Reviewerly/exHarmony huggingface dataset:
Visual Analysis Motivated Rate-Distortion Model for Image Coding

Optimized for pixel fidelity metrics, images compressed by existing image codec are facing systematic challenges when used for visual analysis tasks, especially under low-bitrate coding. This paper proposes a visual analysis-motivated rate-distortion model for Versatile Video Coding (VVC) intra compression. The proposed model has two major contributions, a novel rate allocation strategy and a new distortion measurement model. We first propose the region of interest for machine (ROIM) to evaluate the degree of importance for each coding tree unit (CTU) in visual analysis. Then, a novel CTU-level bit allocation model is proposed based on ROIM and the local texture characteristics of each CTU. After an in-depth analysis of multiple distortion models, a visual analysis friendly distortion criteria is subsequently proposed by extracting deep feature of each coding unit (CU). To alleviate the problem of lacking spatial context information when calculating the distortion of each CU, we finally propose a multi-scale feature distortion (MSFD) metric using different neighboring pixels by weighting the extracted deep features in each scale. Extensive experimental results show that the proposed scheme could achieve up to 28.17% bitrate saving under the same analysis performance among several typical visual analysis tasks such as image classification, object detection, and semantic segmentation.
A unified general type‐2 fuzzy PID controller and its comparative with type‐1 and interval type‐2 fuzzy PID controller

Abstract For the type reduction of general type‐2 fuzzy sets based on α ‐plane representation was converted to type reduction of several interval type‐2 fuzzy sets, so it was time consuming in real applications. In this paper, a unified general type‐2 fuzzy PID (UGT2‐FPID) controller using the upper and lower bounds of one α ‐plane is proposed, which has higher real time. The UGT2‐FPID controller contains another two adjust parameters, and the analytical structure of UGT2‐FPID controller is obtained by adapting input combination method. Furthermore, this paper discusses the parameter adjustment methods to achieve better controlling effects by the mathematical expressions of UGT2‐FPID controller. The robustness and effectiveness of proposed general type‐2 fuzzy PID controller are tested by two production process objects. The simulation results show that the controlling effects of proposed general type‐2 fuzzy PID controller are better than PID, type‐1 fuzzy PID, and interval type‐2 fuzzy PID controller even if there are exit uncertainties, like controlled object structure changing, controller disturbance, or output noise.
Analyzing the Overhead of File Protection by Linux Security Modules

Over the years, the complexity of the Linux Security Module (LSM) is keeping increasing (e.g. 10,684 LOC in Linux v2.6.0 vs. 64,018 LOC in v5.3), and the count of the authorization hooks is nearly doubled (e.g. 122 hooks in v2.6.0 vs. 224 hooks in v5.3). In addition, the computer industry has seen tremendous advancement in hardware (e.g., memory and processor frequency) in the past decade. These make the previous evaluation on LSM, which was done 18 years ago, less relevant nowadays. It is important to provide up-to-date measurement results of LSM for system practitioners so that they can make prudent trade-offs between security and performance. This work evaluates the overhead of LSM for file accesses on Linux v5.3.0. We build a performance evaluation framework for LSM. It has two parts, an extension of LMBench2.5 to evaluate the overhead of file operations for different security modules, and a security module with tunable latency for policy enforcement to study the impact of the latency of policy enforcement on the end-to-end latency of file operations.
Multiple-Instance Learning from Similar and Dissimilar Bags

Multiple-instance learning (MIL) is an important weakly supervised binary classification problem, where training instances are arranged in bags, and each bag is assigned a positive or negative label. Most of the previous studies for MIL assume that training bags are fully labeled. However, in some real-world scenarios, it could be difficult to collect fully labeled bags, due to the expensive time and labor consumption of the labeling task. Fortunately, it could be much easier for us to collect similar and dissimilar bags (indicating whether two bags share the same label or not), because we do not need to figure out the underlying label of each bag in this case. Therefore, in this paper, we for the first time investigate MIL from only similar and dissimilar bags. To solve this new MIL problem, we propose a convex formulation to train a bag-level classifier based on empirical risk minimization and theoretically derive a generalization error bound. In addition, we also propose a strong baseline for this new MIL problem, which aims to train an instance-level classifier by minimizing the instance-level empirical risk. Extensive experimental results clearly demonstrate that our proposed baseline works well, while our proposed convex formulation is even better.
A Brief History of Blockchain

This chapter reviews the history of blockchain from its ideation in the early 1990s to the widespread development efforts of private enterprise and governments two decades into the 21st century. The chapter covers the early developments in securing the digital record; the origins of the immutable, decentralized ledger; and trustless digital transactions. The key people in blockchain's development are addressed including the pseudonymous Satoshi Nakamoto, developer of the first cryptocurrency, Bitcoin, which became for a while synonymous with blockchain. The successes and failures of blockchain are covered in this history as well as its status and the problems that need to be solved for blockchain to have a global economic and cultural impact.
Towards Multi-Client Forward Private Searchable Symmetric Encryption in Cloud Computing

