Reviewerly/exHarmony huggingface dataset:
Exploring the path of constructing career planning system for college students of construction under 1+X certificate system

In the National Vocational Education Reform Program, the pilot project of "academic certificate + several vocational skills certificates" (1+X certificate for short) system is proposed, in which several vocational skills certificates reflect the comprehensive quality required for individual career development.Therefore, based on the demand of the construction industry for highly skilled talents with skill level certificates, the current situation of career planning of senior construction students, the analysis of the main problems of career and career planning courses, the construction of a personalized and targeted career planning system for senior construction students combined with "1+X" certificates The purpose of this paper is to analyze the current situation of career planning and career planning courses, analyze the main problems of career planning and career planning courses, and build a personalized and targeted career planning system combining
VOICE ASSISTANT DRUG RECOMMENDATION

Since the coronavirus emerged, the shortage of appropriate clinical resources—such as specialists, care personnel, the right equipment and medications, etc. has reached an all-time high. The medical profession as a whole is in difficulty, which leads to many people passing away. Due to inconvenience, people began taking medications frequently without getting proper consultation, which made their health situation worse than usual. Recently, machine learning has proven useful in many areas, and creative work for automation is on the rise. It aims to provide a drug recommendation system that will significantly reduce the workload of doctors. We are developing a method for prescribing medications that analyzes patient feedback to forecast the emotional response to various vectorization techniques, including Bow, TF-IDF, Word2Vec, and Manual Feature Analysis, which may facilitate suggest the highest drug for a given sickness by totally different classification algorithms. The anticipated sentiments were evaluated by preciseness, recall, flscore, accuracy, and AUC score. The results show that classifier LinearSVC victimization TF-IDE vectorization outperforms all different models with ninety-three accuracies.
Forensic investigation of Cisco WebEx desktop client, web, and Android smartphone applications

Digital forensic analysis of videoconferencing applications has received considerable attention recently, owing to the wider adoption and diffusion of such applications following the recent COVID-19 pandemic. In this contribution, we present a detailed forensic analysis of Cisco WebEx which is among the top three videoconferencing applications available today. More precisely, we present the results of the forensic investigation of Cisco WebEx desktop client, web, and Android smartphone applications. We focus on three digital forensic areas, namely memory, disk space, and network forensics. From the extracted artifacts, it is evident that valuable user data can be retrieved from different data localities. These include user credentials, emails, user IDs, profile photos, chat messages, shared media, meeting information including meeting passwords, contacts, Advanced Encryption Standard (AES) keys, keyword searches, timestamps, and call logs. We develop a memory parsing tool for Cisco WebEx based on the extracted artifacts. Additionally, we identify anti-forensic artifacts such as deleted chat messages. Although network communications are encrypted, we successfully retrieve useful artifacts such as IPs of server domains and host devices along with message/event timestamps.
VIBO: A SMART INTERACTIVE COLLEGE CHATBOT

A Chatbot is a smart agent that interacts with human beings using a digital device. Chatbots play a vital role in the automation of responding to user queries. Chatbots were initially designed with an idea to fool humans. The design today has become one of the most important components of many businesses. The literature presents a smart interactive text-based chatbot VIBO designed to respond to user queries related to a University.
RESTAURANT RECOMMENDATION SYSTEM

A recommendation system is a collection of algorithms that learn from data and, after processing, can make recommendations to clients. Because of their application in a variety of fields, recommendation systems have recently gained a lot of attraction. Recommender systems are commonly used to forecast user preferences for objects, and they are popular for assisting users in finding restaurants and other products. The Restaurant Recommendation System's main goal is to recommend the best restaurant in the area, as well as food, based on the user's tastes. This programme considers the users' culinary tastes and ratings while making food recommendations.
AN ADVERSARIAL ROBUSTNESS ANALYSIS FRAMEWORK FOR CLASSIFICATION MODEL

In the ML/DL algorithms developed by researchers/developers, the only constraint faced by them is the robustness of the model. The model may be experimented with certain data sets but for the model to get tested with all the permutation and combination of data sets under different conditions is a challenge. For the model to give efficient and accurate results under all circumstances, the users are demanding a more robust model that has been analyzed thoroughly. We aim to create an algorithm that is unique, efficient and solves a real-world problem and build an Adversarial Robustness analysis framework that works on reinforcement learning, applies utility metrics of attacks and defense to give a comprehensive robustness report
Anomaly detection-based condition monitoring

