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With the maturity and popularity of Internet of Things (IoT), the notion of Social Internet of Things (SIoT) has been proposed to support novel applications and networking services for the IoT in more effective and efficient ways. In this paper, we propose a new problem family, namely, Task-Optimized SIoT Selection (TOSS), to find the best group of IoT objects for a given set of tasks in the task pool. TOSS aims to select the target SIoT group such that the target SIoT group is able to easily communicate with each other while maximizing the accuracy of performing the given tasks. We propose two problem formulations, named Bounded Communication-loss TOSS (BC-TOSS) and Robustness Guaranteed TOSS (RG-TOSS), for different scenarios and prove that they are both NP-hard and inapproximable. We propose a polynomial-time algorithm with a performance guarantee for BC-TOSS, and an efficient polynomial-time algorithm to obtain good solutions for RG-TOSS. Moreover, as RG-TOSS is NP-hard and inapproximable within any factor, we further propose Structure-Aware Reinforcement Learning (SARL) to leverage the Graph Convolutional Networks (GCN) and Deep Reinforcement Learning (DRL) to effectively solve RG-TOSS. Experimental results indicate that our proposed algorithms outperform the other deterministic and learning-based baseline approaches.

Energy harvesting creates an emerging intermittent computing paradigm but poses new challenges for sophisticated applications such as intermittent deep neural network (DNN) inference. Although model compression has adapted DNNs to resource-constrained devices, under intermittent power, compressed models will still experience multiple power failures during a single inference. Footprint-based approaches enable hardware-accelerated intermittent DNN inference by tracking footprints, independent of model computations, to indicate accelerator progress across power cycles. However, we observe that the extra overhead required to preserve progress indicators can severely offset the computation progress accumulated by intermittent DNN inference. This work proposes the concept of model augmentation to adapt DNNs to intermittent devices. Our middleware stack, JAPARI, appends extra neural network components into a given DNN, to enable the accelerator to intrinsically integrate progress indicators into the inference process, without affecting model accuracy. Their specific positions allow progress indicator preservation to be piggybacked onto output feature preservation to amortize the extra overhead, and their assigned values ensure uniquely distinguishable progress indicators for correct inference recovery upon power resumption. Evaluations on a Texas Instruments device under various DNN models, capacitor sizes, and progress preservation granularities show that JAPARI can speed up intermittent DNN inference by 3× over the state of the art, for common convolutional neural architectures that require heavy acceleration.

Hashtag is an important advertising tool and a musthave feature for social media nowadays. In the past, many hashtag recommendation methods have been proposed from different perspectives of images, texts, and users. However, most previous works consider neither the mutual influence between multi-modalities, nor the visual similarity between images. In this paper, we devise a novel model, named Triplet-Attention Graph Network (TAGNet). The rationale behind our method is that visually similar images share some common hashtags. Therefore, we build an image graph, and apply a new Aggregated Graph Convolution (AGC) module to propagate information in a collective way. Furthermore, it is noted that text and user also have rich content information within posts, and we hence propose a Triplet Attention (TA) module to incorporate multi-modalities into node features. Experiments on the large-scale dataset collected from Instagram show that TAGNet achieved significant improvement in recall rate over the best state-of-the-art method.

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), that takes into consideration both existing friendships and the likelihood of new friend making in organization of the targeted in person friend-making social activities. To find a set of attendees for such social activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships, and the objective of maximizing the likelihood of friend making. We propose an error-bounded approximation algorithm, MaxGF, to efficiently obtain the solutions very close to the optimal solutions. To boost the performance, we devise two graph-theoretical pruning strategies, namely Neighbor Pruning and Core Pruning, which can effectively avoid redundant graph explorations to improve the performance of HMGF. We conduct a user study to validate our problem formulation and perform extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm. The experimental results manifest that our proposed algorithms outperform the baselines, including the ILP formulation.

When exposed to an item in a recommender system, a user may consume it (known as success exposure) or neglect it (known as failure exposure). The recently proposed methods that consider both success and failure exposure merely regard failure exposure as a constant prior, thus being capable of neither modeling various user behavior nor adapting to overdispersed data. In this paper, we propose a novel model, hierarchical negative binomial factorization, which models data dispersion via a hierarchical Bayesian structure, thus alleviating the effect of data overdispersion to help with performance gain for recommendation. Moreover, we factorize the dispersion of zero entries approximately into two low-rank matrices, thus reducing the updating time linear to the number of nonzero entries. The experiment shows that the proposed model outperforms state-of-the-art Poisson-based methods merely with a slight loss of inference speed.

