Reviewerly/exHarmony huggingface dataset:
The Suitability Level of CSI Factors and Problematic Conditions in Crime Scene Investigation of Inquiry Officers Under Thailand's Provincial Police Region 7

This research aims to study the suitability level of CSI factors from problematic conditions and limitations of CSI factors based on the opinions of inquiry officers affiliated with the Thailand’s Provincial Police Region 7 who were on duty between October 2018 and March 2020, quantitative data were collected by questionnaires from a sample of 300 inquiry officers selected by purposive sampling from the population of 664 inquiry officers under Provincial Police Region 7, and analyzed using descriptive statistics, including frequency, percentage, mean and standard deviation and collecting qualitative data though in-depth interviews with 18 inquiry officers as deputy superintendent, inspector and deputy inspectors. Studies have shown that CSI factors had a moderate average level of suitability. The personnel factor with the highest level of suitability was forensic police and the lowest level of suitability was the inquiry officer. For the CSI supporting factors and legal and regulatory factors, there are average levels of suitability at a fair and moderate level respectively. Major problems and limitations of crime scene investigations in current condition include a shortage of personnel, especially inquiry officers, shortage of supplied and equipment to investigate crime scene and inconsistency of relevant laws and regulations. There are guidelines for the development of crime scene investigations, as follows; increase the number of personnel who have sufficient competence to perform the crime scene investigation tasks. Tackling sustainable personnel shortages solve the problem of the shortage of personnel sustainably, develop investigative work at the police station level to the Royal Thai Police. And review relevant laws and regulations to be consistent and enforced effectively and appropriately.
Forecasting Banknote Circulation During the Covid-19 Pandemic Using Structural Time Series Models

As part of the Eurosystem's annual banknote production planning, the national central banks draw up forecasts estimating the volumes of national-issued banknotes in circulation for the three years ahead. As at the end of 2021, more than 80 per cent of euro banknotes in circulation (cumulated net issuance) had been issued by the national central banks of France, Germany, Italy and Spain ('4 NCBs'). To date, the 4 NCBs have been using ARIMAX models to forecast the banknotes issued nationally in circulation by denomination ('benchmark models'). This paper presents the structural time series models developed by the 4 NCBs as an additional forecasting tool. The forecast accuracy measures used in this study show that the structural time series models outperform the benchmark models currently in use at each of the 4 NCBs for most of the denominations. However, it should be borne in mind that the statistical informative value of this comparison is limited by the fact the projection period is only twelve months.
Trustless Trust and Antitrust: A Synthesis

Authors have written of antitrust’s demise in the face of blockchain technology which, supposedly, achieves the goals of the law, through private ordering, without a need for the law. Most importantly of all, public permissionless blockchains offer the vision of disintermediation – the end of the platform economy troubling many scholars today. At the same time, blockchain technology presents challenges to the doctrine and enforcement of antitrust. Finally, blockchain community governance allows for private ordering of antitrust, i.e., enforcement of rules attempting to protect competition, which are at the same time illegal, not coming from the courts or agencies they constitute competition wrongs themselves.I argue that all three claims are overstated and propose a synthesis of the law and code. The legal doctrine can be modified to tackle the novel technological landscape quite easily, with adoption of novel legal fictions. This is necessary since blockchains – both public and even more so private ones – while ingenious, do not remove a need for the law to protect the market from anti-competitive conduct. Indeed, even public ledgers have power structures allowing for abuse, while private blockchains may, in fact, allow for its proliferation. The law, thus, needs to find a regulatory access point to the ledgers. This is not an easy task: however, cooperation of blockchains with the law, and encoding of antitrust rules on the ledgers themselves, offers a possibility of a reconciliation between the law and the code. At the same time, this lends legitimacy to pro-competitive actions of those cyberspace communities and ensures a preservation of the rule of law. This is the blockchain antitrust synthesis.
2022 CCCC Chair’s Letter

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Phishing Attack Detection Using Machine Learning

Phishing, an online scam in which people are tricked into sensitive personal and account information, is a serious threat both to consumers and institutions doing business on the web. Statistics show a continued increase in phishing attacks. Not only that, but these scams are becoming more and more elaborate and therefore more difficult to detect. Thus, there is a need to employ intelligent algorithms to solve this problem. One of the most successful methods for detecting these malicious activities is machine learning. This is because most phishing attacks have some common characteristics which can be identified by machine learning methods. In this article, we select important phishing URL, and website content-based features are extracted. We used machine learning algorithms like Gradient Boosting Algorithm to give greater accuracy (97%). We compare the performance of other related techniques with our scheme. The proposed scheme performs better compared to other selected related work. This shows that our approach can be used for real-time applications in detecting phishing URLs.
Positive Socialization of a Younger Schoolchildren in the Process of Mastering the Course "Robotics"

