Reviewerly/exHarmony huggingface dataset:
Information Encryption Transmission Method of Automobile Communication Network Based on Neural Network

In order to ensure the information security of automobile communication network, an encryption transmission method of automobile communication network information based on neural network is proposed. Use neural network to generate random key to encrypt vehicle communication network information. Design encryption transmission scheme, including data encryption module, dynamic key generation module and data transmission module. The encrypted transmission scheme is used to transmit the ciphertext of the vehicle communication network information to the other end. The results show that compared with des and RSA encryption methods, the proposed method has shorter encryption time and higher key sensitivity, indicating that the proposed encryption transmission method is more efficient and secure.
Machine Learning Based Method for Quantifying the Security Situation of Wireless Data Networks

Wireless data networks are vulnerable to Trojans, viruses, malware and other attacks, resulting in important data being leaked and tampered with. Therefore, a quantitative evaluation method of wireless data network security situation based on machine learning is proposed. Based on the principles of independence, completeness and scientificity, network security situation assessment indicators are selected, combined with CVSS scoring standards, and network security situation assessment indicators are quantitatively processed to obtain time series of wireless data networks. Based on machine learning and support vector machine algorithm, the process of security situation quantification is established and the parameters of support vector machine algorithm are optimized. Experimental results show that the minimum delay of the proposed method is 4 s, the result is consistent with the actual result, and the minimum error is 5.6%, which fully proves the effectiveness of the proposed method.
Pairwise Decomposition of Directed Graphic Models for Performing Amortized Approximate Inference

Exact inference for large directed graphic models (known as Bayesian networks (BN)) is difficult as the space complexity grows exponentially in the tree-width of the model. Approximate inference, such as generalized belief propagation (GBP), is used instead. GBP treats inference as an energy function optimization problem. The solution is found using iterative message passing, which is inefficient and convergent problematic. Recent progress on amortized technique for GBP is an attractive alternative solution that can optimize the energy function using (deep) neural networks, requiring no message passing. Despite being efficient, the amortized technique for GBP is so far only applied to undirected graphic models with specific structures and factors, with no guarantee of the approximation quality for general models. This is because the energy function to be amortized is defined by a region graph that is ad-hoc and difficult to construct. To ensure the amortized technique for GBP is applied to BN inference for practical use, we propose (i) A new pairwise conversion (PWC) algorithm converts all the conditional probability distributions in the BN into pairwise factors to facilitate neural network parameterization and the region graph construction. (ii) An improved loop structured region graph (LSRG) algorithm to generate a valid region graph satisfying desired region properties. (iii) The energy function defined by the PWC-LSRG region graph can be directly amortized using (deep) neural networks and yield sensible approximations. Experiments show that the proposed amortized PWC-LSRG algorithm improves significantly in accuracy and efficiency compared to conventional algorithms.
Applying thesauruses in expanding user search queries: From local use to linked data

The subject search in natural languages is the most difficult one due to phraseological ambiguities. To solve the problem, the information systems mobilize the terms in controlled dictionaries, e. g. thesauruses. The authors examine the classifications, thesauruses, subject headings, normative (authority) files within the context of the open networked space of the Linked Open Data environment (LOD). These links enable to enhance (complement) user queries with the words from other dictionaries, and to navigate through the other libraries’ systems for the resources. The authors explore the possibility of practical application of EUROVOC and GEMET thesauruses to expand search queries initiated by the users of RNPLS&amp;T’s Single Open Information Archive (SOIA), Portal of Electronic Library (PEL) of the Parliamentary Library of the RF Federal Assembly and the thematic database “Ecology: Science and technologies”, which records could be potentially linked. The authors cite the study findings and characterize the problems revealed. The article is prepared within the framework of the Government Order “Information support of scientific research of scientists and specialists on the basis of the RNPLS&amp;T Open Archive as the scientific knowledge aggregation system, (FNEG-2022-003)” for the years 2022–2024.
Information Systems Strategy and Security Policy: A Conceptual Framework

As technology evolves, businesses face new threats and opportunities in the areas of information and information assets. These areas include information creation, refining, storage, and dissemination. Governments and other organizations around the world have begun prioritizing the protection of cyberspace as a pressing international issue, prompting a renewed emphasis on information security strategy development and implementation. While every nation’s information security strategy is crucial, there has not been much work conducted to define a method for gauging national cybersecurity attitudes that takes into account factors and indicators that are specific to that nation. In order to develop a framework that incorporates issues based on the current research in this area, this paper will examine the fundamentals of the information security strategy and the factors that affect its integration. This paper contributes by providing a model based on the ITU cybersecurity decisions, with the goal of developing a roadmap for the successful development and implementation of the National Cybersecurity Strategy in Greece, as well as identifying the factors at the national level that may be aligned with a country’s cybersecurity level.
Challenges in the translation of legal texts: the case in Kosovo

