Reviewerly/exHarmony huggingface dataset:
Legilinguistic Features of a Semantic Field: COVID-19 in Written News/Media in Hebrew and Arabic

This paper examines and compares some legilinguistic features in news/media reports in Hebrew, and in Arabic in Israel and Egypt during the COVID-19 pandemic in 2020 and the beginning of 2021. The goal was to find frequent and innovated expressions in the communication media during the COVID-19 period. The research question was, since there are linguistic differences between these language-varieties: would differences be found also on the legilinguistic level? Hebrew and Arabic were studied because of their different status. In Israel, Hebrew is the dominant official language, while Arabic is a minority language. In Egypt, Arabic is the official and dominant language. The material was collected from two news/media channels in each of Modern Hebrew and Arabic in Israel, and two from Egypt. Selected news texts were analyzed and examples of their lexical and syntactic elements are presented. The texts revealed similar methods for controlling the pandemic, and some linguistic (lexical and syntactic) differences. The comparison with legilinguistic literature suggests that the findings of these Hebrew and Arabic texts' features during the pandemic differ from features discussed in other studies.
Denoising of Tourist Street Scene Image Based on ROF Model of Second-Order Partial Differential Equation

The noise pollution in tourist street view images is caused by various reasons. A major challenge that researchers have been facing is to find a way to effectively remove noise. Although in the past few decades people have proposed many methods of denoising tourist street scene images, the research on denoising technology of tourist street scene images is still not outdated. There is no doubt that it has become a basic and important research topic in the field of digital image processing. The evolutionary diffusion method based on partial differential equations is helpful to improve the quality of noisy tourist street scene images. This method can process tourist street scene images according to people’s expected diffusion behavior. The adaptive total variation model proposed in this paper is improved on the basis of the total variation model and the Gaussian thermal diffusion model. We analyze the classic variational PDE-based denoising model and get a unified variational PDE energy functional model. We also give a detailed analysis of the diffusion performance of the total variational model and then propose an adaptive total variational diffusion model. By improving the diffusion coefficient and introducing a curvature operator that can distinguish details such as edges, it can effectively denoise the tourist street scene image, and it also has a good effect on avoiding the step effect. Through the improvement of the ROF model, the loyalty term and regular term of the model are parameterized, the adaptive total variation denoising model of this paper is established, and a detailed analysis is carried out. The experimental results show that compared with some traditional denoising models, the model in this paper can effectively suppress the step effect in the denoising process, while protecting the texture details of the edge area of the tourist street scene image. In addition, the model in this paper is superior to traditional denoising models in terms of denoising performance and texture structure protection.
A similarity measure model based on the dissimilarity degree between users

This article offers a new users-similarity measure to improve recommender systems’ accuracy even when a few ratings are available. This model is based on the degree of dissimilarity between common ratings. On real data sets, we conducted our experiments to evaluate the proposed model’s accuracy. Experiments demonstrate the model’s capacity to locate a suitable neighborhood, resolve the user’s cold-start problem, and significantly improve recommendation accuracy. Thus, this research may be considered a supplement to previous studies on user cold-start and good neighbor selection difficulties in user-based collaborative filtering algorithms.
Post-Quantum Cryptosystem of Niederreiter, Algorithm and Encryption Scheme: Modification and Optimization

The article discusses issues related to the post-quantum public key cryptosystem based on algebraic coding—the Niederreiter cryptographic system. Comparison of this cryptosystem with others that are also based on linear codes. The value, uniqueness, cryptographic strength, as well as the encryption algorithm and scheme of this system, its modification and optimization are considered. A description of the Niederreiter cryptosystem is proposed. The developed algorithm is a modification of the Niederreiter cryptosystem with improved cryptographic strength. To improve the software implementation, the proposed algorithm was also optimized, analytically analyzed and all its advantages were proved. The work of the embedded software with the Niddereiter cryptosystem and with the McEliece cryptosystem is shown. We performed a comparative analysis of the performance of cryptographic systems not only with each other according to the software algorithm, but also considered individual cases with Hamming codes and with Reed-Muller codes. Comparative characteristics of each of the cryptosystems, their advantages and disadvantages are shown.
Modal Logic via Global Consequence