The impact of an anomaly is domain-dependent. In a dataset of network activities, an anomaly can imply an intrusion attack. Other objectives of anomaly detection are industrial damage detection, data leak prevention, identifying security vulnerabilities or military surveillance. Anomalies are observations or a sequence of observations in which the distribution deviates remarkably from the general distribution of the whole dataset. A large majority of the dataset consists of normal (healthy) data points. The anomalies form only a very small part of the dataset. Anomaly detection is the technique used to find these observations and its methods are specific to the type of data. While there is a wide spectrum of anomaly detection approaches available today, it becomes increasingly difficult to keep track of all the techniques. In fact, it is not clear which of the three categories of detection methods, ie statistical approaches, machine learning approaches or deep learning approaches, is more appropriate to detect anomalies in time-series data, which are mainly used in industry. A typical industrial device has multi-dimensional characteristics. It is possible to measure voltage, current, active power, vibrations, rotational speed, temperature, pressure difference, etc, on such a device. Early detection of the anomalous behaviour of industrial devices can help reduce or prevent serious damage, which could lead to significant financial loss. This paper presents a summary of the methods used to detect anomalies in condition monitoring applications.
Ensuring High Value National e-Health Solutions Using the Business Value of IT

This paper provides insights from a pilot study which is part of a larger longitudinal research project focused on assessing the value of different national digital health solutions. In this study, the focus is on Australia’s My Health Record and the German e-Health Card. The adopted methodology is a multicase qualitative approach which enables deeper insights to be uncovered. Data collection is from multiple sources including semi-structured interviews, surveys and the analysis of key documents. An initial model for assessing the value of the digital health solution is presented and findings are analyzed against this model to provide recommendations and understand critical success factors for designing, developing and deploying national digital health solutions.
SECRET IMAGE SHARING AND IDENTIFYING FAKE IMAGE USING BLOCK CHAIN

The detection of fraudulent images and the attribution of image sources has become a hot topic of debate in both the news industry and social media. Anyone may quickly create or edit digital content in the age of digitalization, and they can then simply broadcast it on social media networks. The use of these social networking sites has created new difficulties for practical application, such as the viral transmission of incorrect or misleading material with harmful intentions. On the one hand, they greatly facilitate modern communication. A currently very emerging technology for data sharing and application is block chain. By using data encryption, timestamps, and distributed consensus, it is possible to exchange decentralised information in distributed systems without relying on one another, which increases the effectiveness of data sharing and application. The massive data remote sensing image system can fully exploit this technology, and the multi-system shared node storage system can be controlled effectively and uniformly to increase the system's economic efficiency. This study suggests important research technologies, builds the shared architecture based on block chain technology, and offers a theoretical framework for non-engineering practise.
RESUME ANALYZER

Resume analysis is the process in which a machine analyses a resume based on given requirements of the job description. With the flood of resumes received by companies, it is not effective and also not possible for a person to go through a number of resumes to select a candidate. They have become very popular among the companies in the process of determining candidate selection. The main objective of the project is to be able to match the requirements and skills from a job description to the resume applied. This gives an instantaneous result on whether the resume is accepted or rejected. The end process allows the company itself to be able to select candidates without the requirement of a third party and thus is also cost effective. A big number of resumes could be used in this project to sort the necessary application using various classifiers. Following classifications, the top n candidates will be sorted in accordance with the job description using content-based recommendation and cosine similarity. The project employs k-NN to determine which CVs are most similar to the supplied job description. Through machine learning, the system evaluates a resume for a particular position using NLP.
Academic scientific publishing and its role in enriching the Arabic content on the Internet, and its obstacles

The study aimed to assess the view of faculty members at Al-Ahliyya Amman University towards academic scientific publishing houses in enriching Arabic content on the Internet and to reveal the most important obstacles and problems that hinder Arab academic publishing on the Internet. A questionnaire was built that included (28) items, prepared via Google Drive and distributed electronically via WhatsApp to a sample size of (217) faculty members. The results of the study showed that the average responses of faculty members in the field of academic scientific publishing houses in enriching Arabic content on the Internet came at a high degree, and also their average response to the field of obstacles to Arab academic publication to a high degree. It also showed no statistically significant differences between the mathematical averages of respondents in the academic scientific publishing houses in enriching Arabic content on the Internet due to the changing scientific and college rank. There are statistically significant differences between the mathematical averages of respondents in the field of obstacles to Arab academic publication due to the college variable for scientific colleges.
Item Response Theory