Our paper conducts a contrastive analysis between Albanian and English lexical units in the language of laws using corpora analysis. It fills a literature gap related to Corpus linguistics in order to better comprehend patterns of legal lexicon. We use KWIC Concordance to extract the top frequently words and concordance lines in order to analyse differences and similarities among lexical units in Albanian (Source Language) and English (Target Language) laws, compared to those identified in respective corpora. Additionally, we identify types of errors in translation, difficulties in translating legal texts, and factors that influence translators’ errors in translating certain laws of the Republic of Kosovo from Albanian into English. According to our analysis, both languages have in common the use of the same grammatical patterns such as: conjunctions, prepositions, and the use of the common noun forms. The rest have significant differences in their usage, especially with regards to particles and determiners. Our contrastive approach demonstrates that some of these laws were not originally written in Albanian and then translated into English language, as many articles within these laws lack the appropriate word structure and word order and in some cases are semantically ill-formed in the Source Language.&#x0D; Key words: analysis, corpus, language, legal, translation, KWIC Concordance.&#x0D;
MD3: Model-Driven Deep Remotely Sensed Image Denoising

Remotely sensed images degraded by additive white Gaussian noise (AWGN) have low-level vision, resulting in a poor analysis of their contents. To reduce AWGN, two types of denoising strategies, sparse-coding-model-based and deep-neural-network-based (DNN), are commonly utilized, which have their respective merits and drawbacks. For example, the former pursue enjoyable performance with a high computational burden, while the latter have powerful capacity in completing a specified task efficiently, but this limits their application range. To combine their merits for improving performance efficiently, this paper proposes a model-driven deep denoising (MD3) scheme. To solve the MD3 model, we first decomposed it into several subproblems by the alternating direction method of multipliers (ADMM). Then, the denoising subproblems are replaced by different learnable denoisers, which are plugged into the unfolded MD3 model to efficiently produce a stable solution. Both quantitative and qualitative results validate that the proposed MD3 approach is effective and efficient, while it has a more powerful ability in generating enjoyable denoising performance and preserving rich textures than other advanced methods.
A hybrid anomaly detection method for high dimensional data

Anomaly detection of high-dimensional data is a challenge because the sparsity of the data distribution caused by high dimensionality hardly provides rich information distinguishing anomalous instances from normal instances. To address this, this article proposes an anomaly detection method combining an autoencoder and a sparse weighted least squares-support vector machine. First, the autoencoder is used to extract those low-dimensional features of high-dimensional data, thus reducing the dimension and the complexity of the searching space. Then, in the low-dimensional feature space obtained by the autoencoder, the sparse weighted least squares-support vector machine separates anomalous and normal features. Finally, the learned class labels to be used to distinguish normal instances and abnormal instances are outputed, thus achieving anomaly detection of high-dimensional data. The experiment results on real high-dimensional datasets show that the proposed method wins over competing methods in terms of anomaly detection ability. For high-dimensional data, using deep methods can reconstruct the layered feature space, which is beneficial for gaining those advanced anomaly detection results.
Nature and geochemical characteristics of ore-forming fluids in the Zhaoxian gold deposit, Jiaodong gold province, eastern China

DATA REPORT article Front. Earth Sci., 12 January 2023Sec. Economic Geology Volume 10 - 2022 | https://doi.org/10.3389/feart.2022.1085398
Opportunistic Load Balancing for Virtual Machines Scheduling in a Cloud Environment

One of the Internet&rsquo;s fastest-growing technologies is cloud computing, which needs each user to share various resources and virtual computers to access the services they demand. The difficulty is in allocating the user&rsquo;s tasks among the many resources at hand while maintaining a balanced workload. In this work, we present the design of a new scheduling approach for cloud virtual machines in order to process events (related to resource overuse or underuse). This reduces the amount of effort consumed while maintaining the required performance levels in the cloud data center. To evaluate the effectiveness of the approach with real load paths, we conducted an experiment using the open-source cloud computing platform OpenNebula.
Zero-shot stance detection: Paradigms and challenges

