Reviewerly/exHarmony huggingface dataset:
Perfect Data Protection, Storage, and Reputation Management Scheme for Ride-Sharing Services

Ride-sharing services emerging in recent years provide a convenient, green, and indispensable mode of trip. Nevertheless, the lack of confidentiality as well as traceability of the travel data, the reliance of most ride-sharing services on centralized storage structures, and the issue of trust between passengers and drivers are pressing problems to be addressed. To mitigate these problems, a perfect data protection, storage, and reputation management (DPSRM) scheme is proposed for ride-sharing services. First, a passenger utilizes the proposed ciphertext-policy attribute-based encryption (CP-ABE) algorithm with partially hidden-policy, decryption test, and traceability, to encrypt her/his travel data. An authorized driver can successfully decrypt the ciphertext to acquire the passenger's travel data, then drive to the point of departure and wait for the passenger. Second, the data stored on the blockchain, such as the hash value of the ciphertext and the storage address of the ciphertext, can be used for digital forensics to achieve the distributed storage and traceability of data. Third, a reputation management based on the drivers' past behavior is proposed, which is a quantitative method to forecast the reliability of the drivers' future behavior and can be applied to settle the trust issue between passengers and drivers. Furthermore, the security analysis and performance comparisons manifest that the proposed DPSRM scheme is secure and efficient in ride-sharing services.
Exploitation of exponential and logarithmic functions for data encryption and decryption

The need and the demand to hide the content of written messages from prying eyes has arisen as long as writing itself has existed. Particularly, in recent times the protection of information is considered to be quite crucial due to the increased use and development of streaming applications. Therefore, the urge to have more sophisticated, stronger and hard to break data encryption and decryption systems is increasing. In order to attain these requirements, cryptography plays an important role, where many researchers have come up with different proposals and developed algorithms that have helped out a little in ensuring the confidentiality, integrity and authentication of the given information. However, even the internet security through modern cryptography is quite complex and depends on the difficulty of certain computational problems in mathematics. Modern systems of encryption are based on complex mathematical algorithms and carry out a combination of symmetric and asymmetric key encryption schemes to secure communication. For those a significant background in algebra, number theory and geometry is required. In this paper a new cryptosystem is presented, which is based on compound commutative functions such as exponential and logarithmic functions. After the necessary theoretical deliberation in this paper, we will provide the encryption and decryption approach accompanied with relevant examples
CAT: Re-Conv Attention in Transformer for Visual Question Answering

Visual Question Answering (VQA) is a challenging task that obtains the correct answer based on an image and a question related to the picture. Images often contain more information and local spatial relationships than text in this task. However, many current VQA models only utilize the original Transformer to capture the global relationships when performing image processing while ignoring the equally important local relationships. This paper proposes a novel Re-Conv Attention in the Transformer module (CAT) to solve the above problem. Specifically, self-attention is first used to extract the correlation between features (the global relationship). Then, depthwise separable convolution is utilized to extract exciting local information. Finally, the weight generated by the local essential information works on the global relationship extracted by self-attention, developing the local-guided global feature, which constitutes our re-attention mechanism, so that the module can capture the global and local relationships simultaneously. We combine the re-attention mechanism, FFN, and Layer-norm to form CAT. To validate CAT, we conduct extensive experiments on six benchmark datasets of VQA, Image-Text Matching (ITM), and Referring Expression Comprehension (REC) and achieve superior performance gains than the standard Transformer and a bunch of stats-of-the-art methods.
Poisoning Attack against Online Regression Learning with Maximum Loss for Edge Intelligence

Recent trends in the convergence of edge computing and artificial intelligence (AI) have led to a new paradigm of “edge intelligence”, which are more vulnerable to attack such as data and model poisoning and evasion of attacks. This paper proposes a white-box poisoning attack against online regression model for edge intelligence environment, which aim to prepare the protection methods in the future. Firstly, the new method selects data points from original stream with maximum loss by two selection strategies; Secondly, it pollutes these points with gradient ascent strategy. At last, it injects polluted points into original stream being sent to target model to complete the attack process. We extensively evaluate our proposed attack on open dataset, the results of which demonstrate the effectiveness of the novel attack method and the real implications of poisoning attack in a case study electric energy prediction application.
Selective Detail Enhancement Algorithm for Finger Vein Images