Item response theory (IRT; e.g., Lord, 1980; Hambleton and Swaminathan, 1984; Toyoda, 2012; van der Linden, 2016) is the most popular statistical model today as a background theory of test administration. The Program for International Student Assessment, Trends in International Mathematics and Science Study, and Test of English as a Foreign Language, as well as many global-scale tests, are designed based on IRT. In addition, this theory is increasingly becoming an essential subject, not only for students of educational measurement, but also for students of educational psychology in general.
Local Dependence Biclustering

This chapter introduces local dependence biclustering (LDB; Shojima, 2021), which incorporates biclustering (Chap. 7, p. 259) and a Bayesian network model (BNM; Chap. 8, p. 349). Thus, it is desirable to first read those chapters. Biclustering is a method of simultaneously clustering students into latent classes and items into latent fields. Both the classes and fields are nominal clusters. Meanwhile, ranklustering (Sect. 7.3, p. 274) is used to perform item clustering when students are clustered into ordinal classes (i.e., latent ranks). In addition, BNM is a method for identifying the interitem-dependency structure.
PENYISIHAN KADAR BESI (Fe) DAN MANGAN (Mn) DALAM AIR SUMUR DENGAN MEDIA PASIR TERLAPIS MANGAN DIOKSIDA

Well water often contains considerable iron (Fe) and manganese (Mn). The iron and manganese content often transforms actually clear water into yellowish-brown. When iron and manganese content exceed quality standards, it can harm human health. To manage iron and manganese pollutants in well water, it would require research to reduce iron and manganese deposits in an easy and inexpensive way for people who still use water for domestic purposes, according to the Indonesia republic's health minister No. 492/ Menkes/IV/2010. Based on preliminary tests, the results of the initial iron component analysis of 1.06 mg/L and mangan 0.98 mg/ L. The variable in the study is a high proportion of the media filters of greensand on every reactor which is 30 cm, 50 cm and 70 centimeters with 5, 10, 15 and 20 minutes of operation. The reactor is four inches of PVC pipe and the reactor is 80 cm tall, 100 centimeters and 120 centimeters tall. The fe and mn levels that have experienced the filtration treatment of every reactor have generally decreased. The deposite-based percentage is 0.02 mg/L with a set of exclusion efficiency of 98.11% and between 0.048 mg/L and the results of 95.92%.
RETRACTED CHAPTER: Person Re-identification Based on Transform Algorithm

Person Re-identification (Person ReID) is a new technology emerging in the field of intelligent video analysis in recent years, aiming to solve the problem of person re-identification and retrieval under cross-lenses and scenes. It is also a hot research topic of computer vision in recent years. However, the study of Person ReID faces many challenges, such as low image resolution, visual angle change, attitude change, light change and occlusion. Research on target ReID has focused on Person ReID and vehicle ReID, and most state-of-the-art methods are based on Convolutional neural networks (CNN) structures. CNN, although successful, process only one local region at a time and can cause the detail loss of the data due to the existence of convolutional and down sampling operations. For the above problems, this paper designs a new goal-oriented Person ReID architecture, called Trans ReID. In this approach, the images are first converted into several patches, and strong baselines are built, which is advantageous against CNN-based method studies on several Person ReID datasets. To further enhance the robust features under Transformer for learning, two new modules are proposed. We uses the movement and patch scrambling operations to rearrange the embedding of the patch for better identification performance and wider coverage, and we integrate and analyze some non-visual cues to reduce feature bias during camera view changes. The experimental results show our method achieves excellent performance on Person ReID datasets.
Research on Employment Promotion System Based on Smart Education

With the advent of the 5G era, the wisdom of education has long become an essential part of building an intelligent city. Intelligent education has a variety of forms of education realization to provide for social development. At the same time, it has also put forward the requirements for the shortage of high-end talents and reflected the ultimate goal of education in the grim employment situation. With the ultimate realization of human social resources development and education as the goal, through policy support, the establishment of entrepreneurship and employment promotion system, improve the operation mode of entrepreneurship and employment. Through policy support, we will establish a strategy to promote entrepreneurship and engagement and enhance the operation mode of entrepreneurship and employment. To build an intelligent campus management model as the starting point, comprehensively improve the scope of innovative education, shorten the distance between students and job seekers, the job market. Develop new channels to train professional talents and enhance teaching quality; Decentralized integration of schools, employers, education and training institutions, students and job seekers, and other information resources, unified macro allocation by promoting employment, to create a new model of talent training.
The healthy life-style in the system of value orientations of student youth

