The explosive growth of multimodal web data demands communication that transmits meaning rather than raw bits. Existing semantic-communication systems often fail under noise, missing modalities, and distribution shifts because they optimize surface features instead of modality-invariant knowledge. We present Grasp, a knowledge-centric framework for cross-modal communication. Grasp segments streams into semantic blocks and builds a graph over them; a lightweight Graph Neural Networks (GNN) produces schedulable, importance-weighted representations. At its core is knowledge purification : we minimize a conditional mutual information upper bound to perform a three-way disentanglement—strongly related, weakly related, and task-irrelevant components—so that only essential semantics are transmitted while non-essential factors are suppressed. To maintain synchrony, we introduce one-to-two temporal contrastive learning to achieve triple alignment of video, audio, and text despite sampling asynchrony. For efficient transmission, Grasp uses a cross-modal shared vector-quantization codebook—a discrete knowledge codebook —updated by multimodal attention. At the receiver, a soft-recovery mechanism leverages this shared knowledge to robustly reconstruct semantics under low signal-to-noise ratio (SNR) or missing modalities, yielding graceful degradation. Across web tasks—including cross-modal retrieval and missing-modality inference—Grasp improves knowledge consistency, semantic fidelity, and downstream performance over strong baselines while maintaining low latency. These results show that communication structured around purified knowledge is key to building robust, semantic-aware systems for the modern web.

Knowledge Tracing (KT) aims to predict learners' future responses by modeling historical learning sequences. However, existing works often overlook the severe disruptions in temporal and interaction continuity arising from large time intervals between interactions, which leads to significant prediction errors. Therefore, we identify and validate thisbreakpointeffect through empirical studies, and try to address it via data augmentation (DA). Existing DA methods usually emphasize sequence diversity or similarity while neglecting the breakpoint effect in KT. Hence, we strive to enhance knowledge tracing via breakpoint-aware sequence augmentation and propose Collaborative Learning Path Augmentation (CLPA), a systematic three-phase method to enhance models' representational capacity and alleviate the breakpoint effects. First, identifying boundary interaction pairs that exhibit the temporal gap and response inconsistency. Second, leveraging cross-sequence collaborative information to infer functional pseudo-paths between identified boundaries. Third, reconstructing and inserting pseudo-learning paths into target sequences to restore sequence continuity; moreover, two refinements of the sampling quantity constraint and the distance-based sampling strategy are proposed to ensure augmentation quality. Comprehensive experiments on four real-world datasets validate the effectiveness of CLPA, which significantly enhances the target KT model's representational capacity and alleviates the breakpoint effects.

Along with the advance of low-altitude economy, a variety of applications based on Unmanned Aerial Vehicles (UAVs) have been developed to accomplish diverse tasks. In this paper, we focus on the scenario of multiple UAVs performing Federated Learning (FL) tasks. Specifically, a group of UAVs is scheduled to repeatedly visit some Points of Interest (PoIs), collect the data produced by these PoIs, and jointly train a machine learning model based on the collected data. The most challenging issue is how to schedule UAVs to collect data so as to optimize the generalization and convergence of model training under the case that the distributions of the data produced by PoIs have not been known in advance. To address this issue, we propose a novel framework for online FL on distributed unknown data, named OFL-UD2, which is dedicated to online decision-making for UAVs to optimize model training performance. Concretely, we formulate the optimization problem while considering the convergence and quality of trained models as well as energy constraints. Then, we define a utility metric for the data quality of different PoIs and conduct a rigorous convergence analysis for OFL-UD2. Based on the analysis results, we design a two-stage algorithm to determine the scheduling of UAVs. Extensive simulations demonstrate that OFL-UD2can improve model accuracy and speed up running time compared to existing benchmarks significantly.

