Reviewerly/exHarmony huggingface dataset:
ON THE RESULTS OF THE THIRD ALL-RUSSIAN CONFERENCE “SUSTAINABLE DEVELOPMENT OF RUSSIA”, HELD AT THE RUSSIAN STATE UNIVERSITY FOR THE HUMANITIES

New, previously unfamiliar to mankind global challenges and systemic crises (technogenic, biogenic, sociocultural, environmental), cyber- terrorism and ideological extremism, deep contradictions, growing instability and uncertainty that the world faced at the turn of the 20th – 21st centuries, along with emerging the new geological Anthropocene era, led to the urgent need to realize the importance of sustainable development and rethink the role of education (including the humanities). UN Secretary-General António Guterres recently spoke of a growing catastrophe in the field of education. The independent authoritative International Commission of UNESCO stated that certain approaches to education have largely exhausted themselves, and in April 2021 formulated its updated goal – to show that in times of deep crises in health, social disasters, governance, economics and the environment – educa- tion should become regenerative: aimed at updating existing and restoring lost or damaged values and principles of education. The UNESCO global initiative in the context of rethinking the role of knowledge and education in shaping the future of mankind requires the creation in Russia of new scientific and research centers for sustainable development, which will form new ideas in the field of education and tools for their implementation
8 Discourse markers and dialogue act annotation for computational dialogue systems

The aim of this study is to uncover the interaction between DM functions and dialogue segmentation. More precisely, we investigate to what extent DMs are used as autonomous dialogue acts or as part of a larger dialogue act. Dialogue acts are defined as speech acts following Austin’s definition of illocutionary force. We hypothesize that the dialogual behaviour of DMs is informative of the function they will fulfil in discourse. The aim of the present study is thus to answer the following research question: Which DMs in what types of contexts can perform as dialogue acts on their own? Authentic conversation data in Slovene is used to conduct the analysis. As a result of the analysis, six criteria are defined that help distinguish DMs that are dialogue acts on their own. The importance of contextual bondedness and non-verbal, especially prosodic, features of speech in the interpretation of dialogue acts and DMs is outlined. The findings show that the majority of DM usages are not dialogue acts on their own, and at least some of those that are represent borderline DM usages.
A Physics-Guided Reversible Residual Neural Network Model: Applied to Build Forward and Inverse Models for Turntable Servo System

To satisfy the application requirements of the forward/inverse model for various control algorithms used in servo systems, a physics-guided reversible residual neural network (PGRRNN) model is proposed, which is applied to build forward/inverse model for turntable servo systems. With the special design of the network structure, both forward and inverse models can be obtained through one-way training of the model, so that the modeling efficiency is improved. Combining the gated recurrent unit and the fully connected layer as the nonlinear term of the reversible residual unit, the PGRRNN model can capture the relevant information between time series data in the training process, so that the prediction accuracy can be effectively improved. Moreover, by selecting the linear sweeping frequency signals with different amplitudes in series as the input signal, the system can be fully excited and the generalization ability can be effectively improved. Experimental results show that the forward/inverse model built by the proposed method has high precision and good generalization ability. The prediction accuracy of the forward/inverse model is better than that of the models built by the traditional modeling method and our previous work. In addition, the accuracy and real-time performance can be guaranteed simultaneously by adjusting the length of the input sequence and the number of residual units, so it has a certain practical value.
A Study on the Middle Manager Managerial Core Competencies and Training Requirements in Local Government

The “Chief” of local government is a middle manager within the organization. This role is responsible for the communications and implementation of various policies of the county and city governments externally. Internally, the role is responsible for team building and giving work directions. If it is possible to cultivate the person to become a “knowledge worker” through trainings, this can strengthen one’s job competency and effectively enhance the agency’s administration performance. Public service with better quality can be provided. This study adopts the “survey techniques.” The survey is based on the “managerial core competency items of the mid and high-level management positions in the agencies under the Executive Yuan.” There are 6 items of core competency aspects (problem analysis, project management, performance management, information management, communications and coordination, and team building) established and there are 16 items as basic competencies. Testing is applied to the middle manager, the director and deputy director and chief of staff of the service units of the K City government based on the survey highlights. It is hoped to discuss the managerial core competencies and their training requirements for the middle manager of the local government. The purpose is to enhance the competency at the job in order to strengthen the managerial core competency of the middle manager of the City government. Thank you to K City government for authorizing the use of the original data of the above survey. Based on the research results, it has been confirmed that the Junior rank civil service middle manager established by the Executive Yuan, its managerial core competency items are applicable to local government. Under training resource constraints for the agencies, the trainings for the middle managers of the local government shall be prioritized and strengthened based on the needs: They are competencies in “performance expression and communications” and “job motivation” and continue to maintain the 7 core competency items in “problem identification,” “program planning,” “communication expression,” “conflict management,” “job instruction,” “team management” and “knowledge management.”
Multiple-image encryption algorithm based on the dice-rotation model and improved chaos