The article discusses the positive impact of robotics classes on the process of positive socialization of primary school students, which, in accordance with the requirements of the updated version of the Federal State Educational Standard for Primary General Education, is one of the goals of teaching children of this age group. Based on the examples of extracurricular activities in the course, it has been demonstrated that the Lego constructor is a suitable tool for introducing younger schoolchildren to robotics, and also acts as a “soft” means of their positive socialization.
A COMPARATIVE ANALYSIS OF ENSEMBLE LEARNING METHODS ON SOCIAL MEDIA ACCOUNT DETECTION

Today, social media platforms usage and benefiting rate from these environments are increasing. This rapid spread of social media has also allowed the emergence of fake accounts. Fake accounts are generally created to implement malicious activities through another user account or to spread incorrect information. To prevent the detriment that this situation may cause to real individuals, an effective fake account detection was carried out by using ensemble learning methods (Bagging, Boosting, Stacking, Voting and Blending) in this study. These methods were combined with various machine learning algorithms to measure their effectiveness in detecting fake accounts. The experimental results suggested that Bagging technique attained an accuracy level of 90.441%, Stacking technique 89.706%, Voting technique 88.971% and the Blending technique attained 88.235% in the test phase. While for the Boosting methods, XGboost technique attained accuracy level of 86.765%, whereas the AdaBoost outperformed it with an accuracy level of 91.912% in the test phase. The extant results demonstrates that ensemble learning methods combined with machine learning algorithms are efficient in detecting fake social media accounts. It is considered that additional studies with larger datasets alongside the usage of different ensemble methods can further improve the accuracy of the detection process.
Multilevel Models

This accessible and practical textbook gives students the perfect guide to the use of regression models in testing and evaluating hypotheses dealing with social relationships. A range of statistical methods suited to a wide variety of dependent variables is explained, which will allow students to read, understand, and interpret complex statistical analyses of social data. Each chapter contains example applications using relevant statistical methods in both Stata and R, giving students direct experience of applying their knowledge. A full suite of online resources - including statistical command files, datasets and results files, homework assignments, class discussion topics, PowerPoint slides, and exam questions - supports the student to work independently with the data, and the instructor to deliver the most effective possible course. This is the ideal textbook for advanced undergraduate and beginning graduate students taking courses in applied social statistics.
Survival Analysis

This accessible and practical textbook gives students the perfect guide to the use of regression models in testing and evaluating hypotheses dealing with social relationships. A range of statistical methods suited to a wide variety of dependent variables is explained, which will allow students to read, understand, and interpret complex statistical analyses of social data. Each chapter contains example applications using relevant statistical methods in both Stata and R, giving students direct experience of applying their knowledge. A full suite of online resources - including statistical command files, datasets and results files, homework assignments, class discussion topics, PowerPoint slides, and exam questions - supports the student to work independently with the data, and the instructor to deliver the most effective possible course. This is the ideal textbook for advanced undergraduate and beginning graduate students taking courses in applied social statistics.
Investigating the Feasibility of Using Virtual Reality Devices to Present Construction Information in Both Mixed Reality and Virtual Reality Environments

Mixed reality (MR) and virtual reality (VR) technologies are frequently recommended to present design models of construction projects to perform various visualization tasks, such as improving hazard identification training and constructability analysis. Significant financial investment is commonly highlighted as a major impediment to using these technologies, particularly MR. The cost of purchasing head-mounted display (HMD) hardware, such as Microsoft HoloLens for MR, Oculus Quest, or HTC Vive for VR, is substantial. However, the Oculus Quest manufacturer states that, due to recent software improvements, users can now deploy and run both MR and VR applications on the same Oculus Quest HMD, which was initially built only for the VR environment. This development appears to provide significant financial savings to those in the Architecture, Engineering and Construction (AEC) industry who are interested in gaining access to MR and VR. This article explores the possibility of using the Oculus Quest device to deploy MR and VR environments in construction. First, 3D models required for the experiment were created using Autodesk Revit. Then two applications were created using the Unity game engine, the Mixed Reality Toolkit for MR, and the Oculus Quest software development kit for VR. To ensure that the results were comparable, only one type of device and one construction task model were used in MR and VR settings. The MR and VR functions of Oculus Quest HMD were tested. Data on performance and user feedback were collected and analyzed. MR was easier to use than VR for this particular experiment because MR allowed the user to walk freely and view the room in real time, better understand the task, and get better details, accuracy, and scale. This way, one device can provide both environments, MR and VR.
Copyright Implications of the Use of Generative AI

