Reviewerly/exHarmony huggingface dataset:
Implementation of Supply Chain Management in Agriculture using Blockchain

Block chains are now firmly established as a digital technology that combines cryptographic, data management, networking, and incentive mechanisms to support the verification, execution, and recording of transactions between parties. While block chain technologies were originally intended to support new forms of digital currency for easier and secure payments, they now hold great promise as a new foundation for all forms of transactions. Agribusiness stands to become a key beneficiary of this technology as a platform to execute ‘smart contracts’ for transactions, particularly for high-value produce. First it is important to distinguish between private digital currencies and the distributed ledger and block chain technologies that underlie them. The distributed and cross-border nature of digital currencies like Bitcoin means that regulation of the core protocols of these systems by central banks is unlikely to be effective. Monetary authorities are focused more on understanding ‘on-ramps’ and ‘off-ramps’ that constitute the links to the traditional payments system rather than being able to monitor and regulate the currency itself. In contrast to the digital currency feature of block chain, the distributed ledger feature has the potential for widespread use in agribusiness and trade financing, especially where workflows involve many different parties with no trusted central entity.
Quantum sharing of two unknown single-qubit states by using a six-qubit entangled state

Owing to the advantages of entanglement, in this work, an efficient and simple quantum sharing of two unknown state (QSTUS) protocol based on a six-qubit entangled state as the quantum channel is proposed. During the procession of the protocol, sender Alleyn possesses two unknown quantum states, at the same time, she wishes her four agents to share her quantum states. Hence, Alleyn implements two Bell basis measurements on her qubits, meanwhile the appointed controllers make a single-qubit measurement on her own qubit. Applying the suitable unitary operations, the state receivers can recover the original states. Analysis shows that only if all agents cooperate with each other, the proposed protocol QSTUS will be realized easily and successfully.
Transaction Using Facial Authentication and Random PIN Generation

In this paper we are going to develop a robust automated web application for transacting money in higher level security purpose with high facial recognition. First we have to register our personal details with our face and pass the liveness detection test. Haar cascade-based algorithm has been applied for fast and simple face detection from the input image. The face image is then being converted into grayscale image. After that, the iris, eyebrows, nose, mouth of candidates are extracted from the intensity valleys from the detected face it will consider as datasets now our system can understand who are all authorized and unauthorized. While login our face will be recognized if we authorized will can move to authorized page else our system won’t allow to login. For higher security purpose we have used face recognition module. During the transaction our face will be recognized it will allow only the authorized account holder to transact a money, it doesn’t allow others to transact a money.
Gradle-Autofix: An Automatic Resolution Generator for Gradle Build Error

Gradle is one of the widely used tools to automatically build a software project. While developers execute the Gradle build for projects, they face various build errors in practice. However, fixing build errors is not easy because developers should manually find out the cause of the build error and its resolution on their project. For this reason, developers spend much time fixing them, and especially it can be worse if a developer lacks the experience of handling build errors. To address this issue, we propose a novel approach named Gradle-AutoFix to automatically fix build errors along with providing their causes and resolutions. In this approach, we collect build errors to group their causes and resolutions and then generate feature vectors from build error messages by applying Bag-of-Word (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Bigram, and an embedding layer. The feature vectors are utilized for training two classification models on cause and resolution. Next, we analyze fixing patterns and define seven resolution rules to fix the build error automatically. Based on our trained models and defined resolution rules, we built Gradle-AutoFix. For the evaluation, we measured how appropriately Gradle-AutoFix provides causes of build errors and resolutions. As a result, we obtained 96% and 91% accuracy, respectively. Also, we assessed how properly Gradle-AutoFix fixes the project’s build error based on the seven resolution rules. The outcome showed a 64.5% build error resolution rate for 231 projects.
Forming communicative creativity among technical university students on the example of the discipline «Foreign language»

