Federated Learning (FL) is increasingly adopted in edge computing scenarios, where a large number of heterogeneous clients operate under constrained or sufficient resources. The iterative training process of FL incurs considerable computation and communication overhead, which is unfriendly for resource-constrained devices. One-shot FL is a promising approach to addressing communication issues inherent in conventional FL, and model-heterogeneous FL solves the problem of diverse computing resources across clients. However, existing methods face challenges in effectively managing model-heterogeneous one-shot FL, often leading to unsatisfactory global model performance or reliance on auxiliary datasets. To address these challenges, we propose a novel FL framework named FedMHO, which leverages deep classification models on resource-sufficient clients and lightweight generative models on resource-constrained devices. On the server side, FedMHO involves a two-stage process that includes data generation and knowledge fusion. Furthermore, we introduce FedMHO-MD and FedMHO-SD to mitigate the knowledge-forgetting problem during the knowledge fusion stage, and an unsupervised data optimization solution to improve the quality of synthetic samples. Comprehensive experiments demonstrate the effectiveness of our methods, as they outperform state-of-the-art baselines in various experimental setups.

With the rise of Web 2.0 and microservices, the increasing availability of Web APIs has intensified the need for effective recommendation systems. Existing approaches are generally categorized into two methods: recommendation-type methods, which classify APIs using labels, and match-type methods, which retrieve APIs through matching with mashups. However, three significant challenges remain: 1) semantic ambiguities in comparing API and mashup descriptions, 2) a lack of progressive semantic refinement between mashup requirements and individual API descriptions, and 3) computational inefficiency of exhaustive mashup-API comparisons in large-scale repositories. To tackle these challenges, we propose WARBERT, a hierarchical model based on BERT for Web API recommendation. WARBERT utilizes dual-component feature fusion and attention mechanisms to create accurate semantic representations. It consists of WARBERT(R) for initial candidate filtering using recommendation methods, and WARBERT(M), which focuses on refined similarity matching. The final likelihood of an API-mashup pairing combines predictions from both components, with WARBERT(R) further enhanced by an auxiliary task of predicting mashup categories. Experiments conducted on the ProgrammableWeb dataset demonstrate WARBERT outperforms existing baselines, achieving notable improvements in both accuracy and efficiency.

With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (eg, NMS and RPN), yielding some results but simultaneously constraining their scalability. Moreover, most efforts against ODs stem from image-level attacks originally designed for classification tasks, resulting in redundant computations and disturbances in object-irrelevant areas (eg, background). Consequently, how to design a model-agnostic efficient attack to comprehensively evaluate the vulnerabilities of ODs remains challenging and unresolved. In this paper, we propose NumbOD, a brand-new spatial-frequency fusion attack against various ODs, aimed at disrupting object detection within images. We directly leverage the features output by the OD without relying on its any internal structures to craft adversarial examples. Specifically, we first design a dual-track attack target selection strategy to select high-quality bounding boxes from OD outputs for targeting. Subsequently, we employ directional perturbations to shift and compress predicted boxes and change classification results to deceive ODs. Additionally, we focus on manipulating the high-frequency components of images to confuse ODs' attention on critical objects, thereby enhancing the attack efficiency. Our extensive experiments on nine ODs and two datasets show that NumbOD achieves powerful attack performance and high stealthiness.

Academic conferences serve as a useful platform for researchers and educators to share their work and ideas. Upon the paper acceptance, authors need to register their papers, pay registration fees, and present their work at conferences. International conferences often involve multiple parties of users and cross-border payments with multiple channels that incur additional service charges. Conference organisers and reviewers typically contribute voluntarily without any monetary rewards. The emergence of Web 3.0 technology, leveraging the decentralized and secure nature of blockchain, presents an opportunity for this domain. This research creates a decentralized academic conference management system using Web 3.0 (ConfMan Web 3.0) and Rust programming language that simplifies cross-border payments on different channels by using cryptocurrencies instead of fiat currency, thereby reducing service charges. It aims to provide rewards, recognition of contributions, and consolidated historical records to all conference contributors, including authors, reviewers, programme chairs, and organising committees. The Solana blockchain is used to store conference- related data, and a web application is developed for ConfMan Web 3.0. Various testings are conducted to evaluate its performance. The findings highlight the potential of Web 3.0 technology in transforming the academic conference management landscape.

