Reviewerly/exHarmony huggingface dataset:
Improving Bug Severity Prediction With Domain-Specific Representation Learning

Automating the process of bug severity assignment can accelerate bug triagers’ efficiency in the life-cycle of software maintenance, improving the quality of software products. The mainstream approaches for bug severity prediction mainly use different neural networks due to their automated learning ability. However, there are two problems that make existing approaches fail to predict severities for some bugs: 1) they cannot learn the internal knowledge of bug reports; 2) supervised training is difficult to understand the global context of bug reports. To resolve these two problems, in this paper, we propose a bug severity prediction approach, namely KICL, which combines pre-trained language models and domain-specific pre-training strategies, i.e., Knowledge-Intensified pre-training and contrastive learning pre-training. Specifically, Knowledge-Intensified allows KICL to learn project-specific bug report tokens, deeply understanding internal knowledge of bug reports. As for contrastive learning, it allows KICL to perform sequence-level learning, understanding bug reports from the perspective of the global context. When finishing pre-training, we can fine-tune pre-trained KICL for bug severity prediction. To evaluate the effectiveness of KICL, we choose six baseline approaches and compare their performance on a public dataset. The experimental results show that KICL outperforms all baseline approaches by up to 30.68% in terms of weighted average F1-score, achieving new results for bug severity prediction.
Identification of Malicious Encrypted Traffic Through Feature Fusion

The popularity of encrypted communication has grown due to increased security awareness and rapid internet development.End-to-end encryption can prevent data attacks but also poses new cybersecurity threats.Thus, identifying malicious encrypted traffic is a focus of research in network behavior analysis and anomaly detection.Recently, deep learning has brought new directions for the development of traffic classification and anomaly detection.Based on deep learning technology, this paper starts from the two directions of data preprocessing and model selection, studies and analyzes multi-dimensional traffic characteristics and multi-granularity carrier characteristics, and proposes corresponding solutions, and finally designs and proposes a malware-oriented Identification scheme for encrypted traffic.This paper first proposes a malicious encrypted traffic identification scheme MET-FMF based on fine-grained multifeature fusion.Two sets of comparative experiments were designed to compare the multi-dimensional traffic features and the multi-branch network model on the recognition results.The results show that the combination of three-dimensional traffic characteristics and three-branch network model has the best results, with an average accuracy rate of 95.08%.Finally, compared with other schemes, it is found that this scheme has multi-dimensional traffic feature extraction, multi-granularity carrier feature Features such as early session detection and end-to-end transferable learning.
Dynamic Texture Classification Using Directional Binarized Random Features

Dynamic texture description has been studied extensively due to its wide applications in the field of computer vision. Local binary pattern (LBP) and its various variants account for a large part of dynamic texture description methods because of its advantages, such as good discriminability and low computational complexity. However, many LBP-based methods directly extract feature from pixel intensities and only use a proportion of pixels in a local neighborhood. And their good classification performance is usually achieved at the cost of high feature dimensionality, which would limit their application scenarios. We argue that extracting features from the gradient domain will capture more discriminative features due to the additional directional information, and that making use of all the pixels in a local neighborhood would improve performance. In this paper, we propose a simply but effective dynamic texture descriptor that inherits the advantages of LBP while excluding its disadvantages. The proposed method consists of four stages of data processing: 1) gradients extraction; 2) random feature extraction from gradients; 3) binary hashing of directional random features; and 4) histogramming. Gaussian first-order derivatives are used as gradient filters such that stable gradients could be generated. Then random projection is applied to extract random features from each gradients. Both the above two stages are conducted via 3D filtering, and thus they are efficient. Thirdly, the random features from each gradient are binarized and encoded into integer codes, from which a histogram is built. Finally, the histograms from each gradient are concatenated into a feature vector. Because we use 8-bit codes, The feature dimensionality is very low. We evaluate the proposed method on three benchmark dynamic texture datasets with various test protocols. The results demonstrate its effectiveness and efficiency when comparing to many state-of-the-art methods.
Pixel Difference Unmixing Feature Networks for Edge Detection

