Reviewerly/exHarmony huggingface dataset:
Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System

In the relatively short history of machine learning, the subtle balance between engineering and theoretical progress has been proved critical at various stages. The most recent wave of AI has brought to the IR community powerful techniques, particularly for pattern recognition. While many benefits from the burst of ideas as numerous tasks become algorithmically feasible, the balance is tilting toward the application side. The existing theoretical tools in IR can no longer explain, guide, and justify the newly-established methodologies. With no choices, we have to bet our design on black-box mechanisms that we only empirically understand. The consequences can be suffering: in stark contrast to how the IR industry has envisioned modern AI making life easier, many are experiencing increased confusion and costs in data manipulation, model selection, monitoring, censoring, and decision making. This reality is not surprising: without handy theoretical tools, we often lack principled knowledge of the pattern recognition model's expressivity, optimization property, generalization guarantee, and our decision-making process has to rely on over-simplified assumptions and human judgments from time to time. Facing all the challenges, we started researching advanced theoretical tools emerging from various domains that can potentially resolve modern IR problems. We encountered many impactful ideas and made several independent publications emphasizing different pieces. Time is now to bring the community a systematic tutorial on how we successfully adapt those tools and make significant progress in understanding, designing, and eventually productionize impactful IR systems. We emphasize systematicity because IR is a comprehensive discipline that touches upon particular aspects of learning, causal inference analysis, interactive (online) decision-making, etc. It thus requires systematic calibrations to render the actual usefulness of the imported theoretical tools to serve IR problems, as they usually exhibit unique structures and definitions. Therefore, we plan this tutorial to systematically demonstrate our learning and successful experience of using advanced theoretical tools for understanding and designing IR systems.
Optimized firefly algorithm based on levy flight and Gaussian inertial weight

In order to solve the problem of slow convergence rate and premature stagnation of standard firefly algorithm (FA), an optimized firefly algorithm based on Levy flight and Gaussian function inertia weight (LFAGDIW) was proposed. This algorithm designs a decreasing inertia weight based on Gaussian function and adds it to the position updating formula to improve the convergence speed of the algorithm. Levy flight strategy is introduced to realize the random movement of individual fireflies and make the optimal search result jump out of the local optimal. Simulation test shows that the improved algorithm has a better convergence effect and avoids falling into local optimum.
The competance- based approach in Teacher training at HNUE nowadays Using flipped classroom model in teaching Evolution - Biology grade 12

Flipped classroom is a blended learning model between face-to-face and online learning, in which all teaching activities are carried out “reversed” compared to normal. Students are usually assigned easy and medium teaching tasks to study at home, while difficult tasks that require teacher’s guidance and communication are done in class. In this article, based on previous studies, we propose a process to design and organize teaching of topics using the Flipped classroom model in teaching Evolution - Biology grade 12. Initially, organizing experimental teaching applying the model of Flipped classroom in teaching the topic “Modern General Theory of Evolution” have created initiative and interest in learning for learners and contributed to innovating teaching methods, improved learning efficiency, developed students’ biological cognitive competence.
METHODOLOGICAL SUPPORT FOR THE ORGANIZATION OF EXAMS IN TECHNICAL DISCIPLINES DURING DISTANCE EDUCATION

Current realities have revealed an urgent need for the development and improvement of distance forms of educational processes. The most important of which are to control and obtain assessments of the knowledge of the examinees who are not in direct physical contact with the examiner. This article presents the results of a review and analysis of various forms of organization of distance examinations in technical disciplines. There are revealed the main disadvantages of each form of organization of the remote exam, including those that do not allow recommending them for widespread, widespread use. There is considered in detail physical implementation of the most simple form of conducting a distance exam, called “Assignment by e-mail”. This form has been used many times when organizing remote exams at the BMSTU and can be successfully implemented in educational institutions that do not have their own sufficient experience in distance education.
An automatic matching interactive and visualization system for virtual martial arts