In near-infrared finger vein images, the contrast between vein texture and skin is low, and traditional algorithms based on enhancement filters and grayscale stretching are difficult to effectively enhance the vein structure, and it is easy to introduce noise. Therefore, a selective enhancement algorithm for finger vein images based on adaptive guided filtering is proposed. Firstly, a gradient operator weighted guided filter with better edge-preserving ability is proposed to decompose the vein structure of finger vein images, and obtain smooth layer and detail layer. Secondly, a selective weighting factor is designed for vein detail layer enhancement while reducing noise interference. Finally, the enhanced detail layer is fused with the smooth layer to obtain an enhanced image of finger veins. The experimental results show that the proposed algorithm can effectively improve the quality of finger vein images. In six popular image quality evaluation indicators such as information entropy (IE) and peak signal-to-noise ratio, the image recognition rate after enhancement is also improved due to mainstream algorithms.
Privacy-Preserving Action Recognition via Motion Difference Quantization

The widespread use of smart computer vision systems in our personal spaces has led to an increased consciousness about the privacy and security risks that these systems pose. On the one hand, we want these systems to assist in our daily lives by understanding our surroundings, but on the other hand, we want them to do so without capturing any sensitive information. Towards this direction, this paper proposes a simple, yet robust privacy-preserving encoder called BDQ for the task of privacy-preserving human action recognition that is composed of three modules: , , and . First, the input scene is passed to the Blur module to smoothen the edges. This is followed by the Difference module to apply a pixel-wise intensity subtraction between consecutive frames to highlight motion features and suppress high-level privacy attributes. Finally, the Quantization module is applied to the motion difference frames to remove the low-level privacy attributes. The BDQ parameters are optimized in an end-to-end fashion via adversarial training such that it learns to allow action recognition attributes while inhibiting privacy attributes. Our experiments on three benchmark datasets show that the proposed encoder design can achieve state-of-the-art trade-off when compared with previous works. Furthermore, we show that the trade-off achieved is at par with the DVS sensor-based event cameras. Code available at: https://github.com/suakaw/BDQ_PrivacyAR
Alternate histories and conflicting futures

Despite their central importance to a variety of endeavors and despite widespread use in both industry and academia, version control systems (software for tracking versions of files) have not been extensively studied in fields related to technical communication, rhetoric, and communication design. Git, by far the most dominant version control system today, is largely absent. This study theorizes Git as boundary infrastructure---infrastructure used to facilitate collaboration across disciplines and domains. The unique characteristics of boundary infrastructure explain how something as prominent as Git can be so invisible and help identify dangers posed by boundary infrastructure. Drawing on modes of resistance developed in feminist rhetorics, this article concludes with suggestions to ameliorate the negatives effects such infrastructure might have on collaborative knowledge work.
IDGL: An Imbalanced Disassortative Graph Learning Framework for Fraud Detection

The thriving growth of Internet service not only facilitates our daily lives but also incubates various fraudulent activities with concealment. The traceable interactive behaviors forming the graph-like data provide a great opportunity for graph-based fraud detection. Owing to the stellar performance of assortative graph learning, GNN-based fraud detection methods escalate much attention. However, the fraud graph is not always assortative but more likely disassortative as the fraudsters usually camouflage themselves via building numerous connections with normal users. Additionally, the GNN-based fraud detection methods also suffer from graph imbalance issues as the number of fraudsters is far less than that of the normal users. To address these problems, an imbalanced disassortative graph learning framework (IDGL) is proposed with two key components. First, an adaptive dual-channel convolution filter is developed to adaptively combine the advantage of low- and high-frequency signals from its neighbors so as to assimilate the nodes with assortative edges and discriminate the nodes with disassortative edges. Second, a label-aware nodes and edges sampler is designed with the consideration of nodes’ popularity and corresponding label class frequency, which helps the model simultaneously eliminate the bias towards the major classes and pay more attention to the valuable connections (fraud-fraud, fraud-benign). Extensive experiments on two public fraud datasets demonstrate the effectiveness of our method.
PBGAN: Path Based Graph Attention Network for Heterophily

Typical Graph Neural Networks (GNNs) have achieved noticeable performance in node classification tasks with the strategy of neighbor aggregation, which benefits from the homophily assumption of graphs (nodes with the same class tend to be connected). However, the performance of many models degrades tensely when data tend to be heterophilic. Therefore, we need to optimize the neighbor aggregation strategy to deal with these two different types of graphs. In this paper, we propose a Path Based Graph Attention Network (PBGAN) to address this issue. It utilizes structure and path information in the high-order neighborhood and employs Recurrent Neural Network (RNN) to learn attention coefficients according to the shortest paths between node pairs. This learning method makes the attention coefficients better reflect the relational importance between nodes, which is practical for processing heterophilic graphs. Empirical results demonstrate that the PBGAN achieves comparable performance in both homophilic and heterophilic datasets, proving the effectiveness of the proposed attention learning method.
Efficient Differential Privacy Federated Learning Mechanism for Intelligent Selection of Optimal Privacy Protection Levels