Matrix factorization (MF) has earned great success on recommender systems. However, the common-used regression-based MF not only is sensitive to outliers but also unable to guarantee that the predicted values are in line with the user preference orders, which is the basis of common measures of recommender systems, e.g., nDCG. To overcome the aforementioned drawbacks, we propose a framework for personalized ranking of Poisson factorization that utilizes learning-to-rank based posteriori instead of the classical regression-based ones. Owing to the combination, the proposed framework not only preserves user preference but also performs well on a sparse matrix. Since the posteriori that combines learning to rank and Poisson factorization does not follow the conjugate prior relationship, we estimate variational parameters approximately and propose two optimization approaches based on variational inference. As long as the used learning-to-rank model has the 1st and 2nd order partial derivatives, by exploiting our framework, the proposed optimizing algorithm can maximize the posteriori whichever the used learning-to-rank model is. In the experiment, we show that the proposed framework outperforms the state-of-the-art methods and achieves promising results on consuming log and rating datasets for multiple recommendation tasks.

Current peripheral execution approaches for intermittently powered systems require full access to the internal hardware state for checkpointing or rely on application-level energy estimation for task partitioning to make correct forward progress. Both requirements present significant practical challenges for energy-harvesting, intelligent edge Internet-of-Things devices, which perform hardware-accelerated deep neural network (DNN) inference. Sophisticated compute peripherals may have an inaccessible internal state, and the complexity of DNN models makes it difficult for programmers to partition the application into suitably sized tasks that fit within an estimated energy budget. This article presents the concept of inference footprinting for intermittent DNN inference, where accelerator progress is accumulatively preserved across power cycles. Our middleware stack, HAWAII, tracks and restores inference footprints efficiently and transparently to make inference forward progress, without requiring access to the accelerator internal state and application-level energy estimation. Evaluations were carried out on a Texas Instruments device, under varied energy budgets and network workloads. Compared to a variety of task-based intermittent approaches, HAWAII improves the inference throughput by 5.7%–95.7%, particularly achieving higher performance on heavily accelerated DNNs.

Three essential criteria are important for social activity planning: (1) finding attendees familiar with the initiator, (2) ensuring most attendees have tight social relations with each other, and (3) selecting an activity period available to all. In this paper, we propose the Social-Temporal Group Query (STGQ) to find suitable time and attendees with minimum total social distance. We first prove that the problem is NP-hard and inapproximable within any ratio. Next, we design two algorithms, SGSelect and STGSelect , which include effective pruning techniques to substantially reduce running time. Moreover, as users may iteratively adjust query parameters to fine tune the results, we study the problem of Subsequent Social Group Query (SSGQ) . We propose the Accumulative Search Tree and Social Boundary , to cache and index intermediate results of previous queries in order to accelerate subsequent query processing. Experimental results indicate that SGSelect and STGSelect are significantly more efficient than baseline approaches. With the caching mechanisms, processing time of subsequent queries can be further reduced by 50-75 percent. We conduct a user study to compare the proposed approach with manual activity coordination. The results show that our approach obtains higher quality solutions with lower coordination effort, thereby increasing the users’ willingness to organize activities.

Heterogeneous domain adaptation (HDA) addresses the task of associating data not only across dissimilar domains but also described by different types of features. Inspired by the recent advances of neural networks and deep learning, we propose a deep leaning model of transfer neural trees (TNT), which jointly solves cross-domain feature mapping, adaptation, and classification in a unified architecture. As the prediction layer in TNT, we introduce transfer neural decision forest (transfer-NDF), which is able to learn the neurons in TNT for adaptation by stochastic pruning. In order to handle semi-supervised HDA, a unique embedding loss term is introduced to TNT for preserving prediction and structural consistency between labeled and unlabeled target-domain data. Furthermore, we show that our TNT can be extended to zero shot learning for associating image and attribute data with promising performance. Finally, experiments on different classification tasks across features, datasets, and modalities would verify the effectiveness of our TNT.