An automatic matching interactive and visualization system for virtual martial arts is proposed in this paper. The system is developed to improve the user's interest in the intangible cultural heritage of martial arts. Four basic actions in Wing Chun are designed as an example. The body joint coordinate information retrieval is employed to estimate the matching degree between the player's and template actions. We visualize the user's hand movement data according to the speed. The results of user study show that the system is helpful for the beginner to understand martial arts, and it also attracts the user's curiosity and indirectly improves the user's interest in martial arts. Moreover, the system is relatively comfortable in usability, because the movement is captured by the Kinect, which has little restraints on the user's body.
Kesalahan Berbahasa pada Bahasa Melayu di Thailand dan Solusinya dalam Perspektif Hasil Studi di Indonesia

This study is conducted to answer Language errors in Malay and their solutions. This study deals with difficulties coped by the teachers in planning, conducting and evaluating the learning. Methods of collection data using the interview method. The technique of data analysis is done by using reduction, presentation, conclusion drawing and data verification. The amount research respondents are around 8 people, namely the teachers at Tadika Al-Hidayah Bendang Jerambung, Southern Thailand. Based on the results of the study, it shows that there are still language errors in Malay, such as the use of the word contamination of the Patani area in Malay, the illogical use of sentences, the use of inappropriate punctuation marks, and errors in the use of prepositions. The factors that cause language errors in Malay are the lack of literacy activities, the habit of using everyday language, the lack of seriousness in learning time in class, and the Malay language learning curriculum cannot fulfill. Based on the perspective of learning in Indonesia, the solutions that can be offered are the implementation of the School Literacy Movement, the preparation of good learning plans, and the application of text-based learning.Kata kunci: Language Error, Malay Language, Tadika Teacher
A novel deep learning approach for tracking with soft computing technique

Purpose In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best performance. The purpose of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG and Harris are used for the process of feature extraction. The authors’ proposed method will give the best results when compared with other existing methods. Design/methodology/approach The visual tracking of many real-world applications such as robotics, smart monitoring systems, independent driving and human-computer interactions are a major and current research problem in the field of computer vision. This refers to the automated trajectory prediction of an arbitrary target object, often given in the first frame in a bounding box while moving about in successive video frames. In the community of visual tracking or object tracking, DCF has gained more importance. Discriminative trackers strive to train a classifier that differentiates the target item from the background. The fundamental concept is to train a correlation filter that creates high responses around the target and low responses elsewhere. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG and Harris are used for the process of feature extraction. Through experimental analysis, the authors have evaluated several performance assessment metrics such as accuracy, precision, F-measure and specificity. The authors’ proposed method will give the best results when compared with other existing methods. Findings This process involved DCF which gained more importance. When it comes to speed, DCF gives the best performance. The main objective of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique for tracking the objects and these results will be used for identifying the action of the object. Originality/value The main theme exists in the process is to identify the tracking motion of the object by using convolution regression with varied features. This method proves that it will provide better results when compared to state of art methods.
Arabic handwritten recognition based on deep convolutional neural network

Automatic handwritten recognition is a simulation of the human reading process by converting a handwritten input to an editable typed form. A successful deployment of such a system requires a robust character handwritten recognition module. Research papers conducted on the Arabic language are scarce compared to other languages due to the complexity and cursive nature of the Arabic language where characters are written in a conjoined and owing manner. We propose two deep learning models based on a Convolutional Neural Network (CNN) to classify Arabic handwritten characters using two publicly available datasets AIA9K and AHCD. Pre-processing and data augmentation have been successfully utilized to improve performance. We employed an Arabic word segmentation algorithm to disassemble a word into its letters, which makes the input to the classification system. The experimental results demonstrate solid performance with an average accuracy of 96.72% and 98.21% on AIA9k and AHCD datasets, respectively. The performance is boosted to 97.54% and 98.82%, with data augmentation on the respective datasets. Furthermore, the two augmented datasets were merged to build a generalized model that achieved outstanding accuracy of 98.27% using a modified approach of LeNet-5.
Computer crime: Russia on the Way to Corporate Criminal Responsibility