This thesis will be divided into three chapters: In the first chapter, I will talk about the copyright implications of training AI, which I refer to as inputs. To that end, I will acquaint the reader, on the one hand, with the statutory provisions concerning the temporary reproduction in article 5(1) of the InfoSoc Directive and the TDM exceptions in articles 3 and 4 of the DSM Directive. On the other hand, I will introduce the U.S. fair use doctrine and walk through how that would apply to Generative AI. In the second chapter, I will talk about the copyright implications of the AI-generated works, which I refer to as outputs. Since the EU has not harmonized the infringement on the right of reproduction and adaptation (also known as derivative right), I will focus instead on the U.S. alone, since it is also the country hosting most AI companies. To this end, I will delve into the right of reproduction and, by extension, the right of derivatives. Finally, I will go through the fair use arguments from the perspective of the outputs. In the third and last chapter, I will ponder whether AI can be authors for the purposes of Copyright Law. If they cannot because they are not human, I will determine which, either the AI owner or the user, may own the copyright (if any) arising from a given AI-generated work. To achieve this, I will go through how the situation is settled in the US, the EU and the UK.
Impact of Implementing Six Sigma in the Software Development Process

The business environment and economics of the modern world have helped the software sector to become one of the most strategically important industries. Applications and systems that run on software are the primary motivating factor behind today's company processes. However, the process of developing software has been difficult and difficult due to its complexity. Projects related to software development have the greatest risk of falling behind schedule or being scrapped entirely. Researchers identified three fundamental problems that have a significant role in the failure of software projects. Erroneous assessment, inaccurate reporting of the current state, and poor-quality control are some of the issues. In addition to this, top executives also have a role in the development of software issues.
QUANT-NET: A testbed for quantum networking research over deployed fiber

QUANT-NET is a three-node quantum network research testbed funded by the U.S. Department of Energy (DOE). The goal is to establish this network between two sites, Lawrence Berkeley National Lab (LBNL) and University of California, Berkeley, connected with an entanglement swapping substrate over optical fiber and managed by a quantum network protocol stack. On top of this entanglement swapping substrate the research team will implement the most basic building blocks of distributed quantum computing and quantum repeater by teleporting a controlled-NOT gate between two far trapped-ion quantum computation nodes. This paper presents QUANT-NET, its design, key technologies, and progress.
Augmented Reality Apps on Electronic Devices for Education Using Image-based Technique

Engineering education is evolving with technology and educational methods and techniques for teaching and learning need to be renewed as technology continuously develops. The conventional way of teaching and learning is becoming least effective today as students tend to lose their focus during lectures. This research presents the development of Augmented Reality (AR) on electronic devices for education with Android smartphones. 3D models with Augmented Reality marker were developed using multiple Vuforia and Blender 3D software. The AR mobile application was designed using Unity 3D. Image based technique was used to track and scan the AR marker and display five engineering devices with detailed information. Virtual Studio Code is used to program the UI from Unity 3D. Result presents successful mobile AR apps for the five devices which created a new environment for teaching and learning. Sampling of 32 students were tested using the AR apps that shows they are immersive to learn due to the interaction with the 3D model. Analysis presents the effectiveness of augmented reality in education with 75% vote very effective and 25% for medium effectiveness. The respondents also contribute opinions on implementing augmented reality in virtual classes with agreement at 87.5% to implement the AR. Overall, a positive response with 90% of the testers finding Augmented Reality is helpful and stimulating in todays and future education.
Research on 0–1 Backpack Problem based on Improved Quantum Tabu Genetic Algorithm

The 0–1 backpack problem(0-1KP) is one of the very common combinatorial optimization problems, and is also a widely studied NP-complete problem. In this paper, we propose a Dynamic Strategy Tabu Search Operator Based Quantum Genetic Algorithm (DTS-QGA), which introduces the tabu search operator on the basis of the quantum genetic algorithm. To verify the feasibility and superiority of the algorithm, we have completed multiple independent experiments on 10 sets of 0–1 backpack test data with different data dimensions. The experiments demonstrate that the algorithm presented in this paper has a strong capability to perform global search, avoids local optima and achieves faster convergence.
A Research on Cross-Language Fake Reviews Identification Based on ERNIE and SGAN

