The development of mobile web services has brought significant attention to spatial crowdsourcing. The uneven distribution of tasks and workers has led to recent research on Cross-Platform Spatial Crowdsourcing (CPSC), aiming for a multi-win situation for platforms, workers, and task requesters. Previous studies on CPSC problems focused on task assignment and worker selection performance, overlooking the importance of privacy preservation. This article addresses the existing challenges of privacy preservation and service quality by formulating a Privacy-Preserving Cross-Platform Spatial Crowdsourcing (PP-CPSC) problem and proves it to be NP-hard. We propose an Evolutionary Differential Privacy (Evo-DP) approach to optimize PP-CPSC. Evo-DP’s evolutionary framework enables efficient and flexible optimization of privacy budget allocation. Within Evo-DP, each solution to the privacy budget allocation is represented as an individual in the population. To approximate the optimal solution, three evolutionary operations—mutation, crossover, and scaling—are employed for population updates, along with a selection process. A hybrid population model is introduced to balance exploration and exploitation abilities. Experimental results demonstrate Evo-DP’s superiority over previous strategies in terms of solution quality, convergence speed, and scalability.

Multimodal recommendation provides richer and more accurate personalized recommendations by jointly modeling user’s historical behaviors and different modality of items, such as text, image, audio, and video in online platforms. Most existing work of multimodal recommendation focuses on leveraging modal features and modal correlation graph structures to learn user preferences. Due to insufficient exploration of user collaborative preferences and the noise during high-order multimodal data connections, valuable information may be lost, leading to deviations in understanding user preferences. Therefore, an I ndependent T opological Preference-Aware and Co operative H ypergraph D iffusion-based M ultimodal Rec ommender Model (ITCoHD-MRec) is necessary for online platforms. This article aims to develop an ITCoHD-MRec that incorporates topological perception as well as generative diffusion models in multimodal hypergraph recommendation to make the model more adaptive and robust in complex environments. Firstly, leveraging a Graph Convolutional Network (GCN), the model independently captures user preference representations for both collaborative relevance and modal relevance from the user-item interaction graph, which contains ID embeddings and modal features. This enables the extraction of deeper associations between users and items. Secondly, leveraging topological pruning techniques, the model learns differentiated features in different modal blocks to prevent node representations from becoming homogenized. This helps further identify user preferred connectivity patterns and removes redundant noisy connections. Finally, by employing the diffusion model, information regarding the higher-order interaction patterns between attributes and items within the hypergraph structure is propagated. This effectively captures the potential global dependencies between attributes and items, thereby providing deeper associations enriched with more substantial semantic information for subsequent recommendation tasks. The model autonomously learns different features and higher-order connectivity of nodes, which enables the model to obtain a wider and more accurate perception of user preferences in complex interaction environments. Experimental comparisons with 15 models on four real datasets—Baby, Sports, Clothing, and Electronics show that the model improves the recall by 0.85%–3.57% and the normalized discounted cumulative gain by 2.31%–3.43%, which validates the effectiveness of ITCoHD-MRec.

Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by well-designed perturbations. This could lead to disastrous results on critical applications such as self-driving cars, surveillance security, and medical diagnosis. At present, adversarial training is one of the most effective defenses against adversarial examples. However, in traditional adversarial training, it is still difficult to achieve a good trade-off between clean accuracy and robustness since DNNs still learn spurious features. The intrinsic reason is that traditional adversarial training makes it difficult to fully learn core features from adversarial examples when noise and examples cannot be disentangled. In this paper, we disentangle the adversarial examples into natural and perturbed patterns by bit-plane slicing. We assume the higher bit-planes represent natural patterns and the lower bit-planes represent perturbed patterns, respectively. We propose Feature-Focusing Adversarial Training (F$^{2}$AT), which differs from previous work in that it enforces the model to focus on the core features from natural patterns and reduce the impact of spurious features from perturbed patterns. The experimental results demonstrated that the clean accuracy and adversarial robustness with our F$^{2}$AT can be significantly improved.

This study addresses the Multiple Flying Sidekicks Traveling Salesman Problem (mFSTSP), where parallel Unmanned Aerial Vehicles (UAVs, or Drones) work alongside truck to enhance delivery efficiency. Existing scheduling approaches face challenges in high computational costs and the risk of converging to local optima due to excessive exploration in unknown environments, especially in large-scale mFSTSP. This study proposed a Large Language Model Enhanced Q-Learning Approach (LLM-QL) to solve mFSTSP, which combines the local exploration advantages of Q-Learning with the global understanding of unknown environments provided by LLMs, thus improving the efficiency of path planning. A novel prompt strategy is also provided, transforming the problem modeling into a format easily understood by LLMs, guiding the algorithm's exploration and significantly improving convergence. We also provide a proof of the convergence of LLM-QL. Experimental results demonstrate that LLM-QL achieves up to a 1.35 x improvement in key performance metrics such as total completion time, algorithm runtime, and UAV utilization, compared to existing state-of-the-art methods.

Complex social event summarization is a problem which has been important for real-world applications, including crisis management, rumor control and government policy tracking. However, in many critical situations, social events are complex and context-sensitive, which demands the online summarization of social events in an integrated manner. Motivated by this, we propose an online complex social event summarization approach, namely SOMA, which summarizes the complex social events over multiple attributes including media content and contexts simultaneously. The evaluation shows that our proposed approach outperforms the existing solutions for event summarizaiton in terms of effectiveness and efficiency.

