Reviewerly/exHarmony huggingface dataset:
Impresso Inspect and Compare. Visual Comparison of Semantically Enriched Historical Newspaper Articles

The automated enrichment of mass-digitised document collections using techniques such as text mining is becoming increasingly popular. Enriched collections offer new opportunities for interface design to allow data-driven and visualisation-based search, exploration and interpretation. Most such interfaces integrate close and distant reading and represent semantic, spatial, social or temporal relations, but often lack contrastive views. Inspect and Compare (I&amp;C) contributes to the current state of the art in interface design for historical newspapers with highly versatile side-by-side comparisons of query results and curated article sets based on metadata and semantic enrichments. I&amp;C takes search queries and pre-curated article sets as inputs and allows comparisons based on the distributions of newspaper titles, publication dates and automatically generated enrichments, such as language, article types, topics and named entities. Contrastive views of such data reveal patterns, help humanities scholars to improve search strategies and to facilitate a critical assessment of the overall data quality. I&amp;C is part of the impresso interface for the exploration of digitised and semantically enriched historical newspapers.
E-learning: An effective mode of teaching English as a Second Language

The benefits of e-learning in teaching English as a Second Language (ESL) are huge. The outbreak of the dreaded COVID19 pandemic has forced schools, colleges and universities to temporarily remain closed to fight the contagion. E-learning or online learning is a learning method which has been employed in such a difficult scenario to continue the teaching-learning process. E-learning uses various tools and systems to optimize the teaching-learning process through online platforms. For teaching ESL, the teachers should explore collaborative learning, communicative language teaching, and communicative task-based language teaching to enable learners to effectively participate in the e-learning process. The use of these approaches engages the students to interact and perform the assigned tasks enabling them to effectively learn the language in the online platform. The paper explores the various e-learning practices into making effective teaching of English as a second language. Various e-learning tools like the internet, video conferencing platforms like Zoom, Google Meet and Webex, Google Classroom, YouTube, podcast and vodcast, online-boards, mobile phones, and social networking sites like WhatsApp, Facebook and Twitter are explored to efficiently teach English as a second language. Further, the study investigated and analyzed an ESL classroom where various e-learning tools are used.
Characteristic Analysis of Judgment Debtors Based on Hesitant Fuzzy Linguistic Clustering Method

In most law enforcement cases, judgment debtors have behaviors of evading execution in China, which seriously affects the authority of legal judgments and the judiciary’s credibility. Characteristics analysis of judgment debtors plays a vital role in finding out the concealed property and improving efficiency in handling law enforcement cases. Considering the advantages of hesitant fuzzy linguistic term sets (HFLTSs) representing the judgment debtors’ attributes and keeping all the original evaluation information on judgment debtors, we develop a hesitant fuzzy linguistic agglomerative hierarchical clustering (HFL-AHC) method to cluster judgment debtors and analyze the main characteristic of judgment debtors with concealing property. In some situations, the existing HFLTS distance cannot divide the judgment debtors. Therefore, we propose some new distance measures to classify the judgment debtors. The clustering results show that the judgment debtors who hide property have a poor evaluation of trading behavior, work, credibility, and consumption behavior.
Modeling Method and Application of College Comprehensive Teaching Mode Based on Artificial Intelligence

Under the influence of artificial intelligence, education is undergoing a profound innovation. On the basis of expounding the important position of artificial intelligence in the field of computer, this paper studies the modeling method of college comprehensive teaching mode based on artificial intelligence. This paper analyzes the feasibility of applying the task driven method to the teaching of artificial intelligence course based on the teaching objectives of artificial intelligence course and the characteristics and steps of task driven teaching method. This paper proposes a task driven teaching model of artificial intelligence. Then, this paper explores the specific shift process of instructional design paradigm under the background of artificial intelligence. In the pre intelligence stage, this study uses the paradigm shift analysis framework to verify whether Cai instructional design, multimedia instructional design and information instructional design have migration. For the teaching design in the intelligent stage, this paper first expounds the inevitability of the paradigm shift of teaching design under the background of artificial intelligence from the aspects of the opportunities brought by artificial intelligence to education and teaching, the problems of the original information-based teaching design and the many challenges it faces. At last, the typical application cases under the guidance of intelligent instructional design paradigm are described in detail.
Indian Sign Language Recognition through Hybrid ConvNet-LSTM Networks