In online social networks (OSNs), people usually join groups for communication. Information diffusion often occurs with some cost, either between individuals or within/among groups; and different opinions may compete with each other. The groups can make decisions based on the majority of the group members. This type of group consensus is common in group activities. However, existing research on maximization of competitive influence often neglects the effects of group consensus. To this end, we introduce the process of group consensus reaching in influence maximization and propose a novel Group consensus-based Competitive Linear Threshold (GCLT) propagation model; then we study the Budgeted Competitive Influence Maximization (BCIM) problem under the GCLT model. We reveal that the problem is NP-hard, and the objective function is proven to be neither submodular nor supermodular. To this end, we construct an equivalent Group consensus-based Competitive Live-Edge (GCLE) model of GCLT by sampling method. Based on GCLE, develop two submodular functions of the upper and lower bounds. Then, we propose the SBG algorithm by applying the Sandwich Approximation framework for the BCIM problem under the GCLT model. In SBG , we provide an approximate solution to the lower bound and the upper bound by the proposed OPIM-B algorithm. Then, we select the seed set of solutions that achieves the best influence spread in Monte Carlo simulations. We also propose two strategies to optimize SBG . The experiments on six real social network datasets verify the effectiveness and scalability of our method and validate the impact of group consensus on the competitive influence dissemination process, as well as the importance of considering the process of reaching group consensus.

In recommender systems, it is frequently presumed that missing ratings adhere to a missing at random (MAR) mechanism, implying the absence of ratings is independent of their potential values. However, this assumption fails to hold in real-world scenarios, where users are inclined to rate items they either strongly favor or disfavor, introducing a missing not at random (MNAR) scenario. To tackle this issue, prior researchers have utilized explicit MAR feedbacks to infer the propensities of unobserved, implicit MNAR feedbacks. Nonetheless, acquiring explicit MAR feedbacks is resource-intensive and time-consuming and may not reflect users’ true preferences. Furthermore, most methods have only been tested on synthetic or small-scale datasets, thus their applicability and effectiveness in real-world settings without MAR feedbacks remain unclear. Along these lines, we aim to predict MNAR ratings without MAR prior propensities by exploring the consistency between MAR and MNAR feedbacks and narrowing the gap between them. From the empirical study and preliminary experiment, we hypothesize thatuser preferencescan be treated as the common prior propensity for both MAR and MNAR generative processes. In this way, we extend this hypothesis to a more general MNAR scenario: user preferences learned from MNAR can partially substitute for the prior propensities derived from MAR feedbacks for MNAR recommendation tasks. To validate our hypothesis and approach, we develop a lightweight iterative probabilistic matrix factorization framework (lightIPMF) as a practical method of our methodology, utilizing user preferences extracted from MNAR, not MAR, to estimate MNAR feedbacks. Finally, the experimental results show that modeling user preferences can effectively improve MNAR feedback estimation without MAR feedback, and our proposed lightIPMF outperforms the state-of-the-art MNAR methods in predicting MNAR feedbacks.

The serendipity recommendation tries to burst the filter bubble while still meeting user interests. However, serendipity itself has not been well understood in the recommendation system. Thus, factor investigation in recommendation serendipity has attracted much attention, for which two challenges hinder follow-up research: (1) Ambiguity of factors . Different works exploit different factors, and the meanings of factors are inconsistent in various works. (2) Lack of complete impact validation . The importance of these factors in different domains is not yet fully understood. The common approach of user surveys costs much, but the results are usually less objective and limited in quantity. To this end, we strive to comprehensively identify and clarify serendipity factors and explore objective data-driven approaches to validate factor impacts in large-scale cross-domain scenarios. We first conduct a comprehensive literature review to identify all possible factors, from which we find that some factors are being used indistinguishably. To address this issue, we propose two principles of meaning coverage and factor independence to clarify and disentangle serendipity factors. Next, we propose a general experimental framework to explore the impacts of factors. Then, we implement one such framework and run experiments on nine representative datasets to study factor importance on serendipity. We also propose a quantitative method to measure the degree of disentanglement of factors and to test the effects of factor combinations. We gain several useful findings: (1) relevance , diversity , and random are critical factors affecting serendipity; (2) domain features affect factor importance and can guide serendipity recommendation; (3) the disentanglement quantification method benefits the understanding of serendipity and the combination of factors. To our knowledge, this is the first work to comprehensively investigate serendipity factors and experimentally compare their impacts in an objective data-driven approach.

