Reviewerly/exHarmony huggingface dataset:
An Enhanced and Adaptive Proximity Measure Algorithm for the Screening of Fake and Clone Social-Media Profiles

Abstract: The term "semantics" has been brought up in a broad range of text mining studies due to the significant role that text semantics plays in establishing the meaning of a document. On the other hand, there is a dearth of research that synthesises the several subfields of research and offers a synopsis of the works that have been produced. This paper gives an in-depth mapping of research that focuses on semantics and text mining. In order to produce an accurate map, the researchers followed a regimented set of protocols throughout their work. The results were gleaned from a total of 1693 pieces of study that were selected from a pool of 3984 publications that were housed in five different online libraries. Academics who are working on semantics-focused text mining may find the mapping that was generated to be useful as a reference. The mapping gives a fundamental overview of the subject matter, pinpoints certain places that need the construction of primary or secondary studies, and identifies particular regions in need of the creation of either. It shows that the processing of semantic components in text mining is still an open research issue even though various studies have been developed despite the fact that these studies have been established.
Comparative Analysis of Sentiment Analysis Using the Support Vector Machine and Naive Bayes Algorithm on Cryptocurrencies

Objective – Cryptocurrency is growing overtime even being adopted as a legal money in a country out there. Besides can be used as a money, cryptocurrency also can be used as a digital goods to be trade and investment assets. To do some investing in cryptocurrency, there’s a need to evaluate the fundamental and sentiment of that cryptocurrency. This study aims to evaluate cryptocurrency based on responses of Twitter user.Methodology – The Algorithms used in this sentiment analysis study are Support Vector Machine and Naïve Bayes because it’s already proven that these 2 algorithm able to give a good accuracy and performance and using CRISP – DM framework for the study flow.Findings – This research predicts the sentiment for Bitcoin, Ethereum, Binance Coin, Dogecoin, and Ripple using the CRISP - DM method and using Support Vector Machine and Naïve Bayes algorithm.Novelty – This study calculate the sentiment on cryptocurrency using Rapidminer tools.Limitations - This study uses Bitcoin, Ethereum, Binance Coin, Dogecoin, and Ripple using tools such as rapidminerKeywords — Cryptocurrency, Naïve Bayes, Sentiment Analysis, Support Vector Machine
Retracted: Multiple Influences of Intelligent Technology on Network Behavior of College Students in the Metaverse Age

[This retracts the article DOI: 10.1155/2022/2750712.].
Are critical success factors for cybersecurity just technical issues Cybersecurity management and the human factor

Purpose- With the rapid advancement of information and communication technologies, businesses are facing growing security risks. The prevalence, intensity, and complexity of cyber attacks worsen these vulnerabilities, leading to a rising focus on cybersecurity. Enterprises exposed to such cyberattacks might not only face considerable financial losses but also experience data breaches, operational interruptions, harm to their reputation, regulatory penalties, legal expenses, reduced competitive standing, and increased insurance premiums. In this concept study discusses the importance of human factors in cybersecurity management. While organizations spend billions on information technology systems and software to detect and prevent cyber threats, individuals play a critical role in managing these risks. Methodology- Through a review of literature and statistical data, study examines the factors contributing to cybersecurity breaches, the allocation of resources to address them, and proposes potential solutions. Findings- In the workplace, most research on cybersecurity focuses on employees as the most important source of vulnerability. In the literature review, it is understood that an employee’s carelessness and lack of awareness pose the greatest risk to cybersecurity. However, businesses often fail to show sufficient attention to human behavior in their efforts to keep organizational data secure and to plan security strategies. It is important to note that effective cybersecurity management requires not only technical controls but also the management of human factors. Meanwhile, security expenditures in enterprises are often disproportionately allocated to technology investments, with 97% being spent on technology investments, despite the fact that over 85% of breaches are attributable to human factors. Conclusion- In the literature review, it is understood that cybersecurity management is not only related to technical controls, but also the management of human factors is of critical importance. The management of individuals is also an essential cybersecurity responsibility. It is important to adopt a holistic approach to cybersecurity management includes both technical and human perspectives. Cybersecurity awareness has significant benefits for businesses to effectively manage cybersecurity which can be achieved by developing appropriate training programs and foster a cybersecurity culture. Keywords: Cybersecurity, cybersecurity management, cybersecurity awareness, technology investments, human factor JEL Codes: M12, M15, L86
The lexical constructional model meets syntax: guidelines of the formalized lexical-constructional model (FL_CxG )

