Reviewerly/exHarmony huggingface dataset:
Substation Condition Evaluation Based on Fuzzy Analytic Hierarchy Process

In recent years, with the continuous development of the power industry, as an important part of the power industry, it is very necessary to accurately evaluate the overall operation state of the substation. This paper proposes a method to evaluate the substation state based on fuzzy analytic hierarchy process, establishes the index system to evaluate the substation state and the operation state of different levels of the substation, and finally completes the evaluation of the key state by extracting the membership degree between the key equipment and parameters of the substation. The example shows that the comprehensive evaluation method of substation state based on fuzzy analytic hierarchy process can effectively and accurately evaluate the operation state of substation.
Research on the Prediction Model of Key Personnel's Food Crime Based on Stacking Model Fusion

Crime prediction is of great significance to the food safety defense work of major events. The traditional crime prediction depends on the experience of police officers, which is highly subjective and can not be predicted in advance. This paper analyzes and processes food safety police data, combines the characteristics of food crimes, uses stacking model fusion method to predict the food crime tendency of key personnel, and verifies the model through the Recall Rate. The results show that the integrated learning model has high accuracy and can effectively predict the food crime tendency of key personnel.
Novel Architecture for Reducing Sports Injuries in Football

This paper proposes a novel architecture to reduce sports injury in the game of football.The research framework non-invasively intrudes the captured postures of athletes during their respective playing sessions using computer vision and wearable devices.The inclusion of computer vision transforms the postures and gestures as semantic moves, which after processed using the proposed framework predicts the injury in advance depending upon the moves of the respective player.
A Lightweight Block Storage Security Scheme based on LT Coding for 6G Network

The 6G is the space-terrestrial integrated network combining the Internet of Things and satellite network. And the data security problem of 6G network has become a research hotpot. The introduction of blockchain technology in 6G network can effectively improve the safety of data storage. However, with the continuous addition of IoT nodes and satellite nodes, more and more data are stored in blocks, which makes the problems of large storage space and low storage efficiency prominent. Therefore, for 6G network, this paper proposes a lightweight block storage security scheme based on LT coding. In the proposed storage scheme, satellite nodes are responsible for the complete storage of the blockchain. The blocks are encoded by using LT code and stored at the satellite nodes in the form of slices, while the latest blocks are stored in IoT nodes. This greatly reduces the storage burden of 6G network. To suit large-scale nodes of 6G network, the traditional blockchain indexing method has been improved. The IoT nodes can calculate the jump block number according to their own block number, thus realizing the jump indexing and verification of the block. Finally, the simulation results prove that the proposed storage scheme has superior performance in block recovery success rate and block storage occupancy.
Evaluation Of Reservoir Performance On Flood Discharge Plan Using HEC-HMS Modelling (Case Study: Selorejo Reservoir

Abstract Water is a valuable resource for humanity, because of this fact humanity has to manage the amount of water resource whether it is in excess condition or scarce. excess water is a problem in water management. Because it can cause natural disasters and waste water in vain. Therefore, water needs to be properly regulated. One way to regulate water is by structural means or it can also be called water reservoirs. Reservoir is one of the structures that are suitable to hold water and become a means to regulate the water itself. Referring to the POW (dam operation pattern) and ROTW (annual reservoir operation plan) from the Ministry of PUPR, it is said that the condition of the reservoir must be reviewed for development and changes in its utilization at least every 5 years. The solution chosen to evaluate the selorejo reservoir is to use the Rain-runoff Relationship Model in this case using HEC-HMS version 4.2.1, to find out whether the reservoir is still able to accommodate the flood discharge that was planned at the beginning of construction. Due to the age of the Selorejo Reservoir which has reached 48 years, the Selorejo Reservoir itself is planned with an effective age of 50 years. From analysis result we can conclude that even there is change in reservoir capacity due to sedimentation, Selorejo Reservoir still able to accommodate 1000-year Period Flood and if the reservoir kept in +610 m level, it will be able to accommodate PMF Flood.
Classification of Software Contracts: Analysis of the European Union and the American Legal Systems

We live in a time when the use of technology is simply a way of life.All ages and sexes have access to the software; younger audiences use gaming software and music software apps, while older audiences have access to much more software such as computer operating systems.The classification of software for contract purposes affects the choice of law, remedies, and defences that parties can exercise to an agreement.This paper seeks to provide a clear insight into how software contracts are classified in two different legislative regimes, the European Union and the United States of America, to add to the relatively scarce literature on the importance of classification of software for commercial contracting law.The paper concludes that emerging economies such as China have now entered the software arena, indicating a clear need to unify the current laws that regulate software contracts.
The Impact of Cooperative Learning on EFL Achievers’ and Underachievers’ Motivation Based on Marginal Utility