Viral marketing on social networks, also known as Influence Maximization (IM), aims to select k users for the promotion of a target item by maximizing the total spread of their influence. However, most previous works on IM do not explore the dynamic user perception of promoted items in the process. In this paper, by exploiting the knowledge graph (KG) to capture dynamic user perception, we formulate the problem of Influence Maximization based on Dynamic Personal Perception (IMDPP) that considers user preferences and social influence reflecting the impact of relevant item adoptions. We prove the hardness of IMDPP and design an approximation algorithm, named Dynamic perception for seeding in target markets (Dysim), by exploring the concepts of dynamic reachability, target markets, and substantial influence to select and promote a sequence of relevant items. We evaluate the performance of Dysim in comparison with the state-of-the-art approaches using real social networks with real KGs. The experimental results show that Dysim effectively achieves at least 6 times of influence spread in large datasets over the state-of-the-art approaches.

With the successful creation of high-quality image-to-image (Img2Img) translation GANs comes the non-ethical applications of DeepFake and DeepNude. Such misuses of img2img techniques present a challenging problem for society. In this work, we tackle the problem by introducing the Limit-Aware Self-Guiding Gradient Sliding Attack (LaS-GSA). LaS-GSA follows the Nullifying Attack to cancel the img2img translation process under a black-box setting. In other words, by processing input images with the proposed LaS-GSA before publishing, any targeted img2img GANs can be nullified, preventing the model from maliciously manipulating the images. To improve efficiency, we introduce the limit-aware random gradient-free estimation and the gradient sliding mechanism to estimate the gradient that adheres to the adversarial limit, i.e., the pixel value limitations of the adversarial example. Theoretical justifications validate how the above techniques prevent inefficiency caused by the adversarial limit in both the direction and the step length. Furthermore, an effective self-guiding prior is extracted solely from the threat model and the target image to efficiently leverage the prior information and guide the gradient estimation process. Extensive experiments demonstrate that LaS-GSA requires fewer queries to nullify the image translation process with higher success rates than 4 state-of-the-art black-box methods.

Sparse reward tasks are always challenging in reinforcement learning. Learning such tasks requires both efficient exploitation and exploration to reduce the sample complexity. One line of research called self-imitation learning is recently proposed, which encourages the agent to do more exploitation by imitating past good trajectories. Exploration bonuses, however, is another line of research which enhances exploration by producing intrinsic reward when the agent visits novel states. In this paper, we introduce a novel framework Explore-then-Exploit (EE), which interleaves self-imitation learning with an exploration bonus to strengthen the effect of these two algorithms. In the exploring stage, with the aid of intrinsic reward, the agent tends to explore unseen states and occasionally collect high rewarding experiences, while in the self-imitating stage, the agent learns to consistently reproduce such experiences and thus provides a better starting point for subsequent stages. Our result shows that EE achieves superior or comparable performance on variants of MuJoCo environments with episodic reward settings.

The explosive growth in popularity of social networking leads to the problematic usage. An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD) , that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy. To increase the scalability of STM, we further improve the efficiency with performance guarantee. Our framework is evaluated via a user study with 3,126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results manifest that SNMDD is promising for identifying online social network users with potential SNMDs.

Information diffusion is a natural phenomenon occurring in social networks. The adoption behavior of a node toward an information piece in a social network can be affected by different factors, e.g., freshness and hotness. Previously, many diffusion models are proposed to consider one or several fixed factors. In fact, the factors affecting adoption decision of a node are different from one to another and may not be seen before. For a different scenario of diffusion with new factors, previous diffusion models may not model the diffusion well, or are not applicable at all. Moreover, uncertainty of information exposure intrinsically exists between two connected nodes, which causes modeling diffusion more challenge in social networks. In this work, our aim is to design a diffusion model in which factors considered are flexible to be extended and changed and the uncertainly of information exposure is explicitly tackled. Therefore, with different factors, our diffusion model can be adapted to more scenarios of diffusion without requiring the modification of the learning framework. We conduct comprehensive experiments to show that our diffusion model is effective on two important tasks of information diffusion, namely activation prediction and spread estimation.

