The integration of machine learning (ML) into OS kernels is severely hampered by the high latency of offloading to discrete GPUs (dGPUs), where data transfers across the PCIe bus can consume over 93% of the total execution time. This paper argues that for many latency-sensitive kernel tasks, the solution is not a more powerful dGPU but a fundamental shift to an I/O-efficient architecture: the integrated GPU (iGPU) found in modern APUs.

The persistent challenges of data sparsity and class imbalance have long limited the development of recommender systems. Fortunately, line graph theory offers a novel perspective to overcome these issues. By transforming the user-item interaction bipartite graph into a line graph, the problems of data sparsity and class imbalance are elegantly reformulated as those of insufficient labeled nodes and imbalanced label distribution in the line graph domain. This reformulation allows us to directly apply mature techniques from node classification and imbalanced graph learning to address these core challenges. Inspired by this insight, we propose a Line Graph Data Augmentation (LGDA) strategy, which features two distinct characteristics. Firstly, it is a plug-and-play module that resolves data sparsity and imbalance without modifying the underlying recommendation framework. Secondly, it employs a targeted augmentation and confidence filtering mechanism to generate high-quality, balanced augmented data. Extensive experiments on four real-world datasets validate that LGDA effectively alleviates data sparsity and class imbalance, leading to significant improvements in both recommendation performance and system robustness.

Variational autoencoders (VAEs) have been widely used for node clustering, with existing methods mainly focusing on enhancing the expressiveness of their latent space. Recently, the integration of diffusion models with VAEs has provided new opportunities to achieve this objective. However, the mechanism by which the diffusion model improves performance remains unclear. To bridge this gap, we conduct an empirical analysis from the perspective of graph spectral theory, revealing that the signal modulation induced by diffusion models closely aligns with the low-frequency spectral characteristics of VAEs, which in turn explains their effectiveness. Nevertheless, further experiments highlight that diffusion models exhibit limitations in modulating high-frequency signals, which diverge from the spectral characteristics of VAEs. Moreover, existing diffusion methods fail to enable the latent space to adequately capture and reflect cluster-specific characteristics. To address these challenges, we propose a novel plug-and-play method, FVD, to improve the performance of VAE-based methods in node clustering tasks. Specifically, we incorporate the graph wavelet transform as a secondary signal modulator, enabling independent adjustments of specific frequency bands to better align with the spectral characteristics of VAEs. Additionally, we introduce the Student's t-distribution as a conditional constraint in the reverse process of FVD, deriving a more compact variational lower bound. This enhancement preserves fine-grained node information while focusing on clustering details, effectively mitigating the cluster collapse phenomenon. Comprehensive experimental results demonstrate that integrating FVD with existing methods achieves competitive performance improvements in most cases.

Variational autoencoder performs well in community detection on static networks, but it is difficult to directly extend to continuous dynamic networks. The main reason is that traditional methods mainly rely on adjacency structures to complete the inference and generation processes. However, continuous dynamic networks cannot be described by this structure because the inherent timeliness and causality information of the network would be lost. To address this issue, we propose a novel variational autoencoder, CT-VAE, for community detection in continuous dynamic networks, along with its scalable variant, CT-CAVAE. By conceptualizing node interactions as event streams and adopting the Hawkes process to capture temporal dynamics and causality, and incorporating them into the inference process, CT-VAE can effectively extend the traditional inference approach to continuous dynamic networks. Additionally, in the generation phase, CT-VAE combines pseudo-labeling and compact constraint strategies to facilitate the reconstruction process of non-adjacent structures. For the scalable variant, CT-CAVAE, end-to-end community detection is achieved by cleverly combining Gaussian mixture distribution. Extensive experimental results demonstrate that the proposed CT-VAE and CT-CAVAE achieve more favorable performance compared with the state-of-the-art baselines.

Link prediction is an important task that has numerous applications, including in recommender systems and social network analysis. Autoencoder is an effective method for solving the link prediction task. However, most existing autoencoder-based methods neither fully utilize the attribute information of the nodes nor fully take into account the potential data distribution in the graph. In this article, we propose a novel method named attribute-augmented adversarial variational graph autoencoder (A${}^{3}$VGAE), which can effectively solve the above two problems. The method first constructs the attribute structure graph based on the attribute information. Then, it inputs the topological structure graph, the attribute structure graph, and the attribute information into the shared encoder to obtain two latent representations. Besides, the topology structure graph, attribute structure graph, and attribute information are reconstructed by the dual decoder. The adversarial mechanism is introduced to ensure that the two latent representations match specific prior distributions. Extensive experiments conducted on four real-world graph datasets demonstrate the superiority of our proposed A${}^{3}$VGAE in link prediction tasks.

Identifying positive influence dominating set (PIDS) with the smallest cardinality can produce positive effect with the minimal cost on a social network. The purpose of this article is to propose new approximation algorithms for the minimum PIDS problem and its variants such as the minimum connected PIDS and the minimum PIDS of multiplex networks, with the aim of finding target sets with smaller cardinality. Through the design of novel submodular potential function, we theoretically prove that new approximation algorithms yield approximation ratios with same order compared with existing algorithms. We further demonstrate the performance of our algorithm by showcasing its efficacy on several real-world and publicly available instances of social networks, thereby providing additional evidence that our proposed algorithm can identify PIDS with smaller cardinality.

Methods based on variational bayes theorytare widely used to detect community structures in networks. In recent years, many related methods have emerged that provide valuable insights into variational bayes theory. Remarkably, a fundamental assumption remains incomprehensible. Variational bayes-based methods typically employ a posterior distribution that follows a gaussian distribution to approximate the unknown prior distribution. However, the complexity and irregularity of node distributions in real-world networks prompt us to consider what characteristics of network information are suitable for the posterior distribution. Mathematically, inappropriate low- and high-frequency signals in expectation inference and variance inference can intensify the adverse effects of community distortion and ambiguity. To analysis these two phenomena and propose reasonable countermeasures, we conduct an empirical study. It is found that appropriately compressing low-frequency signals during expectation inference and amplifying high-frequency signals during variance inference are effective strategies. Based on these two strategies, this paper proposes a novel variational bayes plug-in, namely VBPG, to boost the performance of existing variational bayes-based community detection methods. Specifically, we modulate the frequency signals during expectation and variance inference to generate a new gaussian distribution. This strategy improves the fitting accuracy between the posterior distribution and the unknown true distribution without altering the modules of existing methods. The comprehensive experimental results validate that methods using VBPG achieve competitive performance improvements in most cases.

Graph data is becoming dynamic and large-scale, demanding high-performance and large-capacity graph storage. Therefore, due to the performance approaching DRAM and the larger capacity than DRAM, persistent memory (PM) has been adopted in large-scale dynamic graph storage systems. However, existing PM-based dynamic graph storage systems have issues, especially PM write amplification caused by the unsorted data structure used to store edges. To improve this issue, we propose a PM-based dynamic graph storage system, HDGraph, using sorted data structure to store edges on DRAM-PM hybrid memory architecture. To better adapt the sorted data structure on PM, HDGraph employs edge buffering on DRAM, merging small writes to reduce write amplification in PM. Moreover, HDGraph also triggers buffer flushing based on a heat evaluating strategy to alleviate DRAM space pressure. Finally, HDGraph maintains a buffering log in PM for edge-level data consistency, enabling quick recovery after a crash. Experimental results show that HDGraph achieves 1.09× to 1.52× higher edge ingestion performance, compared with the only PM-based dynamic graph storage system XPGraph, which use unsorted data structure to store edges.

The training process of large language models (LLMs) sometimes may raise an error, often necessitating the usage of checkpointing mechanisms to save training states periodically. However, this can lead to a waste of training time and impact performance due to frequent I/O operations. Intel Optane Data Center Persistent Memory Module (DCPMM) is a novel memory device that combines non-volatility, high bandwidth, and low latency. Intuitively, deploying LLMs on DCPMM could enhance I/O performance, but the DCPMM cannot fully adapt to the current deep learning frameworks and the read/write granularity mismatch of DCPMM needs to be resolved. To this end, we propose PMCKPT, a checkpointing scheme customized for hybrid memory architectures (DRAM-PM). PMCKPT provides a storage engine to mitigate the impact of frequent I/O operations on training. Furthermore, PMCKPT ensures data persistence and consistency through a persistence scheme based on the persistent memory development kit (PMDK). Finally, it also incorporates a checkpointing management strategy to handle historical version checkpointing and optimize storage management. We implement PMCKPT in ChatGLM and the experimental results demonstrate that PMCKPT can effectively reduce training time by about 34% compared to traditional schemes which use SSDs.