This study explores the change of EFL learners' motivation in learning English with the increase of CL time based on marginal utility. Thirty learners from an intact class in Grade 10 were selected through their performance on a piloted sample Preliminary English Test. Learners were assigned to achievers and underachievers groups. The questionnaire of motivation, based on the Attitude/Motivation Test Battery (AMTB), was given to both groups as a pretest. All participants underwent the same amount of teaching time and same material with the same teacher during seven-week CL, 35 sessions taking 45 minutes each. The same questionnaire was administered again at the end of one week, three-week, five-week and seven-week treatments respectively to both groups and their scores on the questionnaires were compared through an analysis of Paired Samples t-test and ANOVA. The findings showed that after one week CL, both the achievers’ and underachievers’ motivations were significantly improved compared with those in pretest; after three-week CL, the underachievers’ intrinsic and extrinsic motivations and achievers’ extrinsic motivation declined significantly compared with those of Week One, while the latter’s intrinsic motivation has no significant change; after five-week CL, underachievers’ motivation didn’t change significantly compared with their motivation in the pretest, which means that the marginal utility took place in underachievers’ motivation during three-week to five-week CL; after seven-week CL, the achievers’ motivation increased significantly compared with their motivation in the pretest, indicating there was no marginal utility for achievers’ motivation during seven-week CL.
Compact Deep Neural Networks via Soft and Smooth Filter Pruning

Filters pruning methods are widely used to accelerate the inference process of Deep Neural Networks (DNNs). Among them, Soft Filter Pruning (SFP) has achieved increasing attention due to its compatibility and flexibility. However, conventional SFP directly sets the pruned filters to zero during training phase, discarding the training information of the pruned filter completely. In order to solve the above problem, this paper proposed a soft and smooth pruning method to retain the training information among pruned filters. Instead of the zeroing the pruned filters, our method imposes a filter weakening strategy, which gradually forces the pruned filters to zeros. Such a gradual pruning framework will give the pruned model more chances to recover the lost information and boost the pruning performance. To verify the the actual effectiveness of our proposed method, we conduct several experimental results on one dataset of metal surface images captured in a controlled industrial environment, i.e.,KSDD, using Resnet-20 with various pruning rates. Experimental results show that our filter weakening strategy consistently achieves superior performance over the compared methods, especially when a large amount of filters is pruned. By pruning 60% of filters, our method only drops 2.52% on Accuracy.
Peningkatan Kemampuan Bahasa Inggris Siswa Kelas III SMA Islam Terpadu Al-Kamal NW dengan Metode Audio-Visual

This service activity aims at training the English ability of the third grade students of SMA Islam Terpadu Al-Kamal NW in Narmada. This service is to solve the lack of human sources in English teachers in this school in which the headmaster plans to encourage students’ ability in English speaking. It is conducted for three months started from March to May in 2021. To implement the service, the team makes some stages consisting of planning, coordination, preparation, training, and evaluation. In training, the audio-visual method is used to increase the students’ ability in mastering English. The results shows that the average score of all students are 74 for pronunciation, 73 for fluency and 72.94 for grammar. The average score indicate that their ability in English mastery is quite good and need to be developed for further. Thus, team expects that this service run continuously and get supports from related stakeholders.
The Optimal Inference Rules Selection for Unstructured Data Multi-Classification

The Fuzzy Inference System (FIS) is frequently utilized in a variety of Text Mining applications. In the text processing domains, where the amount of the processed data is vast, inserting manual rules for FIS remains a real issue, especially in the text processing domains, where the size of the processed databases is enormous. Therefore, an automated and optimal inference rules (IR) selection strengthens the FIS process. In this work, we propose to apply the FP-Growth as an association model algorithm and an automatic way to identify IR for fuzzy text vectorization. Once the fuzzy vectors are generated, we call the selection variables algorithms, e.g., Info Gain and Relief, to reduce the given descriptor dimensionality. To test the new descriptor performance, we propose multi-classes text classifification systems using several machine learning algorithms. Applying benchmarked databases, the new technique to produce Fuzzy descriptors achieves a signifificant gain in time, precision rules, and weighting quality. Moreover, comparing the classifification systems, the accuracy is improved by 10% comparing with other approaches.
Language, Attitude and Behaviour of Youths Towards Voting in Nigeria