Because Online Social Networks (OSNs) have become increasingly important in the last decade, they have motivated a great deal of research on Social Network Analysis (SNA). Currently, SNA algorithms are evaluated on real datasets obtained from large-scale OSNs, which are usually sampled by Breadth-First-Search (BFS), Random Walk (RW), or some variations of the latter. However, none of the released datasets provides any statistical guarantees on the difference between the sampled datasets and the ground truth. Moreover, all existing sampling algorithms only focus on sampling a single OSN, but each OSN is actually a sampling of a complete social network. Hence, even if the whole dataset from a single OSN is sampled, the results may still be skewed and may not fully reflect the properties of the complete social network. To address the above issues, we have made the first attempt to explore the joint sampling of multiple OSNs and propose an approach called Quality-guaranteed Multi-network Sampler (QMSampler) that can jointly sample multiple OSNs. QMSampler provides a statistical guarantee on the difference between the sampled real dataset and the ground truth (the perfect integration of all OSNs). Our experimental results demonstrate that the proposed approach generates a much smaller bias than any existing method. QMSampler has also been released as a free download.

In this paper, we propose SUbsequence Matching framework with cell MERgence (SUMMER) for online subsequence matching between histogram-based stream synopsis structures under the dynamic time warping distance. Given a query synopsis pattern, SUMMER continuously identifies all the matching subsequences for a stream as the bins are generated. To effectively reduce the computation time, we design a Weighted Dynamic Time Warping (WDTW) algorithm, which computes the warping distance directly between two histogram-based synopses. Furthermore, a Stack-based Overlapping Filter Algorithm (SOFA) is provided to remove the overlapping subsequences to avoid the redundant information. Finally, we design an optional refinement module to relax the subsequence range limit and improve the matching accuracy. Our experiments on real datasets show that the proposed method significantly speeds up the pattern matching without compromising the accuracy required when compared with other approaches.

While recent works on multiuser MIMO (MU-MIMO) mainly focus on boosting the throughput by enabling concurrent transmissions, less attention is paid to recovering concurrent erroneous streams. We however notice that uplink MU-MIMO is especially vulnerable to errors because error in any stream corrupts most of the other concurrent streams. Even worse, carrier sense and rate adaptation become more challenging in MU-MIMO, which further decreases reliability. Existing systems however recover errors by retransmitting all the streams in a corrupted packet, thereby taking away the significant performance benefit of MU-MIMO. To effectively harness the ideal gain of MU-MIMO, we develop Concurrent Packet Recovery ( $\sf {CPR}$ ), a recovery protocol customized for MU-MIMO. It has two distinctive features: (i) It judiciously selects the minimum number of streams to be retransmitted to support successful decoding; (ii) during retransmission, it utilizes the full degrees of freedom by allowing new streams to be sent in parallel. Our evaluation via testbed experiments and trace-driven simulations shows that can efficiently recover both normal losses and collisions. For three-antenna AP scenarios, the throughput gain is up to 31.1 percent when no hidden terminal exists, and is 3.16 $\times$ when 20 percent pairs of contending clients are hidden terminals.

Big-data computing is a new critical challenge for the ICT industry. Engineers and researchers are dealing with data sets of petabyte scale in the cloud computing paradigm. Thus, the demand for building a service stack to distribute, manage, and process massive data sets has risen drastically. In this paper, we investigate the Big Data Broadcasting problem for a single source node to broadcast a big chunk of data to a set of nodes with the objective of minimizing the maximum completion time. These nodes may locate in the same datacenter or across geo-distributed datacenters. This problem is one of the fundamental problems in distributed computing and is known to be NP-hard in heterogeneous environments. We model the Big-data broadcasting problem into a LockStep Broadcast Tree (LSBT) problem. The main idea of the LSBT model is to define a basic unit of upload bandwidth, $r$ , such that a node with capacity $c$ broadcasts data to a set of $\lfloor c/r\rfloor$ children at the rate $r$ . Note that $r$ is a parameter to be optimized as part of the LSBT problem. We further divide the broadcast data into $m$ chunks. These data chunks can then be broadcast down the LSBT in a pipeline manner. In a homogeneous network environment in which each node has the same upload capacity $c$ , we show that the optimal uplink rate $r^*$ of LSBT is either $c/2$ or $c/3$ , whichever gives the smaller maximum completion time. For heterogeneous environments, we present an $O(n \text{log}^2 n)$ algorithm to select an optimal uplink rate $r^*$ and to construct an optimal LSBT. Numerical results show that our approach performs well with less maximum completion time and lower computational complexity than other efficient solutions in literature.