Abstract Insider threats pose a critical risk to organisations, impacting their data, processes, resources, and overall security. Such significant risks arise from individuals with authorised access and familiarity with internal systems, emphasising the potential for insider threats to compromise the integrity of organisations. Previous research has addressed the challenge by pinpointing malicious actions that have already occurred but provided limited assistance in preventing those risks. In this research, we introduce a novel approach based on bidirectional long short-term memory (BiLSTM) networks that effectively captures and analyses the patterns of individual actions and their sequential dependencies. The focus is on predicting whether an individual would be a malicious insider in a future day based on their daily behavioural records over the previous several days. We analyse the performance of the four supervised learning algorithms on manual features, sequential features, and the ground truth of the day with different combinations. In addition, we investigate the performance of different RNN models, such as RNN, LSTM, and BiLSTM, in incorporating these features. Moreover, we explore the performance of different predictive lengths on the ground truth of the day and different embedded lengths for the sequential features. All the experiments are conducted on the CERT r4.2 dataset. Experiment results show that BiLSTM has the highest performance in combining these features.

Situation-awareness (SA) has been important for natural disaster management and smart decision making. Traditionally, security officers recognize disaster situations through emergency reporting with phone calls. However, due to the busy phone lines or power outages caused by disasters, traditional SA has been limited in terms of time and mitigation response, which may cause high loss in life and properties in disaster areas. Social media-based SA has been studied recently. However, existing systems are designed for events without nonconsecutive migrations over location and time. In this demo, we design HSAP, the first human-in-the-loop social media-based SA platform for effective and efficient management of disasters with complex event migrations. HSAP is designed with a number of novel techniques, including complex social event detection, summarization, and human-in-the-loop result filtering. We demonstrate the usage of HSAP via Nepal earthquake 2015.

Data transparency is beneficial to data participants’ awareness, users’ fairness, and research work’s reproducibility. However, when addressing transparency requirements, we cannot ignore data privacy. This article defines the multi-objective data publishing (MODP) problem, optimizing data privacy and transparency at the same time. Accordingly, we propose a distributed cooperative coevolutionary genetic algorithm (DCCGA) to optimize the MODP problem. In the population of DCCGA, each individual represents an anonymization solution to MODP. Three modules in DCCGA, i.e., grouping module, cooperative coevolutionary module, and evolving module, are proposed for distributed sub-population update and evaluation, improving DCCGA’s optimization performance and parallel efficiency. Moreover, a matrix-based crossover operator and a matrix-based mutation operator are designed to exchange and adjust anonymization information in the individuals efficiently. Experimental results demonstrate that the proposed DCCGA outperforms the competitors with respect to solution accuracy, convergence speed, and scalability. Besides, we verify the effectiveness of all the proposed components in DCCGA.

Software vulnerabilities, also known as flaws, bugs or weaknesses, are common in modern information systems, putting critical data of organizations and individuals at cyber risk. Due to the scarcity of resources, initial risk assessment is becoming a necessary step to prioritize vulnerabilities and make better decisions on remediation, mitigation, and patching. Datasets containing historical vulnerability information are crucial digital assets to enable AI-based risk assessments. However, existing datasets focus on collecting information on individual vulnerabilities while simply storing them in relational databases, disregarding their structural connections. This article constructs a compact vulnerability knowledge graph, VulKG, containing over 276 K nodes and 1 M relationships to represent the connections between vulnerabilities, exploits, affected products, vendors, referred domain names, and more. We provide a detailed analysis of VulKG modeling and construction, demonstrating VulKG-based query and reasoning, and providing a use case of applying VulKG to a vulnerability risk assessment task, i.e., co-exploitation behavior discovery. Experimental results demonstrate the value of graph connections in vulnerability risk assessment tasks. VulKG offers exciting opportunities for more novel and significant research in areas related to vulnerability risk assessment. The data and codes of this article are available at https://github.com/happyResearcher/VulKG.git .

Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose an automatic QTS anomaly detection framework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to yield a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3% accuracy, TCQSA exceeds two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs.

Transcriptome-based survival modeling is a critical yet complicated task in cancer treatment due to the strong association between the prognosis of a patient and the progression of tumors based on heterogeneous molecular mechanisms. An effective prognosis model needs to consider the uncertainty that naturally stems from the high molecular heterogeneity together with numerous unforeseeable factors related to prognosis, and requires a perfect match between the selected covariates and the modeling approach of the joint distribution between covariates and right-censored survival time. Unfortunately, these necessary factors have not been fully considered in existing survival models. In this paper, we propose a novel conditional variational deep survival model (CVaDeS for short) for survival analysis. CVaDeS accurately predicts the risk values of patients and effectively distinguishes high-risk and low-risk individuals. Also, the proposed model CVaDeS can be effectively combined with Cox feature selection for survival analysis. Extensive experimental results demonstrate that the combination of Cox feature selection with CVaDeS significantly improves the predictive performance of the model, surpassing baseline models.

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.

Complex social event summarization is a problem which has been shown having great utility for real-world applications, including crisis management, rumor control and government policy tracking. In recent years there has been significant research effort spent on effectively extracting meaningful textual descriptions of an event. However, in many critical situations, social events are complex and context-sensitive, which demands the online summarization of social events in an integrated manner. In this paper, we propose the first online complex social event summarization approach, namely SOMA, which summarizes the complex social events over multiple attributes including media content and contexts simultaneously. Specifically, we first propose a deep learning model that comprehensively summarizes events in regards to the text description and locations that they appear in, by utilizing their hidden connections in posts. We then propose a summary generator over time, text and location to achieve a maximal coverage of the summary over the original social event and minimal redundancy of the summary. Furthermore, we propose a location estimation method to address the location sparsity issue of complex events by mining the correlation between text and location. The evaluation over four real-event datasets and three benchmark datasets shows that our proposed approach outperforms the existing solutions for event summarizaiton in terms of effectiveness and efficiency.

