In federated recommendation systems, model poisoning attacks aim to manipulate the gradient information of multiple target items sent back from local clients to the central server, with the goal of abnormally increasing their exposure across the system. Existing multi-target attack approaches directly manipulate multiple target items and apply a uniform attack strategy to all target items, which may lead to suboptimal promotion effectiveness. To address this issue, we introduce ProitMTA, a novel multi-target model poisoning attack framework that introduces proxy items and provides tailored attack strategies for target items. ProitMTA employs a three-stage process that balances the promotion of multiple target items while preserving recommendation quality. First,proxy item generationuses a Gaussian Mixture Model to create proxy items that represent diverse attack strategies. Second,proxy attack constructiondesigns customized gradient manipulation strategies for each proxy item. Finally,proxy-based target item attacktransfers these strategies to actual target items, enhancing their promotion while minimizing the negative impact on system performance. Through comprehensive experiments on multiple base federated recommendation frameworks and diverse real-world datasets, we demonstrate that ProitMTA outperforms existing attack methods, achieving higher success rates in target item promotion with minimal system-wide performance degradation. Our research highlights the vulnerability of federated recommendation systems when facing multi-target poisoning attacks and underscores the importance of researching effective defense mechanisms We have released our code athttps://github.com/zdy769243418/ProitMTA.

With the rapid development of wireless communication and localization technologies, the easier collection of trajectory data can bring potential data-driven value. Recently, there has been an increasing interest in how to publish trajectory dataset without revealing personal information. However, since the large-scale and real-world sequential trajectory dataset presents a heterogeneous regional distribution, the existing study ignores the relationship between privacy budget allocation and spatial characteristics, resulting in unreasonable continuity and mapping distortion, and thus lowering the utility of the synthetic dataset. To address this problem, we propose a probability distribution model named Adaptive grid-based Weighted Differential Privacy (AWDP). First, trajectories are adaptively discretized into the multi-resolution grid structures to make trajectories more uniformly distributed and less disturbed by the noise. Second, we allocate different weighted budgets for different grids according to density-based regional characteristics. Third, a spatio-temporal continuity maintenance method is designed to solve unrealistic direction- and density-based continuity deviations of synthetic trajectories. An application system is developed for demonstration purposes which is available online athttp://qgailab.com/awdp/. The extensive experiments on three datasets demonstrate that AWDP performs significantly better than the state-of-the-art model in preserving the density distribution of the original trajectories with differential privacy guarantee and high utility.

Complicated temporal patterns can provide important information for accurate time series forecasting. Existing long short-term memory (LSTM) model with attention mechanism have achieved significant performance. However, the exponential decay of long-term memory of LSTM has not be resolved yet in these efforts, remaining a longstanding open problem in recurrent nature. This problem exhibits a bottleneck which restricts the performance of existing studies. Recently, spiking neural networks (SNNs) have shown high efficiency in capturing temporal patterns via the surrogate gradient (SG) method to resolve this issue. However, the concept-drift environment makes it impossible to pre-set the variance into the standard SG method due to time-varying data distribution. In this paper, we propose a novel adaptive and hybrid spiking (AHS) module embedded LSTM, collaborating with two attention mechanisms (called HSN-LSTM) to resolve above-mentioned problems. First, the AHS module is analyzed theoretically can remain long-term memory. Moreover, our smooth SG method avoids pre-setting of variance, which is not sensitive in the above scenarios. Besides, we use the negative log-likelihood function to adjust the attention score for alleviating the negative impact from the concept-drift. Experiment results show the HSN-LSTM outperformed the state-of-the-art models on several multivariate time series datasets.

Traffic signal control aims to coordinate traffic signals across intersections to improve the traffic efficiency of a district or a city. Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated promising performance where each traffic signal is regarded as an agent. However, there are still several challenges that may limit its large-scale application in the real world. On the one hand, the policy of the current traffic signal is often heavily influenced by its neighbor agents, and the coordination between the agent and its neighbors needs to be considered. Hence, the control of a road network composed of multiple traffic signals is naturally modeled as a multi-agent system, and all agents’ policies need to be optimized simultaneously. On the other hand, once the policy function is conditioned on not only the current agent's observation but also the neighbors’, the policy function would be closely related to the training scenario and cause poor generalizability because the agents in various scenarios often have heterogeneous neighbors. To make the policy learned from a training scenario generalizable to new unseen scenarios, a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method is proposed to learn the decentralized policy for each intersection that considers neighbor information in a latent way. Specifically, we formulate the policy learning as a meta-learning problem over a set of related tasks, where each task corresponds to traffic signal control at an intersection whose neighbors are regarded as the unobserved part of the state. Then, a learned latent variable is introduced to represent the task's specific information and is further brought into the policy for learning. In addition, to make the policy learning stable, a novel intrinsic reward is designed to encourage each agent's received rewards and observation transition to be predictable only conditioned on its own history. Extensive experiments conducted on CityFlow demonstrate that the proposed method substantially outperforms existing approaches and shows superior generalizability.