As a useful cryptographic primitive, searchable symmetric encryption (SSE) has been intensively studied to achieve the secure and efficient retrieval of encrypted data. In order to process update operations, dynamic SSE schemes have been proposed. But recently, file-injection attack has threatened the security of traditional dynamic SSE protocols. Therefore, designing dynamic SSE schemes with forward privacy becomes a new demand to resist the above attack. Meanwhile, multi-client setting is another requirement in SSE techniques where multiple clients can be delegated and have access to the database. However, most of previous forward private schemes were constructed for single-client setting and cannot directly extended to multi-client environment efficiently. To solve the problem, we propose a forward private SSE scheme with support for multi-client in cloud computing. The proposed scheme is based on XOR-homomorphic function and involves two new data structures as private link and public search tree. Security proof demonstrates the proposed scheme can meet the desired secure features. We then conduct experimental evaluation of the proposed scheme and make comparison with related schemes. The result shows that the proposed scheme tends to have high efficiency.
Deep Feature Selection-And-Fusion for RGB-D Semantic Segmentation

Scene depth information can help visual information for more accurate semantic segmentation. However, how to effectively integrate multi-modality information into representative features is still an open problem. Most of the existing work uses DCNNs to implicitly fuse multi-modality information. But as the network deepens, some critical distinguishing features may be lost, which reduces the segmentation performance. This work proposes a unified and efficient feature selection-and-fusion network (FSFNet), which contains a symmetric cross-modality residual fusion module used for explicit fusion of multi-modality information. Besides, the network includes a detailed feature propagation module, which is used to maintain low-level detailed information during the forward process of the network. Compared with the state-of-the-art methods, experimental evaluations demonstrate that the proposed model achieves competitive performance on two public datasets.
Cluster-Reduce

Sketches, a type of probabilistic algorithms, have been widely accepted as the approximate summary of data streams. Compressing sketches is the best choice in distributed data streams to reduce communication overhead. The ideal compression algorithm should meet the following three requirements: high efficiency of compression procedure, support of direct query without decompression, and high accuracy of compressed sketches. However, no prior work can meet these requirements at the same time. Especially, the accuracy is poor after compression using existing methods. In this paper, we propose Cluster-Reduce, a framework for compressing sketches, which can meet all three requirements. Our key technique nearness clustering rearranges the adjacent counters with similar values in the sketch to significantly improve the accuracy. We use Cluster-Reduce to compress four kinds of sketches in two use-cases: distributed data streams and distributed machine learning. Extensive experimental results show that Cluster-Reduce can achieve up to 60 times smaller error than prior works. The source codes of Cluster-Reduce are available at Github anonymously[1].
Near-Real Feature Generative Network for Generalized Zero-Shot Learning

Due to the powerful feature synthesis ability, Generative Adversarial Networks (GAN) is well adapted to the Generalized Zero-Shot Learning (GZSL) task and has achieved great success. Most GAN models for GZSL usually employ random noise with normal distribution to synthesize unseen samples. However, the generated samples often have the same normal distribution as the input noise, which is unrealistic in most circumstances. Therefore, in this paper, we consider that the distribution of unseen classes should be follow that of seen classes and propose a near-real feature generative network (NereNet), which utilizes the most semantically similar seen samples to generate the noise for the unseen classes. Specifically, we first calculate the most similar seen classes for the unseen classes, and then train an encoder network to generate the corresponding noise, which is subsequently combined with the unseen classes attributes to generate unseen samples with GAN. Extensive experiments are conducted on four datasets, and the results demonstrate the effectiveness of our proposed method.
Robust Online Learning against Malicious Manipulation and Feedback Delay With Application to Network Flow Classification

Malicious data manipulation reduces the effectiveness of machine learning techniques, which rely on accurate knowledge of the input data. Motivated by real-world applications in network flow classification, we address the problem of robust online learning with delayed feedback in the presence of malicious data generators that attempt to gain favorable classification outcome by manipulating the data features. When the feedback delay is static, we propose online algorithms termed ROLC-NC and ROLC-C when the malicious data generators are non-clairvoyant and clairvoyant, respectively. We then consider the dynamic delay case, for which we propose online algorithms termed ROLC-NC-D and ROLC-C-D when the malicious data generators are non-clairvoyant and clairvoyant, respectively. We derive regret bounds for these four algorithms and show that they are sub-linear under mild conditions. We further evaluate the proposed algorithms in network flow classification via extensive experiments using real-world data traces. Our experimental results demonstrate that the proposed algorithms can approach the performance of an optimal static offline classifier that is not under attack, while outperforming the same offline classifier when tested with a mixture of normal and manipulated data.
SAFIN: Arbitrary Style Transfer with Self-Attentive Factorized Instance Normalization