Dynamic hand gesture recognition is a challenging task of Human-Computer Interaction (HCI) and Computer Vision. The potential application areas of gesture recognition include sign language translation, video gaming, video surveillance, robotics, and gesture-controlled home appliances. In the proposed research, gesture recognition is applied to recognize sign language words from real-time videos. Classifying the actions from video sequences requires both spatial and temporal features. The proposed system handles the former by the Convolutional Neural Network (CNN), which is the core of several computer vision solutions and the latter by the Recurrent Neural Network (RNN), which is more efficient in handling the sequences of movements. Thus, the real-time Indian sign language (ISL) recognition system is developed using the hybrid CNN-RNN architecture. The system is trained with the proposed CasTalk-ISL dataset. The ultimate purpose of the presented research is to deploy a real-time sign language translator to break the hurdles present in the communication between hearing-impaired people and normal people. The developed system achieves 95.99% top-1 accuracy and 99.46% top-3 accuracy on the test dataset. The obtained results outperform the existing approaches using various deep models on different datasets.
Edge-Aware Filter Based on Adaptive Patch Variance Weighted Average

Edge-aware smoothing is an essential tool for computer vision, graphics and photography.In this paper, we develop a new and efficient local weighted average filter for edge-aware smoothing.The proposed filter can use guidance information which permits an iterative filtering process.Since the weights of the proposed filter depend on the local variance, the implementation requires linear filters only, leading to O(N pix ) computational complexity.We also present statistical analysis and simulations which provides new insights into its computational efficiency and its relationship with the bilateral filter.The performance of the proposed filter is comparable to those state-of-the-art filters in many applications including: edge-preserving smoothing, compression artifact removal, structure separation, edge extraction, non-photo realistic image rendering, salience detection, detail magnification and multi-focus image fusion.
A Binary Quantifier for Definite Descriptions for Cut Free Free Logics

Abstract This paper presents rules in sequent calculus for a binary quantifier I to formalise definite descriptions: Ix [ F , G ] means ‘The F is G ’. The rules are suitable to be added to a system of positive free logic. The paper extends the proof of a cut elimination theorem for this system by Indrzejczak by proving the cases for the rules of I . There are also brief comparisons of the present approach to the more common one that formalises definite descriptions with a term forming operator. In the final section rules for I for negative free and classical logic are also mentioned.
Multi-Target Tracking Algorithm Based on 2-D Velocity Measurements Using Dual-Frequency Interferometric Radar

Multi-target tracking (MTT) generally requires either a network of Doppler radar receivers distributed at different locations or a phased array radar. The targets moving with small/no radial velocity or angular velocity only cannot be detected and localized completely by deploying Doppler radar without antenna arrays or multiple receivers. To resolve this issue, we present a new MTT algorithm based on 2-D velocity measurements, namely, radial and angular velocities, using dual-frequency interferometric radar. The contributions of the proposed research are twofold: First, we introduce the mathematical model and implementation of the proposed algorithm by explicitly establishing the relationship between 2-D velocity measurements and kinematic state of the target in terms of Cartesian coordinates. Based on 2-D velocity measurement function, the proposed MTT algorithm comprises the following steps: (i) data association using global nearest neighbor (GNN) method (ii) target state estimation using interacting multiple model (IMM) estimator combined with square-root cubature Kalman filter (SCKF) (iii) track management using rule-based M/N logic. Second, performance of the proposed algorithm is evaluated in terms of tracking accuracy, computational complexity and IMM mean model probabilities. Simulation results for different scenarios with multiple targets moving in different tracks have been presented to verify the effectiveness of the proposed algorithm.
BERTtoCNN: Similarity-preserving enhanced knowledge distillation for stance detection

In recent years, text sentiment analysis has attracted wide attention, and promoted the rise and development of stance detection research. The purpose of stance detection is to determine the author’s stance (favor or against) towards a specific target or proposition in the text. Pre-trained language models like BERT have been proven to perform well in this task. However, in many reality scenes, they are usually very expensive in computation, because such heavy models are difficult to implement with limited resources. To improve the efficiency while ensuring the performance, we propose a knowledge distillation model BERTtoCNN, which combines the classic distillation loss and similarity-preserving loss in a joint knowledge distillation framework. On the one hand, BERTtoCNN provides an efficient distillation process to train a novel ‘student’ CNN structure from a much larger ‘teacher’ language model BERT. On the other hand, based on the similarity-preserving loss function, BERTtoCNN guides the training of a student network, so that input pairs with similar (dissimilar) activation in the teacher network have similar (dissimilar) activation in the student network. We conduct experiments and test the proposed model on the open Chinese and English stance detection datasets. The experimental results show that our model outperforms the competitive baseline methods obviously.
The Singleton Fallacy: Why Current Critiques of Language Models Miss the Point