Existing compression artifacts reduction methods aim to restore images on pixel-level, which can improve the human visual experience. However, in many applications, large-scale images are collected not for visual examination by humans. Instead, they are used for many high-level vision tasks usually by Deep Neural Networks (DNN). In this paper, we find that these methods have limited performance improvements to high-level tasks, even bring negative effects. Therefore, inspired by the teacher-student network framework, we propose a compression artifacts reduction framework (ARF) for DNN. In addition, we generalize our method to the unsupervised setting (U-ARF) where the corresponding original images are unavailable in training. Extensive experiments indicate the proposed methods can help DNNs improve performance on the highly compressed images significantly.

Short videos, ranging from a few seconds to a few minutes, have become popular in many video production. So we need efficient coding tools to compress these videos. AV1 is an emerging open-source and royalty-free video compression format jointly developed and finalized in early 2018 by the Alliance for Open Media. Many coding tools are introduced into AV1 to improve compression performance. However, some coding tools may lead to performance loss to encode these short videos. This paper presents a coding tools performance evaluation of AV1 on short videos. We design an Off experiment by turning off a single coding tool, and the anchor is AV1 with all coding tools on. We list the impact of each open and closed coding tool on the overall performance of the short video.

A common framework of the action recognition is to collect the videos from different cameras into a cloud center firstly, and then perform the 3D CNN on the cloud server. Although directly, this framework will bring a huge burden to the cloud server and video transmission. To handle this challenge, the "front-cloud" collaborative processing architecture can be used. The most import issue is to compress the feature from 3D CNN effectively without significant loss of accuracy. We propose logarithmic quantization with a maximum value threshold and HEVC inter encoding for 3D CNN features. Experimental results on ResNet-50 and InceptionV1 show that the features can be represented by only 1 bit without significant loss of accuracy. The compression ratio of the quantized 1 bit features using HEVC inter coding can reach to 5000 times and the loss of accuracy is less than 1%.

Recently, a novel bio-inspired spike camera has been proposed, which continuously accumulates luminance intensity and fires spikes once the dispatch threshold is reached. It has shown great advantages in capturing fast-moving scene in a frame-free manner with full texture reconstruction capabilities. However, it is difficult to transmit or store the large amount of spike data. By investigating the spatiotemporal distribution of the spikes, we propose an intensity-based measurement for spike train distance and design an efficient coding method to meet the challenge. First, the spike train is transformed into inter-spike intervals (ISIs), and ISIs are adaptively partitioned into multiple segments in temporal. Then, intra-and inter-pixel prediction are performed to find the best reference candidate. The prediction residuals are quantized to achieve lossy compression. Finally, the quantized residuals are fed into an adaptive context-based entropy coder. Overall, to achieve the best performance, each prediction mode will be tried and the one with minimum rate-distortion cost is chosen.

Dynamic vision sensor (DVS) as a bio-inspired camera, has shown great advantages in high dynamic range (HDR) and high temporal resolution (us) in vision tasks. However, how to lossy compress asynchronous spikes for meeting the demand of large-scale transmission and storage meanwhile maintaining the analysis performance still remains open. Towards this end, this paper proposes a lossy spike coding framework for DVS.

In the modern video coding standards, transform coefficient coding takes large portion of output bit-stream. The absolute value of coefficients are usually represented with high-efficiency variable-length code, while the signs are binarized with 1-bit fixed-length code, which ignores the apriority constraints on sign combinations beyond signs. In this paper, we present a fast and efficient sign inference algorithm for improving the coding efficiency of coefficients signs. By considering the correlation of pixels across the transform block boundary, we define a criterion of continuity to constrain the combination space of signs in the block, and shorten the bits will be used. A given number of signs are hidden during bypass coding, and inferred with the high-precision criterion. Then the infer-error is coded with arithmetic coding. Meanwhile a sign bit redefining strategy is introduced, which is based on the invariants of the linear orthogonal separable transform. The strategy ensures that the codec is able to choose up to 4 bits for inferring each block, and avoids the complex decision mechanism introduced by rate distortion optimization. Experiment results show that about 0.6% in average and up to 1.1% BD-rate saving is archived and only less than 1% encoding time complexity increased compared with HEVC baseline.

As an emerging kind of retinomorphic camera, the dynamic vision sensors (DVS) have shown great advantages in wide dynamic range and high temporal resolution in various applications such as autonomous driving and high-speed motion photography. However, how to compress the output spike data of DVS still remains a big challenge. To address this challenge, this paper firstly analyzes the spike firing mechanism and the redundancies of the spike data generated from DVS, and then introduces an efficient cube-based coding framework. Typically, a spike in DVS contains the location (the x-, y- addresses, the timestamp) and the polarity (On/Off). Three key strategies are designed to exploit the spatial and temporal characteristics of the spike location information for compression, including the adaptive macro-cube partitioning structure, the address-prior mode and the time-prior mode. Finally, the experimental results demonstrate that our approach achieves an impressive coding performance, with the average compression ratio of 19.519 over the original spike data, which is much higher than the results of conventional lossless coding algorithms.