Artistic style transfer aims to transfer the style characteristics of one image onto another image while retaining its content. Existing approaches commonly leverage various normalization techniques, although these face limitations in adequately transferring diverse textures to different spatial locations. Self-Attention based approaches have tackled this issue with partial success but suffer from unwanted artifacts. Motivated by these observations, this paper aims to combine the best of both worlds: self-attention and normalization. That yields a new plug-and-play module that we name Self-Attentive Factorized Instance Normalization (SAFIN). SAFIN is essentially a spatially-adaptive normalization module whose parameters are inferred through attention on the content and style image. We demonstrate that plugging SAFIN into the base network of another state-of-the-art method results in enhanced stylization. We also develop a novel base network composed of Wavelet Transform for multi-scale style transfer, which when combined with SAFIN, produces visually appealing results with lesser unwanted textures.
An evolutionary parallel multiobjective feature selection framework

• We propose a robust framework for feature selection that uses four-cores of a CPU. • To exploit diversity, we implement 40 different execution settings. • We analyze the effects of each execution setting through extensive experiments. • We introduce six setting selection schemes and compare them with empirical results. • Experiments show that diverse execution settings lead to better results. Feature selection has become an indispensable preprocessing step in data mining problems as high amount of data become prevalent with the advances in technology. The objective of feature selection is twofold: reducing data amount and improving learning performance. In this study, we leverage the multi-core nature of a regular PC to build a robust framework for feature selection. This framework executes the feature selection algorithm on four processors, in parallel. As per the No Free Lunch Theorem, we facilitate 40 different execution settings for the processors by employing two multiobjective selection algorithms, four initial population generation methods, and five machine learning techniques. Besides, we introduce six setting selection schemes to decide the most fruitful setting for each processor. We carry out extensive experiments on 11 UCI benchmark datasets and analyze the results with statistical tests. Finally, we compare our proposed method with state-of-the-art studies and record remarkable improvement in terms of maximum accuracy.
DOMREP–An Orthogonal Countermeasure for Arbitrary Order Side-Channel and Fault Attack Protection

Protection against physical attacks is a major requirement for cryptographic implementations on devices which can be accessed by attackers. Side-channel and fault injection attacks are the most common types of physical attacks. In this work we present a novel generic solution for simultaneous protection against side-channel and fault attacks with arbitrary order. We combine domain oriented masking and repetition codes in an orthogonal way and call this approach DOMREP. The resistance against side-channel attacks and fault attacks can be scaled independently of each other, for the protection against higher-order side-channel analysis and the injection of multiple faults including SIFA. We develop the generic concept of orthogonal protection, and implement the DOMREP concept on GIMLI, a round two NIST LWC competition candidate, on a Xilinx Artix-7 FPGA. Our implementation of GIMLI is verified to be resistant against univariate first-order side-channel attacks by TVLA. The resistance against SIFA is verified by means of fault emulation of single as well as multiple bit faults. Our implementation of GIMLI achieves the expected security level according to these measurements. We also provide numbers for the area overhead for our protected implementation of GIMLI.
Automatic Space-Time Block Code Recognition Using Convolutional Neural Network With Multi-Delay Features Fusion

Automatic signal recognition (ASR) is becoming increasingly important in spectrum identification and cognitive radio, but most existing space-time block code (STBC) recognition algorithms are traditional ones and do not account for the complementarities between different features. To overcome these deficiencies, a multi-delay features fusion scheme for ASR of STBC using a convolutional neural network (CNN) is proposed in this study. The proposed scheme tries to fuse different time-delay features of received STBC signals to obtain more discriminating features. The dilated convolution of different dilation rates is applied to realize automated multi-delay feature extraction. Then, two fusion methods, i.e., max-correlation (MC) fusion and multi-delay average (MDA) fusion, are proposed to combine the features of different time delays, and a residual block is applied to achieve better representation of signals. Finally, the simulation results demonstrate the superior performance of the proposed method. It is notable that the recognition accuracy can reach 97.4% with a signal-to-noise ratio (SNR) of -5 dB. In addition, the proposed multi-delay features fusion CNN (MDFCNN) scheme does not need a priori information, i.e., modulation type, channel coefficients, and noise power, which is well suited to non-collaborative communication.
Extended incremental nonlinear dynamic inversion for optical flow control of micro air vehicles