In connection with the danger of the consequences of the activities of corporations that occupy a leading place in the country's economy, in Russia, the introduction of criminal liability of legal entities is actively discussed in scientific and government circles. However, in the course of discussions, the issue of criminalizing computer crimes, the growth of which is poorly controllable, is overlooked. The increased public danger of illegal activities of legal entities is also associated with the fact that they are often created and serve as a means of committing economic, environmental and other crimes. The issue of criminal liability of legal entities has been resolved in many countries of the world and is recorded in international documents. For this reason, the author's goal is a research analysis of the main threats to the information security of legal entities in Russia and the development of ways to ensure it. Taking into account the widespread use of legal entities in the commission of economic and other crimes, the author, based on the analysis of statistics and comparative research of foreign experience and the insufficient effectiveness of the measures taken to combat computer crime in Russia, proposed the introduction of criminal liability against legal entities for committing computer crimes.
Psychological competencies of managers and employee motivation

The article considers the problems and specifics of managerial activity in the organization, analyzes the directions of application of managers’ psychological competencies in the framework of labor stimulation and employee motivation management, as well as presents the main schools of management.
Improving Energy Efficiency Using Optimized Energy Model Virtual Machine Algorithm in Cloud Computing

Cloud computing is the emerging trend that provides a variety of applications to promote corporate business across the globe over the internet. Cloud computing offers services to deploy the infrastructure in the specified environment. Different computing techniques are used to manipulate the cloud services. One of the most eminent techniques is Virtual Machine (VM) Migration which enables to set up the compute resources and storages from one to another host without detaching the application or client. Virtual Machine Migration helpful in minimizing energy dissipation, load balancing, and fault management. It is based on the migration and down time with live and non-live categorization. Live VM Migration in data hubs has potential to minimize energy consumption. The proposed Optimized Energy Model Virtual Machine Algorithm is used to calculate each host in the data hub, while the energy consumed by the system in each hour is increasing exponentially then the proposed algorithm is also responsible for reorders the node and the minimizing the energy after reordering.
Cognitive mapping for strategic thinking and knowledge sharing

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings This research paper concentrates on the shared mental models of one company's top management team (TMT). Cognitive mapping was applied to represent knowledge sharing within the group and actionable strategies. The main concern of the TMT in relation to the impact of Brexit was their trade being negatively impacted by external elements like border delays, added tariffs, and extra bureaucracy being imposed upon them. The cognitive mapping process helped the participants to transparently analyze their thinking and actions, as a group, on behalf of the company. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
Intelligent Sensory Control

The use of hand gestures is a natural, inventive, and modern way to communicate without saying anything. Among its many applications are human-computer interaction and sign language. This implementation demonstrates a method to identify hand gestures by identifying features based on the shape of the hand. A single camera is used to capture the user's gesture and provide it as input to the system. The primary goal of gesture recognition is to develop systems that can recognize specific human gestures and use them to control devices. Using real-time gesture recognition, for example, a user can control a computer by making a specific gesture in front of the video camera. Using the OpenCV module, we create a hand gesture volume and brightness control system.
Tool Recognition Based on Computer Vision in Nuclear Power Motor Maintenance Scene

Based on the frequent occurrence of foreign object events in the current nuclear power motor maintenance scene, a foreign object detection method using computer vision is designed. By collecting the image of maintenance tools and using the method of computer vision to identify the content of the image, the limitations of the current detection algorithm for the identification of maintenance tools are found, and the detection algorithm of slender tools is proposed. Research is carried out from the aspects of data set balance, network depth optimization strategy and anchor group expansion and customization. Balanced data sets reduce the bias of network for slender tools. Network depth optimization is proved to be beneficial to the research of slender tools, and the customized anchor plays a significant role. The customized aspect ratio for slender tools greatly improves the receptive field of anchor box, which is more conducive to the detection of slender tools. Compared with the original algorithm, the detection accuracy of the optimized algorithm is improved by 6.91%.
Probabilistic weighted voting model using multiple machine learning methods for fault detection and classification