Telegram is a widely used instant messaging app that has gained popularity due to its high level of privacy protection. Telegram has standout social network features like channels, which are virtual rooms where only administrators can post and broadcast messages to all subscribers. However, these same features have also led to the emergence of problematic activities and a significant number of fake accounts. To address these issues, Telegram has introduced verified and scam marks for channels, but only a small number of official channels are currently marked as verified, and only a few fakes as scams. In this research, we conduct a large-scale analysis of Telegram by collecting data from 120,979 different public channels and over 247 million messages. We identify and analyze two types of channels: Clones and fakes. Clones are channels that publish identical content from another channel in order to gain subscribers and promote services. Fakes, on the other hand, are channels that impersonate celebrities or well-known services by posting their own messages. To automatically detect fake channels, we propose a machine learning model that achieves an F1-score of 85.45%. By applying this model to our dataset, we find the main targets of fakes are political figures, well-known people such as actors or singers, and services.

Many researchers primarily rely on modeling user interests for sequential recommendation. However, dynamic user behaviors often accompany unstable interaction histories, and modeling user interests alone is insufficient for comprehensive user features. Some studies notice the problem of interest drift, but they are usually limited to modeling at the item-level, unable to perceive the subtle changes at the feature-level. To this end, we propose a Stability-aware Preference Model (SAPM), which consists of three modules. The LSI module for extracting long and short-term interests, the FLC module for extracting feature-level candidate information, and the SAF module for fusing them according to the stability score. In particular, we propose a Multi-head GRU (MHGRU) structure in the LSI module, which is more efficient than the general GRU and has stronger expression ability. Through extensive experiments, our framework shows significant mitigation of the impact of unstable interactions. On the two real data sets, we improve MRR by 5.7% and 14.0% compared with the recent baselines. Moreover, we conduct an in-depth analysis of user interaction stability and obtain several interesting findings that can benefit future studies.

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy; meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches.

In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic and economic interplay between content caching, pricing, and content sharing. Therefore, we propose a joint Mean-Field Game framework for mobile edge Caching and Pricing (MFG-CP) in large-scale dynamic MEC systems, which can facilitate distributed optimal decision-making based on the mean-field game theory. Specifi-cally, we first formulate the competitive content placement issue among EDPs as a non-cooperative stochastic differential game. To significantly reduce the communication and computation complexity, we further devise a mean-field model to approximate the collective impact of all EDPs on caching, trading, and sharing, by which each EDP can quickly estimate some unknown information without considerable interactions. Then, we develop a distributed best response scheme based on iterative learning, enabling each EDP to solely customize its optimal caching strategy and pricing policy. Besides, we theoretically prove the existence of a unique MFG equilibrium. Finally, trace-driven simulations demonstrate the effectiveness of MFG-CP compared with some baselines.

In this paper, we consider the large-scale Area Under the Precision-Recall Curve (AUPRC) maximization problem for the imbalanced data classification task. Existing optimization methods for AUPRC maximization only focus on the single-machine setting, which are not applicable to the distributed data. To address this problem, we propose a novel decentralized stochastic compositional gradient descent method for large-scale AUPRC maximization. Our theoretical analysis shows that it can achieve a better sample complexity 𝒪 (1/ϵ4) than 𝒪 (1/ϵ6) of existing decentralized methods, but has the same communication complexity 𝒪 (1/ϵ4). To further reduce the communication cost, we developed a novel communication-efficient decentralized stochastic compositional gradient descent method, whose communication complexity is improved to 𝒪 (1/ϵ4–4α) (where α ∈ (0,1/4)). To the best of our knowledge, this is the first work achieving such favorable sample and communication complexities. Finally, we conduct extensive experiments for imbalanced data classification and the empirical results confirm the superior performance of our proposed methods.