Due to the capability of encoding auxiliary information for alleviating the data sparsity issue, knowledge graph (KG) has gained an increasing amount of attention in recent years. With auxiliary knowledge about items, the KG-based recommender systems have achieved better performance compared with the existing methods. However, the effectiveness of the KG-based methods highly depends on the quality of the KG. Unfortunately, KGs are usually with the problem of incompleteness and sparseness. Besides, the existing KG-based methods could not discriminate the importance of different factors that users consider when making decisions, which may degrade the interpretability of the methods. In this article, we propose a recommendation model named community enhanced knowledge graph for recommendation (CEKGR). By adding entities and relations, the KG is enriched with more semantic information, which would help mine users’ preference for better recommendation. With weights of each path, the interpretability of the recommendation can be improved. To validate the effectiveness of the proposed method, we conduct experiments on three public datasets. Experiment results have shown the improvement compared with other state-of-the-art methods. Besides, case study has illustrated the interpretability of the proposed recommendation model.

Knowledge graphs (KGs) are structured knowledge bases that represent information as a collection of interconnected entities and relations. Link prediction in KGs aims to infer missing or potential links between entities based on triple facts. Among different link prediction methods, knowledge graph embedding (KGE) has gained widespread popularity, with the goal of learning low-dimensional representations for KGs. However, most present KGE methods struggle to capture both local and global neighborhood information efficiently. Additionally, many hybrid methods have limitations in modeling and capturing interactions between triples. In this article, we propose an entity-relation-guided random walk (ERGRW) method for link prediction in KGs. Unlike conventional approaches that solely focus on entity-based walks, ERGRW creatively introduces relations as objects to walk as well. Inspired by distance-based methods, we design novel random walk rules based on the translation principle within triples. Thus, the ERGRW not only captures local and global neighborhood information but also discovers potential semantic relationships and interactions in the KGs. Furthermore, the encoder–decoder framework of ERGRW is able to learn comprehensive representation and improve link prediction performance. Extensive experiments conducted on four standard datasets demonstrate the superiority of ERGRW for link prediction.

Although previous graph-based multi-view clustering (MVC) algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view-consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the k -means for discretization of the spectral embeddings, which lack the ability to directly learn the graph with discrete cluster structure. In light of this, this article presents an efficient MVC approach via u nified and d iscrete b ipartite g raph l earning (UDBGL). Specifically, the anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views, upon which the bipartite graph fusion is leveraged to learn a view-consensus bipartite graph with adaptive weight learning. Furthermore, the Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures (with a specific number of connected components). By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size. Experiments on a variety of multi-view datasets demonstrate the robustness and efficiency of our UDBGL approach. The code is available at https://github.com/huangdonghere/UDBGL.

Information theoretical-based methods have attracted a great attention in recent years and gained promising results for multilabel feature selection (MLFS). Nevertheless, most of the existing methods consider a heuristic way to the grid search of important features, and they may also suffer from the issue of fully utilizing labeling information. Thus, they are probable to deliver a suboptimal result with heavy computational burden. In this article, we propose a general optimization framework global relevance and redundancy optimization (GRRO) to solve the learning problem. The main technical contribution in GRRO is a formulation for MLFS while feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, which can avoid repetitive entropy calculations to obtain a global optimal solution efficiently. To further improve the efficiency, we extend GRRO to filter out inessential labels and features, thus facilitating fast MLFS. We call the extension as GRROfast, in which the key insights are twofold: 1) promising labels and related relevant features are investigated to reduce ineffective calculations in terms of features, even labels and 2) the framework of GRRO is reconstructed to generate the optimal result with an ensemble. Moreover, our proposed algorithms have an excellent mechanism for exploiting the inherent properties of multilabel data; specifically, we provide a formulation to enhance the proposal with label-specific features. Extensive experiments clearly reveal the effectiveness and efficiency of our proposed algorithms.

Link prediction is a hot research topic in graph data analytics, which aims to identify the likelihood of a future connection between two nodes in a network. Among the approaches to link prediction, network embedding has gained tremendous popularity in tackling link prediction tasks. Basically, there are several network embedding methods, such as matrix factorization, random walk, and deep learning. However, most of the existing methods for network embedding solely rely on structure information or fail to effectively combine network structure and feature data. To this end, this paper proposes a novel method that learns network embedding with enhanced feature representations for link prediction (EFRLP). Specifically, structure graphs, bipartite graphs, and co-feature graphs are generated from the attributed network, weighted bipartite graphs are then built over bipartite graphs and correlation weights between features. We further adopt a biased random walk to obtain more representative node sequences. With the help of enriched feature representations, not only can the higher-order semantic relationships be effectively captured but also network sparsity can be alleviated. Then, node sequences are selected into the Skip-Gram model for learning low-dimensional embeddings. Finally, the probability of the existence of edges between nodes is predicted by deep fusion. The effectiveness of the EFRLP method is demonstrated by extensive comparison experiments with several baselines on two real-world datasets.

Community detection based on information diffusion is a relatively niche direction. This paper proposes a social network community detection model based on the inner-community information diffusion simulation. The model uses the inner-community information diffusion algorithm adjusted on the naive infectious disease algorithm for node representation, including the dynamic spreading parameter and repeatable infected nodes. Model clusters nodes into communities according to their representation. Experiment results prove that our model outperforms previous related models on synthetic and real datasets. We verify the interpretability of the inner-community information diffusion representation algorithm meanwhile.

Existing community detection methods for attributed multiplex networks focus on exploiting the complementary information from different topologies, while they are paying little attention to the role of attributes. However, we observe that real attributed multiplex networks exhibit two unique features, namely, consistency and homogeneity of node attributes. Therefore, in this paper, we propose a novel method, called ACDM, which is based on these two characteristics of attributes, to detect communities on attributed multiplex networks. Specifically, we extract commonality representation of nodes through the consistency of attributes. The collaboration between the homogeneity of attributes and topology information reveals the particularity representation of nodes. The comprehensive experimental results on real attributed multiplex networks well validate that our method outperforms state-of-the-art methods in most networks.

Since existing methods are often not effective to detect communities with multiple topics in attributed networks, we propose a method named SSAGCN via Autoencoder-style self-supervised learning. SSAGCN firstly designs an adaptive graph convolutional network (AGCN), which is treated as the encoder for fusing topology information and attribute information automatically, and then utilizes a dual decoder to simultaneously reconstruct network topology and attributes. By further introducing the modularity maximization and the joint optimization strategies, SSAGCN can detect communities with multiple topics in an end-to-end manner. Experimental results show that SSAGCN outperforms state-of-the-art approaches, and also can be used to conduct topic analysis well.

Abstract With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challenging task. An important research topic is to accurately discover potential friends of learners to build implicit learning groups and obtain personalized collaborative recommendations of similar learners according to the learning content. This paper proposes a personalized explainable learner implicit friend recommendation method (PELIRM). Methodologically, PELIRM utilizes the learner's multidimensional interaction behavior in social networks to calculate the degrees of trust between learners and applies the three-degree influence theory to mine the implicit friends of learners. The similarity of research interests between learners is calculated by cosine and term frequency–inverse document frequency. To solve the recommendation problem for cold-start learners, the learner's common check-in IP is used to obtain the learner's location information. Finally, the degree of trust, similarity of research interests, and geographic distance between learners are combined as ranking indicators to recommend potential friends for learners and give multiple interpretations of the recommendation results. By verifying and evaluating the proposed method on real data from Scholar.com, the experimental results show that the proposed method is reliable and effective in terms of personalized recommendation and explainability.

Academic knowledge graph is an important application of knowledge graph in the vertical field of academia. At present, the construction of the academic knowledge graph is mainly completed by extracting published academic papers, authors, publications, and other information from related databases. However, academic information is not just information of published papers. Scholars’ academic activities include participation in academic conferences, visiting and making presentation, and so on. However, the above academic information is hidden in natural language texts and cannot be directly stored in academic knowledge graph. This article proposes an approach named construct-SCHOLAT knowledge graph to construct an academic event knowledge graph based on academic social network SCHOLAT. The construction framework mainly consists of two parts: data preprocessing and event extraction. In the data preprocessing, we propose a knowledge graph embedding method to represent scholars’ academic social feature. In the event extraction, we concatenate the preprocessed scholar vector with academic we-media blog text into the extraction model based on BiLSTM-CRF fused with attention mechanism. The extracted events are added to academic knowledge graph, and a public relationship exists between the event and the scholar. Compared to the previous methods, our framework has an excellent performance after experimental verification. To the best of our knowledge, this is the first study to use the scholar academic social information of the scholar who edited the text as the event extraction input information. In addition, we publish a Chinese event extraction dataset SCHOLAT academic event extraction.1The dataset includes academic we-media blog and the social behavior embedding vector of the scholar. All the data in this dataset are derived from the academic social network SCHOLAT.1https://www.scholat.com/research/opendata

Community detection is a fundamental problem in complex network analysis that aims to find closely related groups of nodes. Recently, network embedding techniques have been integrated into community detection in two manners to capture the intricate relationships between nodes. The two-staged manner generates node embedding vectors and obtains communities by running a clustering algorithm on them. The single-staged manner simultaneously obtains node embedding vectors and communities by optimizing a hybrid objective concerning with node–community relationships. The general-purpose network embedding algorithms used in the first manner do not emphasize retaining node–community relationships. The second manner ignores the influence of a node’s location in a community (at the center or boundary) and its attributes on community generation. In this article, we propose a biased-random-walk-based community detection (BRWCD) algorithm to tackle the issues. First, a topology-weighted degree is designed to enhance the random walk at the boundary of and inside a community to extract communities precisely. Second, we design an attribute-to-node influence index and an attribute-weighted degree to distinguish different attributes’ influence on node transition to obtain communities with high internal cohesion. Comprehensive experiments on the real-world and synthetic networks demonstrate that BRWCD achieves nearly 10% higher accuracy at most than the state-of-the-art algorithms.