In modal logic, semantic consequence is usually defined locally by truth preservation at all worlds in all models (with respect to a class of frames). It can also be defined globally by truth preservation in all models (with respect to a class of frames). The latter is called global consequence, which is much less studied than the standard local one. In this paper we first study the relationship between local and global consequence. Then we give some correspondence results for global consequence. Finally, we illustrate two applications of global consequence, connecting it with informational consequence and update consequence proposed in formal semantics. Some results in the paper are already known, which are collected in the paper for the sake of completeness. The others appear to be new. We suggest that global consequence is not only interesting theoretically, but also useful for applications.
FIRST-ORDER AXIOMATISATIONS OF REPRESENTABLE RELATION ALGEBRAS NEED FORMULAS OF UNBOUNDED QUANTIFIER DEPTH

Abstract Using a variation of the rainbow construction and various pebble and colouring games, we prove that RRA, the class of all representable relation algebras , cannot be axiomatised by any first-order relation algebra theory of bounded quantifier depth. We also prove that the class At(RRA) of atom structures of representable, atomic relation algebras cannot be defined by any set of sentences in the language of RA atom structures that uses only a finite number of variables.
Handling Class Imbalance in k-Nearest Neighbor Classification by Balancing Prior Probabilities

It is well known that recall rather than precision is the performance measure to optimize in imbalanced classification problems, yet most existing methods that adjust for class imbalance do not particularly address the optimization of recall. Here we propose an elegant and straightforward variation of the k-nearest neighbor classifier to balance imbalanced classification problems internally in a probabilistic interpretation and show how this relates to the optimization of the recall. We evaluate this novel method against popular k-nearest neighbor-based class imbalance handling algorithms and compare them to general oversampling and undersampling techniques. We demonstrate that the performance of the proposed method is on par with SMOTE yet our method is much simpler and outperforms several competitors over a large selection of real-world and synthetic datasets and parameter choices while having the same complexity as the regular k-nearest neighbor classifier.
Target detection optimization model based on fine-grained feature fusion

The paper optimizes the YOLOv4 detection model incorporating on the unmanned vehicle detection scenario. A hybrid attention mechanism combined channel and spatial attention mechanisms is introduced into the backbone network of the detction model. The high level semantic feature map and the shallow fine-grained feature map are merged into the detection neck network. The hybrid attention mechanism strengthens the process of screening the fine-grained features. The effect of using hybrid attention mechanism is better than that of using channel attention mechanism. Through the experiments, the results were drawn: (1) For the CSPDarknet-53 network, the effect of using hybrid attention mechanism is better than that of using channel attention mechanism. (2) On VOC 2007+2012 dataset, the model combined shallow layer feature maps and hybrid attention mechanism in this paper can improve can improve the detection accuracy by about 4%, compared with the original YOLOV4 object detection model. At the same time, for the KITTI dataset images closed to the actual scene, the image are used to verify the actual driving scene. The improved model achieves excellent detection effect on KITTI dataset.
The letters of academician V.A. Obruchev from the collection of the Geological institute of RAS and their importance for the history of geosciences

The paper presents a general overview of 870 letters from Russian academician Vladimir Afanasyevich Obruchev (1863-1956) collected at the Geological Institute of the Russian Academy of Sciences. The main topics of the letters are considered: the Obruchev's publications, editor works, scientific expeditions, activity at the institutes of the Academy of Sciences of the USSR, the study of the Quaternary and mineral deposits. The chronology, authors and main topics of the letters are also indicated. The history of geology studies, organised by V.A. Obruchev and V.V. Tikhomirov, is described separately. Obruchev's epistolary heritage is very important for the history of science and needs further study.
SleuthTalk: Identifying Historical Photos with Intelligent Shortlists, Private Collaboration, and Structured Feedback

Identifying people in photographs is an important task in many fields, including history, journalism, genealogy, and collecting, but accurate person identification remains challenging. Researchers especially struggle with the “last-mile proble” of historical person identification, where they must make a selection among a small number of highly similar candidates. We present SleuthTalk, a web-based collaboration tool integrated into the public website Civil War Photo Sleuth which addresses the last-mile problem in historical person identification by providing support for shortlisting potential candidates from face recognition results, private collaborative workspaces, and structured feedback.
On the comparison of methods for asymmetric execution of cryptographic primitives and protocols in the context of using small parameters and short keys

