Reviewerly/exHarmony huggingface dataset:
An Ensemble Machine Learning Approach for Detecting Fraudulent Banking Transactions

With the increased number of banking institutions and their clients, the number of fraudulent transaction has increased dramatically nowadays. Banks and financial institutions are passing a difficult time in safeguarding their online transaction channels. One of the main reasons is the availability of online access channels to the customers and those are increasingly exploited by the attacker. In this paper, a fraud detection approach using an ensemble of machine learning classifiers has been proposed. In the model, an ensemble of K-Nearest Neighbor, Random Forest, and XGBoost algorithms are combined to detect frauds in online transactions. To archive better accuracy in detection, an exploratory data analysis is conducted. Feature engineering is used to add useful features to the data to improve detection accuracy and oversampling is done to solve the imbalances in data. The proposed classifier shows better results compared to some well-known standalone and ensemble classifiers in terms of accuracy, f1 score and roc-auc score.
A Case Study on Defending against Cyber Crimes

Cyber terrorism is the premeditated, politically motivated assault on information, computer systems, computer programmes, and data that results in violence against property, the government, and the general populace.In the age of globalisation, the use of steganography as a form of internet terrorist communication -Red Fort case, E-mail threats in Taj Mahal case, and Supreme Court E-mail Threat case.The use of the internet by terrorists to organise and execute the September 11 World Trade Centre attack reflects the current state of affairs and provides the answer to the question, "Is the internet the new boon or bane of science?"In India, cyber laws prohibit any crime committed with the aid of technology, where a computer is a tool for cybercrime.Cybercrime rules prevent citizens from sharing sensitive information with strangers online.Since the establishment of cyber laws in India, the IT Act 2000 was enacted in 2000 and updated in 2008 to cover all sorts of cyber offences in India.Without a doubt, India's Cyber security or Cyber laws give protection against cybercrime.However, prevention is always preferable to treatment.Cyber law and cyber crimes have also become more complicated in today's technologically advanced society.Internet and technology were developed for research purposes and to make human life easier, but as the number of people using the internet in India expanded, the necessity for Cyber Laws became apparent.Due to the anonymous nature of the internet, it is simple to commit cybercrimes.Consequently, numerous individuals could abuse this component so extensively.Therefore, India has a need for cyber law.
Analyzing the Relationships Between Organizational Justice and Job Performance

The current study aimed to investigate the relationship between organizational justice and job performance at selected private businesses in Kurdistan region of Iraq. Using a quantitative search strategy, this study analyzed and measured organizational justice practice and its effect on achieving job performance using a descriptive analytic approach. The effect of organizational justice on workers' productivity was measured with a survey done in the field. Researchers used a questionnaire as their main data collection tool and put it through rigorous testing to make sure it was valid and reliable. Twenty percent of the community study's 120 staff members working in Iraq's Kurdistan region used a simple, random sample. All participants were asked to fill out a questionnaire, and 120 were returned. However, 8 were disqualified from further statistical analysis due to technical flaws, leaving a total of 112 usable questionnaires. The findings revealed that there is a positive and significant relationship between organizational justice and job performance. Moreover, it was found that procedural justice has the strongest relationship with job performance.
Hall Effect Sensor Design Optimization With Multi-Physics Informed Gaussian Process Modeling

Abstract Magnetic field sensor devices have been widely used to track changes in magnetic flux concentration, and the Hall sensors are promising in many engineering applications. Design optimization of the Hall effect sensor is required to ensure the quality and capability of the device when in service. Even though there has been empirical models established from experiments to guide the design of the Hall effect sensor, the underlying relationship in Hall effect sensor design parameters and corresponding performances has not been looked into thoroughly. This paper presents a physics-informed machine learning technique to optimize the geometry design of Hall magnetic sensors for a low offset and high sensitivity characteristic. Multi-physics based finite element models were first developed to simulate and predict the Hall voltage, offset voltage and sensor sensitivity of different Hall effect sensors with various geometries. In addition, to improve the design efficiency, Gaussian Process (GP) based surrogate models were constructed from multiphysics-based simulation results to effectively investigate the Hall sensor performances with an adaptive sampling strategy. Three types of geometries of Hall sensor were studied and optimized with the proposed physics-informed GP model, the obtained results were consistent with the empirical experimental result.
Web-Based Bag and Luggage Sales Information System