In the digital age, network transmission of batch images is increasingly frequent. To prevent the information leak of image content and improve the security of encryption during the network transmission, we design an improved chaotic system, establish a dice-rotation model, and propose a multiple-image encryption algorithm. First, k plain images are combined into an image cube; second, the image cube is divided into dice blocks (i.e., small image cube); third, the dice blocks are rotated and scrambled according to the designed rules, which are controlled by chaotic sequences; finally, the forward addition and modular operations in the row and column directions are performed by the improved chaos. Experimental results and algorithm analyses indicate that the proposed algorithm can encrypt multiple interactive images at once with desirable encryption effect and have strong security.
Data Cleansing

Once a dataset has been shaped and structured (as covered in the previous two chapters), it probably still needs a good few modifications to make it ready for consumption. Many of these modifications are, at their heart, a selection of fairly simple yet necessary techniques that you apply to make the data cleaner and more standardized. I have chosen to group these approaches under the heading data cleansing.
Development of information system for digital dialogue in teaching using RESTful service

Digital dialogue in teaching, as an asymmetric, multi-user communication system, represents an indirect connection between teachers and students in real time. For this type of communication, it is necessary to ensure the fastest response of clients and to ensure parallel processing as well. The features of RESTful web service, such as: possibility of caching, uniform interface and explicit use of HTTP methods, recommends it as a key component in the information system for digital dialogue in teaching. This paper presents a project for development of such information system using RESTful web service and a description of its advantages compared to other solutions.
Optimization of Skipping Rope Training Method Based on Chaotic Logistics

Skipping training belongs to a competitive event, which can not only lose weight but also improve the physical quality of trainers. At present, there are some problems in skipping training, such as unreasonable plan and unsatisfactory actual training effect. The original rough set method cannot solve the analysis of multivariate data in skipping training, and the ability to evaluate the training effect of skipping is poor. Aiming at the problems existing in the training of skipping rope, this paper puts forward a skipping rope training method based on a chaotic logistics algorithm, aiming at improving the training effect of skipping rope. First, the chaos theory is used to classify the training data. Different data classifications correspond to different training results, which eliminates irrelevant information in the training scheme and reduces the amount of single data analysis. Finally, the data after classification are optimized in a single stage, and the optimal results of different training items are obtained and summarized. After the MATLAB test, the chaotic logistics algorithm is superior to the rough set method in accuracy and convergence speed and meets the actual training needs. Therefore, the chaotic logic analysis method proposed in this paper is suitable for the evaluation of skipping training.
RANCANG BANGUN SMART PARKING PADA AREA KAMPUS BERBASIS ARDUINO MENGGUNAKAN QR CODE

Abstract&#x0D; &#x0D; The number of students using motorized vehicles which increases every year causes its own problems for a campus that has a minimal parking area, this is exacerbated by the irregular placement of vehicles which can cause damage to the vehicle, this condition can also cause other students to have difficulty finding a parking position for his vehicle. Good parking area management can help the campus to take advantage of the minimal parking area optimally, by utilizing microcontroller technology combined with QR code on student identification cards as vehicle access and website-based applications to monitor the availability of parking spaces in real time. used in this research is Research and Development (R&amp;D) in the process of making and developing the system. With this system, besides making it easier for the campus to manage the parking area it has, it can also help students to park their vehicles properly and save the time needed to find available parking areas.&#x0D; &#x0D; Keyword---R&amp;D, Smart Parking, QR Code, Microcontroller
Dual-IMU-WIP: An easy-to-build Walk-in-Place System based on Inertial Measurement Units