This paper discusses the current critique against neural network-based Natural Language Understanding solutions known as language models . We argue that much of the current debate revolves around an argumentation error that we refer to as the singleton fallacy : the assumption that a concept (in this case, language, meaning, and understanding) refers to a single and uniform phenomenon, which in the current debate is assumed to be unobtainable by (current) language models. By contrast, we argue that positing some form of (mental) “unobtanium” as definiens for understanding inevitably leads to a dualistic position, and that such a position is precisely the original motivation for developing distributional methods in computational linguistics. As such, we argue that language models present a theoretically (and practically) sound approach that is our current best bet for computers to achieve language understanding. This understanding must however be understood as a computational means to an end.
AGILE SOFTWARE DEVELOPMENT

Agile Software Development plays a quintessential part in modern day software development. The term Agile refers to frequent reassessment and adaptation of plans and techniques and dividing tasks into shorter tasks for efficiency. Agile Software Development differs considerably from Traditional Software Development Methodology. Agile methodology aims to deliver features of a software project in small steps within a short duration of time (i.e., iterations). Hence, it becomes necessary to use agile software development methodology in todays’ fast-paced revolutionizing software industry. This paper discusses the important subtopics of Agile Software Development which gathered by reviewing/surveying of research papers. First, is the Agile Planning Life Cycle which consists of various stages such as pre-planning, planning, release planning and product backlog management. In the next section, principles such as Scrum, Extreme Programming, Kanban and Lean are discussed. The last section comprises the impact of Agile principles on software quality.
Quantum nonlocality without entanglement depending on nonzero prior probabilities in optimal unambiguous discrimination

Abstract Nonlocality without entanglement(NLWE) is a nonlocal phenomenon that occurs in quantum state discrimination of multipartite separable states. In the discrimination of orthogonal separable states, the term NLWE is used when the quantum states cannot be discriminated perfectly by local operations and classical communication. In this case, the occurrence of NLWE is independent of nonzero prior probabilities of quantum states being prepared. Recently, it has been found that the occurrence of NLWE can depend on nonzero prior probabilities in minimum-error discrimination of nonorthogonal separable states. Here, we show that even in optimal unambiguous discrimination, the occurrence of NLWE can depend on nonzero prior probabilities. We further show that NLWE can occur regardless of nonzero prior probabilities, even if only one state can be locally discriminated without error. Our results provide new insights into classifying sets of multipartite quantum states in terms of quantum state discrimination.
Investigation of Geochemical Sections in Exploratory Boreholes of Mesgaran Copper Deposit in Iran

Abstract: Mesgaran copper deposit is located in Sarbisheh city of South Khorasan province of Iran. To investigate the geochemical changes of the elements with increasing depth in the deposit, geochemical sections for important minerals related to mineralization in the area including copper, lead, zinc and cobalt were drawn for 9 exploratory boreholes. In these sections, the trend of changes of different elements versus depth is shown and the resulting diagrams were drawn for different boreholes. As a result, there is a significant relationship between the elements. With increasing the drilling depth, the amount of copper decreased and the amount of copper in each section was determined according to its cause. Keywords: Geochemistry, Copper deposit, Exploratory borehole, geochemical section.
Annotating argumentative structure in English-as-a-Foreign-Language learner essays

Abstract Argument mining (AM) aims to explain how individual argumentative discourse units (e.g. sentences or clauses) relate to each other and what roles they play in the overall argumentation. The automatic recognition of argumentative structure is attractive as it benefits various downstream tasks, such as text assessment, text generation, text improvement, and summarization. Existing studies focused on analyzing well-written texts provided by proficient authors. However, most English speakers in the world are non-native, and their texts are often poorly structured, particularly if they are still in the learning phase. Yet, there is no specific prior study on argumentative structure in non-native texts. In this article, we present the first corpus containing argumentative structure annotation for English-as-a-foreign-language (EFL) essays, together with a specially designed annotation scheme. The annotated corpus resulting from this work is called “ICNALE-AS” and contains 434 essays written by EFL learners from various Asian countries. The corpus presented here is particularly useful for the education domain. On the basis of the analysis of argumentation-related problems in EFL essays, educators can formulate ways to improve them so that they more closely resemble native-level productions. Our argument annotation scheme is demonstrably stable, achieving good inter-annotator agreement and near-perfect intra-annotator agreement. We also propose a set of novel document-level agreement metrics that are able to quantify structural agreement from various argumentation aspects, thus providing a more holistic analysis of the quality of the argumentative structure annotation. The metrics are evaluated in a crowd-sourced meta-evaluation experiment, achieving moderate to good correlation with human judgments.
An insight review on multimedia forensics technology