The development of information gathering and extraction technology has led to the popularity of multi-view data, which enables samples to be seen from numerous perspectives. Multi-view clustering (MVC), which groups data samples by leveraging complementary and consensual information from several views, is gaining popularity. Despite the rapid evolution of MVC approaches, there has yet to be a study that provides a full MVC roadmap for both stimulating technical improvements and orienting research newbies to MVC. In this article, we review recent MVC techniques with the purpose of exhibiting the concepts of popular methodologies and their advancements. This survey not only serves as a unique MVC comprehensive knowledge for researchers but also has the potential to spark new ideas in MVC research. We summarise a large variety of current MVC approaches based on two technical mechanisms: heuristic-based multi-view clustering (HMVC) and neural network-based multi-view clustering (NNMVC). We end with four technological approaches within the category of HMVC: nonnegative matrix factorisation, graph learning, latent representation learning, and tensor learning. Deep representation learning and deep graph learning are two technical methods that we demonstrate in NNMVC. We also show 15 publicly available multi-view datasets and examine how representative MVC approaches perform on them. In addition, this study identifies the potential research directions that may require further investigation in order to enhance the further development of MVC.

The signaling of the associations between drugs and adverse drug reactions (ADRs) is a challenging task in pharmacovigilance, especially when an association is infrequent or has never previously been reported. Most existing methods for ADR signaling are based on analyzing the frequency with which drugs tend to co-occur with ADRs. In this article, we propose a kernelized multitask learning model, KEMULA, in which information is learned and transferred from the clinical data of other patients as collaborative information to rank distinct lists of ADRs for different patients. We comprehensively compare the performance of KEMULA against three baseline methods, two state-of-the-art ADR signaling methods, and two KEMULA variants. The method is tested on adverse drug event reports retrieved from the FDA Adverse Event Reporting System (FAERS), which includes 4,106,633 unique adverse drug event reports, 7,824 unique ADRs, 114 unique biotech drugs, 1,151 unique small molecule drugs, and 3,363 unique medical conditions. The experimental results demonstrate the advantages of our method and show that it not only can signal frequent ADRs but also has the power to signal infrequent ADRs that cannot be signaled by most existing methods.

Data summarization that presents a small subset of a dataset to users has been widely applied in numerous applications and systems. Many datasets are coded with hierarchical terminologies, e.g., gene ontology, disease ontology, to name a few. In this paper, we study the weighted tree summarization. We motivate and formulate our${\mathsf {kWTS}}$-${\mathsf {problem}}$as selecting a diverse set of$k$nodes tosummarize a hierarchicaltree$T$withweighted terminologies. We first propose an efficient greedy tree summarization algorithm${\mathsf {GTS}}$. It solves the problem with$(1-1/e)$-approximation guarantee. Although${\mathsf {GTS}}$achieves quality-guaranteed answers approximately, but it is still not optimal. To tackle the problem optimally, we further develop a dynamic programming algorithm${\mathsf {OTS}}$to obtain optimal answers for${\mathsf {kWTS}}$-${\mathsf {problem}}$in$O(nhk^3)$time, where$n, h$are the node size and height in tree$T$. The algorithm complexity and correctness of${\mathsf {OTS}}$are theoretically analyzed. In addition, we propose a useful optimization technique of tree reduction to remove useless nodes with zero weights and shrink the tree into a smaller one, which ensures the efficiency acceleration of both${\mathsf {GTS}}$and${\mathsf {OTS}}$in real-world datasets. Moreover, we illustrate one useful application of graph visualization based on the answer of$k$-sized tree summarization and show it in a novel case study. Extensive experimental results on real-world datasets show the effectiveness and efficiency of our proposed approximate and optimal algorithms for tree summarization. Furthermore, we conduct a usability evaluation of attractive topic recommendation on ACM Computing Classification System dataset to validate the usefulness of our model and algorithms.

Event popularity prediction over social media is crucial for estimating information propagation scope, decision making, and emergency prevention. It has been widely inves-tigated by existing approaches focusing on predicting single attribute occurrences which are not comprehensive enough for representing complex social event propagation. Motivated by this, we propose a novel hashtag-influence-based event popularity prediction by mining the impact of an influential hashtag set on the event propagation. We have conducted extensive experiments to prove the effectiveness and efficiency of the proposed approach.

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.

Event popularity prediction over social media is crucial for estimating information propagation scope, decision making, and emergency prevention. However, existing approaches only focus on predicting the occurrences of single attribute such as a message, a hashtag or an image, which are not comprehensive enough for representing complex social event propagation. In this paper, we predict the event popularity, where an event is described as a set of messages containing multiple hashtags. We propose a novel hashtag-influence-based event popularity prediction by mining the impact of an influential hashtag set on the event propagation. Specifically, we first propose a hashtag-influence-based cascade model to select the influential hashtags over an event hashtag graph built by the pairwise hashtag similarity and the topic distribution of event-related hashtags. A novel measurement is proposed to identify the hashtag influence of an event over its content and social impacts. A hashtag correlation-based algorithm is proposed to optimize the seed selection in a greedy manner. Then, we propose an event-fitting boosting model to predict the event popularity by embedding the feature importance over events into the XGBOOST model. Moreover, we propose an event-structure-based method, which incrementally updates the prediction model over social streams. We have conducted extensive experiments to prove the effectiveness and efficiency of the proposed approach.

Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose anautomaticQTSanomalydetectionframework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to achieve a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3 percent accuracy, TCQSA outperforms two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs. Additionally, the effectiveness of the attention mechanisms is quantitatively and qualitatively demonstrated.

Abstract By breaking sensitive associations between attributes, database fragmentation can protect the privacy of outsourced data storage. Database fragmentation algorithms need prior knowledge of sensitive associations in the tackled database and set it as the optimization objective. Thus, the effectiveness of these algorithms is limited by prior knowledge. Inspired by the anonymity degree measurement in anonymity techniques such as k-anonymity, an anonymity-driven database fragmentation problem is defined in this paper. For this problem, a set-based adaptive distributed differential evolution (S-ADDE) algorithm is proposed. S-ADDE adopts an island model to maintain population diversity. Two set-based operators, i.e., set-based mutation and set-based crossover, are designed in which the continuous domain in the traditional differential evolution is transferred to the discrete domain in the anonymity-driven database fragmentation problem. Moreover, in the set-based mutation operator, each individual’s mutation strategy is adaptively selected according to the performance. The experimental results demonstrate that the proposed S-ADDE is significantly better than the compared approaches. The effectiveness of the proposed operators is verified.

Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model's development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

Positive unlabeled time series classification (PUTSC) refers to classifying time series with a set PL of positive labeled examples and a set U of unlabeled ones. Model selection for PUTSC is a largely untouched topic. In this paper, we look into PUTSC model selection, which as far as we know is the first systematic study in this topic. Focusing on the widely adopted self-training one-nearest-neighbor (ST-1NN) paradigm, we propose a model selection framework based on active learning (AL). We present the novel concepts of self-training label propagation, pseudo label calibration principles and ultimately influence to fully exploit the mechanism of ST-1NN. Based on them, we develop an effective model performance evaluation strategy and three AL sampling strategies. Experiments on over 120 datasets and a case study in arrhythmia detection show that our methods can yield top performance in interactive environments, and can achieve near optimal results by querying very limited numbers of labels from the AL oracle.

Absence epilepsy is one of the most common types of epilepsy. The diagnosis of absence epilepsy is among the greatest challenges faced by clinical neurologists due to a lack of easily observable symptoms that are present in conventional epilepsy (e.g. spasm and convulsion), and highly relies on the detection of Spike and Slow Waves (SSWs) in Electroencephalogram (EEG) signals. Recently, graph representations called complex networks have been increasingly applied to characterizing 1D EEG signals. However, existing methods often fail to effectively represent SSWs, struggling to capture the differences between SSW waveforms and their non-SSW counterparts, such as minute differences and distinct shapes. Addressing this issue, in this work, we propose two simple yet effective complex networks, Overlook Graph (OG) and Weighted Overlook Graph (WOG), which have been customized to expressively represent SSWs. Built upon OG and WOG, we then develop a 2D Convolutional Neural Network (2D-CNN) to further learn latent features from the graph representations and accomplish the detection task. Extensive experiments on a real-world absence epilepsy EEG dataset show that the proposed OG/WOG-2D-CNN method can accurately detect SSWs. Additional experiments on the well-known Bonn dataset further show that our method can generalize to the conventional epilepsy seizure detection task with highly competitive performances.

Abstract Review helpfulness prediction aims to prioritize online reviews by quality. Existing methods largely combine review texts and star ratings for helpfulness prediction. However, star ratings are used in a way that has either little representation capacity or limited interaction with review texts. As a result, rating information has yet to be fully exploited during the combination. This paper aims to overcome the two drawbacks. A deep interactive architecture is proposed to learn the text–rating interaction (TRI) for helpfulness modeling. TRI enlarges the representation capacity of star ratings while enhancing the influence of rating information on review texts. TRI is evaluated on six real-world domains of the Amazon 5-Core dataset. Extensive experiments demonstrate that TRI can better predict review helpfulness and beat the state of the art. Ablation studies and qualitative analysis are provided to further understand model behaviors and the learned parameters.

Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach.

As one of the most popular services over online platforms, social recommendation has attracted increasing research efforts recently. Among all the recommendation tasks, an important one is item recommendation over high speed social media streams. Existing stream recommendation techniques are not effective for handling social users with diverse interests. Meanwhile, approaches for recommending items to a particular user are not efficient when applied to a huge number of users over high speed streams. In this paper, we propose a novel framework for the social recommendation over streams. Specifically, we first propose a novel Bi-Layer Hidden Markov Model (BiHMM) that adaptively captures the users' behaviors and their interactions with influential official accounts to predict their long-term and short-term interests. Then, we design a new probabilistic entity matching scheme for identifying the relevance score of a streaming item to a user. Moreover, we propose a novel index scheme called CPPse-index for improving the efficiency of our solution. Extensive tests are conducted to prove the superiority of our approach in terms of the recommendation quality and time cost.

With the explosive growth of online service platforms, increasing number of people and enterprises are doing everything online. In order for organizations, governments, and individuals to understand their users, and promote their products or services, it is necessary for them to analyse big data and recommend the media or online services in real time. Effective recommendation of items of interest to consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. Driven by the business successes, academic research in this field has also been active for many years. Through many scientific breakthroughs have been achieved, there are still tremendous challenges in developing effective and scalable recommendation systems for real-world industrial applications. Existing solutions focus on recommending items based on pre-set contexts, such as time, location, weather etc. The big data sizes and complex contextual information add further challenges to the deployment of advanced recommender systems. This workshop aims to bring together researchers with wide-ranging backgrounds to identify important research questions, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of context-aware recommender systems and big data analytics.