Optical flow-based control strategies have always inspired robotic scientists, especially those in the field of Micro Air Vehicles (MAVs), thanks to their computational efficiency and relative simplicity. A major problem is that the success of optical flow control is governed by the availability of distance estimates, while optical flow provides only the ratio of velocity to distance. Therefore, with only monocular visual information, the inherent nonlinearity of optical flow observables has imposed several challenges in the controller design. In this paper, we propose a newly formulated controller, Extended Incremental Nonlinear Dynamic Inversion (EINDI), to deal with nonlinearities in the system output, such as optical flow control problems. The proposed method unlocks the potential of its predecessor (INDI) in output feedback control by removing the common assumption of time-scale separation, allowing internal dynamics to exist, and requiring only the input and output measurements. Furthermore, the EINDI method has been implemented on an MAV and tested successfully for optical flow landing in a simulation and a real-world outdoor environment. Both simulation and flight test results show 1) good tracking performance of the EINDI control compared to the conventional feedback control, 2) smooth landing trajectories without any oscillation, and 3) fast adaptation of the EINDI control even for landings at different heights and desired setpoints.
Recommendation algorithm combining ratings and comments

The rating data of recommendation systems are too sparse, and many existing studies introduce review information to alleviate this phenomenon, but the connection between reviews and the importance of each review is not fully considered in the feature extraction of the information of reviews. To address this problem, this paper proposes the Local-Global Awareness Attention Model (LGAA) to model the comment information. Through the local attention mechanism, the neighborhood attention score is calculated by fully considering the association between each comment and other comments; then the global attention mechanism is used to calculate the global attention score of each comment and weight the sum of all comments, and fuse the scoring information to form a feature vector; finally, it is fed into a factorization machine and a fully connected network for scoring prediction. The results show that LGAA can achieve better performance than the benchmark algorithm.
Diversity driven Query Rewriting in Search Advertising

Retrieving keywords (bidwords) with the same intent as query, referred to as close variant keywords, is of prime importance for effective targeted search advertising. For head and torso search queries, sponsored search engines use a huge repository of same intent queries and keywords, mined ahead of time. Online, this repository is used to rewrite the query and then lookup the rewrite in a repository of bid keywords contributing to significant revenue. Recently generative retrieval models have been shown to be effective at the task of generating such query rewrites. We observe two main limitations of such generative models. First, rewrites generated by these models exhibit low lexical diversity, and hence the rewrites fail to retrieve relevant keywords that have diverse linguistic variations. Second, there is a misalignment between the training objective - the likelihood of training data, v/s what we desire - improved quality and coverage of rewrites. In this work, we introduce CLOVER, a framework to generate both high-quality and diverse rewrites by optimizing for human assessment of rewrite quality using our diversity-driven reinforcement learning algorithm. We use an evaluation model, trained to predict human judgments, as the reward function to finetune the generation policy. We empirically show the effectiveness of our proposed approach through offline experiments on search queries across geographies spanning three major languages. We also perform online A/B experiments on Bing, a large commercial search engine, which shows (i) better user engagement with an average increase in clicks by 12.83% accompanied with an average defect reduction by 13.97%, and (ii) improved revenue by 21.29%.
Learning Temporal-Correlated and Channel- Decorrelated Siamese Networks for Visual Tracking

Recently, Siamese network based trackers have attracted growing popularity in visual tracking, which tackle the tracking by template matching between the initial template and successive search regions. The initial template patch is generally encoded into a convolutional feature for matching. However, the limited representational capability of the template feature limits the tracking accuracy. Besides, this fixed representation also fails to adapt to the target appearance changes. To alleviate these issues, we improve the Siamese trackers by introducing temporal correlation and channel decorrelation mechanisms. On the one hand, we consider the channel-wise correlations between the initial and historical template features to adaptively aggregate informative channel-wise representations for template update. On the other hand, we propose a decorrelation regularization to weaken the channel-wise correlations of individual template features. By end-to-end training, we learn a more complete and adaptive template for accurate object tracking. We demonstrate the generality of our approach by applying it to two prevalent Siamese trackers, i.e., SiamFC and SiamRPN. Extensive experiments on seven benchmark datasets verify the effectiveness of our method.
Towards Next-Generation Cybersecurity with Graph AI

Cybersecurity professionals are inundated with large amounts of data, and require intelligent algorithms capable of distinguishing vulnerable from patched, normal from anomalous, and malicious from benign. Unfortunately, not all machine learning (ML) and artificial intelligence (AI) algorithms are created equal, and in this position paper we posit that a new breed of ML, specifically graph-based machine learning (Graph AI), is poised to make a significant impact in this domain. We will discuss the primary differentiators between traditional ML and graph ML, and provide reasons and justifications for why the latter is well-suited to many aspects of cybersecurity. We will present several example applications and result of graph ML in cybersecurity, followed by a discussion of the challenges that lie ahead.