A$k$-core is the special cohesive subgraph where each vertex has at least$k$degree. It is widely used in graph mining applications such as community detection, visualization, and clique discovery. Because dynamic graphs frequently evolve, obtaining their$k$-cores via decomposition is inefficient. Instead, previous studies proposed various methods for updating$k$-cores based on inserted (removed) edges. Unfortunately, the parallelism of existing approaches is limited due to their theoretical constraints. To further improve the parallelism of maintenance algorithms, we refine the$k$-core maintenance theorem and propose two effective parallel methods to update$k$-cores for insertion and removal cases. Experimental results show that our methods outperform the state-of-the-art algorithms on real-world graphs by one order of magnitude.

Anomaly detection on attributed networks has received an increasing amount of attention in recent years. Despite the success, most of the existing methods only focus on detecting the abnormal nodes while fail to detect the abnormal subgraphs. In this paper, we define a new problem of hybrid-order anomaly detection on attributed networks, which aims to detect both of the abnormal nodes and subgraphs. To this end, a new deep learning model called Hybrid-Order Graph Attention Network (HO-GAT) is developed, which is able to simultaneously detect the abnormal nodes and motif instances in an attributed network. In order to model the mutual influence between nodes and motif instances, the learning procedures of the node representation and the motif instance representation are integrated into a unified graph attention network with a novel hybrid-order self-attention mechanism. After learning the node representation and the motif instance representation, two decoders are respectively designed to reconstruct the attribute information of the nodes and motif instances, and the hybrid-order topological structure among nodes and motif instances. And finally, the reconstruction errors are utilized as the abnormal score of nodes and motif instances respectively. Extensive experiments conducted on real-world datasets have confirmed the effectiveness of the HO-GAT method.

Nowadays, the learning management system (LMS) has been widely used in different educational stages from primary to tertiary education for student administration, documentation, tracking, reporting, and delivery of educational courses, training programs, or learning and development programs. Towards effective learning outcome assessment, the exam script generation problem has attracted many attentions recently. But the research in this field is still in its early stage. Two essential issues have been ignored largely by existing solutions. First, given a course, it is unknown yet how to generate an quality exam script which concurrently has (i) the proper difficulty level, (ii) the coverage of essential knowledge points, (iii) the capability to distinguish academic performances between students, and (iv) the student scores in normal distribution. Second, while frequently encountered in practice, it is unknown so far how to generate a pair of high quality exam scripts which are equivalent in assessment (i.e., the student scores are comparable by taking either of them) but have significantly different sets of questions. To fill the gap, this paper proposes ExamGAN (Exam Script Generative Adversarial Network) to generate high quality exam scripts, and then extends ExamGAN to T-ExamGAN (Twin-ExamGAN) to generate a pair of high quality exam scripts. Based on extensive experiments on three benchmark datasets, it has verified the superiority of proposed solutions in various aspects against the state-of-the-art. Moreover, we have conducted a case study which demonstrated the effectiveness of proposed solution in the real teaching scenarios.

Community detection is one of the hottest research topics in attributed networks analysis. Nonnegative matrix factorization (NMF) is widely used in community detection of attributed networks because of its high interpretability and extensibility. However, the existing NMF based methods still encounter some obstacles which affect the performance of community detection. Firstly, it is impossible to solve the problem of sparse semantic description. Besides, these methods cannot integrate the heterogeneity of topology structure and nodes attributes. Obviously, these methods cannot accurately identify community structure and assign specific semantic descriptions to each community. To overcome the aforementioned problems, we propose a novel method which combines graph neural networks with weighted-traction regularization. Moreover, we use graph neural networks to discover the semantic characteristics between adjacent nodes which can alleviate the problem of sparse semantic description. Furthermore, the regularizer we proposed can improve the performance of community detection in attributed networks. Experiments on some real attributed networks show that the method we proposed not only is better than some representative related methods but also can assign specific semantic descriptions to each community at the same time.

The non-professional and uncertain testers in crowdsourced testing could lead to the problems of uneven test report quality, substandard test requirement coverage, a large number of repeated bug reports, and low efficiency of report reviewing. This paper designs a crowdsourced testing task assignment approach based on knowledge graph, trying to make full use of the individual advantages and crowd intelligence of crowdsourced workers in crowdsourced testing through personalized task assignment, with the goal to improve the quality of test reports and test completion efficiency. The approach includes three modules: 1) knowledge graph data acquisition: the concept of collaborative crowdsourced test is introduced, and a complete crowdsourced report submission platform is built to obtain the required data for the knowledge graph. 2) Knowledge graph feature learning: building an internal knowledge graph of the crowdsourced testing field based on the data in the platform and combining the historical task records of crowdsourced workers as input, using the machine learning model to get the crowdsourced workers’ preference for specific tasks, and integrates the three-level page coverage and bug-like status. 3) Knowledge graph task assignment: assign test tasks and audit tasks to crowdsourced workers in order to improve the coverage of test requirements and overall test efficiency. We compare the quantity and quality of bug reports in a crowdsourced test task between the task assignment system based on a knowledge graph and the system based on collaborative filtering, which proves the effectiveness of our task assignment technique.

Many retrieval applications can benefit from multiple modalities, for which how to represent multimodal data is the critical component. Most deep multimodal learning methods typically involve two steps to construct the joint representations: 1) learning of multiple intermediate features, with each intermediate feature corresponding to a modality, using separate and independent deep models; 2) merging the intermediate features into a joint representation using a fusion strategy. However, in the first step, these intermediate features do not have previous knowledge of each other and cannot fully exploit the information contained in the other modalities. In this paper, we present a modal-aware operation as a generic building block to capture the non-linear dependencies among the heterogeneous intermediate features, which can learn the underlying correlation structures in other multimodal data as soon as possible. The modal-aware operation consists of a kernel network and an attention network. The kernel network is utilized to learn the non-linear relationships with other modalities. The attention network finds the informative regions of these modal-aware features that are favorable for retrieval. We verify the proposed modal-aware feature learning in the multimodal hashing task. The experiments conducted on three public benchmark datasets demonstrate significant improvements in the performance of our method relative to state-of-the-art methods.

Graph convolutional network (GCN)-based recommendation has recently attracted significant attention in the recommender system community. Although current studies propose various GCNs to improve recommendation performance, existing methods suffer from two main limitations. First, user–item interaction data is generally sparse in practice, highlighting these methods' ineffectiveness in learning user and item feature representations. Second, they usually perform a dot-product operation to model and calculate user preferences on items, leading to inaccurate user preference learning. To address these limitations, this study adopts a design idea that sharply differs from existing works. Specifically, we introduce the knowledge distillation concept into GCN-based recommendation and propose a two-phase knowledge distillation model (TKDM) improving recommendation performance. In Phase I, a self-distillation method on a graph auto-encoder learns the user and item feature representations. This auto-encoder employs a simple two-layer GCN as an encoder and a fully connected layer as a decoder. On this basis, in Phase II, a mutual-distillation method on a fully connected layer is introduced to learn user preferences on items with triple-based Bayesian personalized ranking. Extensive experiments on three real-world data sets demonstrate that TKDM outperforms classic and state-of-the-art methods related to GCN-based recommendation problems.

Recently, hybrid-order community detection has been proposed for addressing the hypergraph fragmentation issue suffered by the motif-based higher-order community detection. However, the existing attempts of hybrid-order community detection inadvertently damage the lower-order connectivity pattern and the higher-order connectivity pattern when constructing the fusion model. Additionally, like the higher-order community detection approaches, they also adopt a two-phase strategy that separately applies the existing graph node clustering methods to the proximity matrix derived from the lower-order connectivity pattern and the higher-order connectivity pattern. Therefore, the higher-order connectivity pattern is only utilized for constructing proximity matrix and hence has no direct effect on the final community results. In this paper, to address the above issues, we propose a Generative model for Hybrid-Order Community detection (GHOC). The main idea lies in defining a likelihood function of a generative model that finds the optimal community membership strength vectors of nodes, based on which the original lower-order connectivity pattern and the higher-order connectivity pattern can be directly reconstructed simultaneously. From the community membership strength vectors, the final community structure can be derived. Extensive experiments have been conducted on several data sets, and the results have confirmed the superiority of the proposed GHOC method.