Introduction. In the era of globalization of the world economy the role of mutually advantageous dialogue in the world professional space is increasing. In this regard all forms of communication, interaction and mutual development among specialists in professional sphere including innovative engineering and scientific research activity are a priority. The purpose of the study is to test the model of forming communicative creativity among technical university students in the process of foreign language training. Methods. 163 senior students of Moscow Polytechnic university took part in the experiment (baccalaureate). Classes were conducted both in full-time form and remotely using the virtual learning environment LMS (Learning management system). The entrance and final diagnostics of the level of communicative creativity were conducted with the use of communicative tasks, test tasks and tasks on LMS platform. In this connection the following parameters were evaluated: ease of creating large number of set of behaviors; flexibility in person’s changing response tactics; originality in creating nonstandard solutions of communicative situations. Pearson criterion χ2 was used to evaluate statistical significance of differences and validity of the data obtained between the groups. Results. The model of forming communicative creativity in innovative engineering and scientific research activity among technical university students and pedagogical conditions of its realization were developed. The course content «Foreign language» was complemented with the Tutorial «Guide to Innovation» which contains the universal module «Innovation classification. Types of research and their results» representing the terminological minimum for innovative engineering and scientific research activity. The changes in student learning outcomes are non-random, and the effect of the changes is due to the application of a set of innovations (χ2= 6.378; p &lt; 0.05). Conclusions. The results of the study show that the novelties offered and means of their realization are effective and the results of the experiment are reliable.
Perancangan Aplikasi E-Learning Berbasis WEB pada SMA Negeri 5 Batanghari

e-learning is one of the learning media which supports the teaching and learning process by using the internet. Currently at SMA Negeri 5 Batanghari the teaching and learning process is carried out during the pandemic using social media such as whatsapp. There are limitations of the whatsapp application so that teaching and learning activities at the school do not go well, such as sending assignments that can carry out automatic assessments. Based on that, it is necessary to develop a web-based e-learning application with the aim of research to make it easier for teachers to send and process grades automatically. The method used in this research is on system development using the waterfall. While the method of data collection is done by interviewing, observing and reviewing the literature into related research. In this research, a web-based e-learning application was produced using the PHP programming language and the MySQL database. The views obtained in this e-learning application are teacher data, student data, grade data, assignment delivery, and reports for teachers in the progress of activities during teaching and learning at SMA Negeri 5 Batanghari.&#x0D; Keywords: Design, Website, E-learning&#x0D;
Model Checking Functional Integration of Human Cognition and Machine Reasoning

Functional integration of human cognition and machine reasoning is an industry-wide problem where failure risks health or safety. Differences in human versus machine functioning obscure conventional integration. We introduce cognitive work problems (CWP) for rigorous, verifiable functional integration. CWP specify the cognitive problem that integrated designs must solve. They are technology-neutral, abstract work objects, allowing people and computing to share and transform them in coordination. The end-to-end method is illustrated on a system that employs AI for remote patient monitoring (RPM) during COVID-19 home care. The CWP specified actionable risk awareness as the medical problem RPM must solve. Graphical modeling standards enabled user participation: CWP as finite state machines and system behavior in BPMN. For model checking, the CWPs logical content was translated to linear temporal logic (LTL) and the BPMN into Promela as inputs to the SPIN model checker. SPIN verified the Promela implements the LTL correctly. We conclude this CWP-derived RPM design solves the medical problem and enhances patient safety. The method appears general to many critical systems.
The IoT Based Exercise Cycle

The "IOT Based Exercise Cycle" project combines a variety of technologies, including computer hardware and software, to provide bikers with a new modern home exercise solution that immerses them in their surroundings. As a result, a cyclist can monitor some of the most significant training data online via a web page, including speed, distance covered by the rider, heart rate, and oxygen level during exercise. Additionally, depending on the amount of peddling, a cyclist can view flashing pictures on a web page. This cycle is linked to the cyclist's heart rate and temperature, allowing him or her to keep track of their BPM (Heart Beats Per Minute) and body temperature. The system consists of both hardware and software components. Hardware is in charge of gathering, measuring, processing, and transmitting data from sensors. A server contains a Front-End Web page that displays and controls data for cyclists on the software side. Finally, there is a web page that displays all of the parameters that the rider can use to track his or her route.
Data Security of Dynamic and Robust Role Based Access Control from Multiple Authorities in Cloud Environment

Abstract: Data integrity maintenance is the major objective in cloud storage. It includes audition using TTP for unauthorized access. This work implements protecting the data and regeneration of data if someone mishandles it. This job will be assigned to a Proxy server. The data of the users will be stored in public and private area of the cloud. So that only public cloud data will be accessed by user and private cloud will remain more secured. Once any unauthorized modification is made, the original data in the private cloud will be retrieved by the Proxy server and will be returned to the user. Cloud storage generally provides different redundancy configuration to users in order to maintain the desired balance between performance and fault tolerance. Data availability is critical in distributed storage systems, especially when node failures are prevalent in real life. This research work explores secure data storage and sharing using proposed AES 128 encryption algorithm and Role Base Access Control (RBAC) for secure data access scheme for end user. This work also carried out backup server approach it works like proxy storage server for ad hoc data recovery for all distributed data servers. The experiment analysis has proposed in public as well as private cloud environment. Keywords: RBAC, Elgamal encryption scheme; secure user access policy; Proxy Key Generation, Role Base Access Control (RBAC), advanced encryption standard (AES), etc.
Deep Learning Approach for Suspicious Activity Detection from Surveillance Video