Recent studies reveal the vulnerability of the image segmentation foundation model SAM to adversarial examples. Its successor, SAM2, has attracted significant attention due to its strong generalization capability in video segmentation. However, its robustness remains unexplored, and it is unclear whether existing attacks on SAM can be directly transferred to SAM2. In this paper, we first analyze the performance gap of existing attacks between SAM and SAM2 and highlight two key challenges arising from their architectural differences: directional guidance from the prompt and semantic entanglement across consecutive frames. To address these issues, we propose UAP-SAM2, the first cross-prompt universal adversarial attack against SAM2 driven by dual semantic deviation. For cross-prompt transferability, we begin by designing a target-scanning strategy that divides each frame into k regions, each randomly assigned a prompt, to reduce prompt dependency during optimization. For effectiveness, we design a dual semantic deviation framework that optimizes a UAP by distorting the semantics within the current frame and disrupting the semantic consistency across consecutive frames. Extensive experiments on six datasets across two segmentation tasks demonstrate the effectiveness of the proposed method for SAM2. The comparative results show that UAP-SAM2 significantly outperforms state-of-the-art (SOTA) attacks by a large margin.

One underlying assumption of recent Federated Learning (FL) paradigms is that all local models share an identical network architecture. However, this assumption is inefficient for heterogeneous systems where devices possess varying computation and communication capabilities. The presence of such heterogeneity among devices negatively impacts the scalability of FL and slows down the training process due to the existence of stragglers. To this end, this paper proposes a novel federated compression framework for heterogeneous models , named FedHM, distributing the heterogeneous low-rank models to clients and then aggregating them into a full-rank global model. Furthermore, FedHM significantly reduces communication costs by utilizing low-rank models. Compared with state-of-the-art heterogeneous FL methods under various FL settings, FedHM is superior in the performance and robustness of models with different sizes. Additionally, the convergence guarantee of FL for heterogeneous devices is first theoretically analyzed.

Due to the limited resources of end devices, the task of Convolutional Neural Network (CNN) inference on the end-side is moving towards edge-end collaboration. However, existing collaborative methods mainly focus on offloading CNN inference tasks from end devices to a single-edge server, which leads to inefficient use of computational resources among nearby edge servers. Moreover, offloading the CNN inference task to a single third-party server may raise privacy concerns. To address these challenges, we propose a framework named AdapCP that introduces a collaborative and adaptive parallel acceleration strategy that utilizes the end device and multiple edge servers. AdapCP consists of two stages: (1) offloading to nearby servers and (2) parallel processing of the CNN inference. For the offloading phase, we use integer linear programming to find the partition points at the inter-layer level. For the parallel phase, we first investigate intra-layer structural splitting methods tailored for both convolutional and fully connected layers. Then, we employ a Deep Deterministic Policy Gradient (DDPG) algorithm based on the Dirichlet distribution to decide the partition points. Finally, we set a periodic update index to enhance AdapCP’s adaptability to dynamic environments. Empirical evaluations conducted on the Jetson nano demonstrate that AdapCP significantly reduces the total latency of CNN inference by an average factor of 2.21 \(\times\) compared to existing solutions.

The Sparse General Matrix-Matrix multiplication (SpGEMM) is a fundamental component for many applications, such as algebraic multigrid methods (AMG), graphic processing, and deep learning. However, the unbearable latency of computing high-dimensional, large-scale sparse matrix multiplication on GPUs hinders the development of these applications. An effective approach is heterogeneous cores collaborative computing, but this method must address three aspects: (1) irregular non-zero elements lead to load imbalance and irregular memory access, (2) different core computing latency differences reduce computational parallelism, and (3) temporary data transfer between different cores introduces additional latency overhead. In this work, we propose an innovative framework for collaborative large-scale sparse matrix multiplication on CPU-GPU heterogeneous cores, named ApSpGEMM. ApSpGEMM is based on sparsity rules and proposes reordering and splitting algorithms to eliminate the impact of non-zero element distribution features on load and memory access. Then adaptive panels allocation with affinity constraints among cores improves computational parallelism. Finally, carefully arranged asynchronous data transmission and computation balance communication overhead. Compared with state-of-the-art SpGEMM methods, our approach provides excellent absolute performance on matrices with different sparse structures. On heterogeneous cores, the GFlops of large-scale sparse matrix multiplication is improved by 2.25 to 7.21 times.

