Reviewerly/exHarmony huggingface dataset:
Sniffing Cybercrimes in M-Banking via WhatsApp: Comparative Legal Framework and Implications

This normative legal research investigates the regulatory landscape and legal responsibilities surrounding sniffing cybercrimes in mobile banking (M-Banking) operations via the WhatsApp application. The study uniquely aligns its analysis with established laws like the Information and Electronic Transaction Law (UU ITE) and the Personal Data Protection Law (UU PDP), elucidating the nuances in sanctions stipulated by these respective legislations. It reveals that the UU ITE and its amendments carry more severe sanctions than the UU PDP. However, based on the lex specialis derogate legi generalis principle and teleological/sociological interpretations of the law's purpose, it concludes that Article 67 (1) of UU PDP governs such sniffing offenses in M-Banking via WhatsApp. The implicated legal liability includes a maximum prison sentence of five years and/or a fine up to five hundred billion rupiah. Moreover, the research underscores the accountability of banking institutions to compensate for the losses suffered by victims, encompassing the replacement of the full or partial account balance or other agreed forms of responsibility. These findings have critical implications for cybersecurity regulations, and the banking sector's duty of care towards customers in the digital era.Highlights:&#x0D; &#x0D; Disparity: Penalties under UU ITE and UU PDP differ for sniffing cybercrimes.&#x0D; Governing Law: Sniffing crimes in M-Banking via WhatsApp fall under UU PDP, Article 67 (1).&#x0D; Liability: Banks are responsible for compensating victims' losses.&#x0D; &#x0D; Keywords: Sniffing Cybercrimes, M-Banking, WhatsApp, Legal Framework, Bank Accountability.
Some Issues in Robust Clustering

Abstract Some key issues in robust clustering are discussed with focus on the Gaussian mixture model based clustering, namely the formal definition of outliers, ambiguity between groups of outliers and clusters, the interaction between robust clustering and the estimation of the number of clusters, the essential dependence of (not only) robust clustering on tuning decisions, and shortcomings of existing measurements of cluster stability when it comes to outliers.
Data Clustering and Representation Learning Based on Networked Data

Abstract To deal simultaneously with both, the attributed network embedding and clustering, we propose a new model exploiting both content and structure information. The proposed model relies on the approximation of the relaxed continuous embedding solution by the true discrete clustering. Thereby, we show that incorporating an embedding representation provides simpler and easier interpretable solutions. Experiment results demonstrate that the proposed algorithm performs better, in terms of clustering, than the state-of-art algorithms, including deep learning methods devoted to similar tasks.
Parsimonious Mixtures of Seemingly Unrelated Contaminated Normal Regression Models

Abstract In recent years, the research into linear multivariate regression based on finite mixture models has been intense. With such an approach, it is possible to perform regression analysis for a multivariate response by taking account of the possible presence of several unknown latent homogeneous groups, each of which is characterised by a different linear regression model. For a continuous multivariate response, mixtures of normal regression models are usually employed. However, in real data, it is not unusual to observe mildly atypical observations that can negatively affect the estimation of the regression parameters under a normal distribution in each mixture component. Furthermore, in some fields of research, a multivariate regression model with a different vector of covariates for each response should be specified, based on some prior information to be conveyed in the analysis. To take account of all these aspects, mixtures of contaminated seemingly unrelated normal regression models have been recently developed. A further extension of such an approach is presented here so as to ensure parsimony, which is obtained by imposing constraints on the group-covariance matrices of the responses. A description of the resulting parsimonious mixtures of seemingly unrelated contaminated regression models is provided together with the results of a numerical study based on the analysis of a real dataset, which illustrates their practical usefulness.
Fuzzy Clustering by Hyperbolic Smoothing

Abstract We propose a novel method for building fuzzy clusters of large data sets,using a smoothing numerical approach. The usual sum-of-squares criterion is relaxed so the search for good fuzzy partitions is made on a continuous space, rather than a combinatorial space as in classical methods [8]. The smoothing allows a conversion from a strongly non-differentiable problem into differentiable subproblems of optimization without constraints of low dimension, by using a differentiable function of infinite class. For the implementation of the algorithm, we used the statistical software R and the results obtained were compared to the traditional fuzzy C –means method, proposed by Bezdek [1].
Trends in Data Stream Mining