Community detection can exhibit the aggregation behavior of complex networks. Network motifs are the fundamental building blocks which can reveal the higher-order structure of complex networks. Label propagation algorithm has the advantage of approximately linear time complexity, unfortunately, the randomness of label update is a major but unsolved issue. For these reasons, this paper proposes a novel community detection method, named motif-based embedding label propagation algorithm (MELPA). First, complex network topology is reconstructed by merging higher-order topology with lower-order connectivity features, where higher-order topology is captured by mining network motifs. Second, We design a label propagation characteristic model according to nodes influence, then a new label update rule is formulated based on reconstructed weighted network, the rule integrates frequency among neighbor labels, influence of nodes, propagation characteristics and closeness of nodes to update the node label, the purpose is to overcome the randomness of label selection and identify a better and more stable community structure. Finally, extensive experiments on synthetic networks and real-world complex networks are conducted to verify the effectiveness of MELPA, especially for the complex networks with unobvious community structure, MELPA will get unexpected results.

In a big data environment, traditional recommendation methods have limitations such as data sparseness and cold start, etc. In view of the rich semantics, excellent quality, and good structure of knowledge graphs, many researchers have introduced knowledge graphs into the research about recommendation systems, and studied interpretable recommendations based on knowledge graphs. Along this line, this paper proposes a scholar recommendation method based on the high-order propagation of knowledge graph (HoPKG), which analyzes the high-order semantic information in the knowledge graph, and generates richer entity representations to obtain users’ potential interest by distinguishing the importance of different entities. On this basis, a dual aggregation method of high-order propagation is proposed to enable entity information to be propagated more effectively. Through experimental analysis, compared with some baselines, such as Ripplenet, RKGE and CKE, our method has certain advantages in the evaluation indicators AUC and F1.

Sentence representation approaches have been widely used and proven to be effective in many text modeling tasks and downstream applications. Many recent proposals are available on learning sentence representations based on deep neural frameworks. However, these methods are pre-trained in open domains and depend on the availability of large-scale data for model fitting. As a result, they may fail in some special scenarios, where data are sparse and embedding interpretations are required, such as legal, medical, or technical fields. In this paper, we present an unsupervised learning method to exploit representations of sentences for some closed domains via topic modeling. We reformulate the inference process of the sentences with the corresponding contextual sentences and the associated words, and propose an effective context-enhanced process called the bi-Directional Context-enhanced Sentence Representation Learning (bi-DCSR). This method takes advantage of the semantic distributions of the nearby contextual sentences and the associated words to form a context-enhanced sentence representation. To support the bi-DCSR, we develop a novel Bayesian topic model to embed sentences and words into the same latent interpretable topic space called the Hybrid Priors Topic Model (HPTM). Based on the defined topic space by the HPTM, the bi-DCSR method learns the embedding of a sentence by the two-directional contextual sentences and the words in it, which allows us to efficiently learn high-quality sentence representations in such closed domains. In addition to an open-domain dataset from Wikipedia, our method is validated using three closed-domain datasets from legal cases, electronic medical records, and technical reports. Our experiments indicate that the HPTM significantly outperforms on language modeling and topic coherence, compared with the existing topic models. Meanwhile, the bi-DCSR method does not only outperform the state-of-the-art unsupervised learning methods on closed domain sentence classification tasks, but also yields competitive performance compared to these established approaches on the open domain. Additionally, the visualizations of the semantics of sentences and words demonstrate the interpretable capacity of our model.

Community detection is of great help to understand the structures and functions of complex networks. It has become one of popular research topics in the field of complex networks analysis. Due to the simplicity, flexibility, effectiveness and better interpretability, Nonnegative Matrix Factorization (NMF)-based methods have been widely employed for community detection. However, most existing NMF-based community detection methods are linear and their performance is limited when facing networks with diversified structure information. In view of this, we propose a nonlinear NMF-based method named NMFGAAE, which is composed of two main modules: NMF and Graph Attention Auto-Encoder (GAAE). This approach can boost the performance of NMF-based community detection methods by the aid of graph neural networks and deep clustering. More specifically, GAAE introduces an attention mechanism directed by NMF-based community detection to learn the node representations, while NMF can simultaneously factor these representations to uncover the community structure. We design a unified framework to jointly optimize GAAE and NMF modules, which is very beneficial to obtain better community detection results. We conduct extensive experiments on synthetic and real-world networks. The results show that our NMFGAAE not only performs better than state-of-the-art NMF-based community detection methods, but also outperforms some network representation based baselines. More importantly, NMFGAAE indeed can boost the performance of NMF-based community detection methods.

Community detection is one of the popular research topics in the field of complex networks analysis. It aims to identify communities, represented as cohesive subgroups or clusters, where nodes in the same community link to each other more densely than others outside. Due to the interpretability, simplicity, flexibility, and generality, nonnegative matrix factorization (NMF) has become a very ideal model for community detection and lots of related methods have been presented. To facilitate research on NMF-based community detection, in this article, we make a comprehensive review on NMF-based methods for community detection, especially the state-of-the-art methods presented in high prestige journals or conferences. First, we introduce the basic principles of NMF and explain why NMF can detect communities and design a general framework of NMF-based community detection. Second, according to the applicable network types, we propose a taxonomy to divide the existing NMF-based methods for community detection into six categories, namely, topology networks, signed networks, attributed networks, multilayer networks, dynamic networks, and large-scale networks. We deeply analyze representative methods in every category. Finally, we summarize the common problems faced by all methods and potential solutions and propose four promising research directions. We believe that this survey can fully demonstrate the versatility of NMF-based community detection and serve as a useful guideline for researchers in related fields.

We introduce a unified system for SCHOLAT to import information into Encyclopedia, called Semi-automatic Scholar Encyclopedia Generating System, which consists of four modules: website display page, backstage administration module, version comparison and website security module. Scholar Encyclopedia is implemented as a subsystem of Semi-automatic Scholar Encyclopedia Generating System. Scholar Encyclopedia is a scholar information website that has been independently compiled by the scholars and filled in by personal information. Scholar Encyclopedia Import Model consists of three modules: user evaluation, information import and manual review. At present, Semi-automatic Scholar Encyclopedia Generating System has been used, and the beta version of Scholar Encyclopedia has been deployed in our school network center. The online application shows that Scholar Encyclopedia Import Model has good accuracy and practicability.

In modern academic social network, it is very difficult for scholars to find academic papers consistent with their research direction. Time is a critical factor in paper recommendation. As time goes on, the impact of an academic paper would gradually fade. Likewise, the research interests of users may also change. Therefore, we propose a temporal perceptual neural collaborative filtering model that integrates the multi-dimensional features of papers. We conducted our experiments on the dataset from CiteULike, comparing the recommended results by using four time-decay functions and evaluating our model with multiple evaluation indicators. The satisfactory results show that our model is effective in filtering out the expired papers by considering the characteristics of papers and the changes of scholars' interests.

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Social networks are changing our daily lives. In order to meet the needs of research and teaching, we designed a social network named SCHOLAT, which provides a platform for scholars to cooperate in research and teaching. In this talk, I will briefly introduce the usage of SCHOLAT through real examples, analysis the big data in SCHOLAT, and propose an application mode of SCHOLAT+. Finally, I will introduce several applications based on SCHOLAT.

In this paper we focus on a research of the overlapping community structures. Towards this goal, we propose DOCLPA (Detecting Overlapping Community based Label Propagation Algorithm), a fast label propagation method is designed to detect overlapping community in GraphChi (a parallel sliding windows method) with only network topology. Here the label initialization is designed to reduce labels redundancy by local maximal cliques. We focus in particular on propagation characteristics between nodes, as far as possible, let the label propagation process be similar to the real information dissemination. Crucially, the DOCLPA method can detect high quality communities adaptively and does not require limited parameter. The experimental findings on real-world networks show that DOCLPA is effectiveness and accuracy in comparison with GCE, COPRA and LPA. Furthermore, by adjusting the overlapping coefficient to reveal the behavior characteristics of overlapping nodes, we prove that overlapping communities on the real-world networks are very naturally social phenomenon. In addition, our experiments on social, product, collaboration and communication networks demonstrate that overlapping nodes often represent active users or products who involve multiple different communities.

In today's society, social networking has been integrated into everyone's life. The detection of network community has been a hotspot in recent years, and it has been widely used in fraud prevention, personalized recommendation, risk control and other fields. In this paper, we present an improved community detection algorithm for fusing topology and attribute information (FTAI) based on Non-Negative Matrix Factorization (NMF). Firstly, we use the attribute similarity matrix instead of a binary matrix based on string matching to cope with the sparsity of the attribute matrix. Next, we introduce the transfer matrix and the attribute feedback adjustment method to fuse the topological matrix and the attribute matrix. Then, we deduce the iteration formula of each matrix with a rigorous mathematical method, which shows the reliability of the algorithm. Finally, we evaluate our method with extensive experiments by using the data set from a real academic social network (SCHOLAT). Experimental results show that FTAI is superior to other baseline algorithms. The results also indicate that FTAI is more flexible, robust and suitable for community detection in social networks with complex attribute information.