With the wide adoption of blockchains in data-intensive applications, enabling verifiable queries over a blockchain database is urgently required. Aiming at reducing costs, previous solutions embed a small-sized authenticated data structure (ADS) in each block header, so that a user can verify search results without maintaining a full copy of blockchain databases. However, existing studies focus on exact queries with difficulty to guarantee the freshness of search results. In this article, we propose two frameworks, called$\mathsf{veffChain}$and$\mathsf{veffChain++}$, to realize freshness authentication of rich queries over blockchain databases. Specifically,$\mathsf{veffChain}$concerns about verifiable latest-$K$exact queries and employs RSA accumulator to generate constant-size ADSs;$\mathsf{veffChain++}$integrates RSA accumulator into the Trie tree to further authenticate latest-$K$fuzzy queries. For improved scalability, an adaptive keyword splitting (AKS) solution is proposed to enable ADSs to be incrementally updated. Compared with the state-of-the-art work, our frameworks have the following merits: (1)Freshness Guarantee. The user can efficiently retrieve the freshest data from a blockchain database in a verifiable way. (2)Flexibility. The user can specify different query patterns on demand to retrieve data as accurately as possible. The detailed security analysis and extensive experiments validate the practicality of our frameworks.

Due to the large-scale data streams produced by distributed terminals, hybrid-storage blockchain (HSB) that combines on-chain and off-chain storages has emerged as a promising solution for secure data storage in decentralized applications. Because all the raw data is outsourced to an untrusted service provider (SP), existing solutions suggest to utilize an on-chain authenticated data structure (ADS) to verify query results retrieved off-chain. However, existing solutions support onlycoarse-grained authenticationmaking a user abandon all the query results once the validation fails. In this paper, we focus on realizingfine-grained authenticationfor range queries, enabling a user to distinguish authentic data from falsified results. Considering the heavy gas consumption of on-chain storage, we propose two multi-dimensional parity-based verification (MPV) schemes with a trade-off between off-chain and on-chain efficiencies. Our main idea is to design an accumulator-based ADS to summarize well-designed verifiable hypercubes, so that fake results can be quickly located by combining multi-dimensional faces failed validation. Compared with previous solutions, our MPV schemes allow a user to make efficient use of query results by filtering out errors, and thus have higher data utility. The detailed security analysis and extensive experiments demonstrate the security and effectiveness of our MPV schemes, respectively.

Points-of-interest (POIs) recommendation plays a vital role in location-based social networks (LBSNs) by introducing unexplored POIs to consumers and has drawn extensive attention from academia and industry. Existing POI recommender systems usually learn fixed latent vectors to represent both consumers and POIs from historical check-ins and make recommendations under the spatio-temporal constraints. However, we argue that the existing works still suffer from the challenges of explaining consumers’ complicated check-in actions. To this end, we first explore the interpretability of recommendations from the POI aspect, i.e., for a specific POI, its function usually changes over time, so representing a POI with a single fixed latent vector is not sufficient to describe the dynamic nature of POIs. Besides, check-in actions to a POI are also affected by the zone where it is located. In other words, the zone's embedding learned from POI distributions, road segments, and historical check-ins could be jointly utilized to enhance POI embeddings. Along this line, we propose aTime-zone-spacePOI embedding model (ToP), which integrates multi-knowledge graphs and topic model to introduce not only spatio-temporal effects but also sentiment constraints into POI embeddings for strengthening interpretability of recommendation. Specifically, ToP learns multiple latent vectors for a POI in a different period with spatial constraints via knowledge graph learning. To add sentiment constraints, ToP jointly combines these vectors with the zone's representations learned by topic models to make explainable recommendations. ToP considers the time, space, and sentiment of POI in a unified embedding framework, which benefits the POI recommendations. Extensive experiments on real-world Changchun city datasets demonstrate that ToP achieves state-of-the-art performance in terms of common metrics and provides more insights for consumers’ POI check-in actions.

Online reviews and ratings play an important role in shaping the purchase decisions of customers in e-commerce. Many researches have been done to make proper recommendations for users, by exploiting reviews, ratings, user profiles, or behaviors. However, the dynamic evolution of user preferences and item properties haven’t been fully exploited. Moreover, it lacks fine-grained studies at the aspect level. To address the above issues, we define two concepts of user maturity and item popularity, to better explore the dynamic changes for users and items. We strive to exploit fine-grained information at the aspect level and the evolution of users and items, for dynamic sentiment prediction. First, we analyze three real datasets from both the overall level and the aspect level, to discover the dynamic changes (i.e., gradual changes and sudden changes) in user aspect preferences and item aspect properties. Next, we propose a novel model of Aspect-based Sentiment Dynamic Prediction (ASDP), to dynamically capture and exploit the change patterns with uniform time intervals. We further propose the improved model ASDP+ with a bin segmentation algorithm to set the time intervals non-uniformly based on the sudden changes. Experimental results on three real-world datasets show that our work leads to significant improvements.