Abstract Learning from data streams is a hot topic in machine learning and data mining. This article presents our recent work on the topic of learning from data streams. We focus on emerging topics, including fraud detection and hyper-parameter tuning for streaming data. The first study is a case study on interconnected by-pass fraud. This is a real-world problem from high-speed telecommunications data that clearly illustrates the need for online data stream processing. In the second study, we present an optimization algorithm for online hyper-parameter tuning from nonstationary data streams.
Latent Block Regression Model

Abstract When dealing with high dimensional sparse data, such as in recommender systems,co-clusteringturnsouttobemorebeneficialthanone-sidedclustering,even if one is interested in clustering along one dimension only. Thereby, co-clusterwise is a natural extension of clusterwise. Unfortunately, all of the existing approaches do not consider covariates on both dimensions of a data matrix. In this paper, we propose a Latent Block Regression Model (LBRM) overcoming this limit. For inference, we propose an algorithm performing simultaneously co-clustering and regression where a linear regression model characterizes each block. Placing the estimate of the model parameters under the maximum likelihood approach, we derive a Variational Expectation–Maximization (VEM) algorithm for estimating the model’s parameters. The finality of the proposed VEM-LBRM is illustrated through simulated datasets.
Old and New Constraints in Model Based Clustering

Abstract Model-based approaches to cluster analysis and mixture modeling often involve maximizing classification and mixture likelihoods. Without appropriate constrains on the scatter matrices of the components, these maximizations result in ill-posed problems. Moreover, without constrains, non-interesting or “spurious” clusters are often detected by the EM and CEM algorithms traditionally used for the maximization of the likelihood criteria. A useful approach to avoid spurious solutions is to restrict relative components scatter by a prespecified tuning constant. Recently new methodologies for constrained parsimonious model-based clustering have been introduced which include the 14 parsimonious models that are often applied in model-based clustering when assuming normal components as limit cases. In this paper we initially review the traditional approaches and illustrate through an example the benefits of the adoption of the new constraints.
Improving Classification of Documents by Semi-supervised Clustering in a Semantic Space

Abstract In the paper we propose a method for representation of documents in a semantic lower-dimensional space based on the modified Reduced k -means method which penalizes clusterings that are distant from classification of training documents given by experts. Reduced k -means (RKM) enables simultaneously clustering of documents and extraction of factors. By projection of documents represented in the vector space model on extracted factors, documents are clustered in the semantic space in a semi-supervised way (using penalization) because clustering is guided by classification given by experts, which enables improvement of classification performance of test documents. Classification performance is tested for classification by logistic regression and support vector machines (SVMs) for classes of Reuters-21578 data set. It is shown that representation of documents by the RKM method with penalization improves the average precision of classification by SVMs for the 25 largest classes of Reuters collection for about 5,5% with the same level of average recall in comparison to the basic representation in the vector space model. In the case of classification by logistic regression, representation by the RKM with penalization improves average recall for about 1% in comparison to the basic representation.
Clustering Validation in the Context of Hierarchical Cluster Analysis: An Empirical Study

Abstract The evaluation of clustering structures is a crucial step in cluster analysis. This study presents the main results of the hierarchical cluster analysis of variables concerning a real dataset in the context of Higher Education. The goal of this research is to find a typology of some relevant items taking into account both the homogeneity and the isolation of the clusters. Two similarity measures, namely the standard affinity coefficient and Spearman’s correlation coefficient, were used, and combined with three probabilistic (AVL, AVB and AV1) aggregation criteria, from a parametric family in the scope of the VL (Validity Link) methodology. The best partitions were selected based on some validation indices, namely the global STAT levels statistics and the measures P(I2, Σ) and γ, adapted to the case of similarity coefficients. In order to evaluate the clusters and identify their most representative elements, the Mann and Whitney U statistics and the silhouette plot were also used.
An MML Embedded Approach for Estimating the Number of Clusters

Abstract Assuming that the data originate from a finite mixture of multinomial distributions, we study the performance of an integrated Expectation Maximization (EM) algorithm considering Minimum Message Length (MML) criterion to select the number of mixture components. The referred EM-MML approach, rather than selecting one among a set of pre-estimated candidate models (which requires running EM several times), seamlessly integrates estimation and model selection in a single algorithm. Comparisons are provided with EM combined with well-known information criteria – e.g. the Bayesian information Criterion. We resort to synthetic data examples and a real application. The EM-MML computation time is a clear advantage of this method; also, the real data solution it provides is more parsimonious, which reduces the risk of model order overestimation and improves interpretability.
A Proposal for Formalization and Definition of Anomalies in Dynamical Systems