With the rise of social media, the prevalence of fake reviews has surged, causing significant harm to the e-commerce industry's competitive landscape. To tackle the issue of limited publicly available datasets and the challenge of identifying fake reviews, a cross-lingual review dataset is created by amalgamating existing publicly available datasets. A fake review recognition model is devised based on the ERNIE2.0 pretrained language model and a semi-supervised generative adversarial network. Initially, ERNIE is employed to extract high-quality linguistic representations of the review data. Next, a generator in a semi-supervised generative adversarial network is utilized to generate noisy data that has a similar distribution to that of the genuine review text data. Finally, the identification of fake reviews is executed in a discriminator. Experimental validation is conducted using the cross-lingual dataset created, and the results indicate that the method achieves a remarkable 81.43% accuracy in identifying fake reviews with only a small amount of labeled data, thereby affirming its effectiveness.
Design of Interactive Teaching System Based on Computer Vision Technology

With the development of computer technology, visual algorithms and camera technology, non-contact human-computer interaction have gradually become the mainstream of people's daily life, and the campus should also follow the trend of the times to establish an epoch-making and more interactive modern teaching environment. This paper proposes a highly convenient and innovative interactive teaching system that integrates various cutting-edge technologies including gesture recognition technology, face recognition technology based on HOG feature computing, and Internet of Things technology. Teachers can use gestures to control computers after logging in through face recognition. Using subconscious and natural gestures to teach lesson can simplify computer-side operation and improve teachers' lecture efficiency.
Prediction of Wordle Game based on BP neural network optimization based on Grey Wolf algorithm

In 2021, the word guessing game Wordle became an overnight hit around the world. It updated a different “inscription” every day, requiring players to guess a five-letter “inscription” within six times (more than six times deemed unsuccessful). This paper aims to use the BP neural network algorithm optimized by Grey Wolf algorithm to build a multi-input multi-output mathematical model through training and data analysis of the huge data set of Wordle game, and predict the number proportion distribution of future players on six guesses (1,2,3,4,5,6,X). This algorithm can reflect the improvement and enhancement of the prediction accuracy of BP neural network optimized by Grey Wolf algorithm compared with the traditional BP neural network, and show more powerful data processing ability, so as to extend the machine learning model to a wider range of prediction problems.
A Novel Graph Neural Network-Based Automated Fault Localization Technique for Novice Programs

In recently years, Massive Open Online Courses have garnered significant attention, becoming a fundamental learning method for programming. However, debugging poses a challenge for novice programmers lacking programming experience due to its considerable effort, time cost, and professional experience. This has spurred interest in automatically localizing faults for novice programs. Regrettably, traditional fault localization techniques are often insufficient due to the inherent attributes of novice programs, such as concise code, straightforward logic, and limited passed tests. In this paper, we propose a novel fault localization technique, GccvFL, utilizing graph representation learning. Specifically, we first collect variable value sequences in novice programs and merge the dynamic and static features of the code. Then we reserve this detailed information of the faulty program into one graph. A gated graph neural network (GGNN) is used to learn useful features from the graph and output the rank list according to their suspiciousness. Ultimately, GccvFL enhances fault localization by integrating these features within the model. Our evaluation of 89 novice programs from the Beijing University of Chemical Technology Online Judge (BUCTOJ) dataset shows GccvFL significantly outperforms existing fault localization techniques on all the observed metrics. GccvFL localizes eight fault statements more than state-of-the-art techniques in TOP-1, showing that GccvFL's superior fault localization performance in novice programs.
Lightweight Multi-Role Recommendation System in TV live-streaming

Recommendation system has achieved great success in e-commerce business due to its strong modeling capacity for intent user preferences. Especially with more attention paid to live sale, recommendation shows obvious improvement in product CTR and users’ experience. For the TV terminal, the live sale is a mature business. However, since one TV terminal corresponds to several family members, TV live-streaming recommendation is challenging work. It needs to distinguish different family members and make targeted recommendations. Meanwhile, the recommendation system has the ability to quickly calculate and respond to user needs. This paper proposes a personalized TV recommendation system and a lightweight multi-role recommendation model for TV terminals, which can automatically identify multi-user preferences based on feedback from a single terminal. The accuracy of the recommendation system is verified by a public dataset.