The paper systematically considers methods of changing the execution of known standard cryptographic primitives, symmetric and asymmetric, as well as cryptographic protocols using short (low-entropy) keys and small parameters that allow increasing their strength. The proposed changes are related to the introduction of asymmetry in the complexity of the work of legitimate users, which should lead to an increase in the complexity of decryption algorithms for an adversary or cryptanalyst. Security is considered in relation to the use of standard and universal attack methods by the adversary, such as the method of full testing of keys due to the small size of these keys, as well as methods for solving problems of discrete logarithm or factorization of large integers, the size of which is limited by the small size of the cryptosystem parameters. Security is considered in relation to the use of standard and universal attack methods by the adversary, such as the method of full testing of keys due to the small size of these keys, as well as methods for solving problems of discrete logarithm or factorization of large integers, the size of which is limited by the small size of the cryptosystem parameters. This work is of an overview nature, which allows you to look at what is common in the proposed methods for different situations of using cryptography with short keys and small parameters.
Identify Bilingual Patterns and Phrases from a Bilingual Sentence Pair

This paper presents a method for automatically identifying bilingual grammar patterns and extracting bilingual phrase instances from a given English-Chinese sentence pair. In our approach, the English-Chinese sentence pair is parsed to identify English grammar patterns and Chinese counterparts. The method involves generating translations of each English grammar pattern and calculating translation probability of words from a word-aligned parallel corpora. The results allow us to extract the most probable English-Chinese phrase pairs in the sentence pair. We present a prototype system that applies the method to extract grammar patterns and phrases in parallel sentences. An evaluation on randomly selected examples from a dictionary shows that our approach has reasonably good performance. We use human judge to assess the bilingual phrases generated by our approach. The results have potential to assist language learning and machine translation research.
A Residual Network with Focal Loss to Handle Class-imbalance Problem on Nurse Care Activity Recognition

Human Activity Recognition (HAR) is a remarkable basement for the development of the generalized and robust machine learning modeling, as this domain typically has to deal with high-dimensional sensor data that are characterized by a large mutability (e.g., due to the diversity of the user's actions or as the aggregate of noise). However, unlike other domains, HAR is a very challenging task because of the large scale of class-imbalance, lack of a good benchmark dataset for HAR, and different experimental setups (e.g., several types of sensors or changes in their locations). Highly imbalanced data forces the model to show large bias towards the majority class and thus, minority classes are almost ignored. On the “The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data” dataset, we present a deep learning paradigm that is compiled with multi-class focus loss and residual block network. In our proposed model, the residual block network prevents the model from vanishing gradient problems in a deep learning network and focal loss overcomes the crucial class-imbalance problem. On the test data, we achieved a promising accuracy of 34.47%, beating the previous state-of-the-art algorithms.
Design of computer aided translation system for English communication language based on grey clustering evaluation

In order to facilitate communication and communication, this paper studies the optimization of the current computer-aided translation system, and proposed a design method of English communication language computer-aided translation system based on grey clustering evaluation. By optimizing the hardware configuration and algorithm function keys of the system, the English translation mechanism of multilanguage interaction, the design idea of editing and modifying after English translation and knowledge database management are realized, and the system function framework was constructed, including the system transceiver unit, automatic translation unit, manual correction unit, task management unit and memory management unit, the performance of task management unit and memory management unit is analyzed. On this basis, the specific work flow of the design system mainly includes the English translation service flow integrating multilanguage interaction and the project-based multilanguage interaction English translation service flow design, which realizes the English translation online assistance under the multilanguage interaction environment. The experimental results show that the design system has the advantages of high online translation speed, pronunciation success rate and multilanguage translation success rate.
Software Fault-Prone Module Classification Using Learning Automata based Deep Neural Network Model

The software systems usually include some defects that will show adverse effects on the use of the application. Software fault-prone module will help software professionals to release the software within given time and cost. Software industries have to spend most of the budget in the testing phase only. If the fault-prone software modules are identified before testing phase, the limited testing resources will be allocated to the fault-prone modules to reduce the cost. In this paper, we have proposed a novel approach that leverages deep learning techniques to classify the software modules into faulty and non-faulty modules using learning automata. The deep learning model is trained using learning automata. We have validated our proposed approach using publicly available data set taken from public repository. We have also applied over sampling techniques on four data sets to balance the data set in order to obtain better performance measures. We have compared the results of proposed approaches with some existing models and we found our proposed approach is able to provide better prediction performance.
Verification and Analysis of Two-dimensional Path Planning Objective Function Optimization Based on Classical Particle Swarm Optimization Algorithm