Abstract Although many scientists strongly focus on anomaly detection in different applications and domains, there currently exists no universally accepted definition of anomalies and outliers. Using an approach based on control theory and dynamical systems, as well as a definition for anomalies as described by philosophy of science, the authors propose a generalized framework viewing anomalies as key drivers of progress for a better understanding of the dynamical systems around us. By mathematically defining anomalies and delimiting deviations within expectations from completely unforeseen instances, this paper aims to be a contribution to set up a universally accepted definition of anomalies and outliers.
Utilizing GANs for Credit Card Fraud Detection: A Comparison of Supervised Learning Algorithms

The evolution and improvements in electronic commerce and communications around the world have stimulated credit card use. With the support of smartphone wallets, electronic payments have become the most popular payment method for personal and business use; however, the past few years have also seen a major increase in fraudulent transactions. Corporations and individuals experience very negative impacts from such fraud. Therefore, fraud detection systems have received a lot of attention recently from major financial institutions. This paper proposes a fraud detection approach that deals with small and imbalanced datasets using Generative Adversarial Networks (GANs) for sample generation. Six machine-learning algorithms were applied to real-world data. The accuracy of all six algorithms was above 85% and the precision was above 95%. Five of the six algorithms had a recall score greater than 90%. Furthermore, the Receiver Operating Characteristics (ROC), which measure performance at different thresholds, demonstrated scores greater than 0.90, except Naïve Bayes, which scored 0.81. The proposed approach outperformed the same algorithms in other studies.
Stability of Mixed-Type Cluster Partitions for Determination of the Number of Clusters

Abstract For partitioning clustering methods, the number of clusters has to be determined in advance. One approach to deal with this issue are stability indices. In this paper several stability-based validation methods are investigated with regard to the k -prototypes algorithm for mixed-type data. The stability-based approaches are compared to common validation indices in a comprehensive simulation study in order to analyze preferability as a function of the underlying data generating process.
An Entertaining Application to Mix Exercise with Fun using Pose Estimate and Unity

We address the rising issue of increasing weight and declining health due to sedentary lifestyles and unhealthy diets. Our solution involves a camera tracking system that monitors users' movements and offers six engaging exercise games, bridging the gap between fitness and enjoyment. The project comprises three interconnected components: pose estimation, game information, and mini-games, with C# code utilized for pose estimation and individualized coding for seamless integration of each game.Throughout the development process, three notable challenges emerged: occasional sensory issues with the pose estimate, animation complexities, and the absence of an effective scoring system. To enhance the efficiency of the pose estimate, we conducted three rounds of trials, each consisting of ten arm circles, revealing that proximity to sensors was a common cause of issues.The animation challenge was overcome by incorporating free, simple, and readily available animations from online sources into human models for games requiring user interaction with models. To address the scoring dilemma, we clarified game goals by providing text instructions, guiding users on how to achieve success.This application caters to a younger audience, offering affordability, visual appeal, intense exercise, and swift results. Balancing fitness and fun, it presents an ideal solution for those seeking an engaging and effective exercise regimen.
The Development Stages of Metaverse and Its Cultural Consequences

The discussion on the metaverse requires considering its cultural consequences through its artistic imagination and technological realization. The current stage of the metaverse presents a sharp contrast between the full-fledged artistic imagination and the limited technological realization. The primary stage is a virtual world created by virtual reality technology while the future advanced stage should be the integration of “real world” and “virtual world”. The primary form of “metaverse” emphasizes the relative independence of cognition from the human body and tries to establish a “unmanned” virtual world with “no body participation”; while the advanced one proposes the human physical factor of “physical cognition” that must be fully considered in the seamless connection between the real world and the virtual one. The cognitive mode of “metaverse” is a complex of “disembodied cognition”, “embodied cognition”, “embodied cognition” and the combination of the two. The primary stage is establishing the aesthetic form of “Aesthetics of Isolation”, but it is opposed to the concept of “Internet of everything” in information technology. While “metaverse” is developing into the advanced stage, its aesthetic form will be open and compatible, so as to generate the emerging “Aesthetics of Connection” transformation.
First Break Traveltime Picking Using Unsupervised Machine Learning and Super‐Virtual Refraction Interferometry