The edge detection model based on deep learning significantly improves performance, but its generally high model complexity requires a large pretrained Convolutional Neural Networks (CNNs) backbone, and hence large memory and computing power. To solve this problem, we carefully choose proper components for edge detection, introduce a Multiscale Aware Fusion Module based on self-attention and a feature-unmixing loss function, and propose a lightweight network model, Pixel Difference Unmixing Feature Networks (PDUF). The backbone network of proposed model is designed to adopt skip long-short residual connection and does not use pre-trained weights, and requires straightforward hyper-parameter settings. Extensive experiments on the BSDS, NYUD, and Multi-cue datasets, we found that the proposed model has higher F-scores than current state-of-the-art lightweight models (those with fewer than 1 million parameters) on BSDS500 (ODS F-score of 0.818), NYUDv2 depth datasets (ODS F-score of 0.767) and Multi-Cue dataset (ODS F-score 0.871(0.002)), with similar performance compared with some large models (with about 35 million parameters).
Publisher Z.I. Grzhebin in Letters to Scientist, Socialist, Writer V.S. Voitinsky

The Paris period of the life and activity of the expatriate book publisher Z.I. Grzhebin (1925-1927) is extremely insufficiently studied. A collection of nine letters to V.S. Voitinsky and his wife, discovered at the International Institute of Social History (Amsterdam, the Netherlands), makes it possible to clarify a number of episodes that remained outside the framework of the domestic historiography of the issue. Biographical information on the social, political and scientific activities of V.S. Voitinsky in Russia and abroad is given. The text of some Z.I. Grzhebins letters, previously unknown to specialists, as well as the agreement on the credit debt of his Berlin publishing house, which actually put an end to the fate of his company, is introduced into scientific circulation.
The Development of the Russian Film Industry in the Era of Digital Media Exemplified by A. Fedorchenko’s Film &lt;i&gt;Big Snakes of Ulli-Kale&lt;/i&gt;

The relevance of this research lies in understanding of Russian cinema as a rapidly developing cultural industry with the influence of digital media on these processes. A clear example of this process is A. Fedorchenkos creative work, including his last major work, Big Snakes of Ulli-Kale. According to the author of the article, digital media, providing new opportunities for creative self-realization, contribute to the directors traditional desire for the most complete control over the means of artistic expressiveness of film production.
How good do you think you are with computers? The link between teachers’ perceived digital literacy, occupational efficacy, and psychological distress

The present study focused on teachers' perceived digital literacy, occupational self-efficacy, and psychological distress. Our sample included 279 Romanian teachers aged 20 to 66 (M = 31.92, SD = 11.72), with professional experience ranging from 1 to 46 years (M = 8.90). We tested a moderated mediated model, exploring occupational self-efficacy as a mediator of the relationship between perceived digital literacy (moderated by gender, controlling for age and professional experience) and psychological distress. Our findings suggested that higher levels of perceived digital literacy led to higher levels of occupational self-efficacy, which led to lower levels of psychological distress. Gender moderated this relationship, i.e., the observed indirect effects were significant for both genders, but the effects were stronger for male participants. We discuss our results concerning their practical implications for teachers' mental health and professional activity and the perspectives following the COVID-19 pandemic.
Research on cross-modality person re-identification based on generative adversarial networks and modal compensation

Cross-modality person re-identification aims to match specific pedestrian images from different modal image galleries. In order to solve the problem of excessive image modal differences, a cross-modal person re-identification method based on generative adversarial network and feature-level modal compensation is proposed. Firstly, generating corresponding cross-modal images by cycle-consistent generative adversarial networks based on knowledge distillation to solve the problem of fewer image sample pairs; secondly, designing a feature extraction network with self-attention mechanism to extract more discriminative pedestrian features; on this basis, in order to make up for the defects of low-quality generated images and more interference information, a feature-level modality compensation module is designed, firstly the single-modality features are decomposed into modality-specific features and modality-shared features, then the corresponding missing modality-specific features are generated through the modality-shared features, and finally the existing features and the generated features are fused, which enables the model to Generating modality-specific features that are relevant to people and ignoring those that are irrelevant can improve the performance of feature extraction. Through comparative experiments on the RegDB and SYSU-MM01 datasets, the results of the method on the cumulative matching characteristic and the average precision mean were analyzed. The accuracy rate has been improved to a certain extent.
Risk assessment model of Internet of Things based on BP neural network algorithm

The internet of things (IOT) is a novel concept. With the rapid development of wireless communication technology, it has quickly become a hot topic. Its basic concept is to communicate, communicate and cooperate with each other around all kinds of things and objects around us through the unique address mode in the world, so as to achieve the common goal. Through in-depth research and investigation, it is found that at present, most banks in China generally adopt the scoring system for personal credit risk assessment (RBI), and the assessment indicators have not kept pace with the times for many years, and basically there is no big change. Obviously, this evaluation method is highly subjective, and the evaluation indicators are relatively backward, slightly rigid and single. In this regard, this paper studies the relatively mature evaluation criteria and methods in the field of information security, and analyzes the information security status of small IOT systems. Integrate the information security features of the IOT to construct a RBI index system for small IOT systems.
A DeepFake compressed video detection method based on dense dynamic CNN