The basic I/O operations of a system can be categorized as two distinct modes: synchronous (sync) I/O and asynchronous (async) I/O, whose performance varies on the system statues, workloads and storage devices. Appropriately applying I/O modes is critical to the system performance. However, the I/O access of diverse applications in a server, especially in a cloud, is volatile and irregular. As a result, this can lack a flexible and adaptive I/O modes, leading to the sub-optimal I/O performance. To tackle this problem, in this paper, we propose IObrain, an intelligent I/O mode recommendation system, which can adopt the appropriate I/O mode in a dynamic and self-adaptive manner according to both application needs and system statuses. IObrain first trains a lightweight recommendation model with decision tree. Then, a query hook is interposed into the storage engine to intercept the read/write operations from upper application. In this way, IObrain queries the recommendation model first before executing a read/write operation to find the right I/O mode. In addition, two techniques, called inference cache and gRPC bridge, are proposed to reduce the inherent query latency. We practically implement IObrain and verify the advantage of IObrain based on the prototype system. The experimental results show that, compared to existing approach, IObrain improves the I/O performance by up to 1.33× with mild running costs.

The relational database service in cloud usually achieves energy efficiency by using virtualization technology, in which it consolidates multiple independent database systems into a single physical machine while enforcing the hardware-level isolation among them. However, the disk I/O performance is inevitably hurt due to the resource contention on the shared device. We propose VMSQL, a novel disk I/O model for the virtualized relational database management system (RDBMS). VMSQL has two innovations over the original disk model of virtualized database systems. First, VMSQL enforces the synchronous operation in guest operating system to handle with the transaction commitment. Due to its simplicity, a portion of CPU cycles is decoupled from I/O buffer management and then used to serve the upcoming requests, thereby improving their response times. Second, in host system, VMSQL asynchronizes the storage path of transactions which are committed from the different co-located guest databases. An obvious advantage of this procedure is that systems can apply host-level improvements into the disk I/O performance of virtualized RDBMS, relieving the random I/O and enhancing the throughput of whole system. We implement a prototype of this Sync-Async model in QEMU-KVM hypervisor, in which the InnoDB engine is deployed in the guest operating system. Extensive experiments are conducted to verify its advantages and the results are positive without any loss of ACID-compliance. In the meanwhile, VMSQL incurs moderate overhead at the hypervisor layer.

Automatic short answer scoring (ASAS) is a research subject of intelligent education, which is a hot field of natural language understanding. Many experiments have confirmed that the ASAS system is not good enough, because its performance is limited by the training data. Focusing on the problem, we propose MDA-ASAS, multiple data augmentation strategies for improving performance on automatic short answer scoring. MDA-ASAS is designed to learn language representation enhanced by data augmentation strategies, which includes back-translation, correct answer as reference answer, and swap content. We argue that external knowledge has a profound impact on the ASAS process. Meanwhile, the Bidirectional Encoder Representations from Transformers (BERT) model has been shown to be effective for improving many natural language processing tasks, which acquires more semantic, grammatical and other features in large amounts of unsupervised data, and actually adds external knowledge. Combining with the latest BERT model, our experimental results on the ASAS dataset show that MDA-ASAS brings a significant gain over state-of-art. We also perform extensive ablation studies and suggest parameters for practical use.

Information theoretical based methods have attracted a great attention in recent years, and gained promising results to deal with multi-label data with high dimensionality. However, most of the existing methods are either directly transformed from heuristic single-label feature selection methods or inefficient in exploiting labeling information. Thus, they may not be able to get an optimal feature selection result shared by multiple labels. In this paper, we propose a general global optimization framework, in which feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, thus facilitating multi-label feature selection. Moreover, the proposed method has an excellent mechanism for utilizing inherent properties of multi-label learning. Specially, we provide a formulation to extend the proposed method with label-specific features. Empirical studies on twenty multi-label data sets reveal the effectiveness and efficiency of the proposed method. Our implementation of the proposed method is available online at: https://jiazhang-ml.pub/GRRO-master.zip.

Summary Traditional feature‐based semantic similarity (SS) approaches exploit the Wikipedia features in term of sets. They evaluate the similarity of concepts based on the commonalities among their feature sets. However, these feature‐based approaches treat all the features equally in similarity evaluation. Therefore, they ignore the underlying statistics of the features and consequently lose the essential semantic details about them. One solution is that each feature can be assigned a specific weight using its statistics. This weight will reflect the relative importance of a feature in similarity evaluation. Therefore, in this paper, based on two statistical models, ie, information content and TFIDF, we propose some hybrid semantic similarity measurement methods. Firstly, we propose some new methods called weighting functions to compute the weights of the features and feature sets in Wikipedia. Secondly, based on the weighting functions, we propose some new weighted feature‐based SS approaches for Wikipedia concepts. Thirdly, we evaluate the proposed methods on well‐known benchmarks for English, German, French, and Spanish languages. Finally, we compare the performance of our methods with the traditional feature‐based and some state‐of‐the‐art SS approaches. The experimental evaluation shows that our weighted methods perform better than the traditional feature‐based and some state‐of‐the‐art approaches in similarity evaluation.

Location-based recommendation has recently received a lot of attention in the communities of information service and mobile application. Its task is to provide personalized recommendations of points of interest (POIs) to users at a certain time and location. However, existing location-based recommendation models have at least two main drawbacks: first they cannot adequately capture semantic features of POIs and users, which may lead to unsatisfactory recommendations and second they cannot effectively address the cold-start problem. To address the above drawbacks, in this article, we first propose a novel deep representation learning-based model (DRLM) for improving the recommendation accuracy. In DRLM, we mainly focus on learning to accurately represent semantic features of POIs and users. Specifically, four co-occurrence matrices are constructed to produce four different original features for each POI, and a principal component analysis (PCA) algorithm is utilized to generate a semantic feature of each POI from its four original features. On the other hand, a three-modal simple recurrent unit (TMSRU) network is given to constructed semantic features of users using semantic features of POIs, times, and locations. We further propose minimum description length (MDL)-based and skyline-based strategies to address the cold-start issues for new users and new POIs, respectively. Through experiments on two real-world data sets, we show that compared with the state-of-the-art approaches, the proposed model DRLM can achieve the superior performance in terms of high recommendation accuracy and effectiveness in handling the cold-start problem.

In the Internet of Things (IoT) community, wireless sensor network (WSN) is a key technique to enable ubiquitous sensing of environments and provide reliable services to applications. WSN programs, typically interrupt-driven, implement the functionalities via the collaboration of interrupt procedure instances (IPIs, namely executions of interrupt processing logic). However, due to the complicated concurrency model of WSN programs, the IPIs are interleaved intricately and the program behaviors are hard to predicate from the source codes. Thus, to improve the software quality of WSN programs, it is significant to disentangle the interleaved executions and develop various IPI-based program analysis techniques, including offline and online ones. As the common foundation of those techniques, a generic efficient and real-time algorithm to identify IPIs is urgently desired. However, the existing instance-identification approach cannot satisfy the desires. In this paper, we first formally define the concept of IPI. Next, we propose a generic IPI-identification algorithm, and prove its correctness, real-time, and efficiency. We also conduct comparison experiments to illustrate that our algorithm is more efficient than the existing one in terms of both time and space. As the theoretical analyses and empirical studies exhibit, our algorithm provides the groundwork for IPI-based analyses of WSN programs in IoT environment.

Compared with traditional course websites, the open online course platforms have a larger number of courses, and course recommendation is becoming increasingly important. In this paper, we shall propose a course recommendation model based on academic social networks, a hybrid method combing with association rules algorithm and an improved multi-similarity algorithm of multi-source information, which can recommend courses according to potential relationships between courses and users implicit interests. The proposed model is applied to SCHOLAT. Judging from our experimental results, the new model is capable of reducing cold-start problem and providing better accuracy.

In order to solve the problem of subset community in some community detection algorithms which is based on label propagation, this paper proposes a method that merges the core network in advance, and constructs a scholars recommendation model for academic social network. The model processes the problem of subset community through the GraphChi framework in parallel and recommends the scholar users within the community according to the relevant recommendation rules to achieve the scholars' recommendation service. Experimental results show that the proposed model effectively reduces the running time of community detection algorithm and achieves good recommendation results.

With the development of modern technology, more and more research papers have been published and shared in various digital databases. However, it is time-consuming for researchers to find suitable scholars who study the same research field with them. To address this issue, we focus on proposing a keyword-based scholar recommendation framework, which can help users to advance their research by recommending scholars that align with the users interests. We first utilize keywords that are extracted from abstract to construct a word-word co-occurrence graph in each query. Based on the graph, we propose an approach to find core nodes to solve cold start problem by treating these core nodes as the users interests. We then combine these core nodes and authors to build a bipartite graph, and adopted the PersonalRank algorithm to rank authors based on the bipartite graph. Finally, we design a recommendation evaluation criterion by comparing our recommendation lists with the results lists of Microsoft Academic search. Experimental results show that our recommendation framework can effectively and efficiently generate scholar recommendation in the situation that lack of the citations times, user behavior and other important indicators.