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities. We study the different ways that weather factors may impact these two kinds of activities. We also introduce a new factor of event duration. By integrating the above factors with user interest and user-event distance, we build a model of attendance prediction with the weather named GBT-W , based on the Gradient Boosting Tree. Furthermore, we develop a platform to help event organizers estimate the possible number of activity attendance with different settings (e.g., different weather, location) to effectively plan their events. We conduct extensive experiments, and the results show that our method has a better prediction performance on both the outdoor and the indoor activities, which validates the reasonability of considering weather and duration.

Serendipity recommendation has attracted more and more attention in recent years; it is committed to providing recommendations which could not only cater to users’ demands but also broaden their horizons. However, existing approaches usually measure user-item relevance with a scalar instead of a vector, ignoring user preference direction, which increases the risk of unrelated recommendations. In addition, reasonable explanations increase users’ trust and acceptance, but there is no work to provide explanations for serendipitous recommendations. To address these limitations, we propose a Directional and Explainable Serendipity Recommendation method named DESR. Specifically, we extract users’ long-term preferences with an unsupervised method based on GMM (Gaussian Mixture Model) and capture their short-term demands with the capsule network at first. Then, we propose the serendipity vector to combine long-term preferences with short-term demands and generate directionally serendipitous recommendations with it. Finally, a back-routing scheme is exploited to offer explanations. Extensive experiments on real-world datasets show that DESR could effectively improve the serendipity and explainability, and give impetus to the diversity, compared with existing serendipity-based methods.

Recommendation systems provide good guidance for users to find their favorite movies from an overwhelming amount of options. However, most systems excessively pursue the recommendation accuracy and give rise to over-specialization, which triggers the emergence of serendipity. Hence, serendipity recommendation has received more attention in recent years, facing three key challenges: subjectivity in the definition, the lack of data, and users' floating demands for serendipity. To address these challenges, we introduce a new model called HAES, a H ybrid A pproach for movie recommendation with E lastic S erendipity, to recommend serendipitous movies. Specifically, we (1) propose a more objective definition of serendipity, \em content difference and \em genre accuracy, according to the analysis on a real dataset, (2) propose a new algorithm named JohnsonMax to mitigate the data sparsity and build weak ties beneficial to finding serendipitous movies, and (3) define a novel concept of elasticity in the recommendation, to adjust the level of serendipity flexibly and reach a trade-off between accuracy and serendipity. Extensive experiments on real-world datasets show that HAES enhances the serendipity of recommendations while preserving recommendation quality, compared to several widely used methods.

The mission of set reconciliation (also called set synchronization) is to identify those elements which appear only in exactly one of two given sets. In this paper, we extend the set reconciliation problem into three design rationales: (i) multiset support; (ii) near 100% reconciliation accuracy; (iii) communication-friendly and time-saving. Prior reconciliation methods fail to realize the three rationales simultaneously. To this end, we redesign Trie and Fenwick Tree (FT), to near-accurately represent and reconcile two types of multisets that we refer to as unsorted and sorted multisets, respectively. Comprehensive evaluations are conducted to quantify the performance of our proposals. The trace-driven evaluations demonstrate that Trie and FT achieve near-accurate multiset reconciliation, with 4.31 and 2.96 times faster than the CBF-based method, respectively.