A phrase is a natural, meaningful, and essential semantic unit. In topic modeling, visualizing phrases for individual topics is an effective way to explore and understand unstructured text corpora. However, from phrase quality and topical cohesion perspectives, the outcomes of existing approaches remain to be improved. Usually, the process of topical phrase mining is twofold: phrase mining and topic modeling. For phrase mining, existing approaches often suffer from order sensitive and inappropriate segmentation problems, which make them often extract inferior quality phrases. For topic modeling, traditional topic models do not fully consider the constraints induced by phrases, which may weaken the cohesion. Moreover, existing approaches often suffer from losing domain terminologies since they neglect the impact of domain-level topical distribution. In this paper, we propose an efficient method for high quality and cohesive topical phrase mining. A high quality phrase should satisfy frequency, phraseness, completeness, and appropriateness criteria. In our framework, we integrate quality guaranteed phrase mining method, a novel topic model incorporating the constraint of phrases, and a novel document clustering method into an iterative framework to improve both phrase quality and topical cohesion. We also describe efficient algorithmic designs to execute these methods efficiently. The empirical verification demonstrates that our method outperforms the state-of-the-art methods from the aspects of both interpretability and efficiency.

Sparse topic models (STMs) are widely used for learning a semantically rich latent sparse representation of short texts in large scale, mainly by imposing sparse priors or appropriate regularizers on topic models. However, it is difficult for these STMs to model the sparse structure and pattern of the corpora accurately, since their sparse priors always fail to achieve real sparseness, and their regularizers bypass the prior information of the relevance between sparse coefficients. In this paper, we propose a novel Bayesian hierarchical topic models called Bayesian Sparse Topical Coding with Poisson Distribution (BSTC-P) on the basis of Sparse Topical Coding with Sparse Groups (STCSG). Different from traditional STMs, it focuses on imposing hierarchical sparse prior to leverage the prior information of relevance between sparse coefficients. Furthermore, we propose a sparsity-enhanced BSTC, Bayesian Sparse Topical Coding with Normal Distribution (BSTC-N), via mathematic approximation. We adopt superior hierarchical sparse inducing prior, with the purpose of achieving the sparsest optimal solution. Experimental results on datasets of Newsgroups and Twitter show that both BSTC-P and BSTC-N have better performance on finding clear latent semantic representations. Therefore, they yield better performance than existing works on document classification tasks.

Extracting web content is to obtain the required data embedded in web pages, usually including structured records, such as product information, and text content, such as news. Web pages use a large number of HTML tags to organize and to present various information. Both knowing little about the structures of web pages and mixing kinds of information in web pages are making the extraction process very challenging to guarantee extraction performance and extraction adaptability. This study proposes a unified web content extraction framework that can be applied in various web environments to extract both structured records and text content. First, we construct a characteristic container to hold kinds of characteristics related with extraction objectives, including visual text information, content semantics(instead of HTML tag semantics), web page structures, etc. Second, the above characteristics are integrated into an extraction framework for extraction decisions on different web sites. Especially, we put forward different strategies, path aggregation for extracting text content and HMM model for structured records, to locate the extraction area by exploiting both those extraction characteristics. Comparative experiments on multiple web sites with popular extraction methods, including CETR, CETD and CNBE, show that our proposed extraction method can provide better extraction precision and extraction adaptability.

This article presents an unsupervised multi-view hierarchical embedding (UMHE) framework to sufficiently reveal the intrinsic topical knowledge in social events. Event-oriented topics are highly related to such events as it can provide explicit descriptions of what have happened in social community. In many real-world cases, however, it is difficult to include all attributes of microblogs, more often, textual aspects only are available. Traditional topic modelling methods have failed to generate event-oriented topics with the textual aspects, since the inherent relations between topics are often overlooked in these methods. Meanwhile, the metrics in original word vocabulary space might not effectively capture semantic distances. Our UMHE framework overcomes the severe information deficiency and poor feature representation. The UMHE first develops a multi-view Bayesian rose tree to preliminarily generate prior knowledge for latent topics and their relations. With such prior knowledge, we design an unsupervised translation-based hierarchical embedding method to make a better representation of these latent topics. By applying self-adaptive spectral clustering on the embedding space and the original space concomitantly, we eventually extract event-oriented topics in word distributions to express social events. Our framework is purely data-driven and unsupervised, without any external knowledge. Experimental results on TREC Tweets2011 dataset and Sina Weibo dataset demonstrate that the UMHE framework can construct hierarchical structure with high fitness, but also yield topic embeddings with salient semantics; therefore, it can derive event-oriented topics with meaningful descriptions.

Data summarization that presents a small subset of a dataset to users has been widely applied in numerous applications and systems. Many datasets are coded with hierarchical terminologies, e.g., the international classification of Diseases-9, Medical Subject Heading, and Gene Ontology, to name a few. In this paper, we study the problem of selecting a diverse set of k elements to summarize an input dataset with hierarchical terminologies, and visualize the summary in an ontology structure. We propose an efficient greedy algorithm to solve the problem with (1-1/e)≈ 62%-approximation guarantee. Preliminary experimental results on real-world datasets show the effectiveness and efficiency of the proposed algorithm for data summarization.