Studies show that a person is willing to join a social group activity if the activity is interesting, and if some close friends also join the activity as companions. The literature has demonstrated that the interests of a person and the social tightness among friends can be effectively derived and mined from social networking websites. However, even with the above two kinds of information widely available, social group activities still need to be coordinated manually, and the process is tedious and time-consuming for users, especially for a large social group activity, due to complications of social connectivity and the diversity of possible interests among friends. To address the above important need, this paper proposes to automatically select and recommend potential attendees of a social group activity, which could be very useful for social networking websites as a value-added service. We first formulate a new problem, named Willingness mAximization for Social grOup (WASO). This paper points out that the solution obtained by a greedy algorithm is likely to be trapped in a local optimal solution. Thus, we design a new randomized algorithm to effectively and efficiently solve the problem. Given the available computational budgets, the proposed algorithm is able to optimally allocate the resources and find a solution with an approximation ratio. We implement the proposed algorithm in Facebook, and the user study demonstrates that social groups obtained by the proposed algorithm significantly outperform the solutions manually configured by users.

This paper focuses on the problem of short text summarization on the comment stream of a specific message from social network services (SNS). Due to the high popularity of SNS, the quantity of comments may increase at a high rate right after a social message is published. Motivated by the fact that users may desire to get a brief understanding of a comment stream without reading the whole comment list, we attempt to group comments with similar content together and generate a concise opinion summary for this message. Since distinct users will request the summary at any moment, existing clustering methods cannot be directly applied and cannot meet the real-time need of this application. In this paper, we model a novel incremental clustering problem for comment stream summarization on SNS. Moreover, we propose IncreSTS algorithm that can incrementally update clustering results with latest incoming comments in real time. Furthermore, we design an at-a-glance visualization interface to help users easily and rapidly get an overview summary. From extensive experimental results and a real case demonstration, we verify that IncreSTS possesses the advantages of high efficiency, high scalability, and better handling outliers, which justifies the practicability of IncreSTS on the target problem.

Late fusion is one of the most effective approaches to enhance recognition accuracy through combining prediction scores of multiple classifiers, each of which is trained by a specific feature or model. The existing methods generally use a fixed fusion weight for one classifier over all samples, and ignore the fact that each classifier may perform better or worse for different subsets of samples. In order to address this issue, we propose a novel sample specific late fusion (SSLF) method. Specifically, we cast late fusion into an information propagation process that diffuses the fusion weights of labeled samples to the individual unlabeled samples, and enforce positive samples to have higher fusion scores than negative samples. Upon this process, the optimal fusion weight for each sample is identified, while positive samples are pushed toward the top at the fusion score rank list to achieve better accuracy. In this paper, two SSLF methods are presented. The first method is ranking SSLF (R-SSLF), which is based on graph Laplacian with RankSVM style constraints. We formulate and solve the problem with a fast gradient projection algorithm; the second method is infinite push SSLF (I-SSLF), which combines graph Laplacian with infinite push constraints. I-SSLF is a $l_{\infty }$ norm constrained optimization problem and can be solved by an efficient alternating direction method of multipliers method. Extensive experiments on both large-scale image and video data sets demonstrate the effectiveness of our methods. In addition, in order to make our method scalable to support large data sets, the AnchorGraph model is employed to propagate information on a subset of samples (anchor points) and then reconstruct the entire graph to get the weights of all samples. To the best of our knowledge, this is the first method that supports learning of sample specific fusion weights for late fusion.