The article is devoted to the issue of studying attitude of students to their own health, to healthy lifestyle, to regional health care system. The results of sociological survey carried out in February-April 2021 are presented. The factors and conditions contributing to preservation and strengthening of youth health were identified. The conclusions and recommendations concerning formation of value orientations for healthy lifestyle of students were formulated.
Facial Recognition Software Use on Surgically Altered Faces: A Systematic Review

Objective: Facial recognition software (FRS) is becoming pervasive in society for commercial use, security systems, and entertainment. Alteration of the facial appearance with surgery poses a challenge to these algorithms, but several methods are being studied to overcome this issue. This study systematically reviews methods used in facial recognition of surgically altered faces. Materials and Methods: A systematic review was performed by searching PubMed and Institute of Electrical and Electronics Engineers (IEEE) databases to identify studies addressing FRS and surgery. On initial review, 178 manuscripts were identified relating to FRS and surgery and allowed division into multiple subgroups. The decision was made to focus on the recognition of surgically altered faces. Results: Eligible studies included those reports in English on FRS of surgically altered faces, and 39 papers were included. Surgical procedures range from affecting skin surface, such as skin peeling, to altering facial features, such as rhinoplasty, mentoplasty, malar augmentation, brow lift, facelift, orthognathic surgery, facial reanimation, and facial feminization. Methods were classified into appearance-based, feature-based, and texture-based. Descriptive versus experimental protocols were characterized by different reporting outcomes and controls. Accuracy ranged from 19.1% to 85.35% using various analysis methods. Conclusions: Knowledge of available limitations and advantages can aid in counseling patients regarding personal technology use, security, and quell fears about surgery to evade authorities. Surgical knowledge can be utilized to improve FRS algorithms for postsurgical recognition.
The Role of Personality Type and Self-Determination of Students Majoring in Non-Philological Specialities While Building English for Professional Purposes Competence

The purpose of the present research was to prove the assumption that considering students’ personality type and self-determination, including motivation about their profession choice, has a direct positive effect on building their English for professional purposes competence. Considering obtained theoretical and empirical data, we can state that personality type and self-determination are interrelated with developing English for professional purposes competence of students majoring in non-philological specialities, namely in Sciences. To collect the data, we employed theoretical analysis and conducted two questionnaire polls to define the significant personality types of students and their motivation for their occupational choice. English for professional purposes competence is an integral characteristic of occupational and personal qualities of future specialists majoring in non-philological specialities. Acquisition of linguistic, specialised and cultural knowledge and building abilities and skills in speaking, writing, listening, and reading involve many psychophysiological mechanisms. Some conscious acts alter into a series of automatic, unconscious operations in language learning. Thus, building English for professional purposes competence involves focusing on activities consistent in the gradual accomplishment of different tasks, concentrating on structured performance according to the instructions, performing a series of automatic, unconscious operations and building associative links. In this way, constructing cognitive maps will bring fruitful results.
Exploring the Origin of the Present-Day Human Self on the Fringes of Linguistic Advancement

The paper explores the possibility of the evolution of the present-day adult human self (PHS) with linguistic advancements. Considering fringe mentality as a genuine issue during the evolution of PHS, the paper favours that there can be various types of mentalities associated with various kinds of minds, among which present-day adult human mind (PHM) having PHS is only one kind. It explores the logical possibility of a different mentality in our remote ancestors to broaden the contours of the concept of self and mind. Part one is expository in nature and discusses the distinctive features of the present-day adult human self (PHS). Following Sleutels’s (2013) approach, the second part analyses the assumption that the PHS is an innately given inner experience by positing the ‘fringe mind’ problem. Julian Jaynes’s (1976) claim is approached with the help of the linguistic mechanism involved in the emergence of propositional attitudes (PA) to argue that PHS can also be a matter of linguistic advancement rather than mere biological or psychological adaptive advancement. An attempt to address Sleutels (2006) less developed notion of B-mind and A-mind is also made.
Introduction to the Artificial Literature

The study aims to introduce a new literary term which is “The artificial literature”. The study suggests a number of questions which investigate the most prominent transformation witnessed by the digital literature. It also explores the limits reached by the intermingling between technology and its applications in literature. The study scrutinizes the important issues relating to this case and the main challenges and obstacles. It also answers the question of the purpose of such a study enlisting the shortcomings related to it as the antagonists of the concept see the intermingling between literature and technology. The study concludes that “the artificial literature” applications started to pay attention to literature and seek to make electronic applications and intelligent robots which are able to simulate the human literary production in a way that necessitate a literary and technological research of the outcomes of such work.