This paper offers the basic guidelines of a formalized version of the Lexical Constructional Model (LCM; Ruiz de Mendoza &amp; Mairal Usón, 2008, 2011; Ruiz de Mendoza &amp; Galera, 2014), the Formalized Lexical-Constructional Grammar (FL_CxG), which will pave the way for future computational developments, such as parsers or lexical databases. The FL_CxG deploys (i) the typologically oriented syntactic apparatus of Role and Reference Grammar (Van Valin, 2005; Van Valin &amp; LaPolla, 1997), (ii) the catalogue of constructional units arranged in a 4-layer typology, as proposed by the LCM, and (iii) some insights for semantic representations from the Generative Lexicon Theory (Pustejovsky, 1995; Pustejovsky &amp; Batiukova, 2019), and Minimal English (Goddard, 2018). All the components of the FL_CxG (lexical units and construct(ion)s) are formally encoded as Typed Feature Structures in the format of Attribute Value Matrixes. These units are to be understood as constraints operating in the unification processes which underlie the generation/decoding of a given fragment of language.
Online love fraud and the experiences of indonesian women: a qualitative study

Online love fraud is a cybercrime. Victims typically lose around 500 billion rupiahs every year in Indonesia. The research related to the experiences of victims of love scammers is limited, and the authorities often ignore victims who report. This study aimed to explore the experiences of Indonesian women as a victim. The study is qualitative with a phenomenological design utilizing purposive sampling. Twelve victims of online love fraud participated in the study. Data was collected through semi-structured interviews via video chat. Data processing was carried out by verbatim transcription of the video chats. The resulting texts were then rigorously read and re-read, coded, and grouped according to emerging themes. The victims described the experiences of sending large sums of money and naked photos to the perpetrators. The themes reflect the psychosocial impact of love fraud on women: shock, sadness, anger, low self-esteem, anxiety, depression, and suicidal thoughts. Online love fraud impacts the psychosocial functioning of victims. Victims feel a double burden in losing money and self-respect. These findings highlight the importance of policy, mental health practice, and research. Conclusions suggest psychosocial intervention is vital for the survivor.
Why Do Scientists Disagree?

Scientific research is often characterized by schools of thought. We investigate whether these divisions arise in part from differences in researchers’ cognitive traits such as their tolerance for ambiguity and the vividness of their visual imagery. These differences may guide researchers to prefer different types of problems, to tackle the same problems in different ways, and perhaps even reach different conclusions when studying the same problems in the same way. We surveyed 7973 researchers in psychology and allied disciplines and investigated links between what they research (area, topic, methodology), their stances on open questions in psychological science, and a set of validated scales assessing a variety of cognitive traits and dispositions. We also linked responses to publication histories from which we constructed landscapes of similarity and difference based on publication titles and abstracts, citations and co-authorship patterns. Our results show that researchers’ stances on scientific questions are, as expected, predictable from what kind of research they do. But they are also predictable from their cognitive traits, even when controlling for their research areas, methods and topics and co-authorship and citation patterns. These findings support the idea that divisions in scientific fields reflect not only differences in the kinds of data brought to bear on a topic, but also differences in the researchers themselves, hinting that the divisions may be more difficult to bridge than suggested by the traditional model of data-driven scientific consensus.
False Data Detector for Electrical Vehicles Temporal-Spatial Charging Coordination Secure Against Evasion and Privacy Adversarial Attacks

As the number of electric vehicles on roads significantly increases, spatial-temporal charging coordination mechanisms have been introduced for balancing charging demand and energy supply. However, electric vehicles could send false data, such as state-of-charge (SoC), to the charging coordination mechanism for gaining high charging priority illegally. Machine Learning models can be used to detect false data. However, in our application the detector is trained on a dataset that contains sensitive information, such as the locations and SoC values of the electric vehicles. Therefore, attackers could launch adversarial attacks against the detector, such as membership inference and model inversion, for revealing sensitive information on the drivers whose data are used to train the detector. Furthermore, attackers could launch evasion attacks against the detector by computing false SoC values that are classified benign by the detector. Addressing the three attacks simultaneously makes the problem more complicated because a countermeasure to one attack may degrade the model's accuracy and unintentionally make the model more susceptible to other attacks. Accordingly, in this paper, we propose a deep-learning training approach for false data detector in spatial-temporal charging coordination. Our approach can deal with the tradeoffs and balance the detector's accuracy and robustness against the adversarial attacks. Specifically, our approach combines three techniques, including mimic learning, dropout, and differential privacy, in a certain way that makes the detector highly accurate in detecting false data and also robust against adversarial attacks. To validate our approach, we have conducted a set of experiments and the given results demonstrate the robustness and accuracy of our detector.
Discovery of Silhouette--a New Threat to Privacy--and Our Efforts to Counter It