With the development of the Internet, more and more data can be found on texts information. People produce texts information via writing blogs, product reviews, microblogs, film reviews and so on, which contain sentiments or opinions of the writer. User comments usually can reflect their intuitive feelings for a product. We can dig out relatively large value through sentiment analysis for these comments. Sentiment classification, as one of the most important tasks in sentiment analysis for many real world applications, is our main focus in this article. To improve the accuracy of sentiment classification, we propose a deep neural network fusion framework, which is composed of a multi-window CNN-LSTM model and a multi-window CNN-CNN model with the fusion of the probability of two models to generate final output. Experimental results instruct that our framework is quite feasible.

Memory object caching systems, such as Memcached and Redis, have been proved to be a simple and high-efficient middleware for improving the performance of Internet of Things (IoT) devices querying the database in cloud. However, its performance guarantee is built on the fact that the target data, queried by the IoT device, will be accessed many times and hit in the caching system. Therefore, when database system is handling the unrepeated IoT queries, it usually presents the suboptimal performance, which greatly impairs the efficiency of real-time data processing on IoT devices. To improve this issue, we propose Seer-MCache, the memory object caching system with a smart prefetching (read-ahead) function, to fill up the caching system with the desired data before the intensive IoT queries arriving. Seer-MCache includes a set of rules to launch the specific behaviors of read-head. These rules are able to be customized according to the workload characteristics and system load. We implement a prototype system in Redis (caching layer) and MySQL server (database system). Extensive experiments are conducted to verify the effectiveness of Seer-MCache, the results show that Seer-MCache can improve the performance of read-intensive workload up to 61% (39.5% in average). Meanwhile, the cost of the read-ahead behavior is moderate and controllable.

Authorship attribution is an active research direction due to its legal and financial importance. The goal is to identify the authorship of anonymous texts. In this paper, we propose a Topic Drift Model (TDM), monitoring the dynamicity of authors’ writing style and latent topics of interest. Our model is sensitive to the temporal information and the ordering of words, thus it extracts more information from texts.

Based on dynamic research perspectives of time slicing, this paper shows an endeavor on mining dynamic features of the scientific teams. Traditionally the static method of network structure analysis can successfully be used to analyze the distribution of network resource structure. But it cannot be used to explore the dynamic features of research groups, because of its lacks on the influence of some variables in research activities. These variables include researchers, research hotspots and research funds, etc. which are all in varying state due to the changing world. This paper proposes a new approach to analyze the dynamic characteristics of scientific teams, by using time slicing incorporated with traditional static method. KP(core members Keep-Rate) is used as an index of the dynamic transitions of scientific teams in two sequential time slices, and an algorithm is proposed to identify the successor team(s).

Cancer diseases are caused by combination of genetic, environmental, and lifestyle factors. Therefore, it is difficult for health organization to treat this disease. This study focuses on identifying the landscape of Cancer research by using bibliometric methods and social network analysis methods based on a number of research articles related to Cancer retrieved from PubMed. To deeply understand the landscape of research on this disease, we adopt productivity analysis which consists of author, university/institution, country and frequent MeSH terms analysis. We specifically perform the concept graph-based network analysis by applying four centrality measures and analyzing co-occurrence of MeSH terms. In the end, we propose a method to predict the Rising Star that may be the active researcher in the field of Cancer disease in the next few years. With this method, we can possibly find more academic cooperation via academic social networks. The encouraging results show that our work is highly feasible.

Summary Online social networks (OSNs) have become more and more popular and have attracted a great many users. Friend recommendation, which is one of the important services in OSN, can help users discover their interested friends and alleviate the problem of information overload. However, most of existing recommendation methods only consider either user link or content information and hence are not effective enough to provide high quality recommendations. In this paper, we propose a topic community‐based method via Nonnegative Matrix Factorization (NMF). This method first applies joint NMF model to mine topic communities existing in OSN by combing link and content information. Then it computes user pairwise similarities and makes friends recommendation based on topic communities. Furthermore, this method can be implemented using the MapReduce distributed computing framework. Extensive experiments show that our proposed method not only has better recommendation performance than state‐of‐the‐art methods but also has good scalability to deal with the problem of friend recommendation in large‐sale OSNs. Moreover, the application case demonstrates that it can significantly improve friend recommendation service in the real world OSN. Copyright © 2016 John Wiley & Sons, Ltd.

The data stored in E-Learning and academic social networking system databases can reveal useful insight into the behavior of the users of these systems. The statistical analysis and machine learning techniques can be used to make meaningful inferences from this data. Moreover, Education Data Mining (EDM) and Learning Analytics (LA) practices can help educators and administrators to take educated decisions related to the wellbeing of learners and the learning system overall. In this paper, we present descriptive statistical analysis of SCHOLAT E-Learning data to expose interesting information about the behavior of learners and the data gathering practices. The data was fetched from SCHOLAT Courses module database, and then univariate and bivariate analysis was performed to get an insight. This data represents the activities of students when they were undergoing a blended learning course. The analysis revealed that students' interest lies in activities which are related to improve their grades, e.g. submitting homework on time where mean value approaches the maximum value. However, they do not show the same enthusiasm in other activities. Moreover, a loophole was also discovered in data gathering practices as a result of analysis. The analysis lays a foundation for more complicated analysis in future and improving the system's data gathering practices.

One of the fundamental questions is how we can enhance our relationships with the people in our social networks. The combination of frequent engagement, deep interaction, and time spent together are what constitute to build a stronger relationship. With this knowledge in mind, it is not surprising to see many scholars claim that social networking sites may help us to re-connect some lost relationships, but has limited ability to strengthen an existing relationship. Surprisingly, from a survey we conducted, we found that most people who use Facebook or scholat.com (a scholar-oriented social networking site) claim that their relationships with some of their friends are improved, even though they do not have any in-depth or personal conversation with them and they can understand their friends better even without any communication. It contradicts with what we have known. It seems that a relationship can be improved with Facebook or scholat.com alone. In this paper, we try to analyze this finding and explain it using communication model and visualized analysis. We conclude how communication model changes as time goes and how it re-defines the meaning of communication.

With the social network being widely used, people would like to use the friends recommendation provided by a social websites. There are lots of methods to make the recommendation results more accurately and efficiently. By considering the feature selection strategy in the stage of data preprocessing, we propose a novel friend recommendation system using a classification model, which formulates the recommendation problem. We compare the performance of four classifiers, and draw a conclusion that our proposed method can get higher accuracy.

Network analysis has been shown to be an effective and cheap way to screen genes that are associated to diseases and chemicals. The identification of features that are used to order potentially related genes is key to do this job. Though many network models and structure based features have been proposed in the literature, they do not perform well enough for such gene prioritization task, especially when the heterogeneity of such networks is taken account. In this paper, a type of heterogeneous network called Generalized Bi-relational Network (GBN) is formalized. A series of path based features on GBN are defined. Though some of the features have been used in other literature, it is the first time to evaluate them in both supervised and unsupervised learning models. The experiment on real chemical-disease-gene networks shows that the features proposed in this paper gain promising performance in both supervised and unsupervised framework.

In a virtualized environment, when multiple co-located relational database engines update their data files on a shared storage device simultaneously, the transaction log files, which be scattered within different guest image files, introduce the random logging I/O easily. To cope with this issue, we propose vTrans, a novel I/O driver of virtualized transaction log file. Generally, vTrans uses the split I/O driver design. In each guest operating system a front-end driver is added to identify the transaction logging semantic from specific database engine. At the hypervisor layer a dedicated back-end driver consolidates all transaction logging I/Os from guest font-end drivers and then persist them into a consecutive data area on the shared storage device, resulting in the relatively sequential logging I/O. We implement vTrans in a QEMU system which deployed MySQL InnoDB database engine. The experimental result shows that vTrans can effectively improve the performance of random logging I/O in a virtualized system.

Abstract Cloud computing offers a cheap and efficient solution for the deployment of web applications. It results in a big increase of the number of service provider. Users hold multiple identities for using services from different domains. The openness of public clouds requires the authentication system to accept user identities from various domains and to support hybrid authentication protocols. This work proposes a cross‐domain single sign‐on mechanism to address the preceding issues and makes a formal mathematical model to analyze the security issues of the proposed mechanism's authentication architecture; furthermore, an algorithm is proposed to detect the authentication architecture's weak vertex whose failure would lead to a partial failure in the architecture. The proposed mechanism allows service providers to verify user identities in a decentralized way and allows users to unify their identities from various domains in a safe way. The verification process used in this mechanism is able to support hybrid authentication protocols as well as to accelerate the verification of credentials by eliminating single point of failure and single‐point bottleneck. Copyright © 2015 John Wiley & Sons, Ltd.