Purpose The individual machine learning methods used for fault detection and classification have accuracy performance at a certain level. A combined learning model composed of different base classifiers rather than an individual machine learning model is introduced to ensure diversity. In this way, this study aims to improve the generalization capability of fault detection and classification scheme. Design/methodology/approach This study presents a probabilistic weighted voting model (PWVM) with multiple learning models for fault detection and classification. The working principle of this study’s proposed model relies on weight selection and per-class possibilities corresponding to predictions of base classifiers. Moreover, it can improve the power of the prediction model and cope with imbalanced class distribution through validation metrics and F-score. Findings The performance of the proposed PWVM was better than the performance of the individual machine learning methods. Besides, the proposed voting model’s performance was compared with different voting mechanisms involving weighted and unweighted voting models. It can be seen from the results that the presented model is superior to voting mechanisms. The performance results revealed PWVM has a powerful predictive model even in noisy conditions. This study determines the optimal model from among voting models with the prioritization method on data sets partitioned different ratios. The obtained results with statistical analysis verified the validity of the proposed model. Besides, the comparative results from different benchmark data sets verified the effectiveness and robustness of this study’s proposed model. Originality/value The contribution of this study is that PWVM is an ensemble model with outstanding generalization capability. To the best of the authors’ knowledge, no study has been performed using a PWVM composed of multiple classifiers to detect no-faulted/faulted cases and classify faulted phases.
The Modes of Mean(ing)s in Cybercrime Theorising, Analysing, Modelling and More

Developing a more rounded understanding of cybercrime is key to enhancing the ways in which we respond to it. This task has often been associated with the process of definition, though options here remain far from satisfactory. But defining cybercrime is one thing, attaching meaning to these definitions is another. In this paper I review some possible ways forward for adding greater depth and substance to what we mean we talk about cybercrime. I consider the role of data collection, theory and analysis in this context and question how helpful these have been in furthering our understanding of digital offending. I then evaluate what seems to be a third, promising option – the modelling of cybercrime activity, whether formally or informally. I suggest this provides a useful synthesis of theory, analysis and available evidence, as well as providing the basis for a more dynamic sense of cybercrime ‘in action’. I conclude by setting out details of a recent research project where an economic model of cybercrime was developed. Some implications of this model for our understanding of cybercrime are outlined and reflections offered on the extent to which modelling might provide a useful way forward.
THE EFFECT OF ADDITIONAL INTERCEPTORS ON SHIP RESISTANCE AND LIFT FORCE CASE STUDY PATROL SHIP 40 METER

This paper examines the effect of adding Interceptor to ship resistance and ship lift force. The research was carried out by making a model of the Patrol 40 trim by stern without an Interceptor and with the addition of an interceptor with Maxsurf software, then tested with Fine Marine (Numeca) software. The test results showed a decrease in resistance after the addition of the Interceptor at a speed of 12 knots decreased by 16,196 kN or 42.9%, at a speed of 18 knots decreased by 86.356 kN or 63.9% and at a speed of 24 knots decreased by 122,699 kN or 62,92%. The lift force generated at a speed of 12 knots 1,134,977 kN or the stern rises 0.2 cm, at a speed of 18 knots 1,135,598 kN or the stern rises 20.1 cm and at a speed of 24 knots 1,132,403 kN or the stern rises 32.5 cm.
Design of Wireless Sensor Network Architecture for Power Transmission and Transformation Business

In view of the problems that the existing status-aware business of transmission and transformation equipment uses a large number of private protocols which are incompatible with each other and mature wireless communication protocols which cannot meet the actual business needs in collection, transmission, networking, etc., this paper carries out the analysis of power business scenarios and the characteristics of the perceived data, establishes the unified perception layer of the Internet of Things for transmission and substation equipment, and improves the wireless sensor network architecture, realizes the unification of multi-types wireless sensor under complex business scenarios access and connectivity.
Visual cryptography based on dual image watermarking for copyright protection and security using various transforms

In this paper, a novel dual image watermarking method is proposed to provide the copyright protection to digital images. In this proposed method, the lower and higher frequency subbands of the decomposed original image are modified by inserting the watermark information using different transforms. The implementation of a method that combines the visual cryptography with dual image watermarking to provide the security for the digital images during an exchange in open networks. The Arnold transform is used to encrypt the second watermark to employ the secure communication. Based on the proposed, the perceptibility and robustness of the distributed images can be increased. The original and watermarked images look like same and the PSNR and NCC are calculated for analyzing the system performance. This method is more robust and secure than existing methods.