Summary form only given. This paper introduces a spike camera with a distinct video capture scheme and proposes two methods of decoding the spike stream for texture reconstruction. The spike camera captures light and accumulates the converted luminance intensity at each pixel. A spike is fired when the accumulated intensity exceeds the dispatch threshold. The spike stream generated by the camera indicates the luminance variation. Analyzing the patterns of the spike stream makes it possible to reconstruct the picture of any moment which enables the playback of high speed movement.

How to precisely and efficiently detect near-duplicate copies with complicated audiovisual transformations from a large-scale video database is a challenging task. To cope with this challenge, this article proposes a transformation-aware soft cascading (TASC) approach for multimodal video copy detection. Basically, our approach divides query videos into some categories and then for each category designs a transformation-aware chain to organize several detectors in a cascade structure. In each chain, efficient but simple detectors are placed in the forepart, whereas effective but complex detectors are located in the rear. To judge whether two videos are near-duplicates, a Detection-on-Copy-Units mechanism is introduced in the TASC, which makes the decision of copy detection depending on the similarity between their most similar fractions, called copy units (CUs), rather than the video-level similarity. Following this, we propose a CU search algorithm to find a pair of CUs from two videos and a CU-based localization algorithm to find the precise locations of their copy segments that are with the asserted CUs as the center. Moreover, to address the problem that the copies and noncopies are possibly linearly inseparable in the feature space, the TASC also introduces a flexible strategy, called soft decision boundary , to replace the single threshold strategy for each detector. Its basic idea is to automatically learn two thresholds for each detector to examine the easy-to-judge copies and noncopies, respectively, and meanwhile to train a nonlinear classifier to further check those hard-to-judge ones. Extensive experiments on three benchmark datasets showed that the TASC can achieve excellent copy detection accuracy and localization precision with a very high processing efficiency.

Summary form only given. Recently, a low-delay and high-efficiency hierarchical prediction structure (HPS) has been proposed for the forthcoming HEVC. Actually, frames and coding units (CUs) at different HPS positions have different importance to predict following frames and CUs. This paper firstly analyzes what frames and CUs should be quantified less. Based on the analysis, we propose a Hierarchical-and-Adaptive BIT-allocation method with Selective background prediction (HABITS) to optimize the video performance of HEVC. Extensive experiments on HM8.0 show that, HABITS saves 13.3% and 35.5% of the total bit rate for eight HEVC conference videos and eight common used surveillance videos. Even for the normal videos in HEVC's Class B and C, there is still 2.2% bit-saving.

Generally speaking, digital libraries have multiple granularities of semantic units: book, chapter, page, paragraph and word. However, there are two limitations of current eBook retrieval systems: (1) the granularity of retrievable units is either too big or too small, scales such as chapters, paragraphs are ignored; (2) the retrieval results should be grouped by facets to facilitate user's browsing and exploration. To overcome these limitations, we propose a multi-granularity multi-facet eBook retrieval approach.

Keyphrases play a key role in text indexing, summarization and categorization. However, most of the existing keyphrase extraction approaches require human-labeled training sets. In this paper, we propose an automatic keyphrase extraction algorithm, which can be used in both supervised and unsupervised tasks. This algorithm treats each document as a semantic network. Structural dynamics of the network are used to extract keyphrases (key nodes) unsupervised. Experiments demonstrate the proposed algorithm averagely improves 50% in effectiveness and 30% in efficiency in unsupervised tasks and performs comparatively with supervised extractors. Moreover, by applying this algorithm to supervised tasks, we develop a classifier with an overall accuracy up to 80%.

Links among objects contain rich semantics that can be very helpful in classifying the objects. However, many irrelevant links can be found in real-world link data such as Web pages. Often, these noisy and irrelevant links do not provide useful and predictive information for categorization. It is thus important to automatically identify which links are most relevant for categorization. In this paper, we present a contextual dependency network (CDN) model for classifying linked objects in the presence of noisy and irrelevant links. The CDN model makes use of a dependency function that characterizes the contextual dependencies among linked objects. In this way, CDNs can differentiate the impacts of the related objects on the classification and consequently reduce the effect of irrelevant links on the classification. We show how to learn the CDN model effectively and how to use the Gibbs inference framework over the learned model for collective classification of multiple linked objects. The experiments show that the CDN model demonstrates relatively high robustness on data sets containing irrelevant links.

With the exponential growth of both the amount and diversity of the information that the Web encompasses, automatic classification of topic-specific Web sites is highly desirable. We propose a novel approach for Web site classification based on the content, structure and context information of Web sites. In our approach, the site structure is represented as a two-layered tree in which each page is modeled as a DOM (document object model) tree and a site tree is used to hierarchically link all pages within the site. Two context models are presented to capture the topic dependences in the site. Then the hidden Markov tree (HMT) model is utilized as the statistical model of the site tree and the DOM tree, and an HMT-based classifier is presented for their classification. Moreover, for reducing the download size of Web sites but still keeping high classification accuracy, an entropy-based approach is introduced to dynamically prune the site trees. On these bases, we employ the two-phase classification system for classifying Web sites through a fine-to-coarse recursion. The experiments show our approach is able to offer high accuracy and efficient process performance.