Locomotion interfaces for virtual environments remain an active field of research. Walk-in-place (WIP), where the intent to move is communicated to the system by taking steps on the spot, provides a realistic experience to the user without being confined to a limited tracking area. The proposed systems found in the literature have complex setups, require bulky hardware, and use closed source software, which makes reproduction of the results as well as follow-up research difficult or impossible. We propose an easy-to-build wireless WIP system based on consumer grade electronic components which is versatile, affordable, and extensible. We use the rotation and position data of inertial measurement units attached to the user’s left and right shin, perform signal processing directly on the device, and show how to use this data for continuous movement in a virtual environment. Our locomotion interface uses a state machine and features low start and stop latencies as well as the detection of foot gestures for rotation and sideward movements. The proposed device is small enough to be mounted on a pair of shin guards. We developed an exercise application to proof that our system works in a practical scenario, where the user moves through a virtual environment by using her/his legs only. We publish building plans of the device and make our software open source in order to make our results replicable and allow for follow-up research.
An experimental comparison of single-agent and multi-agent learning-and-playing algorithms in a gaming ecosystem

The first version of the RLGame ecosystem, which features a collection of conventional Artificial Intelligence and Reinforcement Learning techniques for learning to play (and, actually, playing) a board game, was adopted for the development of the second version that supports Multiagent Reinforcement Learning and a sequence of games. We present an experimental comparison of a variety of algorithms which are available within the ecosystem and comment on the potential to investigate single-player vs multi-player scenarios, alongside some experimental results which suggest that loosely co-ordinated game play can be superior to fully co-ordinated game play.
Human Activity Recognition on Accelerometer Data using Machine Learning Algorithms

Smart devices provide ample opportunities for the domain of sensor mining since they include a large variety of sensors like accelerometer, GPS, microphone, proximity, light, temperature, etc. Due to its capability of ubiquitous and portability, these smart devices also provide an excellent opportunity for providing services to people’s lives. In this paper, we consider the human activity recognition task as a supervised classification problem where the task entails identifying the physical activity that a user is participating in. To perform this classification task, we utilize WISDM project data where the accelerometer data is primarily used for activity recognition. We present the results of several machine learning algorithms on this dataset and provide the pros and cons of using various ML techniques for human activity recognition.
Comparative Study of Feature Engineering for Automated Short Answer Grading

Recent advancement in the field of Natural Language Processing (NLP) has shown promising results in numerous NLP applications. Automated Short Answer Grading (ASAG) is one of the challenging applications of NLP. Understanding the underlying context and semantics in text will help to evaluate short answer responses and predict the scores more similar to human evaluator. Training machine to understand the domain knowledge of courses and utilize the same for evaluation has demanded for more robust method to generate word vectors which are capable of understanding the meaning of words based on context. In this paper we have studied and experimented with already existing word vector generation techniques TF-IDF, word2vec and BERT. Along with the pre-trained word2vec we have computed domain specific word vectors too. Finally, by the utilization of XGBoost we have computed the category of student answer. The comparison of similarity between human evaluated category and category generated by all studied embedding technique along with XGBoost algorithm is computed with the help of Quadratic Weighted Kappa. The results have shown that domain specific embedding will help to predict correct category of student answer.
Textual Query Translation into Python Source Code using Transformers

Programmers who are new to a language find it difficult to unlearn the known language's syntax and learn the new one from scratch. To deal with this, there should be a system that translates the problem statement into a proper source code. The existing solutions for this problem are systems that works on Recurrent Neural Networks, long short-term memory architecture and other deep learning architectures whereas these technologies are inefficient as compared to the transformer architecture. Another problem with the existing solution is, it does not store the previously searched query which can be useful again in further work. To overcome the above-stated problems, Neural Machine Translation (NMT) can be used to convert the specific query from English to an equivalent Python code. Keeping this in mind, english language to python source code generation using transformers has been achieved in this work. Training the self-attention based transformer with the dataset[18] has been done. BLEU score of 0.78 has been achieved.
Video-Based Human Activity Detection

Human activity detection has become a prime research area in computer vision problem. There are many applications where human activity detection can be incorporated such as analyzing the video data which will help to monitor data generated by closed-circuit television (CCTV) cameras. In this paper, a system is proposed which uses convolutional neural network along with incremental learning to detect different type human activities in videos. The proposed system is evaluated on benchmark dataset, KTH dataset.
Résumés en Français

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Résumés en Français. Am J Intellect Dev Disabil 1 July 2022; 127 (4): 345–346. doi: https://doi.org/10.1352/1944-7558-127.4.345 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
Moderate Complexity Turbo Decoder for Near-Optimum Decoding of Product Codes