News recommendation recently has attracted wide spread research attention because of the fast propagation of information on the Internet. Due to the large volume of information, a recommendation system which can provide the most important and useful information is required. Most of existing researches focus on providing recommendation based on news contents and predict the category of news only, which is inefficient if the news pool is very large or contains a lot of noisy data. In this study, we propose a novel news recommendation system called UBS, which recommends personalized news based on User Behavior Sequence (UBS) with high efficiency. We formulate the mining problem of user behavior sequence for Internet news reading, which can significantly enhance the performance of recommendation. Experimental validation was conducted using real datasets that obtained from news website. The results show that UBS can provide reasonable news recommendation compared to content-based recommendation as well as collaborative filtering.

Social networks are very dynamic objects, since new edges and vertices are added to the graph over the time. Link prediction is an important task in social network analysis and is useful in many application domains. In the recent years, there is significant interest in methods that represent the social network in the form of a graph and leverage topological and semantic measures of similarity between two nodes to make predictions. In this article, we propose a hybrid approach utilizing time-varied weight information of links. We focus on the problem of link prediction particularly in the context of evolving co-authorship. Experiments have shown that the link prediction algorithm based on time-varied weight can reach better result.

The significance of time in information production and consumption has been recognised in information retrieval research. Temporal information plays an important role in the webpage retrieval. The webpage has both the temporal metadata and temporal semantics in the content. However, the existing search engines conduct the information retrieval based on text keywords rather than temporal semantics. To address this issue, a Temporal semantics Information Retrieval (TSIR) System is proposed to deal with the Chinese temporal information retrieval. The TSIR system is deployed on Hadoop and implemented by the means of MapReduce. Firstly, the Chinese temporal regular expression rule is introduced to extract the explicit and implicit temporal phrases in the query keywords and webpages. Secondly, the scores of webpages are re-evaluated by taking text relevance and temporal semantics relevance into account and the returned results are ranked according to re-evaluation. Experiment shows that TSIR system could precisely and effectively match the keywords queries related to temporal expression.

The problem of discovering covert nodes in social network has been widely studied because of its tremendous number of applications in determining critical points in social network, such as detecting terrorist, recommending item for possible customer, finding the source of spreading gossip, etc. In this paper, we utilize game theory to solve this problem. Firstly, we propose the model which analyzes game in the influence transmission. Then we obtain each nodes contribution by calculating the nodes earning in game. The feasibility and effectiveness of our method were verified on a simulation dataset and a real dataset.

IaaS encourages pooled resource management model, which provides transparency on the management and provision of IT resources. The transparency, hiding physical details of the underlying resources, makes it difficult for cloud users/services to identify trusted resources for service deployment, resulting in potential risks of deploying critical services on untrusted resources. This paper proposes a pool oriented trust management mechanism for cloud infrastructures, allowing the construction and identification of trusted clusters consisted of trusted resources, with strict membership management to accept only trusted physical resources. Resources of a trusted cluster expose identical trust properties/attributes to cloud users, enabling users to verify the trust on the resources without the need of identifying individual physical resource. Hence, service deployment and migration can be augmented with the above trust verification to ensure that services are always deployed on trusted resources.

Rank aggregation, which combines multiple individual rank lists toobtain a better one, is a fundamental technique in various applications such as meta-search and recommendation systems. Most existing rank aggregation methods blindly combine multiple rank lists with possibly considerable noises, which often degrades their performances. In this paper, we propose a new model for robust rank aggregation (RRA) via matrix learning, which recovers a latent rank list from the possibly incomplete and noisy input rank lists. In our model, we construct a pairwise comparison matrix to encode the order information in each input rank list. Based on our observations, each comparison matrix can be naturally decomposed into a shared low-rank matrix, combined with a deviation error matrix which is the sum of a column-sparse matrix and a row-sparse one. The latent rank list can be easily extracted from the learned low-rank matrix. The optimization formulation of RRA has an element-wise multiplication operator to handle missing values, a symmetric constraint on the noise structure, and a factorization trick to restrict the maximum rank of the low-rank matrix. To solve this challenging optimization problem, we propose a novel procedure based on the Augmented Lagrangian Multiplier scheme. We conduct extensive experiments on meta-search and collaborative filtering benchmark datasets. The results show that the proposed RRA has superior performance gain over several state-of-the-art algorithms for rank aggregation.

Destination prediction is an essential task for a number of emerging location based applications such as recommending sightseeing places and sending targeted advertisements. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, existing techniques suffer from the "data sparsity problem", i.e., the number of available historical trajectories is far from sufficient to cover all possible trajectories. This problem considerably limits the amount of query trajectories whose predicted destinations can be inferred. In this demonstration, we showcase a system named "DesTeller" that is interactive, user-friendly, publicly accessible, and capable of answering real-time queries. The underlying algorithm Sub-Trajectory Synthesis (SubSyn) successfully addressed the data sparsity problem and is able to predict destinations for almost every query submitted by travellers. We also consider the privacy protection issue in case an adversary uses SubSyn algorithm to derive sensitive location information of users.

In recent years, there has been growing interest in learning to rank. The introduction of feature selection into different learning problems has been proven effective. These facts motivate us to investigate the problem of feature selection for learning to rank. We propose a joint convex optimization formulation which minimizes ranking errors while simultaneously conducting feature selection. This optimization formulation provides a flexible framework in which we can easily incorporate various importance measures and similarity measures of the features. To solve this optimization problem, we use the Nesterov's approach to derive an accelerated gradient algorithm with a fast convergence rate O(1/T(2)). We further develop a generalization bound for the proposed optimization problem using the Rademacher complexities. Extensive experimental evaluations are conducted on the public LETOR benchmark datasets. The results demonstrate that the proposed method shows: 1) significant ranking performance gain compared to several feature selection baselines for ranking, and 2) very competitive performance compared to several state-of-the-art learning-to-rank algorithms.

Collaboration networks are among some of the social networks and offer us the opportunity to study the structure underlying the networks. In this paper, we utilize the social network analysis (SNA) framework to understand what characterizes the social structure of the CSCWD conference's paper co-authorship network. It can potentially provide us with an understanding of the individuals and the network. We consider two scientists to be connected if they have authored a paper together, system like the co-authorship network of the conference is inherently dynamic, and so we represent it as a time-varying graph. Each graph is a static cumulative network. We then give results for the average distance and the diameter they show that this network forms a “small world”. We then also give the clustering coefficient demonstrating the presence of clustering of the collaboration between the scientists, as well as the increasing proportion of the giant component, it can serve as an additional support that the community would work well if it is densely connected. Through those figures, we also want to find some potential actions that can be taken to further develop the conference and other similar conferences.

Lots of scholars depend on the Internet for rapid access to the other scholar's research information and build cooperative relationship. But current systems for finding another scholar who share the same research field or interests do not adequately satisfy the need of scholars; moreover the process of seeking is time-consuming and difficult. In this paper, we use the data from scholar's profile in Schol@t (the social network site we build for scholar) and provide a model base on the semantic analysis and social network analysis to recommend the right partner who has the same interest or in same research area to scholar.

This paper is concerned with a study on the feature selection for ranking. Learning to rank is a useful tool for collaborative filtering and many other collaborative systems, which many algorithms have been proposed for dealing this issue. But feature selection methods receive little attention, despite of their importance in collaborative filtering problems: First, recommender systems always have massive data. Using all these data in learning to rank is unrealistic and impossible. Second, we discuss that not all the features are useful for a user's query. So choosing the most relevant data is necessary and useful. To amend this problem, we describe an algorithm called FBPCRank to choose the most relevant features for ranking. Our method combines two measures of good subsets of features, which not only can decrease the loss objective, but also reduce total similarity scores between any two features. We adopt forward and backward methods to choose the most relative features and use Pearson correlation coefficient to measure the similarity of two features. The experiments indicate that our method can outperform other state-of-the-art algorithms by selecting just small amounts of features.

Cloud computing is an emerging computing paradigm in which IT resources and capacities are provided as services over the Internet. Promising as it is, this paradigm also brings forth new challenges for data security and access control when users outsource sensitive data for sharing on cloud servers, which are likely outside of the same trust domain of data owners. To maintain the confidentiality of, sensitive user data against untrusted servers, existing work usually apply cryptographic methods by disclosing data decryption keys only to authorized users. However, in doing so, these solutions inevitably introduce heavy computation overhead on the data owner for key distribution and data management when fine-grained data access control is desired, and thus do not scale well. In this paper, we present a way to implement, scalable and fine-grained access control systems based on attribute-based encryption (ABE). For the purpose of secure access control in cloud computing, the prevention of illegal key sharing among colluding users is missing from the existing access control systems based on ABE. This paper addresses this challenging open issue by defining and enforcing access policies based on data attributes and implementing user accountability by using traitor tracing. Furthermore, both the user grant and revocation are efficiently supported by using the broadcast encryption technique. Extensive analysis shows that the proposed scheme is highly efficient and provably secure under existing security models.