This study aims to design and create a web-based information system that functions to help manage the sales process at CV. Purnama. This research was conducted in March 2020 at the Informatics Laboratory, University of Muhammadiyah Sidoarjo and CV. Purnama. This research was conducted using the waterfall method, data were collected through interviews, literature study, and problem analysis. The results of this study are the design and manufacture of Web-Based Bag and Suitcase Sales Information Systems at CV. Purnama which aims to increase promotion and facilitate the operational management in CV. Purnama. This information system has two actors, namely admin as manager of information systems and customers as consumers. Based on this research, the conclusion is that with the sales system (e-commerce), CV. Purnama can facilitate the conventional transaction process to become more modern with the availability of online transactions.
Hybrid Strategy of Parallel Tea Picking Robot Trajectory Planning Based on Optimal Time and Jerk

Abstract：A speed curve model method based on the optimal time and jerk is proposed.The background of the current leaf bud picking of famous tea is analyzed, and a famous tea picking robot combining Delta parallel mechanism and visual recognition technology is proposed.The picking trajectory cycle of famous teas was established and the improved speed curve model is applied, and the Lamé curve was used to optimize the picking trajectory to improve the sudden change of acceleration caused by the switching of vertical and horizontal motion in the working process of the Delta mechanism.The adaptive improvement of the harmonic jerk model is completed, and the particle swarm optimization algorithm suitable for the particularity of tea picking is applied to solve the optimal time interval parameters in the model, thereby optimizing the working time in the tea picking trajectory cycle.The model complies with kinematic constraints, comprehensively optimizes the time and jerk of the picking trajectory, thus can maintain a good trajectory smoothness.Experimental results show that the method can shorten the picking time, improve the efficiency, perform better in jerk suppression, and provide an effective way for path planning of famous tea picking robots.
Cascaded Solution for Multi-domain Conditional Question Answering with Multiple-Span Answers

This paper introduces our technical solution for CCKS-2022’s task of “A Dataset of Conditional Question Answering with Multiple-Span Answers”. The solution consists of Data Analysis and Processing, Condition-Answer Extraction, Post-extraction Processing, Condition-Answer Relation Classification, and Post-classification Processing. The rule-based post-extraction and Post-classification Processing modules consist of seven cascaded modules. Because the training data of the task contains multi-domain questions and answers and is constrained, we have designed a prediction method based on the conditions, coarse-grained answers, and fine-grained answers of the fine-tuned pre-trained language model for multi-domain scenarios. Binary classification is used for relation extraction of conditions, coarse-grained answers, and fine-grained answers, and the constraint extraction method is based on rules. The proposed solution obtains an F1 value of 0.74487 on the test set (ranking 3rd), and its effectiveness in multi-domain scenarios is verified.
SSRINDEX tool: An automated tool to measure level of software security readiness index for remote working during Covid-19 pandemic

Resulting from the COVID-19 pandemic, which requires mankind to practice social distancing, companies and government agencies do not have any options but to send their workers home and practice remote working. With very limited guidance, workers are required to work remotely by utilizing various software facilitated by Internet. The increased dependence on the cyberspace opens up the organization to become highly vulnerable to cyber threats/crimes, which may affect their performance. Hence, organizations need to manage their cyber risk by ensuring that they are capable to manage software security. Motivated from these problems, we have developed a prototype tool, called SSRIDEX tool (Software Security Readiness Index tool) to measure the level of software security readiness index among workers working remotely. This SSRINDEX tool which runs in mobile is usable to measure of the level of software security readiness in organization especially during this COVID-19 pandemic where most workers are working remotely.
What happens to semantics and interaction in a software’s automatic transcriptions?

This article addresses the implications of transcriptions for scientific knowledge by focusing on automatic transcription software called speech-to-text. The article presents the general functioning and uses of speech-to-text software, then focuses on the evaluation of a specific software conducted by the author as a social scientist. This evaluation allows one to open the black box of the software by determining in a qualitative way the sources of its systematic errors. While the software undoubtedly provides a reliable semantic environment, it encounters two types of problems of a distinct nature that are intertwined in the computational literature: those generated by the limitations of the software's computational models, which can be solved by improving the system's models, and those related to ordinary conversation of a praxeological nature, which remain beyond the machine's reach. A detailed comparison with transcriptions manually performed in the manner of conversation analysis shows how the software makes interaction disappear from its transcriptions.
Big data analysis and forensics

This study provides an insight into one of digital forensics' needs by analysing big data. Digital forensics is one of the branches of forensic science specialised in recovering data from digital devices for investigation for purposes of computer crime or other goals. The study shows that the main challenges faced by digital investigators are those relating to the storage, management, and analysis of a large amount of data of various types, including organised and semi-organised. Investigators rely on specific tools to handle big data like Hadoop, Spark Apache, and SAS. Hadoop provides a system for storing massive files on distributed files and analysing their components, while Spark Apache provides quick analysis of distributed data without storing it. SAS visual analysis of big data provides fast support for data discovery and visualisation via a memory drive. An overview of these three big data technologies is reviewed through their components and the processes by which these features are compared and then compared. The study shows how the greatest benefit is achieved by bringing these tools together when using rather than relying on one and not the other.
Brought, Sought, and Taught