Benefiting from its superior feature learning capabilities and efficiency, deep hashing has achieved remarkable success in large-scale image retrieval. Recent studies have demonstrated the vulnerability of deep hashing models to backdoor attacks. Although these studies have shown promising attack results, they rely on access to the training dataset to implant the backdoor. In the real world, obtaining such data (e.g., identity information) is often prohibited due to privacy protection and intellectual property concerns. Embedding backdoors into deep hashing models without access to the training data, while maintaining retrieval accuracy for the original task, presents a novel and challenging problem. In this paper, we propose DarkHash, the first data-free backdoor attack against deep hashing. Specifically, we design a novel shadow backdoor attack framework with dual-semantic guidance. It embeds backdoor functionality and maintains original retrieval accuracy by fine-tuning only specific layers of the victim model using a surrogate dataset. We consider leveraging the relationship between individual samples and their neighbors to enhance backdoor attacks during training. By designing a topological alignment loss, we optimize both individual and neighboring poisoned samples toward the target sample, further enhancing the attack capability. Experimental results on four image datasets, five model architectures, and two hashing methods demonstrate the high effectiveness of DarkHash, outperforming existing state-of-the-art backdoor attack methods. Defense experiments show that DarkHash can withstand existing mainstream backdoor defense methods.

Learning a good similarity measure for large-scale high-dimensional data is a crucial task in machine learning applications, yet it poses a significant challenge. Distributed minibatch Stochastic Gradient Descent (SGD) serves as an efficient optimization method in large-scale distributed training, allowing linear speedup in proportion to the number of workers. However, communication efficiency in distributed SGD requires a sufficiently large minibatch size, presenting two distinct challenges. Firstly, a large minibatch size leads to high memory usage and computational complexity during parallel training of high-dimensional models. Second, a larger batch size of data reduces the convergence rate. To overcome these challenges, we propose an Efficient Distributed Sparse Relative Similarity Learning ( \(\mathbf{\mathsf{EDSRSL}}\) ) framework. This framework integrates two strategies: local minibatch SGD and sparse relative similarity learning. By effectively reducing the number of updates through synchronous delay while maintaining a large batch size, we address the issue of high computational cost. Additionally, we incorporate sparse model learning into the training process, significantly reducing computational cost. This article also provides theoretical proof that the convergence rate does not decrease significantly with increasing batch size. Various experiments on six high-dimensional real-world datasets demonstrate the efficacy and efficiency of the proposed algorithms, with a communication cost reduction of up to \(90.89\%\) and a maximum wall time speedup of \(5.66\times\) compared to the baseline methods.

The Vision Transformer (ViT), which benefits from utilizing self-attention mechanisms, has demonstrated superior accuracy compared to CNNs. However, due to the expensive computational costs, deploying and inferring ViTs on resource-constrained mobile devices has become a challenge. To resolve this challenge, we conducted an empirical analysis to identify performance bottlenecks in deploying ViTs on mobile devices and explored viable solutions. In this paper, we propose SPViT, an adaptive split and offloading method that accelerates ViT inference on mobile devices. SPViT executes collaborative inference of ViT across available edge devices. We introduce a fine-grained splitting technique for the vision transformer structure. Furthermore, we propose an algorithm based on the Auto Regression model to predict partition latency and adaptive offload partitions. Finally, we design offline and online optimization methods to minimize the computational and communication overhead on each device. Based on real-world prototype experiments, SPViT effectively reduces inference latency by 2.2x to 3.3x across four state-of-the-art models.

Collaborative learning (CL) is a distributed learning framework that aims to protect user privacy by allowing users to jointly train a model by sharing their gradient updates only. However, gradient inversion attacks (GIAs), which recover users' training data from shared gradients, impose severe privacy threats to CL. Existing defense methods adopt different techniques, e.g., differential privacy, cryptography, and perturbation defenses, to defend against the GIAs. Nevertheless, all current defense methods suffer from a poor trade-off between privacy, utility, and efficiency. To mitigate the weaknesses of existing solutions, we propose a novel defense method, Dual Gradient Pruning (DGP), based on gradient pruning, which can improve communication efficiency while preserving the utility and privacy of CL. Specifically, DGP slightly changes gradient pruning with a stronger privacy guarantee. And DGP can also significantly improve communication efficiency with a theoretical analysis of its convergence and generalization. Our extensive experiments show that DGP can effectively defend against the most powerful GIAs and reduce the communication cost without sacrificing the model's utility.