To prevent damage due to threats that are unknown to users and businesses, it is important to understand potential security issues in systems before the attackers do and to take preventive measures before an attack occurs.This article describes the mechanism of a new privacy threat called Silhouette, discovered in the course of this sort of empirical research on threats, together with a method for handling the threat, and initiatives to reinforce services and browser security functions around the world.
Overlap Makes Perfect: Designing a Meta-Tutor With Live Corrective Feedback for Ubiquitous Motion Learning

It has been deemed as a fundamental difficulty in the task of motion learning for MOOC courses, due to the limitation of MOOC’s defining feature of online teaching. This paper proposes a novel method called Meta-tutor for MOOC motion learning, by using neural style transfer and AR techniques to overcome the above problem. The Meta-tutor constructed by neural style transfer discards the original identity of real-world tutor in the MOOC video and can be regarded as a Meta-human in the Metaverse of MOOC’s virtual learning space. A light-weight wireless framework is designed to support ubiquitous motion learning, where the learner can present at any real-world practising field (e.g., sports court, dance room and training yard) and only needs take a smart mobile phone and wear a commodity AR device. This research is the first step towards a long-term study program.
Trial Service of Secure Computation System San-shi(TM)

Overview of San-shi trial serviceNTT's San-shi has been developed as a system having the advantage of secure computation technology that enables integrated analysis of data without mutual disclosure of data among organizations providing and/or using such data, while keeping the data encrypted.NTT has begun a free trial service of Sanshi to enable many users in a variety of fields to experience this value.The trial period began on August 20, 2018, and runs through March 2019.At present, trial users can experiment with San-shi in various fields including healthcare, manufacturing, and system integration.
Unlocking the Path to Academic Excellence: Unraveling the Impact of 480 Class Hours in Improving Reading Ability for Lecturers

This study seeks to explore the readers' ability experienced by adult learners who attended IELTS preparation courses at the Language Centre Hasanuddin University. It aims at measuring their progress through reading class. The respondents were 44 out of 50 lecturers from 18 different universities in the eastern part of Indonesia, both public and private, sponsored by the Directorate of Higher Education of the Republic of Indonesia. They attended 240 sessions which equaled 480 class hours for three months. The data were collected from the IELTS pre-test, progress-test, mid-test, and post-test. The progress test was given ten times during the preparation. By the end of the course, the participants sat for the real IELTS test administered by IALF Bali. They were also given a self-assessment questionnaire (20 questions) using a Likert scale ranging from "strongly agree to strongly disagree." Besides, the researchers also conducted direct observation as they were the teachers of Reading in their class. Based on the result of all instruments used in this research, the finding indicates that most of the respondents' reading ability is improving.
Research Trends in Post-quantum Cryptography

The growing recognition that quantum computers are soon to be a reality is driving research into postquantum cryptography.This article introduces the Post-Quantum Cryptography Standardization project of the National Institute of Standards and Technology (NIST) in the United States, which is playing a core role in the research and development of post-quantum cryptography, and introduces NTT initiatives and independent research related to that project.
Security R&amp;D for a Safe and Secure Digital Society

Major environmental changes and market transitions are occurring as our digital society is realized, and in line with this trend, NTT Secure Platform Laboratories is conducting research and development on security technologies to build resistance to emerging new security threats and to resolve issues involving the utilization of data.This article introduces some security issues that can arise in a digital society and security measures to deal with them, both defensive and offensive.
QuIP: 2-Bit Quantization of Large Language Models With Guarantees

This work studies post-training parameter quantization in large language models (LLMs). We introduce quantization with incoherence processing (QuIP), a new method based on the insight that quantization benefits from $\textit{incoherent}$ weight and Hessian matrices, i.e., from the weights being even in magnitude and the directions in which it is important to round them accurately being unaligned with the coordinate axes. QuIP consists of two steps: (1) an adaptive rounding procedure minimizing a quadratic proxy objective; (2) efficient pre- and post-processing that ensures weight and Hessian incoherence via multiplication by random orthogonal matrices. We complement QuIP with the first theoretical analysis for an LLM-scale quantization algorithm, and show that our theory also applies to an existing method, OPTQ. Empirically, we find that our incoherence preprocessing improves several existing quantization algorithms and yields the first LLM quantization methods that produce viable results using only two bits per weight. Our code can be found at https://github.com/Cornell-RelaxML/QuIP.
On the Learning Dynamics of Attention Networks