Differential privacy (DP) is considered as an effective privacy-preserving method in federation learning to defend against privacy attacks. However, recent studies have shown that it can be exploited to perform security attacks (e.g., false data injection attacks), leading to degraded Federated Learning (FL) performance. In this paper, we systematically study poisoning attacks using the DP mechanism in order to perform them from an adversarial perspective. We demonstrate that although the DP mechanism provides a certain degree of privacy assurance, it can also be a vector for poisoning attacks by adversaries. As a countermeasure, we propose FedEDP, a concise and effective differential privacy federation learning (DPFL) algorithm that uses the parameters and losses of differential privacy to intelligently generate an optimal privacy level for edge nodes (clients) to defend against possible poisoning attacks. We conducted experiments on the datasets MNIST and CIFAR10, respectively, and the experimental results showthat FedEDP significantly improves the privacy-utility trade-off over the state-of-theart in DPFL.
Intention Identification Algorithm for Hunter-target Problem: A Case Study

Hunter-target problem is a typical problem in multi-agent systems. This paper proposes two intention identification algorithms to identify the intention of other agents for the target agent in this problem. In this paper, the intention of other agents is divided according to the dangerous degree for the target. To implement the intention identification, a data set of the trajectory of hunters is established and the pre-processing method for the predictors is proposed. The intention predictors are trained based on CNN-RNN and CNN-LSTM respectively and tested in some typical scenes. Some performance parameters are obtained on the test set, and the predictors' performance is compared. The results show that the predictor based on CNN-LSTM is better for this problem.
Fidelity Evaluation of Virtual Traffic Based on Anomalous Trajectory Detection

Measuring the fidelity of synthesized virtual traffic has become an important and fundamental concern for evaluating the performance of different traffic simulation techniques and applications of autonomous vehicle testing. In this work, we propose a novel method to evaluate the fidelity of any trajectory data from the perspective of anomalous trajectory detection. First, given the trajectory data to be evaluated as input, the method learns spatio-temporal traffic features and reconstructs the input trajectory through a Long Short-Term Memory (LSTM)-based autoencoder architecture. Then, the anomalous trajectories are detected by comparing the reconstructed trajectories and the input ones using the reconstruction error as the benchmark. Our method can detect eight different kinds of anomalous trajectory in terms of changes in velocity and moving direction. In order to evaluate the fidelity of the input trajectory, we design a perceptual evaluation on virtual traffic fidelity and derive a mapping from the reconstruction error to the evaluation score. We demonstrated the effectiveness and robustness of our metric through many experiments on real-world and synthetic trajectory data containing different types of motion anomalies.
Neural Architecture Adaptation for Object Detection by Searching Channel Dimensions and Mapping Pre-trained Parameters

Most object detection frameworks use backbone architectures originally designed for image classification, conventionally with pre-trained parameters on ImageNet. However, image classification and object detection are essentially different tasks and there is no guarantee that the optimal backbone for classification is also optimal for object detection. Recent neural architecture search (NAS) research has demonstrated that automatically designing a backbone specifically for object detection helps improve the overall accuracy. In this paper, we introduce a neural architecture adaptation method that can optimize the given backbone for detection purposes, while still allowing the use of pre-trained parameters. We propose to adapt both the micro- and macro-architecture by searching for specific operations and the number of layers, in addition to the output channel dimensions of each block. It is important to find the optimal channel depth, as it greatly affects the feature representation capability and computation cost. We conduct experiments with our searched backbone for object detection and demonstrate that our backbone outperforms both manually designed and searched state-of-the-art backbones on the COCO dataset.
Contrastive Class-aware Adaptation for Domain Generalization

Domain generalization (DG) tackles the problem of learning a model that generalizes to data drawn from a target domain that was unseen during training. A major trend in this area consists of learning a domain-invariant representation by minimizing the discrepancy across multiple source domains. This strategy, however, does not apply to the challenging yet realistic single-source scenario. In this paper, in contrast to existing methods that focus on domain discrepancy, we exploit the fact that discrepancies also arise across samples from the same class. We therefore develop a unified framework for both multisource and single-source DG that exploits contrastive learning to maximize the gap between samples from the same class, either from different domains or from the same one, while separating the samples from different classes. Our results on standard multisource and single-source DG benchmark datasets demonstrate the benefits of our method over the state-of-the-art ones in both settings.
Concept Drift-based Checkpoint-restart for Edge Services Rejuvenation