A major challenge in stance detection is the large (potentially infinite) and diverse set of stance topics. Collecting data for such a set is unrealistic due to both the expense of annotation and the continuous creation of new real-world topics (e.g., a new politician runs for office). Furthermore, stancetaking occurs in a wide range of languages and genres (e.g., Twitter, news articles). While zero-shot stance detection in English, where evaluation is on topics not seen during training, has received increasing attention, we argue that this attention should be expanded to multilingual and multi-genre settings. We discuss two paradigms for English zero-shot stance detection evaluation, as well as recent work in this area. We then discuss recent work on multilingual and multi-genre stance detection, which has focused primarily on non-zero-shot settings. We argue that this work should be expanded to multilingual and multi-genre zero-shot stance detection and propose best practices to systematize and stimulate future work in this direction. While domain adaptation techniques are well-suited for work in these settings, we argue that increased care should be taken to improve model explainability and to conduct robust evaluations, considering not only empirical generalization ability but also the understanding of complex language and inferences.
ONA for Autonomous ROS-Based Robots

In this paper we will show that OpenNARS for Applications (ONA) can be used for enhanced autonomous robots operating in the real world. It is done by utilizing state-of-the-art object detection along with Simultaneous Localization and Mapping techniques while inheriting the strengths of means-end reasoning and adding robust learning at runtime into the picture. This is showcased in an experiment where an ONA-controlled mobile robot with manipulator arm is learning about its environment to collect bottles to be returned to a human operator.
Human Activity Recognition using 1D-CNN and Stacked LSTM

Computer vision is an engrossing area of Artificial Intelligence that is growing continuously due to huge amounts of data uploaded online daily. Human Activity Recognition (HAR) is an extensively considered problem in Computer vision. Although a lot of research has been done in the field of activity recognition, still it is a challenging job for researchers to enhance the performance of the network as there are many limitations such as variation in illumination, complex nature of human activities, background cluttering, occlusion, many activities at the same time. HAR is a process of identifying human activities in an environment obtained from data through various modalities like camera, inertial measurement unit (IMU), body-worn, physiological, positional, and environmental sensors. A hybrid model of 1D-CNN and stacked LSTM is proposed for HAR. The proposed method is tested on the WISDM dataset and it gives superior performance compared to the state- of-art-techniques. It has achieved an accuracy of 97%. The analysis is done based on hyperparameters, methodology, and performance metrics to validate the results.
Lightweight Multi-scale Attentional Network for Single Image Dehazing

Neural network-based image dehazing algorithms have obtained extensive research results. However, haze retention is still present in the results for complex outdoor images. Designing complicated networks may be improved, yet they consist of a tremendous number of parameters, which causes slow inference and requires a more demanding computer environment for execution. The network globally adopts a U-Net-like structure and employs two improved modules ARM and SAM based on the attention mechanism to implement multi-scale feature extraction and fusion. Our proposed network combines the high and low level semantics in diverse scale feature images while raising the focus on diverse haze concentration regions. Experimental results reveal that our network can get improved visual results in RESIDE-SOTS (Outdoor) and NTIRE2021 NH-HAZE Image Dehazing Challenge datasets in comparison with other SOTA methods and attains higher Peak Signal to Noise Ratio and Structural Similarity Index.
Traffic Flow Statistics Algorithm Based on Improved YOLOv5

Aiming at the problems of poor real-time and low accuracy of traffic flow statistics, this paper proposes an end-to-end traffic flow statistics algorithm based on improved YOLOv5. Firstly, in order to improve the detection effect of YOLOv5, the regional context module is proposed to capture the global information. Secondly, $\alpha$-CIoU Loss is used as the localization loss to improve the bounding box regression accuracy. Finally, we connect the improved YOLOv5 detection algorithm with Byte data association method and set virtual coils in the video frames to complete the traffic flow statistics. The experimental results show that the improved YOLOv5 algorithm on the UA-DETRAC dataset improves the average accuracy by 3.8% compared with the original YOLOv5 algorithm. Combined with the Byte data association method, the traffic flow statistics accuracy reaches 99.2% and the speed reaches 73frame/s, which can effectively improve the accuracy while meeting the real-time requirements.
Cross-Camera Vehicle Trajectory Estimation towards Traffic Flow

Cross-Camera vehicle tracking(CCVT) is an significant field in intellective transportation systems and intelligent visual perception in smart city. It is challenging due to the large variation of the same vehicle within various cameras. We propose a cross-camera multi-target vehicle tracking framework towards traffic flow parameter estimation, which is mainly divided into three steps:(i) multi-object tracking in a single camera, (ii) multi-camera trajectory association and, (iii) traffic flow parameter estimation. Among them, we design an encoder that integrates appearance and trajectory information for vehicle matching between multiple cameras. To demonstrate the validity of the algorithm, we collected multi-camera videos in real scenes of urban roads and annotated them manually. The experimental results demonstrate the effectiveness and accuracy of the proposed method.
Revisiting the Impact of Anti-patterns on Fault-Proneness: A Differentiated Replication