Determining appropriate statistical distributions for modeling text corpora is important for accurate estimation of numerical characteristics. Based on the validity of the test on a claim that the data conforms to Poisson distribution we propose Poisson decomposition model (PDM), a statistical model for modeling count data of text corpora, which can straightly capture each document's multidimensional numerical characteristics on topics. In PDM, each topic is represented as a parameter vector with multidimensional Poisson distribution, which can be easily normalized to multinomial term probabilities and each document is represented as measurements on topics and thereby reduced to a measurement vector on topics. We use gradient descent methods and sampling algorithm for parameter estimation. We carry out extensive experiments on the topics produced by our models. The results demonstrate our approach can extract more coherent topics and is competitive in document clustering by using the PDM-based features, compared to PLSI and LDA.

Compressing textstreams generated by social networks can both reduce storage consumption and improve efficiency such as fast searching. However, the compression process is a challenge due to the large scale of textstreams. In this article, we propose a textstream compression framework based on compressed sensing theory and design a series of matching parallel procedures. The new approach uses a linear projection technique in the textstream compression process, achieving fast compression speed and low compression ratio. Two processes are executed by designing elaborated parallel procedures for efficient compressing and decompressing of large-scale textstreams. The decompression process is implemented for approximate solutions of underdetermined linear systems. Experimental results show that the new method can efficiently achieve the compression and decompression tasks on a large amount of text generated by social networks.

Diagnosis of neurological diseases is a growing concern and one of the most difficult challenges for modern medicine. According to the World Health Organisation’s recent report, neurological disorders, such as epilepsy, Alzheimer’s disease and stroke to headache, affect up to one billion people worldwide. An estimated 6.8 million people die every year as a result of neurological disorders. Current diagnosis technologies (e.g. magnetic resonance imaging, electroencephalogram) produce huge quantity data (in size and dimension) for detection, monitoring and treatment of neurological diseases. In general, analysis of those medical big data is performed manually by experts to identify and understand the abnormalities. It is really difficult task for a person to accumulate, manage, analyse and assimilate such large volumes of data by visual inspection. As a result, the experts have been demanding computerised diagnosis systems, called “computer-aided diagnosis (CAD)” that can automatically detect the neurological abnormalities using the medical big data. This system improves consistency of diagnosis and increases the success of treatment, save lives and reduce cost and time. Recently, there are some research works performed in the development of the CAD systems for management of medical big data for diagnosis assessment. This paper explores the challenges of medical big data handing and also introduces the concept of the CAD system how it works. This paper also provides a survey of developed CAD methods in the area of neurological diseases diagnosis. This study will help the experts to have some idea and understanding how the CAD system can assist them in this point.

To date, data generates and arrives in the form of stream to propagate discussions of public events in microblog services. Discovering event-oriented topics from the stream will lead to a better understanding of the change of public concern. However, as the massive scale of the data stream, traditional static topic models, such as LDA, are no longer fit for topic detection and tracking tasks. In this paper, we propose a central topic model (CenTM), where a Multi-view Clustering algorithm with Two-phase Random Walk (MC-TRW) is devised to aggregate the LDA's latent topics into central topics. Furthermore, we leverage the aggregation of central topics alternately with MC-TRW and sequential topic inference to improve the scalability in the stream fashion, so as to derive the dynamic central topic model (DCenTM). Specifically, our model is able to uncover the intrinsic characteristics of the central topics and predict the trend of their intensity along a life cycle. Experimental results demonstrate that the proposed central topic model is event-oriented and of high generalization, it therefore can dispose the topic trend prediction effectively and precisely in massive data stream.

The creation of sharing communities has resulted in the astonishing increasing of digital videos, and their wide applications in the domains such as entertainment, online news broadcasting etc. The improvement of these applications relies on effective solutions for social user access to video data. This fact has driven the recent research interest in social recommendation in shared communities. Although certain effort has been put into video recommendation in shared communities, the contextual information on social users has not been well exploited for effective recommendation. In this paper, we propose an approach based on the content and social information of videos for the recommendation in sharing communities. Specifically, we first exploit a robust video cuboid signature together with the Earth Mover's Distance to capture the content relevance of videos. Then, we propose to identify the social relevance of clips using the set of users belonging to a video. We fuse the content relevance and social relevance to identify the relevant videos for recommendation. Following that, we propose a novel scheme called sub-community-based approximation together with a hash-based optimization for improving the efficiency of our solution. Finally, we propose an algorithm for efficiently maintaining the social updates in dynamic shared communities. The extensive experiments are conducted to prove the high effectiveness and efficiency of our proposed video recommendation approach.

Unlike in general recommendation scenarios where a user has only a single role, users in trust rating network, e.g. Epinions, are associated with two different roles simultaneously: as a truster and as a trustee. With different roles, users can show distinct preferences for rating items, which the previous approaches do not involve. Moreover, based on explicit single links between two users, existing methods can not capture the implicit correlation between two users who are similar but not socially connected. In this paper, we propose to learn dual role preferences (truster/trustee-specific preferences) for trust-aware recommendation by modeling explicit interactions (e.g., rating and trust) and implicit interactions. In particular, local links structure of trust network are exploited as two regularization terms to capture the implicit user correlation, in terms of truster/trustee-specific preferences. Using a real-world and open dataset, we conduct a comprehensive experimental study to investigate the performance of the proposed model, RoRec. The results show that RoRec outperforms other trust-aware recommendation approaches, in terms of prediction accuracy.