Attention models are typically learned by optimizing one of three standard loss functions that are variously called -- soft attention, hard attention, and latent variable marginal likelihood (LVML) attention. All three paradigms are motivated by the same goal of finding two models -- a `focus' model that `selects' the right \textit{segment} of the input and a `classification' model that processes the selected segment into the target label. However, they differ significantly in the way the selected segments are aggregated, resulting in distinct dynamics and final results. We observe a unique signature of models learned using these paradigms and explain this as a consequence of the evolution of the classification model under gradient descent when the focus model is fixed. We also analyze these paradigms in a simple setting and derive closed-form expressions for the parameter trajectory under gradient flow. With the soft attention loss, the focus model improves quickly at initialization and splutters later on. On the other hand, hard attention loss behaves in the opposite fashion. Based on our observations, we propose a simple hybrid approach that combines the advantages of the different loss functions and demonstrates it on a collection of semi-synthetic and real-world datasets
Accuracy Improvement in Differentially Private Logistic Regression: A Pre-training Approach

Machine learning (ML) models can memorize training datasets. As a result, training ML models over private datasets can lead to the violation of individuals' privacy. Differential privacy (DP) is a rigorous privacy notion to preserve the privacy of underlying training datasets. Yet, training ML models in a DP framework usually degrades the accuracy of ML models. This paper aims to boost the accuracy of a DP logistic regression (LR) via a pre-training module. In more detail, we initially pre-train our LR model on a public training dataset that there is no privacy concern about it. Then, we fine-tune our DP-LR model with the private dataset. In the numerical results, we show that adding a pre-training module significantly improves the accuracy of the DP-LR model.
Gradient-Based Spectral Embeddings of Random Dot Product Graphs

The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially postulate that edge formation probabilities are given by the dot product of the corresponding latent positions. Accordingly, the embedding task of estimating these vectors from an observed graph is typically posed as a low-rank matrix factorization problem. The workhorse Adjacency Spectral Embedding (ASE) enjoys solid statistical properties, but it is formally solving a surrogate problem and can be computationally intensive. In this paper, we bring to bear recent advances in non-convex optimization and demonstrate their impact to RDPG inference. We advocate first-order gradient descent methods to better solve the embedding problem, and to organically accommodate broader network embedding applications of practical relevance. Notably, we argue that RDPG embeddings of directed graphs loose interpretability unless the factor matrices are constrained to have orthogonal columns. We thus develop a novel feasible optimization method in the resulting manifold. The effectiveness of the graph representation learning framework is demonstrated on reproducible experiments with both synthetic and real network data. Our open-source algorithm implementations are scalable, and unlike the ASE they are robust to missing edge data and can track slowly-varying latent positions from streaming graphs.
EmphasisChecker: A Tool for Guiding Chart and Caption Emphasis

Recent work has shown that when both the chart and caption emphasize the same aspects of the data, readers tend to remember the doubly-emphasized features as takeaways; when there is a mismatch, readers rely on the chart to form takeaways and can miss information in the caption text. Through a survey of 280 chart-caption pairs in real-world sources (e.g., news media, poll reports, government reports, academic articles, and Tableau Public), we find that captions often do not emphasize the same information in practice, which could limit how effectively readers take away the authors' intended messages. Motivated by the survey findings, we present EmphasisChecker, an interactive tool that highlights visually prominent chart features as well as the features emphasized by the caption text along with any mismatches in the emphasis. The tool implements a time-series prominent feature detector based on the Ramer-Douglas-Peucker algorithm and a text reference extractor that identifies time references and data descriptions in the caption and matches them with chart data. This information enables authors to compare features emphasized by these two modalities, quickly see mismatches, and make necessary revisions. A user study confirms that our tool is both useful and easy to use when authoring charts and captions.
Trimming and Building Freezing Sets

We develop new tools for the construction of fixed point sets in digital topology. We define excludable points and show that these may be excluded from all freezing sets. We show that articulation points are excludable. We also present results concerning points that must belong to a freezing set and often are easily recognized. These include points of degree~1 and some local extrema.