Cloud computing has been acknowledged as one of the prevaling models for providing IT capacities. The off-premises computing paradigm that comes with cloud computing has incurred great concerns on the security of data, especially the integrity and confidentiality of data, as cloud service providers may have complete control on the computing infrastructure that underpins the services. This makes it difficult to share data via cloud providers where data should be confidential to the providers and only authorized users should be allowed to access the data. This work aims to construct a system for trusted data sharing through untrusted cloud providers, to address the above mentioned issue. The constructed system can imperatively impose the access control policies of data owners, preventing the cloud storage providers from unauthorized access and making illegal authorization to access the data.

Learning to rank, a task applying machine learning techniques to rank the expected information of the users, such as movie items users might be interested in. It is useful for collaborative filtering, which is regarded as a hot subfield of computer supported collaborative work(CSCW). In this paper, we propose an algorithm based on RankBoost to rank expected information of the users more accurately. The main advantage of the algorithm against RankBoost is to add a Weight Matrix regularizer to rank the relevance levels of the information smoothly and locally based on graph methods. The experimental results on the public LETOR datasets show that the proposed algorithm performs better than the baseline algorithm, indicating that the method is promising.

Role is an important concept in collaboration systems and CSCW systems especially in the aspect of group awareness. This paper proposes a new group awareness model based on role by the research of role in collaboration systems and the analysis of existing awareness models. In the new proposed model, the task is decomposed into activities in order to acquire the activity graph. Then the roles are extracted from activity graph to build the role-relation graph. According to role-relation graph we can compute the awareness intension of the roles in a group.

A role based hierarchical group awareness model (RHGAM) on the basis of the previous research was presented in this paper. Regarding role as the basis of the group cooperation, firstly RHGAM constructed a group cooperation environment (GCE), and then GCE was extended by group awareness content, awareness hierarchy, the task decomposition rule. The model divided the awareness information into four levels by decomposition and recombination using a role-task graph and the thinking of group structure. The time possibility analysis based fuzzy time and the implementation mechanism of the model to provide group cooperation and awareness mechanism in the project Personal Research Information Workspace were also discussed in this paper.

Materialized view maintenance is an important research area in the database and data warehouse area. Currently, most of the view maintenance methods are based on the C/S model, which will lead to overload and even crash at the end of data warehouse. A model of P2P-based collaborative view maintenance is proposed, which can not only exploit the potential merits of P2P and CSCW, but also overcome the above mentioned problems. Furthermore, a collaborative maintenance algorithm is proposed. Finally, the evaluation shows the proposed strategy achieves better performance both on the message number and the total execution time.

Although researchers have long stressed the importance of temporal properties in knowledge representation and reasoning, and a lot of temporal knowledge representation frameworks are proposed, few is carried out in practical use. In this paper, a new temporal production system, in which both rules and facts possess temporal attributes, is introduced. Production system is a knowledge representation mechanism used by most knowledge based systems and expert systems in practice. In this paper its definition and reasoning algorithm are presented, and a sample implementation in a salary system is given in the end.

Question Classification plays an important role in most Question Answering systems. In this paper, we exploit semantic features in Support Vector Machines (SVMs) for Question Classification. We propose a semantic tree kernel to incorporate semantic similarity information. A diverse set of semantic features is evaluated. Experimental results show that SVMs with semantic features, especially semantic classes, can significantly outperform the state-of-the-art systems.

Business Process Execution Language for Web Services is a language, which can be used to define abstract and executable processes. It has became to be the defacto standard of Web Service composition. However, the security aspect of access control is explicitly mentioned to be outside the scope of BPEL. This paper focuses on the implementation of access controls in the BPEL-based process. The existing Task-Based Access Control model was extended. The definition of authorization unit was modified and new types of authorization units were added. Moreover the mapping from the process defined by BPEL to TBAC model was implemented and an approach of using TBAC in BPEL was put forward. The future work was pointed out in the end of the paper.

View maintenance is an important field in the research of dataware house. This paper provides an online view self-maintenance method based on source view's increment to keep the materialized view consistent with the data source. We use the cooperation between the integrator at warehouse and the monitor at data source to maintenance the view, and the method does not require to query back to data source, it can accelerate view maintenance and lessen the burden of communication between dataware house and data sources.

The explosive increase of resources available on the World Wide Web (WWW) not only provides users with valuable information, but also makes the information usage difficult. On the other hand, nowadays it is a trend that more and more people cooperatively and collaboratively work together to complete a task. Efficient coordination and cooperation is required. Thus a cooperative information system is needed. This paper presents the design and implementation of a web-based cooperative information system. It automatically collects information from the WWW, intelligently classifies them into different categories and transforms them into structured records before storing them into the repository. Meanwhile, the system supports cooperative information query and provides a platform that allows users to exchange their ideas with their coworkers. The cooperative features of the system are outlined. Then, the design architecture and implementation techniques are described. Finally its application is illustrated.

XML which is a new language for data representation is expected to become a universal format for data exchange on the Web. Generally speaking, we make some changes on XML documents as time goes by. Because temporal XML can store the successive versions of a document in an incremental fashion, it provides us a high effective method for version management. Of course, XML is more natural than RDB on representing the temporal information. So the topic of representing, querying and updating temporal information in XML has received some attention. In this paper a XML data model for tracking historical information in an XML document are proposed. After presenting an abstract model for bitemporal XML, two different ways of mapping this abstract representation into a (bitemporal) XML document are discussed. Finally all schemas are compared.

This paper considers that CSCW theory is still in immature phase. Effective theory is badly required in order to overcome the chasm in the developmental process. Moreover, CSCW systems consist of many specifications different with most traditional systems. These requirements pose new challenges to the formal description tools. This paper argues that a series of specifications in CSCW systems can be sufficiently expressed based on temporal logic. Then an extended temporal logic CWTL is brought forward on this principle. At the end of the paper, an example is presented showing how to specify a simplified cooperative authoring system with CWTL

Information security and access control is an important part of CSCD applying. RBAC (role based access control) as an access control technology using for distributed system has been widely researched these years. Role hierarchies is one component of RBAC model, it can reduce the workload of permission assignment. Nowadays most research of RBAC has no relation with timing constraint. The main purpose of this article is to study the effect of temporal constraint acting on role hierarchy. The basis of the study is RBAC96 model, first we analyse the change of role state, propose 3 forms of temporal constraint and give the expression of temporal constraint. Then we present an analysis of the effects of temporal constraint on role enabling and role activation which has various implication on a role hierarchy, and at last we formally describe a timing restricted role hierarchies model

Workflow management has been a hot issue in both academic and industrial research. Deadline assignment is of great significance in workflow management. In order to avoid deadline violation, designers need an approach to evaluate the upper bound of turnaround time of a workflow process, especially in the early stage of the process design. In this paper, a method is proposed to evaluate the latest finishing time of a workflow instance. This method is based on our previously proposed model fuzzy temporal workflow nets (FTWF-Nets), which could depict uncertain or imprecise temporal information. Then an efficient algorithm extended from critical path method (CPM) is devised. Finally a case study is given

Performance analysis for workflow models has been recognized as one of the most significant tasks in workflow management. Time performance evaluation methods are needed for workflow designers to estimate the average turnaround time of processes in the early stage of workflow system development. Based on extending fuzzy temporal workflow nets by introducing choice possibilities for transitions in structural conflict, a new workflow model named extended fuzzy temporal workflow nets (EFTWF-nets) has been proposed. And the calculation of temporal elements in EFTWF-nets is given. Then a decomposition algorithm for EFTWF-nets is put forward and a time performance evaluation method based on it is investigated. Finally a case study is given to illustrate how to use this approach

Materialized view (MV) maintenance is an important research area in the database and data warehouse area. Currently, most of the MV maintenance methods are based on the C/S or B/S model, and the MV tasks are recomputed sequentially, which cause the system to overload and even crash. To address the problem, decomposition of the maintenance task is explored based on task balancing collaborative strategy, which is also applicable to the CSCW systems for managing, searching and processing data

As an open standard for data representation, XML breathes new energy into Web applications and electronic commerce. In this paper, the authors elucidate the concepts and connotations of XML, introduce the system structure and functional model of the e-commerce system ESHOPDIY (Electronic Shop Do-It-Yourself) which we developed by integrating three kinds of e-commerce sales styles, [business-to-consumer (B2C), business-to-business (B2B) and consumer-to-consumer (C2C)]; deal with developing technology and the realization of ESHOPDIY; and finally discuss its features.

It is a very important work to improve the quality of voice on transferred Internet. In this paper two aspects about voice quality are analyzed, and an algorithm of dynamic setting audio buffer for improving QoS is proposed. Finally the simulation results of the algorithms and the conclusion are given.

IP QoS is one of the core technologies of IP network. The mechanisms to achieve IP QoS could be sub bandwidth management (SBM), multiple protocols label switching (MPLS), IntServ, DiffServ and so on. But some of the above should be integrated for implementing completed IP QoS. At present, one of the most popular ideas is IntServ on the edge of network and DiffServ on the backbone. In this paper, we present a framework for completed end-to-end adaptive IP QoS by integrating DiffServ and IntServ, put forward the negotiation flowchart of adaptive QoS on the boundary router and the QoS control flow-chart on the Interior router.