Existing research on finding social groups mostly focuses on dense subgraphs in social networks. However, finding socially tenuous groups also has many important applications. In this paper, we introduce the notion of k-triangles to measure the tenuity of a group. We then formulate a new research problem, Minimum k-Triangle Disconnected Group with No-Pair Constraint (MkTG), to find a socially tenuous group from the online social network. We prove that MkTG is NP-hard and inapproximable within any ratio. Two algorithms, namely TERA and TERA-ADV, are designed for solving MkTG effectively and efficiently. Further, we examine the MkTG problem on tree-based social networks, due to their structural resemblance with corporate social networks built upon the supervision relation. Accordingly, we devise an efficient algorithm, namely Tenuity Maximization for Trees (TMT), to obtain the optimal solution in polynomial time. In addition, we study a more general version of MkTG, named Generalized Minimum k-Triangle Disconnected Group without No-Pair Constraint (MkTG-G). We formulate MkTG-G, analyze its inapproximability, and propose a randomized approximation algorithm, named Randomized Ranking with Limited Neighborhood Participation (RLNP). Experimental results on real datasets manifest that the proposed algorithms outperform the baselines in terms of both efficiency and solution quality.

Management of uncertain data in spatial queries has drawn extensive research interests to consider the granularity of devices and noises in the collection and the delivery of data. Most previous works usually model and handle uncertain data to find the required results directly. However, it is more difficult for users to obtain useful insights when data uncertainty dramatically increases. In this case, users are usually willing to invest more resources to improve the result by reducing the data uncertainty in order to obtain more interesting observations with the existing schemes. In light of this important need, this paper formulates a new problem of selecting a given number of uncertain data objects for acquiring their attribute values to improve the result of the Probabilistic k-Nearest-Neighbor (k-PNN) query. We prove that better query results are guaranteed to be returned with data acquisition, and we devise several algorithms to maximize the expected improvement. We first explore the optimal single-object acquisition for 1-PNN to examine the fundamental problem structure and then propose an efficient algorithm that discovers crucial properties to simplify the probability derivation in varied situations. We extend the proposed algorithm to achieve the optimal multi-object acquisition for 1-PNN by deriving an upper bound to facilitate efficient pruning of unnecessary sets of objects. Moreover, for data acquisition of k-PNN, we extract the k-PNN answers with sufficiently large probabilities to trim the search space and properly exploit the result of single-object acquisition for estimating the gain from multi-object acquisition. The experimental results demonstrate that the probability of k-PNN can be significantly improved even with only a small number of objects for data acquisition.

Data clustering has been widely applied to many practical applications, including social network analysis, location-based analysis, and scientific analysis. Considering the ongoing huge increases in the amount of data generated by Internet users and limited memory and computation resources, the design of scalable clustering has become very significant. In this paper, we propose a distributed clustering algorithm named HiClus, which is based on heterogeneous cloud computing. HiClus can efficiently build an adaptive distributed tree in the cloud to utilize computation resources of both central processing unit and general-purpose computing on graphics processing units. The evaluation herein shows that HiClus can scale up better, use less clustering time, and achieve better load balancing than existing MapReduce algorithms.

An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental factors considered in standard diagnostic criteria (questionnaire) cannot be observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the performance. Our framework is evaluated via a user study with 3126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results show that SNMDD is promising for identifying online social network users with potential SNMDs., Comment: 11 pages

Social networks model the social activities between individuals, which change as time goes by. In light of useful information from such dynamic networks, there is a continuous demand for privacy-preserving data sharing with analyzers, collaborators or customers. In this paper, we address the privacy risks of identity disclosures in sequential releases of a dynamic network. To prevent privacy breaches, we proposed novel kw-structural diversity anonymity, where k is an appreciated privacy level and w is a time period that an adversary can monitor a victim to collect the attack knowledge. We also present a heuristic algorithm for generating releases satisfying kw-structural diversity anonymity so that the adversary cannot utilize his knowledge to reidentify the victim and take advantages. The evaluations on both real and synthetic data sets show that the proposed algorithm can retain much of the characteristics of the networks while confirming the privacy protection.

In multiuser MIMO (MU-MIMO) networks, the optimal bit rate of a user is highly dynamic and changes from one packet to the next. This breaks traditional bit rate adaptation algorithms, which rely on recent history to predict the best bit rate for the next packet. To address this problem, we introduce TurboRate, a rate adaptation scheme for MU-MIMO LANs. TurboRate shows that clients in an MU-MIMO LAN can adapt their bit rate on a per-packet basis if each client learns two variables: Its SNR when it transmits alone to the access point, and the direction along which its signal is received at the AP. TurboRate also shows that each client can compute these two variables passively without exchanging control frames with the access point. A TurboRate client then annotates its packets with these variables to enable other clients to pick the optimal bit rate and transmit concurrently to the AP. A prototype implementation in USRP-N200 shows that traditional rate adaptation does not deliver the gains of MU-MIMO WLANs, and can interact negatively with MU-MIMO, leading to low throughput. In contrast, enabling MU-MIMO with TurboRate provides a mean throughput gain of 1.7× and 2.3×, for 2-antenna and 3-antenna APs, respectively.