Crime will turn into the principle issue step by step of our general public so it is important to find a few ways to beat the issue; that is the reason the expression “media legal” is presented. Mixed media measurable can be characterized as a science by which breaking down of an advanced resource for an appraisal for some particular reason to extricate some significant data and in some sort of examination for computerized narrative. Multimedia forensics provides the path to test the digital data from a source that may be an authorized image, order, or any other document that is used to identify in the forensic. In the multimedia forensics images, video, audio, etc. are covered. In multimedia forensic techniques, the focus is to identify the source of the digital device, which may be a mobile phone, digital camera, etc., with the help of media; similarly, the forensic media detects the evidence by which it is obtained. For preventing false detection lens are used with the characteristics of dust spots. All results depend on the lens detection, even under heavy compression and downsampling. Investigation of a crime is a complex process that starts at the crime scene, continues in the lab for in-depth investigation, and ends in the courtroom, where the final judgment is made. Investigators need support in all these steps to make their jobs as effective and efficient as possible. Now the question arises as to why multimedia forensics is required. A few answers are to gather the proof before it is lost and obliterated, utilizing some integral assets for altering.
University as an Environment of Innovative Interactions

Orenburg State University is presented as an environment of multilevel multidimensional interaction of various subjects. The dynamics of value interaction between students and teachers is revealed. In the conditions of modern social and digital transformations, the nature of joint activity is changing from formal-reproductive to value-creative. It is pointed out that the dominant instrumental values of students are shifting due to the information and technological features of distance educational technologies. Youth interactions and socio-cultural practices are integrated into the comprehensive program for the development of the OSU socio-cultural environment on the basis of various project activities. The implementation of the program is characterized by receiving grant support for student initiatives. International aspects of university interaction are represented by the development of forms of scientific and educational export to universities of the Republic of Kazakhstan.
Bidirectional Multiscale Feature Aggregation for Speaker Verification

In this paper, we propose a novel bidirectional multiscale feature aggregation (BMFA) network with attentional fusion modules for text-independent speaker verification. The feature maps from different stages of the backbone network are iteratively combined and refined in both a bottom-up and top-down manner. Furthermore, instead of simple concatenation or element-wise addition of feature maps from different stages, an attentional fusion module is designed to compute the fusion weights. Experiments are conducted on the NIST SRE16 and VoxCeleb1 datasets. The experimental results demonstrate the effectiveness of the bidirectional aggregation strategy and show that the proposed attentional fusion module can further improve the performance.
Educational Organization’s Security Level Estimation Model

During the pandemic, distance learning gained its necessity. Most schools and universities were forced to use e-learning tools. The fast transition to distance learning increased the digitalization of the educational system and influenced the increase of security incident numbers as there was no time to estimate the security level change by incorporating new e-learning systems. Notably, preparation for distance learning was accompanied by several limitations: lack of time, lack of resources to manage the information technologies and systems, lack of knowledge on information security management, and security level modeling. In this paper, we propose a security level estimation model for educational organizations. This model takes into account distance learning specifics and allows quantitative estimation of an organization’s security level. It is based on 49 criteria values, structured into an AHP (Analytic Hierarchy Process) tree, and arranged to final security level metric by incorporating experts’ opinion-based criteria importance coefficients. The research proposed a criteria tree and obtained experts’ opinions lead to educational organization security level evaluation model, resulting in one quantitative metric. It can be used to model different situations and find the better alternative in case of security level, without external security experts usage. Use case analysis results and their similarity to security experts’ evaluation are presented in this paper as validation of the proposed model. It confirms the model meets experts-based information security level ranking, therefore, can be used for simpler security modeling in educational organizations.
Frustratingly Simple Pretraining Alternatives to Masked Language Modeling

Masked language modeling (MLM), a selfsupervised pretraining objective, is widely used in natural language processing for learning text representations.MLM trains a model to predict a random sample of input tokens that have been replaced by a [MASK] placeholder in a multi-class setting over the entire vocabulary.When pretraining, it is common to use alongside MLM other auxiliary objectives on the token or sequence level to improve downstream performance (e.g. next sentence prediction).However, no previous work so far has attempted in examining whether other simpler linguistically intuitive or not objectives can be used standalone as main pretraining objectives.In this paper, we explore five simple pretraining objectives based on token-level classification tasks as replacements of MLM.Empirical results on GLUE and SQUAD show that our proposed methods achieve comparable or better performance to MLM using a BERT-BASE architecture.We further validate our methods using smaller models, showing that pretraining a model with 41% of the BERT-BASE's parameters, BERT-MEDIUM results in only a 1% drop in GLUE scores with our best objective. 1