Personalized federated learning (PFL) is a variant of federation learning, which improves the model performance of each participant through collaborative training while providing a customized local model that meets their unique needs. Balancing global (general) knowledge and local (personalized) knowledge is a core challenge in PFL. Existing PFL methods neglect knowledge forgetting during aggregation and updating, impacting the fusion of both types of knowledge. We empirically confirm the existence of knowledge forgetting, which leads to performance degradation during FL. This observation motivates us to rethink PFL from a knowledge perspective. Therefore, we propose Personalized Federated Learning Framework with Adaptive Model Fusion (pFedAMF). We consider the global model as global knowledge and history state models as local knowledge, and preserve them before forgetting occurs. After local training, we use an adaptive knowledge matrix to fuse knowledge from local, global, and history state models. This fused knowledge is then distilled into an individual model, thus transferring the fused knowledge into global models. Extensive experiments show that pFedAMF consistently outperforms FedAvg, achieving up to 5.22% average accuracy boost, and reducing computation cost by 82% and communication cost by 87%.

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://github.com/CGCL-codes/DarkSAM.

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers.In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six datasets, demonstrate that Gen-AF attains high testing accuracy and robust testing accuracy against state-of-the-art DAEs.

Federated learning, as one enabling technology of edge intelligence, has gained substantial attention due to its efficacy in training deep learning models without data privacy and network bandwidth concerns. However, due to the heterogeneity of the edge computing system and data, many methods suffer from the“client-drift”issue that could considerably impede the convergence of global model training: local models on clients can drift apart, and the aggregated model can be different from the global optimum. To tackle this issue, one intuitive idea is to guide the local model training by global teachers,i.e.,past global models, where each client learns the global knowledge from past global models via adaptive knowledge distillation techniques. Inspired by these insights, we propose a novel approach for heterogeneous federated learning,FedGKD, which fuses the knowledge from historical global models and guides local training to alleviate the“client-drift”issue. In this paper, we evaluateFedGKDthrough extensive experiments across various CV and NLP datasets (i.e.,CIFAR-10/100, Tiny-ImageNet, AG News, SST5) under different heterogeneous settings. The proposed method is guaranteed to converge under common assumptions and outperforms the state-of-the-art baselines in the non-IID federated setting.

Thyroid cancer is the most pervasive disease in the endocrine system and is getting extensive attention. The most prevalent method for an early check is ultrasound examination. Traditional research mainly concentrates on promoting the performance of processing a single ultrasound image using deep learning. However, the complex situation of patients and nodules often makes the model dissatisfactory in terms of accuracy and generalization. Imitating the diagnosis process in reality, a practical diagnosis-oriented computer-aided diagnosis (CAD) framework towards thyroid nodules is proposed, using collaborative deep learning and reinforcement learning. Under the framework, the deep learning model is trained collaboratively with multiparty data; afterward classification results are fused by a reinforcement learning agent to decide the final diagnosis result. Within the architecture, multiparty collaborative learning with privacy-preserving on large-scale medical data brings robustness and generalization, and diagnostic information is modeled as a Markov decision process (MDP) to get final precise diagnosis results. Moreover, the framework is scalable and capable of containing more diagnostic information and multiple sources to pursue a precise diagnosis. A practical dataset of two thousand thyroid ultrasound images is collected and labeled for collaborative training on classification tasks. The simulated experiments have shown the advancement of the framework in promising performance.

A geospatial Knowledge Graph (KG) is a heterogeneous information network, capable of representing relationships between spatial entities in a machine-interpretable format, and has tremendous applications in logistics and social networks. Existing efforts to build a geospatial KG, have mainly used sparse spatial relationships, e.g., a district located inside a city, which provide only marginal benefits compared to a traditional database. In spite of the substantial advances in the tasks of link prediction and knowledge graph completion, identifying geospatial relationships remains challenging, particularly due to the fact that spatial entities are represented with single-point geometries, and textual attributes are frequently missing. In this study, we present GTMiner, a novel framework capable of jointly modeling Geospatial and Textual information to construct a knowledge graph, by mining three useful spatial relationships from a geospatial database, in an end-to-end fashion. The system is divided into three components: (1) a Candidate Selection module, to efficiently select a small number of candidate pairs; (2) a Relation Prediction component to predict spatial relationships between the entities; (3) a KG Refinement procedure, to improve both coverage and correctness of a geospatial knowledge graph. We carry out experiments on four cities' geospatial databases, from publicly-available sources and compare with existing algorithms for link prediction and geospatial data integration. Finally, we conduct an ablation study to motivate our design choices and an efficiency analysis to show that the time required by GTMiner for training and inference is comparable, or even shorter, than existing solutions.