The study emphasized the contribution of language to the attitude and behaviour of youths towards voting.Language has power on attitudes and behaviour of youths as the politicians address and give directives to their supporters and followers through language which in turn influences the attitudes and behaviour of supporters and followers towards the political decision making.Youths are persuaded and used as tools to foment trouble in all part of the country so as to distabilise the peace of the society at large.They are even used by politicians as kingpins or thugs in different electoral offences based on their utterances before, during and after the political process.The negative attitude of these youths manifests in behaviours like thuggery, assassination, protest, indolence, intolerance, assault, defacing of posters, chanting of slogans, threat and apathy.These acts are considered to jeopardise national development as well as the credibility of the electioneering processes.Language may possibly, reverse electoral decision in favour of a particular individual, group or political party.Voter education could be an effort to create among citizens a new set of culturally defining "habits of the heard" which will enhance citizenship, foster a sense of connectedness to a community stretching beyond the schools and ultimately support the practices, basic value and institutions necessary for a democratic process.The paper looked into the concept of voting, voter education, youths' attitude and behaviour towards voting in Nigeria, language and the electoral process, language and politics, language position in Nigeria, effects of language, and strategies for improving youths' attitude and behaviour towards voting in Nigeria.
Automatic irrigation system based on solar energy

The use of proper method of irrigation is important due to the lack of rains and scarcity of land reservoir water. The continuous extraction of water from earth is reducing the water level due to which lot of land is coming slowly in the zones of unirrigated land. The unplanned use of water results a significant waste of water. To solve this riddle the present study deals the use of automatic plant irrigation system. This system drives power from solar energy through a photovoltaic route. Hence, dependency on erratic commercial power is not required. The solar energy is used to operate the irrigation system. The circuit comprises of sensor parts built using op-amp IC. Op-Amp´s are configured here as a comparator. Two stiff copper wires are inserted in the soil to sense whether the soil is wet or dry. A microcontroller is used to control the whole system by monitoring the sensors and when sensors sense dry condition of soil, then the microcontroller will send command to relay driver IC, the contacts of which are used to switch on the motor and it will switch off the motor when the soil is in wet condition. The microcontroller performs the above task as it receives the signal from the sensors through the output of the comparator, and these signals operate under the control of software which is stored in ROM of the microcontroller. The condition of the motor running i.e., ON/OFF is displayed on a 16X2 LCD which is interfaced to the microcontroller.
Cryptoassets

This chapter examines cryptoassets, with special focus on bitcoin. Fiat currencies all function as (a) a store of value, (b) a medium of exchange and (c) a unit of account. In contrast, bitcoin is not a stable store of value and is neither (b) nor (c). The 'Legal statement on Cryptoassets and Smart Contracts' is summarised, along with the decision in AA v Persons Unknown & Ors, Re Bitcoin [2019] EWHC 3556. The UK Government's Cryptoassets Report divides cryptoassets into (a) exchange tokens, (b) security tokens and (c) utility tokens. Treatment of cryptoassets under the Second Electronic Money Directive ("EMD2"), the Payment Services Regulations 2017, FSMA Regulated Activities Order (RAO) Order 2001 and MiFID II. FCA regulation, EU law and international policies on anti-money laundering and cryptoassets. Rules and guidance on retail customers and cryptoasset derivatives. The chapter finishes with reference to Central Bank Digital Currency (CBDC) plans.
Computer Science Technology-Cyber Security Option

Prof. Sheldon has 35+ years of experience from academia, industry and government in various roles working a diverse set of computer science problems within the
Practical Approaches: User Stories Domain Prediction and Keyword Extraction for Insights

This project aims to make a recommendation system for user stories domain prediction and extracting useful keywords and key phrases. It gives an insight to business analysts and helps them write their user stories in a particular well-defined manner. So that next time whenever a client gives a requirement to some new business analyst and at the time of writing user stories, they will know for what domain they are writing and what relevant information he/she needs to ask from the client. Moreover, we are extracting useful keywords and key phrases from user stories which can also be used as tagging and giving insights to them for their business purpose in future. A recommender system or recommendation system is a subdivision of information retrieval and information retrieval system used to predict the categorization or choices of a user on a particular item in general. The recommendation system is widely and primarily used in commercial areas. Later on, many of them are used in wide areas like Facebook, Netflix, Amazon Prime, YouTube, etc. As we have mentioned above, the aim of this project is to give an insight to business analysts to make their user stories fast and in an appropriate manner. So that whenever a client gives some requirements to a new business analyst, then he/she would have the ability to recognize that this user domain story would fall under which category and which domain they are talking about. We are going to make a recommendation system using different deep learning algorithms to analyze and evaluate the performance of different algorithms on it. After that, we are extracting important keywords and key phrases from it by implementing a natural language processing technique named “Named Entity Recognition” which further has multiple uses like tagging, maintaining stocks, helps in making insights of business, etc. We are trying to build an API for it.
Design and Implementation of Animal Medical Interaction APP Based on Android