The Chase-Pyndiah algorithm which is a soft-input/soft-output algorithm is revisited for decoding turbo product codes (TPC). In general, soft-output computation of the algorithm involves a huge amount of compare and save operations. In this paper, a hybrid approach is considered for the soft output computation which reduces the amount of these operations to a moderate level comparing to the original algorithm. For the Hamming code TPCs $(32,26,4)^{2}$ and $(64,57,4)^{2}$, the Chase-Pyndiah algorithm with hybrid approach for the soft output computation has shown no degradation in coding gain when compared to the original algorithm while reducing the algorithm’s complexity.
Virtual reality-based simulated training: evaluation and assessment

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 Adkere et al. (2020) use a multi triangulation approach including biometric measures to complement quantitative and qualitative data and provide and more holistic approach. The inclusion of biometric data alongside quantitative and qualitative approaches offsets some of the disadvantages of these methods and enhances the assessment evaluation of VR-based simulation training. 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.
Novel Cryptographic Hashing Technique for Preserving Integrity in Forensic Samples

Hash functions are used to protect the integrity and authentication of digital information. It takes the input of any length and produces an output of fixed length, called digest. Some of the standard and most popular cryptographic hashing techniques are MD5, SHA-1, and HMAC but due to the more execution time and large digest size of these functions, there is a huge demand for new hashing functions for the integrity of the information. Therefore, the authors have proposed a lightweight novel cryptographic hash algorithm that satisfies the basic properties of hashing. This algorithm has been implemented on MATLAB and is compared with existing hashing techniques using execution time and digest size as a parameter. After simulation, it has been observed that the proposed scheme presents good results.
Challenges in Digital Forensics and Future Aspects

Digital forensics is an important arm of information technology and, more importantly, law enforcement agencies. Since the rise of digital evidence, all law enforcement agencies have a dedicated digital forensics division to carry out examination and investigation. Digital forensic investigations get complex quite often and hit a few hurdles. The challenges faced by a digital forensic expert often go unnoticed or overlooked and need attention from senior authorities. In this chapter, we’ll briefly discuss these challenges and how they hamper the investigation process and how these are being tackled. Each stage of digital forensic investigation has its own challenge – forensic imaging of high-volume storage media will take a lot of time and slow the examination process. Encrypted device/data is not useful until it is decrypted. If advanced methods such as JTAG and Chip-off are required, then the forensic lab must have the setup and skilled personnel to conduct such an advanced level of examination.
Visual development environment and visual programming as an effective tool for data collection and analysis

The paper analyzes the effectiveness of various visual programming languages. Its application in data processing and collection is considered. The complexity of analyzing diverse, poorly structured and often unrelated information requires the use of special software and appropriate skills. The application of visual programming to work with such data is proposed. A prototype of software for obtaining, analyzing and processing data from websites in the form of a visual programming language has been developed. The prototype includes a visual user interface development environment and a visual script editor. The efficiency and optimal functionality of visual programming for working with data are analyzed.
Detection of a fork-like marker of ureaplasmosis in the image of the biological fluid facies

Ureaplasmosis is a common infectious disease of the genitourinary system caused by microorganisms. Diagnosis in the laboratory requires time-consuming and expensive methods. A much faster and cheaper method of early diagnosis is image analysis of the facies (thin film of dried urine). A sign of this disease is the presence of special structures (markers) on the images of facies. One of these markers is in the shape of a fork. This paper proposes an algorithm for detecting this marker in images entered into a computer. Tests of the algorithm on the available facies images have shown its high efficiency.
PROBLEMS AND PERSPECTIVES OF DEVELOPMENT OF CRYPTOCURRENCY MARKER IN KAZAKHSTAN

PROBLEMS AND PERSPECTIVES OF DEVELOPMENT OF CRYPTOCURRENCY MARKER IN KAZAKHSTAN Daulet Turgunov Student, M.Narikbayev Kazguu University, Kazakhstan, Nur-Sultan Roman Dovolnov MA(Social Science) Economics, M.Narikbayev Kazguu Universi
Community Scientists Recover Micrometeorites from Lake Michigan

A team of scientists, educators, and teenagers discovered the objects, some of which may have been delivered by a fireball that streaked across the sky in 2017.