Federated Learning(FL) is an emerging approach in edge computing for collaboratively training machine learning models among multiple devices, which aims to address limited bandwidth, system heterogeneity, and privacy issues in traditional centralized training. However, the existing federated learning methods focus on learning a shared global model for all devices, which may not always be ideal for different devices. Such situations become even worse when each edge device has its own data distribution or task. In this paper, we study personalized federated learning in which our goal is to train models to perform well for individual clients. We observe that the initialization in each communication round causes the forgetting of historical personalized knowledge. Based on this observation, we propose a novelPersonalized Federated Learning(PFL) framework via self-knowledge distillation, named pFedSD. By allowing clients to distill the knowledge of previous personalized models to current local models, pFedSD accelerates the process of recalling the personalized knowledge for the latest initialized clients. Moreover, self-knowledge distillation provides different views of data in feature space to realize an implicit ensemble of local models. Extensive experiments on various datasets and settings demonstrate the effectiveness and robustness of pFedSD.

Entity Resolution (ER) links entities that refer to the same real-world entity from different sources. Existing work usually takes pairs of entities as input and judges those pairs independently. However, there is often interdependence between different pairs of ER decisions, e.g., the entities from the same data source are usually semantically related to each other. Furthermore, current ER approaches are mainly based on attribute similarity comparison, but ignore interdependence between attributes. To address the limits of existing methods, we propose HierGAT, a new method for ER based on a Hierarchical Graph Attention Transformer Network, which can model and exploit the interdependence between different ER decisions. The benefit of our method comes from: 1) The graph attention network model for joint ER decisions; 2) The graph-attention capability to identify the discriminative words from attributes and find the most discriminative attributes. Furthermore, we propose to learn contextual embeddings to enrich word embeddings for better performance. The experimental results on publicly available benchmark datasets show that HierGAT outperforms DeepMatcher by up to 32.5% of F1 score and up to 8.7% of F1 score compared with Ditto.

Searching and reusing code snippets from open-source software repositories based on natural-language queries can greatly improve programming productivity.Recently, deep-learning-based approaches have become increasingly popular for code search. Despite substantial progress in training accurate models of code search, the robustness of these models has received little attention so far.

A geospatial database is today at the core of an ever increasing number of services. Building and maintaining it remains challenging due to the need to merge information from multiple providers. Entity Resolution (ER) consists of finding entity mentions from different sources that refer to the same real world entity. In geospatial ER, entities are often represented using different schemes and are subject to incomplete information and inaccurate location, making ER and deduplication daunting tasks. While tremendous advances have been made in traditional entity resolution and natural language processing, geospatial data integration approaches still heavily rely on static similarity measures and human-designed rules. In order to achieve automatic linking of geospatial data, a unified representation of entities with heterogeneous attributes and their geographical context, is needed. To this end, we propose Geo-ER1, a joint framework that combines Transformer-based language models, that have been successfully applied in ER, with a novel learning-based architecture to represent the geospatial character of the entity. Different from existing solutions, Geo-ER does not rely on pre-defined rules and is able to capture information from surrounding entities in order to make context-based, accurate predictions. Extensive experiments on eight real world datasets demonstrate the effectiveness of our solution over state-of-the-art methods. Moreover, Geo-ER proves to be robust in settings where there is no available training data for a specific city.

The fast growth ofpre-trained models(PTMs) has brought natural language processing to a new era, which has become a dominant technique for variousnatural language processing(NLP) applications. Every user can download the weights of PTMs, then fine-tune the weights for a task on the local side. However, the pre-training of a model relies heavily on accessing a large-scale of training data and requires a vast amount of computing resources. These strict requirements make it impossible for any single client to pre-train such a model. To grant clients with limited computing capability to participate in pre-training a large model, we propose a new learning approach,FedBERT, that takes advantage of the federated learning and split learning approaches, resorting to pre-training BERT in a federated way.FedBERTcan prevent sharing the raw data information and obtain excellent performance. Extensive experiments on seven GLUE tasks demonstrate thatFedBERTcan maintain its effectiveness without communicating to the sensitive local data of clients.