Supported by the technical advances and the commercial success of GPS-enabled mobile devices, geo-tagged photos have drawn plenteous attention in research community. The explosive growth of geo-tagged photos enables many large-scale applications, such as location-based photo browsing, landmark recognition, etc. Meanwhile, as the number of geo-tagged photos continues to climb, new challenges are brought to various applications. The existence of massive near-duplicate geo-tagged photos jeopardizes the effective presentation for the above applications. A new dimension in the search and presentation of geo-tagged photos is urgently demanded. In this paper, we devise a location visualization framework to efficiently retrieve and present diverse views captured within a local proximity. Novel photos, in terms of capture locations and visual content, are identified and returned in response to a query location for diverse visualization. For real-time response and good scalability, a new Hybrid Index structure which integrates R-tree and Geographic Grid is proposed to quickly identify the Maximal Near-duplicate Photo Groups (MNPG) in the query proximity. The most novel photos from different groups are then returned to generate diverse views on the location. Extensive experiments on synthetic and real-life photo datasets prove the novelty and efficiency of our methods.

Data clustering is one of the fundamental research problems in data mining and machine learning. Most of the existing clustering methods, for example, normalized cut and (k)-means, have been suffering from the fact that their optimization processes normally lead to an NP-hard problem due to the discretization of the elements in the cluster indicator matrix. A practical way to cope with this problem is to relax this constraint to allow the elements to be continuous values. The eigenvalue decomposition can be applied to generate a continuous solution, which has to be further discretized. However, the continuous solution is probably mixing-signed. This result may cause it deviate severely from the true solution, which should be naturally nonnegative. In this paper, we propose a novel clustering algorithm, i.e., discriminative nonnegative spectral clustering, to explicitly impose an additional nonnegative constraint on the cluster indicator matrix to seek for a more interpretable solution. Moreover, we show an effective regularization term which is able to not only provide more useful discriminative information but also learn a mapping function to predict cluster labels for the out-of-sample test data. Extensive experiments on various data sets illustrate the superiority of our proposal compared to the state-of-the-art clustering algorithms.

As a prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant ads into a Web page, so as to increase the number of ad-clicks. However, some problems of homonymy and polysemy, low intersection of keywords etc., can lead to the selection of irrelevant ads for a page. In this paper, we present a new contextual advertising approach to overcome the problems, which uses Wikipedia concept and category information to enrich the content representation of an ad (or a page). First, we map each ad and page into a keyword vector, a concept vector and a category vector. Next, we select the relevant ads for a given page based on a similarity metric that combines the above three feature vectors together. Last, we evaluate our approach by using real ads, pages, as well as a great number of concepts and categories of Wikipedia. Experimental results show that our approach can improve the precision of ads-selection effectively.

Microdata protection in statistical databases has recently become a major societal concern and has been intensively studied in recent years. Statistical Disclosure Control (SDC) is often applied to statistical databases before they are released for public use. Micro aggregation for SDC is a family of methods to protect micro data from individual identification. SDC seeks to protect micro data in such a way that can be published and mined without providing any private information that can be linked to specific individuals. Micro aggregation works by partitioning the micro data into groups of at least k records and then replacing the records in each group with the centroid of the group. An optimal micro aggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the micro aggregation process. This paper presents a pair wise systematic (P-S) micro aggregation method to minimize the information loss. The proposed technique simultaneously forms two distant groups at a time with the corresponding similar records together in a systematic way and then anonymized with the centroid of each group individually. The structure of P-S problem is defined and investigated and an algorithm of the proposed problem is developed. The performance of the P-S algorithm is compared against the most recent micro aggregation methods. Experimental results show that P-S algorithm incurs less than half information loss than the latest micro aggregation methods for all of the test situations.

Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, because data-driven mining automatically generates classification outputs that are imprecise, meaningless, and beyond users' control. In this paper, we provide a novel domain-driven classification method that takes advantage of multiple criteria and multiple constraint-level programming for intelligent credit scoring. The method involves credit scoring to produce a set of customers' scores that allows the classification results actionable and controllable by human interaction during the scoring process. Domain knowledge and experts' experience parameters are built into the criteria and constraint functions of mathematical programming and the human and machine conversation is employed to generate an efficient and precise solution. Experiments based on various data sets validated the effectiveness and efficiency of the proposed methods.

A global education system, as a key area in future IT, has fostered developers to provide various learning systems with low cost. While a variety of e-learning advantages has been recognized for a long time and many advances in e-learning systems have been implemented, the needs for effective information sharing in a secure manner have to date been largely ignored, especially for virtual university collaborative environments. Information sharing of virtual universities usually occurs in broad, highly dynamic network-based environments, and formally accessing the resources in a secure manner poses a difficult and vital challenge. This paper aims to build a new rule-based framework to identify and address issues of sharing in virtual university environments through role-based access control (RBAC) management. The framework includes a role-based group delegation granting model, group delegation revocation model, authorization granting, and authorization revocation. We analyze various revocations and the impact of revocations on role hierarchies. The implementation with XML-based tools demonstrates the feasibility of the framework and authorization methods. Finally, the current proposal is compared with other related work.

Due to the inherent correlation among Web objects and the lack of a uniform schema of Web documents, Web community mining and analysis has become an important area for Web data management and analysis. The research of Web communities spans a number of research domains such as Web mining, Web search, clustering and text retrieval. In this talk we will present some recent studies on this topic, which cover finding relevant Web pages based on linkage information, discovering user access patterns through analyzing Web log files, co-clustering Web objects and investigating social networks from Web data. The algorithmic issues and related experimental studies will be addressed. Some research directions are also to be discussed.

Service discovery is one of challenging issues in Service-Oriented computing. Currently, most of the existing service discovering and matching approaches are based on keywords-based strategy. However, this method is inefficient and time-consuming. In this paper, we present a novel approach for discovering web services. Based on the current dominating mechanisms of discovering and describing Web Services with UDDI and WSDL, the proposed approach utilizes Probabilistic Latent Semantic Analysis (PLSA) to capture semantic concepts hidden behind words in the query and advertisements in services so that services matching is expected to carry out at concept level. We also present related algorithms and preliminary experiments to evaluate the effectiveness of our approach.