As an increasing number of social networking data is published and shared for commercial and research purposes, privacy issues about the individuals in social networks have become serious concerns. Vertex identification, which identifies a particular user from a network based on background knowledge such as vertex degree, is one of the most important problems that has been addressed. In reality, however, each individual in a social network is inclined to be associated with not only a vertex identity but also a community identity, which can represent the personal privacy information sensitive to the public, such as political party affiliation. This paper first addresses the new privacy issue, referred to as community identification, by showing that the community identity of a victim can still be inferred even though the social network is protected by existing anonymity schemes. For this problem, we then propose the concept of \textit{structural diversity} to provide the anonymity of the community identities. The $k$-Structural Diversity Anonymization ($k$-SDA) is to ensure sufficient vertices with the same vertex degree in at least $k$ communities in a social network. We propose an Integer Programming formulation to find optimal solutions to $k$-SDA and also devise scalable heuristics to solve large-scale instances of $k$-SDA from different perspectives. The performance studies on real data sets from various perspectives demonstrate the practical utility of the proposed privacy scheme and our anonymization approaches.

An object can move with various speeds and arbitrarily changing directions. Given a bounded area where a set of objects moving around, there are some typical moving styles of the objects at different local regions due to the geography nature or other spatiotemporal conditions. Not only the paths that the objects move along, we also want to know how different groups of objects move with various speeds. Therefore, given a set of collected trajectories spreading in a bounded area, we are interested in discovering the typical moving styles in different regions of all the monitored moving objects. These regional typical moving styles are regarded as the profile of the monitored moving objects, which may help reflect the geoinformation of the observed area and the moving behaviors of the observed moving objects. In this paper, we present DivCluST, an approach to finding regional typical moving styles by dividing and clustering the trajectories in consideration of both the spatial and temporal constraints. Different from the existing works that consider only the spatial properties or just the interesting regions of trajectories, DivCluST focuses more on typical movements in local regions of a bounded area and takes the temporal information into account when designing the criteria for trajectory dividing and the distance measurement for adaptive $(k)$-means clustering. Extensive experiments on three types of real data sets with specially designed visualization are presented to show the effectiveness of DivCluST.

The Support Vector Machines (SVMs) have been widely used for classification due to its ability to give low generalization error. In many practical applications of classification, however, the wrong prediction of a certain class is much severer than that of the other classes, making the original SVM unsatisfactory. In this paper, we propose the notion of Asymmetric Support Vector Machine (ASVM), an asymmetric extension of the SVM, for these applications. Different from the existing SVM extensions such as thresholding and parameter tuning, ASVM employs a new objective that models the imbalance between the costs of false predictions from different classes in a novel way such that user tolerance on false-positive rate can be explicitly specified. Such a new objective formulation allows us of obtaining a lower false-positive rate without much degradation of the prediction accuracy or increase in training time. Furthermore, we show that the generalization ability is preserved with the new objective. We also study the effects of the parameters in ASVM objective and address some implementation issues related to the Sequential Minimal Optimization (SMO) to cope with large-scale data. An extensive simulation is conducted and shows that ASVM is able to yield either noticeable improvement in performance or reduction in training time as compared to the previous arts.