Suspicious Activity is predicting the body part of a person from video. This project will entail detecting suspicious human Activity from video using neural networks. Suspicious human activity detection from surveillance video is an active research area of image processing and computer vision. Using visual surveillance, human activities can be monitored in public areas such as bus stations, railway stations, airports, banks, shopping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, accidents and illegal parking, vandalism, fighting, crime and other suspicious activities. It is very tough to watch public places continuously, so we use an intelligent video surveillance, It is required to monitor the human activities from video and categorize them as usual and unusual activities; and can generate an alert.
Keeping in touch with our hidden side

Previous studies have shown that the sensory modality used to identify regions of the body hidden from sight, but frequently viewed, influences the type of the body representation employed for reaching them with the finger. The question then arises as to whether this observation also applies to body regions which are rarely, if ever, viewed. We used an established technique for pinpointing the type of body representation used for the spatial encoding of targets which consisted of assessing the effect of peripheral gaze fixation on the pointing accuracy. More precisely, an exteroceptive, visually dependent, body representation is thought to be used if gaze deviation induces a deviation of the pointing movement. Three light-emitting diodes (LEDs) were positioned at the participants' eye level at -25 deg, 0 deg and +25 deg. Without moving the head, the participant fixated the lit LED before the experimenter indicated one of the three target head positions: topmost point of the head (vertex) and two other points located at the front and back of the head. These targets were either verbal-cued or tactile-cued and the participants had to reach them with their index finger. We analysed the accuracy of the movements directed to the topmost point of the head, which is a well-defined, yet out of view anatomical point. Based on the possibility of the brain to create visual representations of the body areas that remain out of view, we hypothesized that the position of the vertex is encoded using an exteroceptive body representation, both when verbally or tactile-cued. Results revealed that the pointing errors were biased in the opposite direction of gaze fixation for both verbal-cued and tactile-cued targets, suggesting the use of a vision-dependent exteroceptive body representation. The enhancement of the visual body representations by sensorimotor processes was suggested by the greater pointing accuracy when the vertex was identified by tactile stimulation compared to verbal instruction. Moreover, a control condition showed that participants were more accurate in indicating the position of their own vertex than the vertex of other people. Together, our results suggest that the position of rarely viewed body parts are spatially encoded by an exteroceptive body representation and that non-visual sensorimotor processes are involved in the constructing of this representation.
Formation of Prerequisites for Educational Independence of First-Graders in Mathematics Lessons

The article deals with the actual problem of the formation of educational independence of younger schoolchildren using the means of mathematics, the approaches of various Russian scientists to the organization of this process are revealed. The author gives a description of the concept of "learning independence", gives examples of tasks that contribute to the development of all components of the independence of first grade students.
An Experimental Analysis of the Applications of Datamining Methods on Bigdata

&lt;p align="justify"&gt;Data mining is a procedure of separating covered up, obscure, however possibly valuable data from gigantic data. Huge Data impactsly affects logical disclosures and worth creation. Data mining (DM) with Big Data has been broadly utilized in the lifecycle of electronic items that range from the structure and generation stages to the administration organize. A far reaching examination of DM with Big Data and a survey of its application in the phases of its lifecycle won't just profit scientists to create solid research. As of late huge data have turned into a trendy expression, which constrained the analysts to extend the current data mining methods to adapt to the advanced idea of data and to grow new scientific procedures. In this paper, we build up an exact assessment technique dependent on the standard of Design of Experiment. We apply this technique to assess data mining instruments and AI calculations towards structure huge data examination for media transmission checking data. Two contextual investigations are directed to give bits of knowledge of relations between the necessities of data examination and the decision of an instrument or calculation with regards to data investigation work processes.&lt;/p&gt;
Gender Influence on Speech Behaviour of Commanding Officers of Foreign Armies in Situations of Professional Communication (Based on the Material of the English, German and Norwegian Languages)