The emergence of DeepFake poses serious risks to data privacy and social stability. We propose an end-to-end DeepFake video detection method based on a dense dynamic convolutional neural network (CNN) to address the poor performance of DeepFake video detection on complex compression formats and datasets of different forgery methods. In this method, extracted face images are clustered and cleaned by cosine similarity, and face images are expanded through data augmentation to improve data diversity. Dynamic dense blocks are incorporated in a CNN to address optimization difficulties in deep neural networks, and an attention mechanism further improves generalization power. Convolution kernel pruning increases processing speed by effectively reducing the computational needs due to dynamic convolution. Experiments demonstrate that this method has better results on DeepFake video detection across compression rates and datasets compared to other network models.
SeTGAN: Semantic‐text guided face image generation

Abstract In this article, we propose a method to jointly control face image generation through semantic segmentation maps and text. Existing semantic segmentation maps lack detailed face attributes such as beards, and it is difficult to explicitly represent the gender of the target person by virtue of the semantic maps. State‐of‐the‐art face image generation methods guided by semantic segmentation maps mostly solved this by introducing the original image for supervision, which cannot accurately control the detailed attributes of the target face. At the same time, the text‐guided image generation method perform poorly in controlling the front and side of the face pose. Therefore, we propose an idea that the semantic segmentation map controls the coarse content of the target image and the text controls the fine details of the target image. Through the well‐designed mapping network and content mixing mechanism, the model in this article flexibly draws on the advantages of the two modes, and can generate high‐resolution images that are diverse, high‐quality, and more faithful to the target in detail attributes than most of previous methods. Extensive experiments demonstrate the superior performance of the proposed method in terms of accuracy and fidelity.
Fine-grained insulator defect detection method based on vision-transformer

Insulator defects are unavoidable due to long-term exposure and harsh natural environment. With the development of the computer vision and deep learning, researchers have been drawn to automatic unmanned aerial vehicle-based insulator inspection to improve power transmission safety. However, achieving full automation of fine-grained insulator defect detection is still very challenging due to the visual complexity of defects and the high-resolution image computation complexity. This study focuses on fine-grained insulator defect detection by vision transformer based on deep learning. The proposed method is based on a Swin-transformer framework, which focuses on learning hierarchical image feature representation computed with shifted windows scheme. Experiments were carried out on high-resolution image datasets to evaluate the performance of the proposed method for fine-grained insulator defect detection tasks. The results show that the method takes advantage of vision transformer's capabilities and outperforms the state-of-the-art method in terms of mean average precision (mAP) at 94.2% when the intersection threshold over union is set to 0.5.
The Application of Exercises in English Classroom Teaching based on the Concept of New English Curriculum Standard

Practice is a crucial link in classroom teaching. How to improve students' practice is an important problem that teachers should pay attention to in teaching. This paper mainly studies the application of practice teaching in the classroom, is a essential part of the language teaching practice, and the paper research mainly from contact with listening, speaking, reading, writing and translating a few aspects, analysis of English practice should pay attention to some problems, as well as the students how to use the classroom practice, promote efficiency of learning and learning interest problem is analyzed.
EEM: An elastic event matching framework for content-based publish/subscribe systems

The inevitable workload fluctuations in large-scale IoT applications make it difficult to ensure the QoS of data distribution services. Usually, this problem can be solved by dynamically adjusting resource supply. However, deploying more resources not only takes time, it is also difficult to achieve cost-effective results. In this paper, we propose an elastic event matching (EEM) framework and we explore the idea of endowing matching algorithms with adaptability in performance. The strategy is to enable seamless switching between exact matching and approximate matching, achieving a trade-off between matching precision and matching speed. First, we establish a predicate skipping adjustment (PSA) mechanism which quantifies the relationship between false positives and the number of skipped predicates. In addition, we design a performance adjustment decision (PAD) algorithm according to fluctuating workloads. We implemented an EEM prototype based an Kafka, which uses an existing matching algorithm enhanced by PSA as the engine. The prototype is evaluated through a series of experiments based on both synthetic data and real-world stock traces. Experiment results show that adjusting the performance of matching algorithm at the price of a small false positive rate of less than 0.1% can shorten event latency by up to 14.34 times, which clearly demonstrates the effectiveness of the EEM framework.
Multi-authority anonymous authentication with public accountability for incentive-based applications