As a nascent technique, Mobile Edge Computing (MEC) is mushrooming with a broad application prospect. By transferring abundant computing and storage resources from cloud to edge servers close to users, it allows services to be hosted on edge servers, which greatly reduces service latency. However, due to environmental dynamics, the edge services' reliability fluctuates in real-time. When the reliability of an edge service degrades severely, it may be suffering an anomaly, which can seriously impact its real-time performance and users' quality of experience. To ensure the real-time performance of edge services, this paper presents CDCrest, a concept drift-based checkpoint restart approach for edge service rejuvenation. CDCrest employs L-Detection, a concept drift-based approach to detect edge services' runtime reliability anomalies. L-Detection leverages a sliding window and Locality Sensitive Hashing (LSH)-based sampling to extract features from an edge service's real-time and historic reliability data streams. Then, it calculates the real-time Distribution Change Degree (DCD) based on Jensen-Shannon (JS) divergence to infer whether the edge service is suffering a reliability anomaly. Once an anomaly for an edge service is identified, CDCrest employs a checkpoint restart mechanism to ensure the rapid rejuvenation of the edge service. Extensive experiments conducted based on a popular real-world dataset demonstrate the effectiveness and efficiency of CDCrest against the state-of-the-art approaches.
Interactive Teaching Methods as a Means of Developing Creative Activity of Instrumentalist Students in the Context of Distance Learning

The relevance of the problem is conditioned by the need to adapt musical art to the conditions of distance education. The paper highlights the main approaches to the implementation of interactive teaching methods for instrumentalist students as an effective means of developing creative activity in the context of distance education. In the context of a pandemic, learning is being transformed into a virtual environment, in particular, a remote way of conducting training sessions. This feature requires a separate pedagogical approach to the development of skills in mastering musical instruments. Therefore, the education process needs to be updated and modernised. The purpose of the study was to determine the methods of interactive teaching of instrumentalist students. Theoretical and methodological approaches to the use of interactive teaching methods are substantiated, in particular, the essence of interactive learning is determined, the features of distance learning are determined, and methods for studying the creative activity of instrumentalist students are highlighted. The concepts of interactivity and creativity are revealed. The features of distance learning are analysed, in particular, effective educational technologies and teaching methods are determined, and cloud technologies are identified as the most effective. The method of determining creativity was used to investigate the creativeness of instrumentalist students. The study results became the basis for the effective implementation of interactive learning methods in the process of organising distance learning with instrumentalist students. The practical significance lies in the identification of the most effective interactive methods of development of creativity and the conclusion of appropriate exercises. The expediency of using interactive methods based on the study by researchers as the most effective in the process of distance learning is also substantiated.
Short Autobiography

My full name is Vadim Anatol’evich Yankov. I was born on February 1st 1935, in Taganrog, Russia. During the Second World War, I was evacuated to Sverdlovsk. In 1952, I enrolled in the Department of Philosophy of Moscow State University. When I faced the “troubles” related to the ideologization in the humanities fields in the Soviet time, I decided to move to the Department of Mechanics and Mathematics in 1953. In 1956, I was expelled from the University. The reasons were my sharp criticism against the Komsomol, the participation in a complaint against the condition at the University students’ restaurant, and the publication of an independent students’ newspaper. Later, I was accepted into the University’s remote learning program and obtained my diploma in 1959.
Multi-source Representation Enhancement for Wikipedia-style Entity Annotation

Entity annotation in Wikipedia (officially named wikilinks) greatly benefits human end-users. Human editors are required to select all mentions that are most helpful to human end-users and link each mention to a Wikipedia page. We aim to design an automatic system to generate Wikipedia-style entity annotation for any plain text. However, existing research either rely heavily on mention-entity map or are restricted to named entities only. Besides, they neglect to select the appropriate mentions as Wikipedia requires. As a result, they leave out some necessary annotation and introduce excessive distracting annotation. Existing benchmarks also skirt around the coverage and selection issues. We propose a new task called Mention Detection and Se-lection for entity annotation, along with a new benchmark, WikiC, to better reflect annotation quality. The task is coined centering mentions specific to each position in high-quality human-annotated examples. We also proposed a new framework, DrWiki, to fulfill the task. We adopt a deep pre-trained span selection model inferring directly from plain text via tokens' context embedding. It can cover all possible spans and avoid limiting to mention-entity maps. In addition, information of both inarguable mention-entity pairs, and mention repeat has been introduced as token-wise representation enhancement by FLAT attention and repeat embedding respectively. Empirical results on WikiC show that, compared with often adopted and state-of-the-art Entity Linking and Entity Recognition methods, our method achieves improvement to previous methods in overall performance. Additional experiments show that DrWiki gains improvement even with a low-coverage mention-entity map.