Recently, the development of Bike Sharing Systems (BSSs) brings environmental and economic benefits to the public. However, BSSs frequently suffer from the imbalanced bike distribution, including dock-less BSSs. The underflow or overflow of bikes in a region may lead to a lower service level to BSSs or congestion to the city. In the paper, we consider rebalancing the dock-less BSS by providing users with monetary incentives. The long-term objective is to maximize the number of satisfied users who successfully complete their rides over a period of time. The operator of the dock-less BSS can not only encourage a user to rent bikes at the neighborhood of its source with a source incentive, but also incentivize them to return bikes at the neighborhood of its destination with a destination incentive. To learn the differentiated incentive price for rebalancing bikes across time and space, we extend a novel deep reinforcement learning framework for user incentive. The source and destination incentives are integrated in an adaptive way by adjusting the detour level at the source and/or destination by avoiding bike underflow and overflow. In the experiment, we evaluate our approach in comparison with two existing pricing schemes. The locations of sources and destinations are abstracted from a selected dataset from Mobike. The experiment results show that our adapted learning algorithm outperforms the original one that only considers source incentive as well as another state-of-the-art approach in maximizing the long-term number of satisfied users.

The mission of set reconciliation (also called set synchronization) is to identify those elements which appear only in exactly one of two given sets. In this paper, we extend the set reconciliation problem into three design rationales: (i) multiset support; (ii) near 100 percent reconciliation accuracy; and (iii) communication-friendly and time-saving. These three rationales, if realized, will lead to unprecedented benefits for the set reconciliation paradigm. Generally, prior reconciliation methods are mainly designed for simple sets and thus remain inapplicable for multisets. Methods based on probabilistic data structures, e.g., the Counting Bloom Filter (CBF), support efficient representation, and multiplicity queries. Based on these probabilistic data structures, approximate multiset reconciliation can be enabled. However, they often cannot achieve a statisfying accuracy, due to potential hash collisions. The reconciliations enabled by logs or lists incur high time-complexity and communication overhead. Therefore, existing reconciliation methods, fail to realize the three rationales simultaneously. To this end, we redesign Trie and Fenwick Tree (FT), to near-accurately represent and reconcile two types of multisets that we refer to as unsorted and sorted multisets, respectively. Moreover, to further reduce the communication overhead during the reconciliation process, we design a partial transmission strategy when exchanging two Tries or FTs. Comprehensive evaluations are conducted to quantify the performance of our proposals. The trace-driven evaluations demonstrate that Trie and FT achieve near-accurate multiset reconciliation, with 4.31 and 2.96 times faster than the CBF-based method, respectively. The simulations based on synthetic datasets further indicate that our proposals outperform the CBF-based method in terms of accuracy and communication overhead at most time.

A Bloom filter is an effective, space-efficient data structure for concisely representing a set, and supporting approximate membership queries. Traditionally, the Bloom filter and its variants just focus on how to represent a static set and decrease the false positive probability to a sufficiently low level. By investigating mainstream applications based on the Bloom filter, we reveal that dynamic data sets are more common and important than static sets. However, existing variants of the Bloom filter cannot support dynamic data sets well. To address this issue, we propose dynamic Bloom filters to represent dynamic sets, as well as static sets and design necessary item insertion, membership query, item deletion, and filter union algorithms. The dynamic Bloom filter can control the false positive probability at a low level by expanding its capacity as the set cardinality increases. Through comprehensive mathematical analysis, we show that the dynamic Bloom filter uses less expected memory than the Bloom filter when representing dynamic sets with an upper bound on set cardinality, and also that the dynamic Bloom filter is more stable than the Bloom filter due to infrequent reconstruction when addressing dynamic sets without an upper bound on set cardinality. Moreover, the analysis results hold in stand-alone applications, as well as distributed applications.

Semi-supervised clustering employs limited supervision in the form of labeled instances or pairwise instance constraints to aid unsupervised clustering and often significantly improves the clustering performance. Despite the vast amount of expert knowledge spent on this problem, most existing work is not designed for handling high-dimensional sparse data. This paper thus fills this crucial void by developing a Semi-supervised Clustering method based on spheRical K-mEans via fEature projectioN (SCREEN). Specifically, we formulate the problem of constraint-guided feature projection, which can be nicely integrated with semi-supervised clustering algorithms and has the ability to effectively reduce data dimension. Indeed, our experimental results on several real-world data sets show that the SCREEN method can effectively deal with high-dimensional data and provides an appealing clustering performance.