Anti-patterns manifesting on software code through code smells have been investigated in terms of their prevalence, detection, refactoring, and impact on software quality attributes. In particular, leveraging heuristics to identify fault-fixing commits, Khomh et al. have found that anti-patterns and code smells have an impact on the fault-proneness of a software system. Similarly, Saboury et al. found a relationship between anti-pattern occurrences and fault-proneness, using heuristic to identify fault-fixing commits and fault-inducing changes. However, recent studies question the accuracy of heuristics, and thus the validity of empirical studies that leverage it. Hence, in this work, we would like to investigate to what extent the results of empirical studies using heuristics to identify bug fix commits are affected by the limitations of the heuristics based approach using manually validated bug fix commits as a ground truth. In particular, we conduct a differentiated replication of the work by Khomh et al. We particularly focused on the impact of anti-patterns on fault-proneness as it is the only dependent variable that may be affected by noise in the collected faults data. In our differentiated replication study, (1) we expanded the number of subject systems from 5 to 38, (2) utilized a manually validated dataset of bug-fixing commits from the work of Herbold et al., and (3) answered research questions from Khomh et al., that are related to the relationship between anti-pattern occurrences and fault-proneness. (4) We added an additional research question to investigate if combining results from several heuristic-based approaches could help reduce the impact of noise. Our findings show that the impact of the noise generated by the automatic algorithm heuristic based is negligible for the studied subject systems; meaning that the reported relation observed on noisy data still holds on the clean data. However, we also observed that combining results from several heuristic based approaches do not reduce this noise, quite the contrary.
FPGA based Feature Extraction in Real time Computer Vision-A Comprehensive Survey

Today, Computer Vision algorithms play a vital role in almost every domain of our day-to-day life. This powerful technology has aided in the development of answers to a wide range of difficult and complex challenges encountered across different sectors like defence, security, surveillance, robotics, healthcare, transportation, manufacturing, agriculture, sports, retail etc. Feature Extraction is one of the key components in any computer vision system and it aims at extracting the distinct and relevant features from an image that could clearly characterize different objects present in it. Almost every computer vision application like image matching, object detection and recognition, human or pedestrian detection, robotic vision etc. rely on the feature extraction techniques as one of their primary steps. Recently various hardware accelerators have been developed for these feature extraction processes and among these Field Programmable Gate Array (FPGA) is considered as the most suitable platform because of its added advantages of flexibility, parallelism, low power, and re-configurability. This paper presents, a review of the literature on FPGA implementations of various efficient feature extraction methods, such as Scale Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HoG). The review covers the different implementation techniques followed by researchers for efficient mapping on hardware and provides a comparison of their work on the basis of parameters like speed and resource utilization.
Don't DIY: Automatically transform legacy Python code to support structural pattern matching

As data becomes more and more complex as technology evolves, the need to support more complex data types in programming languages has grown. However, without proper storage and manipulation capabilities, handling such data can result in hard-to-read, difficult-to-maintain code. Therefore, programming languages continuously evolve to provide more and more ways to handle complex data. Python 3.10 introduced structural pattern matching, which serves this exact purpose: we can split complex data into relevant parts by examining its structure, and store them for later processing. Previously, we could only use the traditional conditional branching, which could have led to long chains of nested conditionals. Maintaining such code fragments can be cumbersome. In this paper, we present a complete framework to solve the aforementioned problem. Our software is capable of examining Python source code and transforming relevant conditionals into structural pattern matching. Moreover, it is able to handle nested conditionals and it is also easily extensible, thus the set of possible transformations can be easily increased.
Omnidirectional Gradient and Its Application in Stylized Edge Extraction of Infrared Image

Gradient computing is a low-level technology widely used in image processing. For large gradient magnitude, the pixel value in the field changes a lot, and for small gradient magnitude the pixel in the domain changes little. This is the basis of classical edge extraction algorithms, but it is often necessary to manually set thresholds to differentiate. This paper innovatively brings out the concept of omnidirectional gradient, which uses flexible convolution kernel radius and special law to calculate, and omnidirectional gradient pays more attention to gradient direction and analyzes the relationship and change of the gradient direction with different kernel radius. We present here an algorithm for stylized edge extraction based on omnidirectional gradient, overcoming the drawback of classical edge extraction algorithms that require manual thresholding. Experimental results show that the proposed method outperforms the classical edge extraction methods in terms of adaptive, consistent, and visually friendlier features for infrared imaging. In addition, the algorithm is fast and efficient, its result can be used as real-time input for subsequent applications.