Based on the development status of medical apps in the market and the actual needs of animal medical treatment, an Android-based animal medical interaction system platform is proposed. The design idea, development model, main functional modules and the design and implementation of key technologies of the system are described. The system implements the establishment of Tomcat server and the configuration of MySQL database, and uses Http Client to realize the information exchange between medical client and server. The system provides an interactive platform for farmers and experts, and the collected information will provide necessary data for intelligent interaction in the next step.
Design of Robust Fuzzy Neural Network with α-Divergence

Fuzzy neural network has been considered as an effective model to apply in many applications. However, due to the training mode based on minimizing the mean squared error, the typical fuzzy neural network suffers from poor robustness for disturbances. To overcome this problem, a robust fuzzy neural network with &#x03B1;-divergence is designed and analyzed in this paper. First, a cost function based on &#x03B1;-divergence is developed to describe the discrepancy between the real output and fuzzy neural network output. Then, a training mode, which minimizes the above function, can reduce the sensibility of disturbances to improve the robustness of fuzzy neural network. Second, an adaptive learning algorithm is employed to adjust the parameter of fuzzy neural network. Then, the proposed fuzzy neural network is able to obtain fast convergence in the learning process. Finally, some benchmarks are used to test the merits of fuzzy neural network. The simulation results illustrate that the proposed fuzzy neural network can achieve good robustness.
Anomaly Detection for Imbalanced Data via Deep Neural Network with Concentrated Loss

Deep neural networks have recently been used to address surface anomaly detection in industrial quality control and have achieved much success. However, addressing the data imbalance problem, especially the Easy/Hard Examples (EHE) imbalance problem, remain a challenging task in anomaly detection. To alleviate this problem, we propose a two-stage convolutional neural network with a novel loss function, i.e., concentrated loss function. Specifically, the concentrated loss function enables the model to pay more attention to hard examples and improve the quality of segmentation for imbalanced data. To verify the effect of our method, we implement our method on the surface anomaly detection dataset, i.e., the KolektorSDD2 dataset. The experimental results show the superiority of our method over the other state-of-the-art approaches.
Analysis of Machine Code Using Natural Language Processing

In this paper we have created a Machine Learning Model for Machine Languages, this field of processing machine language and converting/ making meaningful iterations out of it comes under Natural Language Processing. Existing approaches on NLP were focused on human-to-human interactions i.e., English to French, German, Hindi. In this research we try to analyze lexical in machine code, break it down to smaller meaningful bits and make some inferences like the type of languages, number of loops, functionality, and time complexity of code, rewriting of code, converting code from one language to another. We have mainly focused on the recognition of the type of language by testing a small snippet of code. We have also focused on a bigger real-world problem which is conversion of code from one language to another with the same basic meaning and logic, just changing the syntax. We have deployed the machine learning model for public use, and open-sourced Flask API is deployed on the cloud for usage that accepts 3 types of machine languages and returns the type of syntax. We have created a simple Angular App for the demonstration and testing of the model which is hosted and is live online. link (https://nlpapp-8e2fd.web.app/)
Textomatify – A Web Application to Automate Multiple Task like Text Recognition from Image, Speech Recognition, Machine Translation

The aim of our project to automate the application to overcome from the language barrier among countries and also states within the country, the above-mentioned application will perform the various features in the application. Language technology has become pervasive in everyday life. Neural networks are a key component in this technology thanks to their ability to model large amounts of data. Text recognition in images is a research area which attempts to develop a computer system with the ability to automatically read the text from images. Speech is the first important primary need, and the most convenient means of communication between people. The communication among human computer interaction is called human computer interface. Machine translation is a domain of computational linguistics, which explores the use of software to translate text from one language to another. Machine translation simply performs substitution of words in one language for words in other, but that may not assure good translation. In the Project we combine all three domains in one system. Here we integrate Text extract from image, Speech recognition and Text translation from one language to another in one web application.