Skillification is a powerful concept that can drive better outcomes for students, employers, and institutions of higher education (IHEs). Successful use, however, requires IHEs to adopt a systems thinking mindset more than developing a singular taxonomy or exquisite model. Creating a system of skill-driven applications assumes that universities have rich input language that can be translated to skills without extraordinary investment or effort and can do that translation many times over using different algorithms created by different providers as their application needs warrants. Two tests conducted at Northeastern University offer guidance on how to approach this new design: by affirming the feasibility of using syllabi as input for automated skill extraction and identifying data evaluation activity that drives better decisions about third-party partnerships and skill-driven application use.
UI Animation and Effects

Animations and effects are important elements/aspects of modern mobile apps. Each app has dozens of similar apps available in the stores/gallery; you need to make yours stand out so customers will download yours. Beautiful animated UI helps it stand out.
A Study on Perception Before and After Introduction of Autonomous Police System Using Big Data Analysis Technology

The reason for the introduction of the autonomous police system is closely related to the ideology of local autonomy guaranteed by the Constitution, and the democracy of security administration that reflects the participation of residents and the autonomy of local communities is key. Against this background, the main affairs of the self-governing police in Korea are centered on resident-friendly and public security. It is expected that the self-governing police will need the interest, understanding, cooperation, and participation of local communities and local residents to secure the security of the local community and solve various problems.&#x0D; This study aims to derive implications and provide basic data for the development of autonomous police by using the search volume related to autonomous police before and after the introduction of the autonomous police system, news, and SNS. According to the research results, first, search volume and news volume increased according to specific issues, but after the introduction of the system, search volume decreased while news volume increased. It can be understood that a large number of news are derived according to various issues about the autonomous police system, apart from people's interest. In order to attract local residents' attention and help them understand, efforts should be accompanied by exploring and applying promotional means other than media reports, and measures to secure finances to prepare an environment in which various policy methods can be proposed.&#x0D; Second, it can be inferred that there is still a lack of understanding and many questions about the autonomous police in that keywords related to form, timing, and method of introduction were highlighted through analysis of keywords related to news search before and after the introduction of the autonomous police. In order to enhance the public's understanding of the autonomous police and to establish a stable settlement, the government and related ministries should increase state support of cities and provinces and expand the authority of provincial governors to provide a foundation for operating in accordance with the purpose of the system. In addition, it is necessary to make efforts to dispel concerns by preparing measures to compensate for the shortcomings of the autonomous police system.&#x0D; Third, before and after the introduction, people's perception of the autonomous police was found to have changed positively on SNS, and it can be interpreted as expressing expectations for the autonomous police not long after the system was implemented. Therefore, in order for local police to dispel people's concerns and meet the expectations expressed, it will be necessary to supplement legal and institutional devices to secure the expertise and internalization of local police, including related ministries and institutions.
The Design of College Students' Innovation and Entrepreneurship Information Experience System based on Multidimensional Dynamic Algorithms

With the rapid development of information technology, the country needs a large number of innovative and entrepreneurial talents, and college students deserve to become the main force of innovation and entrepreneurship. Colleges and universities provide students with Double creation training, which helps to improve students' comprehensive quality and competitiveness. In today's Internet era, the power of the Internet must be used to make Double creation education more effective. At present, the student Double creation platform of colleges and universities lacks an information experience the system, analyzes the data of the system designs a college student Double creation information experience system, and conducts experiments on the system Performance Testing. The experimental results show that the system data is good and the operation is efficient; it shows that the system data calculated by the multi-dimensional dynamic algorithm is suitable for college students' Double creation experience.
Young Adults’ Punctuation Error in Writing English Daily Story on Instagram

This research focuses on analyzing the young adult instagram users’ punctuation error on their instagram story. This present research uses qualitative design and the type is content analysis. The subjects of this research are taken from instagram users who belong to young adult instagram user and the researcher using snowball. This research employs documentation as the instrument of the study to collect the data. The findings of this research provided into 10 points; the researcher finds that there are 10 kinds of punctuation errors which are made by the young adult instagram users, they are (1) exclamation mark, (2) capital (capitalization), (3) period (full stop), (4) comma, (5) apostrophe, (6) question mark, (7) inverted comma (quotation mark), (8) ellipse, (9) hyphen, and (10) colon. The percentages of each error are exclamation mark 40.1%, capital (capitalization) 25.2%, period (full stop) 20.5%, comma 6.7%, apostrophe 2.3%, question mark 2.3%, inverted comma (quotation mark) 0.8%, hyphen 0.5%, colon 0.5%, and colon 0.2%. The findings of this study indicate that the most common punctuation error made by the young adult Instagram users was exclamation marks with a percentage of 40.1%.
Exploring Knowledge Transferring and Conversion in the Fuzzy Front End of Collaborative Innovation Development