Complex task processing and frequent data communication in Wireless Multimedia Sensor Networks (WMSN) demand for energy-efficient and Quality of Service (QoS) guarantee to support new applications especially in the sensing layer of Internet-of-Vehicles. However, the WMSN is heterogeneous and the energy distribution is not uniform, the current routing protocols do not take energy consumption into account while ensuring QoS. Therefore, to make energy distribution more efficiently while ensuring QoS has become a challenging problem. In this paper, we propose an energy-efficient distributed adaptive cooperative routing (EDACR) for WMSN, taking into account the constraints of QoS and energy consumption. Particularly, we design a reinforcement learning based mechanism to perform QoS and energy balanced routing according to the knowledge of reliability and delay. The simulation results show that the energy consumption is reduced while ensuring QoS compared with the traditional cooperative protocol and the distributed adaptive cooperative routing protocol.

Quality of cloud service (QoS) is one of the crucial factors for the success of cloud providers in mobile cloud computing. Context-awareness is a popular method for automatic awareness of the mobile environment and choosing the most suitable cloud provider. Lack of context information may harm the users' confidence in the application rendering it useless. Thus, mobile devices need to be constantly aware of the environment and to test the performance of each cloud provider, which is inefficient and wastes energy. Crowdsourcing is a considerable technology to discover and select cloud services in order to provide intelligent, efficient, and stable discovering of services for mobile users based on group choice. This article introduces a crowdsourcing-based QoS supported mobile cloud service framework that fulfills mobile users' satisfaction by sensing their context information and providing appropriate services to each of the users. Based on user's activity context, social context, service context, and device context, our framework dynamically adapts cloud service for the requests in different kinds of scenarios. The context-awareness based management approach efficiency achieves a reliable cloud service supported platform to supply the Quality of Service on mobile device.

We investigate how to adopt dual random projection for high-dimensional similarity learning. For a high-dimensional similarity learning problem, projection is usually adopted to map high-dimensional features into low-dimensional space, in order to reduce the computational cost. However, dimensionality reduction method sometimes results in unstable performance due to the suboptimal solution in original space. In this paper, we propose a dual random projection framework for similarity learning to recover the original optimal solution from subspace optimal solution. Previous dual random projection methods usually make strong assumptions about the data, which need to be low rank or have a large margin. Those assumptions limit dual random projection applications in similarity learning. Thus, we adopt a dual-sparse regularized random projection method that introduces a sparse regularizer into the reduced dual problem. As the original dual solution is a sparse one, applying a sparse regularizer in the reduced space relaxes the low-rank assumption. Experimental results show that our method enjoys higher effectiveness and efficiency than state-of-the-art solutions.

For many data mining and machine learning tasks, the quality of a similarity measure is the key for their performance. To automatically find a good similarity measure from datasets, metric learning and similarity learning are proposed and studied extensively. Metric learning will learn a Mahalanobis distance based on positive semi-definite (PSD) matrix, to measure the distances between objectives, while similarity learning aims to directly learn a similarity function without PSD constraint so that it is more attractive. Most of the existing similarity learning algorithms are online similarity learning method, since online learning is more scalable than offline learning. However, most existing online similarity learning algorithms learn a full matrix with d2parameters, where d is the dimension of the instances. This is clearly inefficient for high dimensional tasks due to its high memory and computational complexity. To solve this issue, we introduce several Sparse Online Relative Similarity (SORS) learning algorithms, which learn a sparse model during the learning process, so that the memory and computational cost can be significantly reduced. We theoretically analyze the proposed algorithms, and evaluate them on some real-world high dimensional datasets. Encouraging empirical results demonstrate the advantages of our approach in terms of efficiency and efficacy.

With the increasing popularity of location-based social networks, a large number of users have been involved in the check-ins. The venues where the user frequently repeats check-ins tend to play a very important role in his daily life, as they not only dominate the user's mobility behavior but also imply the user's personal preferences. Therefore, fast discerning of such check-in venues could enable us to improve a wide range of location-based services. In this paper, we propose a new location classification problem for users of location-based social networks, in which we aim to discern, given the observation that a user makes a "new" check-in at a venue, whether he will frequently repeat check-ins at this venue. To solve the problem, we first extract 16 features attached to the user's "new" check-ins. With the publicly available check-in dataset, we then train a location classifier based on Support Vector Machine and compare it with two baselines based on majority voting. The comparison results demonstrate the practicability of the trained location classifier.