The main task of mobile robot path planning is according to the environment model, the mobile robot finds a collision free path from the starting coordinate point to the target coordinate point in the motion space according to one or some optimization criteria, such as the minimum work cost, the shortest walking route, the shortest walking time and so on, Make the robot reach the destination safely. At present, mobile robot path planning methods can be divided into three categories according to the working mode. One is path planning based on environment model, which can deal with the path planning under the condition of completely knowing the position and automatically shape of obstacles. Specific methods such as a topological graph method [1–2], etc.; The second one is the local path planning method based on sensor information. Including: artificial potential field method [3], fuzzy logic method [4], etc.; The third one is the behavior-based path planning method [5–6], The paper shows Verification and analysis of two-dimensional path planning objective function optimization based on classical particle swarm optimization algorithm.
Classification-IoU Joint Label Assignment for End-to-End Object Detection

Using prediction-aware label assignment to choose positive samples has proven to be the key to remove Non-maximum Suppression (NMS) and reach end-to-end object detection. However, existing prediction-aware label assignment methods combine the classification cost and localization cost simply by multiplication or addition. It might produce unstable assignment results since the classification cost and localization cost are used independently to assign labels, and thus degrades the detection accuracy. In this paper, we propose a classification-IoU joint label assignment that takes advantage of the correlation between classification and localization to choose positive samples. In particular, we add category and localization consistency constraints to the cost function for label assignment, and improve the stability of label assignment. Besides, a classification-IoU joint representation is applied to improve the correlation between classification and localization in the process of training. It is trained by the loss function that considers classification and location quality simultaneously. Extensive experiments demonstrate the effectiveness of our method. On COCO dataset, the end-to-end object detector with the techniques we proposed, achieves competitive performance against the state-of-the-art object detectors.
Multiple Exemplars Learning for Fast Image Retrieval

The past decade, we have witnessed rapid progress in compact representation learning for fast image retrieval. In the unsupervised scenario, product quantization (PQ) is one of the promising methods to generate compact image representation for fast and accurate retrieval. Inspired by the great success of deep neural network (DNN) achieved in computer vision, many works attempted to integrate PQ in DNN for end-to-end supervised training. Nevertheless, in existing deep PQ methods, data samples from different classes share the same codebook. Thus, they might be entangled with each other in the feature space. Meanwhile, existing deep PQ methods relying on triplet or pairwise loss require a huge number of training triplets or pairs, which are expensive in computation and scale poorly.
Arabic Computational Linguistics: Potential, Pitfalls and Challenges

Arabic computational linguistics though still relatively new is gaining pace rapidly. While the development of tools for computational linguistics in many languages has come a very long way, and progress has been achieved in creating tools for Arabic, Arabic computational linguistics are in need of much attention. It is not obvious that tools developed for, let us say, English will only need minor modifications before they can be applied to Arabic. Computational tools developed for English rely heavily on enormous work achieved in English linguistics in general, and corpus linguistics more particularly. If Arabic computational linguistics is to achieve its potential, it needs to mirror the hard work done in other languages. Researchers in Arabic computational linguistics should also fully understand the nature of the data they are working with. The present article is not a review of the field, but rather a discussion on the potential, pitfalls, and challenges of Arabic computational linguistics. We will discuss the potential of what research in this field can contribute to linguistic and pedagogical research on Arabic, we will also discuss issues related to defining what ‘Arabic (language)’ is from a linguistic point of view, the nature of the Arabic script, transcription and transliteration, and finally corpus building.
Unsupervised Learning Framework for 3D Reconstruction from Face Sketch

Increasingly attention has been paid to 3D understanding and reconstruction recently, while the inputs of most existing models are chromatic photos. 3D shape modelling from the monochromatic input, such as sketch, largely remains under-explored. One of the major challenges is the lack of paired training data, since it is costly to collect such a database with one-to-one mapping instances of two modalities, e.g., a 2D sketch and its corresponding 3D shape. In this work, we attempt to attack the problem of 3D face reconstruction using 2D sketch in an unsupervised setting. In particular, an end-to-end learning framework is proposed. There are two key modules of the network, the 2D translation network and the 3D reconstruction network. The 2D translation network is utilized to translate an input sketch face into a form of realistic chromatic 2D image. Then an unsupervised 3D reconstruction network is proposed to further transform the 2D image obtained in the previous step into a 3D face shape. In addition, because there is no existing sketch-3D face dataset available, two synthetic datasets are constructed based on BFM and CelebA, namely SynBFM and SynCelebA, to facilitate the evaluation. Extensive experiments conducted on these two synthetic datasets validate the effectiveness of our proposed approach.