The research goal is to investigate the specificity of activities of startup entrepreneurial teams that co-develop new product concepts with stakeholders. We identified a specific pattern of knowledge conversions/transferring through observations of collaborative activities, followed by questionnaires and interviews with members of the entrepreneurial team. The research pool comprised eleven entrepreneurial teams of 4-6 members each. The predominant objects of conversions and transferring among the entrepreneurial team members and external stakeholders were identified as tacit knowledge types: know-how (what, why), insights, intuition, attitudes, beliefs (as a type of attitudes), and entrepreneurial vision. Types of conver­sions and transferring were the same in all the teams but the application of managerial tools (facilitating means) that allowed transferring/conversions varied. The identified specificity of knowledge processing could be used to optimize those tools/methods by elaborating specific measurable criteria of how efficiently tacit knowledge was transferred and converted.
Domain Adaption Based on MSE Criterion and Progressive RKHS Subspace Learning (MSEpRKHS_DA)

Reproducing Kernel Hilbert Space (RKHS) subspace learning is very popular among the domain adaption, which learns a latent RKHS subspace for the source domain and target domain, so that their distribution gap becomes smaller than in the original data space. There is a famous probability theory: two second-order moment random variables are equal if and only if their mean squared error (MSE) is zero. In this paper, firstly, we use second-order moment random variables to model the source domain and target domain. Then, we prove that a second-order moment random variable is still second-order moment after it is transformed into the RKHS subspace. Finally, we propose the MSE criterion to measure the distribution difference between source domain and target domain. To our best knowledge, we are the first to apply the MSE to RKHS subspace learning. And the experiments show the superiority of MSE criterion, which performs better than the common Maximum Mean Difference (MMD) and the Covariance Matrix (CovM) criteria. Furthermore, considering the robustness of the RKHS subspace learning framework to the data dimension, we propose the domain adaption framework of the progressive RKHS subspace learning (pRKHS-DA), which continuously updates the learned RKHS subspace. Each update takes the previous learned subspace as the starting point. The idea of pRKHS-DA is proposed for the first time in this paper. Finally, this paper proposes MSEpRKHS_DA model based on MSE criterion and pRKHS-DA framework. And experiments show that our model achieves higher classification accuracy than some state-of-the-art methods.
Mobile Robot Path Planning Method Based on an Improved A* Algorithm

The traditional A * algorithm has a series of problems such as a blind search direction, several turning points of the planned path, and a discontinuous curvature. To solve these problems, the traditional A * algorithm was improved in this study. First, the search direction was determined according to the connection angle between the expansion node and the target point as well as the difference of the coordinate system to prevent the algorithm from searching unnecessary expansion nodes. In sequence, considering the actual movement of the mobile robot, a diagonal path was processed from the perspective of a safe distance from obstacles. Finally, a combination of the Bezier curve and improved A * algorithm was used to optimize the planned path and eliminate paths with several large corners. A simulation experiment was conducted using the MATLAB 2020b experimental platform. The results revealed that the improved A * algorithm integrating the Bezier curve had fewer expansion nodes and a shorter planned path length, and the generated path did not touch obstacles and was smoother.
STUDENTS' TRANSLATION ERRORS IN TRANSLATING NEWS TEXTS BASED ON VILAR et al. (2006)

The research is aimed at describing the types of translation errors made by the English Literature Students in translating news text from English into Indonesian. The present study employed a descriptive qualitative study. The research subject was the sixth-semester students of the English Literature Study Program, Faculty of Economics, Law, and Humanities, Universitas Ngudi Waluyo who have taken a Translation Course. The news text was taken from the Jakarta Post and it consists of 22 paragraphs. The news texts were then translated into Bahasa Indonesia and they become the target texts as the sources of data to investigate the students’ translation errors. They were then classified into five types of errors according to Vilar et al. (2006, 698). The research results revealed that there were 108 translation errors in the form of incorrect words (60,2%), punctuation (18.5%), missing words (12,03%), and word order (0,9%). It can be concluded that the most frequent errors made by the students are incorrect words, punctuation, and missing words.