The aim of the study is to find out the features of the linguistic and speech behaviour of the female and male military personnel who are the members of the highest command staff of the armed forces of the NATO bloc states. The scientific novelty lies in the fact that for the first time a study of gender-oriented communication strategies is conducted on the basis of interviews with senior officers of the US, German and Norwegian armies. Not only the general characteristics of their communicative style are considered, but also the degree of expressiveness of specific features, which, according to previous studies, are typical of the service men and women. As a result, the degree of the gender factor influence on the choice of language means in the considered communicative situations has been determined. The relationship between the service period and the expressiveness of features in speech that are typical of the military personnel of a certain gender has been established.
Study on oxygen diffusion movement law of artificial oxygenation

The problem of hypoxia at high altitude is particularly prominent, which has caused great difficulties in the development of mineral resources in high altitude areas. Artificial oxygenation is one of the most effective technologies to solve hypoxia at high altitude at present, so it is urgent of setting oxygen supply and ventilation parameters and studying the law of oxygen diffusion.This paper adopts the method of combining field experiment and simulation research to study the law of oxygen diffusion, and to carry out the optimization of local artificial oxygenation technology in metal mine.The results show that when the particle size of the smoke sample is smaller, the effective diffusion coefficient at the initial time is larger, which has higher precision and efficiency under the same pressure.
Rotation object detection methods based on YOLOv5

Recently, deep learning has been rapidly developed in the field of object detection. However, how to accurately and efficiently locate rotating objects is still a challenging task. This work adopts the YOLOv5 algorithm as a rotation detector to locate the rotated objects in images. We first analyze and discuss the angle parameter regression method now commonly used in angle prediction, the angle classification method and the circular smooth label rotation detection method. Then, we validate these methods in the widely recognized rotation target image DOTA dataset. The experiments show that the average accuracy of the three methods is about 72% on rotation objects detection, and among these three methods, the circular smooth label rotation detection method achieves the highest performance on the task of rotation prediction.
Image super-resolution reconstruction method based on residual mechanism

With the development of artificial intelligence, deep learning has been widely used in image super-resolution reconstruction. To solve the problems of feature extraction insufficiency, detail loss, and gradient disappearance in super-resolution reconstruction based on traditional deep learning, we propose a lightweight multihierarchical feature fusion network for single-image super-resolution. An important part of our network is dual residual block. To better extract features and reduce the amount of parameters as much as possible, the dual residual block we designed is an excite-and-squeeze structure. To transmit feature information, webadd autocorrelation weight unit into dual-residual block, which can weight each channel according to the image feature information. Extensive experiments show that our method is significantly better than LapSRN, MSRN, and other representative methods. The PSNR on SET14, URBAN100, and MANGA109 datasets are improved by 5 dB and SSIM is improved by 4% compared with the baseline method.
Personalized e-learning recommender system based on a hybrid approach

In recent years, the development of recommendation systems has aroused growing interest in many areas, especially in e-learning. However, the lack of support and personalization in this context leads the learners to lose their motivation to continue in the learning process. To overcome this problem, we focus on adapting learning resources to the needs of learners using a recommender system. Thus, most of the existing studies in this area don&#x2019;t take into account the differences in the characteristics of learners. This problem can be mitigated by incorporating additional learner information into the recommendation process. Additionally, many recommender techniques experience cold start and rating sparsity issues. This article presents our proposed recommendation system in order to provide learners with appropriate learning resources to follow the learning process and maintain their motivation. We propose hybrid recommender system that combines recommendation techniques to solve the problem of retrieving relevant learning resources for learners and that can alleviate both the cold-start and data sparsity problems. Using this hybridization between different techniques is useful in the personalization of the learner&#x2019;s profile. We consider different learner characteristics such as their preferences, their goal, and their level of knowledge, hence resulting in the generation of more accurate recommendations.
Local Stochastic ADMM for Communication-Efficient Distributed Learning

In this paper, we propose a communication-efficient alternating direction method of multipliers (ADMM)-based algorithm for solving a distributed learning problem in the stochastic non-convex setting. Our approach runs a few stochastic gradient descent (SGD) steps to solve the local problem at each worker instead of finding the exact/approximate solution as proposed by existing ADMM-based works. By doing so, the proposed framework strikes a good balance between the computation and communication costs. Extensive simulation results show that our algorithm significantly outperforms existing stochastic ADMM in terms of communication-efficiency, notably in the presence of non-independent and identically distributed (non-IID) data.