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines., Comment: 12 pages

The support vector machine (SVM) is a widely used tool in classification problems. The SVM trains a classifier by solving an optimization problem to decide which instances of the training data set are support vectors, which are the necessarily informative instances to form the SVM classifier. Since support vectors are intact tuples taken from the training data set, releasing the SVM classifier for public use or shipping the SVM classifier to clients will disclose the private content of support vectors. This violates the privacy-preserving requirements for some legal or commercial reasons. The problem is that the classifier learned by the SVM inherently violates the privacy. This privacy violation problem will restrict the applicability of the SVM. To the best of our knowledge, there has not been work extending the notion of privacy preservation to tackle this inherent privacy violation problem of the SVM classifier. In this paper, we exploit this privacy violation problem, and propose an approach to postprocess the SVM classifier to transform it to a privacy-preserving classifier which does not disclose the private content of support vectors. The postprocessed SVM classifier without exposing the private content of training data is called Privacy-Preserving SVM Classifier (abbreviated as PPSVC). The PPSVC is designed for the commonly used Gaussian kernel function. It precisely approximates the decision function of the Gaussian kernel SVM classifier without exposing the sensitive attribute values possessed by support vectors. By applying the PPSVC, the SVM classifier is able to be publicly released while preserving privacy. We prove that the PPSVC is robust against adversarial attacks. The experiments on real data sets show that the classification accuracy of the PPSVC is comparable to the original SVM classifier.

E-mail communication is indispensable nowadays, but the e-mail spam problem continues growing drastically. In recent years, the notion of collaborative spam filtering with near-duplicate similarity matching scheme has been widely discussed. The primary idea of the similarity matching scheme for spam detection is to maintain a known spam database, formed by user feedback, to block subsequent near-duplicate spams. On purpose of achieving efficient similarity matching and reducing storage utilization, prior works mainly represent each e-mail by a succinct abstraction derived from e-mail content text. However, these abstractions of e-mails cannot fully catch the evolving nature of spams, and are thus not effective enough in near-duplicate detection. In this paper, we propose a novel e-mail abstraction scheme, which considers e-mail layout structure to represent e-mails. We present a procedure to generate the e-mail abstraction using HTML content in e-mail, and this newly devised abstraction can more effectively capture the near-duplicate phenomenon of spams. Moreover, we design a complete spam detection system Cosdes (standing for COllaborative Spam DEtection System), which possesses an efficient near-duplicate matching scheme and a progressive update scheme. The progressive update scheme enables system Cosdes to keep the most up-to-date information for near-duplicate detection. We evaluate Cosdes on a live data set collected from a real e-mail server and show that our system outperforms the prior approaches in detection results and is applicable to the real world.

Three essential criteria are important for activity planning, including: (1) finding a group of attendees familiar with the initiator, (2) ensuring each attendee in the group to have tight social relations with most of the members in the group, and (3) selecting an activity period available for all attendees. Therefore, this paper proposes Social-Temporal Group Query to find the activity time and attendees with the minimum total social distance to the initiator. Moreover, this query incorporates an acquaintance constraint to avoid finding a group with mutually unfamiliar attendees. Efficient processing of the social-temporal group query is very challenging. We show that the problem is NP-hard via a proof and formulate the problem with Integer Programming. We then propose two efficient algorithms, SGSelect and STGSelect, which include effective pruning techniques and employ the idea of pivot time slots to substantially reduce the running time, for finding the optimal solutions. Experimental results indicate that the proposed algorithms are much more efficient and scalable. In the comparison of solution quality, we show that STGSelect outperforms the algorithm that represents manual coordination by the initiator., VLDB2011

Natural phenomena show that many creatures form large social groups and move in regular patterns. However, previous works focus on finding the movement patterns of each single object or all objects. In this paper, we first propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D, which exploits the obtained group movement patterns to reduce the amount of delivered data. The compression algorithm includes a sequence merge and an entropy reduction phases. In the sequence merge phase, we propose a Merge algorithm to merge and compress the location data of a group of moving objects. In the entropy reduction phase, we formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that obtains the optimal solution. Moreover, we devise three replacement rules and derive the maximum compression ratio. The experimental results show that the proposed compression algorithm leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently.

Data broadcast is an advanced technique to realize large scalability and bandwidth utilization in a mobile computing environment. In this environment, the channel bandwidth of each channel is variant with time in real cases. However, traditional schemes do not consider time-variant bandwidth of each channel to schedule data items. Therefore, the above drawback degrades the performance in generating broadcast programs. In this paper, we address the problem of generating a broadcast program to disseminate data via multiple channels of time-variant bandwidth. In view of the characteristics of time-variant bandwidth, we propose an algorithm using adaptive allocation on time-variant bandwidth to generate the broadcast program to avoid the above drawback to minimize average waiting time. Experimental results show that our approach is able to generate the broadcast programs with high quality and is very efficient in a data broadcasting environment with the time-variant bandwidth.