This work proposes a practical payment protocol with scalable anonymity for Internet purchases, and analyzes its role-based access control (RBAC). The protocol uses electronic cash for payment transactions. It is an offline payment scheme that can prevent a consumer from spending a coin more than once. Consumers can improve anonymity if they are worried about disclosure of their identities to banks. An agent provides high anonymity through the issue of a certification. The agent certifies reencrypted data after verifying the validity of the content from consumers, but with no private information of the consumers required. With this new method, each consumer can get the required anonymity level, depending on the available time, computation, and cost. We use RBAC to manage the new payment scheme and improve its integrity. With RBAC, each user may be assigned one or more roles, and each role can be assigned one or more privileges that are permitted to users in that role. To reduce conflicts of different roles and decrease complexities of administration, duty separation constraints, role hierarchies, and scenarios of end-users are analyzed.

An association rule generation algorithm usually generates too many rules including a lot of uninteresting ones. Many interestingness criteria are proposed to prune those uninteresting rules. However, they work in post-pruning process and hence do not improve the rule generation efficiency. We discuss properties of informative rule set and conclude that the informative rule set includes all interesting rules measured by many commonly used interestingness criteria, and that rules excluded by the informative rule set are forwardly prunable, i.e. they can be removed in the rule generation process instead of post pruning. Based on these properties, we propose a direct interesting rule generation algorithm, DIG, to directly generate interesting rules defined by any of 12 interestingness criteria. We further show experimentally that DIG is faster and uses less memory than Apriori.

This paper describes a novel approach to document classification that uses decision-tree machine learning based on a succinct vector of important terms in each document. The succinct vector itself is generated by a machine-learning approach which builds parsers that can identify significant features in a document by partitioning it into regions based on low-level document characteristics. The fact that the feature vector is succinct overcomes the problem of very large term vectors, which have hindered the application of conventional machine learning to document classification. The fact that the parser can be trained to extract only important terms from documents means that small training sets can be used to achieve the same classification accuracy as with conventional approaches.

This paper presents two hyperlink analysis-based algorithms to find relevant pages for a given Web page (URL). The first algorithm comes from the extended cocitation analysis of the Web pages. It is intuitive and easy to implement. The second one takes advantage of linear algebra theories to reveal deeper relationships among the Web pages and to identify relevant pages more precisely and effectively. The experimental results show the feasibility and effectiveness of the algorithms. These algorithms could be used for various Web applications, such as enhancing Web search. The ideas and techniques in this work would be helpful to other Web-related research.

We develop formal authorization allocation algorithms for role-based access control (RBAC). The formal approaches are based on relational structure, and relational algebra and operations. The process of user-role assignments is an important issue in RBAC because it may modify the authorization level or imply high-level confidential information to be derived while users change positions and request different roles. There are two types of problems which may arise in user-role assignment. One is related to the authorization granting process. When a role is granted to a user this role may conflict with other roles of the user or together with this role; the user may have or derive a high level of authority. Another is related to authorization revocation. When a role is revoked from a user, the user may still have the role from other roles. To solve these problems, this paper presents an authorization granting algorithm, and weak revocation and strong revocation algorithms that are based on relational algebra. The algorithms can be used to check conflicts and therefore to help allocate roles without compromising the security in RBAC. We describe how to use the new algorithms with an anonymity scalable payment scheme. Finally, comparisons with other related work are discussed.

This paper proposes a secure, scalable anonymity and practical payment protocol for Internet purchases, and uses role based access control (RBAC) to manage the new payment scheme. The protocol uses electronic cash for payment transactions. In this new protocol, from the viewpoint of banks, consumers can improve anonymity if they are worried about disclosure of their identities. An agent provides a higher anonymous certificate and improves the security of the consumers. The agent will certify re-encrypted data after verifying the validity of the content from consumers, but with no private information of the consumers required. With this new method, each consumer can get the required anonymity level, depending on the available time, computation and cost. We also analyse how to prevent a consumer from spending a coin more than once. Furthermore, we use RBAC to manage the new payment scheme. Each user may be assigned one or more roles, and each role can be assigned one or more privileges that are permitted to users in that role. Security administration with RBAC consists of determining operations that must be executed by persons in particular jobs, and assigning employees to proper roles. RBAC can improve system security and reduce conflicts of different roles. The complexities with RBAC can be decreased by mutually exclusive roles and role hierarchies.

The paper presents a prototype visualization system for text information retrieval and its technical details on algorithm design. The system supports the user in constructing an initial query type and further refining the query by visual interaction. Visualization of text information retrieval is an attractive research area in information retrieval. There are many mathematical algorithms used for information retrieval. The authors investigate an approach to construct visual representations based on singular value decomposition (SVD) of matrices and implement visual interfaces using Java. Experimental results show the feasibility of the proposed approach. The ideas in this approach can also be helpful in other visualization environments.

In order to support the interaction betweeen coexisting traditional short transactions and long cooperative transactions, we propose a novel timestamp ordering approach. With this timestamp ordering method, short transactions can be processed in the traditional way, as if there were no cooperative transactions. Therefore they will not be blocked by cooperative transactions. Cooperative transactions will not be aborted when there is a conflict with short transactions. Rather, they will incorporate the recent updates into their own processing. The serializabilities, among short transactions, and between a cooperative transaction (group) and other short transactions, are all preserved.