Incentive-based applications enable users to obtain rewards after they complete a task, but how to balance the privacy and accountability is one of the most serious concerns currently. Publicly accountable anonymous authentication provides an excellent way verifying a user’s identity in a privacy-preserving way while ensuring public accountability in case of dispute. Although different kinds of these schemes have been proposed, they all assume that there is a single centralized certificate authority issuing a certificate to users, and are not suitable for an actual scenario which always involves multiple authorities. Therefore, a new primitive called multi-authority linkable and traceable anonymous authentication is proposed to address this issue, enabling privacy protection while holding public accountability under a multi-authority setting. We formally define a security model for this new notion and simultaneously design a generic construction while giving the security proof. Additionally, we implement the proposed scheme to show its efficiency.
Risk assessment modeling with application in the accounting cloud-service industry

Accounting cloud services are implemented by applying the Software as a Service (SaaS) model. Accounting cloud service providers face many risks from external competitive environments similar to other SaaS providers (Lemkin, 2015) and also have some specific risks from accounting characteristics such as going concerned and periodicity (Ma & Kauffman, 2014). Risk management has become a critical management issue in the accounting cloud service industry, and risk assessment is the core content of the risk management process. However, fewer researchers paid attention to risk assessment models and applications in the cloud service industry, especially the accounting cloud service industry. Using the evidence reasoning approach with the Gini coefficient, the paper provides a risk assessment model including a systematic framework and a rational process, which could assess the risk level in the accounting cloud service industry. The proposed risk assessment modeling is applied and demonstrated using 535 Chinese accounting cloud service providers.
Practical Steps to Mitigate Cybersecurity Attacks on Radiation Oncology Practices

Radiation oncology, as a technologically intensive discipline that requires communication between multiple and diverse computer systems, is vulnerable to cyberattack. Given the enormous amount of the loss of time, energy, and money that results from a cyberattack, it behooves radiation oncologists and their teams to minimize cybersecurity threats to their practices. In this article, we present practical steps that radiation oncologists can take to prevent, prepare for, and respond to a cyberattack.
Trust Model System for the Energy Grid of Things Network Communications

Network communication is crucial in the Energy Grid of Things (EGoT). Without a network connection, the energy grid becomes just a power grid where the energy resources are available to the customer uni-directionally. A mechanism to analyze and optimize the energy usage of the grid can only happen through a medium, a communications network, that enables information exchange between the grid participants and the service provider. Security implementers of EGoT network communication take extraordinary measures to ensure the safety of the energy grid, a critical infrastructure, as well as the safety and privacy of the grid participants. With the dynamic nature of network communication of the EGoT, the information provided by the customer or the service provider can be falsified by a malicious attacker. Therefore, a trust model is necessary to monitor any abnormal activities. This paper describes a distributed trust model system that meets the need of the EGoT. This paper describes methods for evaluating and improving the distributed trust model using standard hypothesis testing metrics such as true positive, false positive, true negative, false negative, equal error rate, and F1 score. Example calculations are shown based on generated sample data.
SHIFTing to sustainable behavior: An ethical-persuasive approach for mobile application development

Sustainable behavior leads to quality of life. While research has established the link between the two, how to orient our lives with responsible behavior is still unknown. Hence, this study proposes an approach to design a mobile phone application for sustainable behaviors based on the SHIFT framework. The proposed application aims to shift users’ unsustainable behaviors to sustainable ones. Therefore, the Design Science Research (DSR) methodology is adopted to achieve the objective of this research. As a result, we outline ethical concerns, persuasive messages, and key features of the proposed SHIFT App. The proposed artifact of the study is an easy-to-use application that can help users to adopt a sustainable lifestyle. This conceptual work guides future research to develop mobile applications for people to inculcate sustainable behavior in their everyday lives.
Quantum Software Architecture Blueprints for the Cloud: Overview and Application to Peer-2-Peer Energy Trading

Applied quantum computing research has often focused on determining business-driven use-cases and applications of quantum algorithms which may provide advantage, either runtime or solution quality, over their classical counterparts. Often missed, yet equally important, is the discussion on how to actually run quantum software applications or models in a production environment where they run in an event-based or scheduled manner thereby providing the results to the enterprise which has spent time upskilling their development team and developing the quantum software solution.In this paper, we introduce a hybrid classical-quantum cloud architecture blueprint which starts from the code-level software development level, and extends to the production-ready quantum software application level. We then, for the first time, introduce a novel sample optimization application used in the energy industry for Peer-2-Peer energy trading and deploy it using the proposed cloud architecture blueprint. We demonstrate sample output of the live-running application where trading optimizations occurs on an hourly basis via remote connection to a quantum computing cloud backend.