Abstract An accurate and trustworthy first break picking plays a key role in static correction calculations, velocity analysis, and deconvolution. First break traveltimes picking accuracy ensures correct and reliable seismic data processing results. Many methods are available to perform the first break pickings automatically. Most of these methods, however, are not robust when there is a low signal‐to‐noise ratio (SNR). In this study, to automatically determine the traveltimes of the first arrivals, we used the density‐based spatial clustering application with noise (DBSCAN) and the super‐virtual refraction interferometry (SVI). In the case of noisy data, using only DBSCAN for first break picking shows poor accuracy. To deal with this issue, SVI is employed to improve the SNR of the data, and then DBSCAN is applied again to the enhanced data for accurate picking. In the first step, DBSCAN is used to define a muting window for SVI. In the second step, SVI is employed to improve the first arrival SNR. In the third step, DBSCAN is applied to determine the final first arrival traveltime. The proposed approach is tested on synthetic and field data sets, where the manual and the cross‐correlation pickings are compared to the DBSCAN automatic pickings. We further test the proposed approach on a publicly available data set to benchmark our technique for first‐break accuracy with other established methods. Results show that the first arrival traveltime picks using the proposed approach are very accurate compared to manual picking.
Structural lineament analysis of the Bir El-Qash area, Central Eastern Desert, Egypt, using integrated remote sensing and aeromagnetic data

The Bir El-Qash area, located in the Central Eastern Desert of Egypt, is characterized by a diverse range of igneous, metamorphic, and sedimentary rocks with ages spanning from the Late Proterozoic to Quaternary. Integration of remote sensing with aeromagnetic data was conducted to generate surface and subsurface structural lineaments. Shaded relief from digital elevation models, principal component analysis of Landsat-8 data, and ALOS/PALSAR images were utilized to create lineament maps. Airborne magnetic data were employed to reveal subsurface characterizations. The study area has undergone various tectonic activities, resulting in complex structures. Multiple fault trends and fractures were identified, including the NW-SE (Red Sea-Gulf of Suez) trend, the NE-SW trending Syrian arc trend, the N-S trending East African trend and the WNW-ESE trend. By analyzing the tectonic features of the Bir El-Qash area, this study provides insights into the geological history and evolution of the Eastern Desert of Egypt.
Enhancing the Chinese identity of young people in Hong Kong and Macao based on the creation of cinematic virtual reality art, taking the cinematic virtual reality work I love being me: in my place, my home as an example

Abstract The subject of this paper is the use of cinematic virtual reality (CVR) to study the issue of Chinese identity. Guided by theories of immersion and presence, taking a cultural perspective (collective memory) on the context of the relationship between mainland China, Hong Kong, and Macao, we created a CVR work titled I Love Being Me: in my Place, my Home . We used CVR as a stimulus for an experiment with the aim of proving that CVR has a significant effect on enhancing the sense of Chinese identity in young people in Hong Kong and Macao. Based on the results of the experiment, we draw the following two conclusions: (1) CVR provides participants with a greater sense of immersion and presence compared to traditional 2D films; (2) CVR should be tailored to local culture—identity, history, language and politics—to maximize participants’ sense of identity and immersion.
Roadmap for Digital Image Forgery Detection Using Deep Learning

Technology advances are prominent today while influencing all aspects of our lives. Misuse of information has also increased as a result of technical improvements. As a result, investigators have the enormous task of recognizing modified information and distinguishing this from genuine data. Among the most prevalent techniques for electronic image alteration is splicing, which includes replicating a specific section using the same or different photograph and transferring it to a new image. In the wake of this issue, picture identification of forgeries has arisen as a viable approach for confirming the validity of online photographs.&#x0D; Within this paper, we propose a strategy depending on the cutting-edge ResNet50v2 neural networks framework. This study describes an approach intended specifically for detecting splicing, among the most common forms of online picture forgeries. The VGG-16 convolutional neural network model is used in our technique. The recommended network topology accepts picture patchwork as data and generates identification outcomes on the patch, categorizing them as legitimate or fraudulent. We select pieces from the primary picture areas and the inserted splicing boundaries throughout the teaching stage. This paper proposes an effective technique for identifying splices in electronic photos, proving the usefulness of our deep learning-powered strategy and emphasizing its superior results over previous alternatives.
Anaerobic Digestion Potential in Traditional Boarding School

There are approximately 36,600 religious boarding schools in Indonesia accommodating a student population of 3.4 million. Traditional religious boarding school is one of the most significant contributors to waste generation after housing. This study is aimed to measure waste generation and the potential to implement anaerobic digestion in traditional boarding schools. At Al Hikam Boarding School, with a population of 265 residents, the average daily waste generation was measured at 33.6 kg/day, with organic waste constituting 41.8%. Over three months, the study included the stages of seeding, acclimatisation, and operation of anaerobic digestion using organic waste generated by the boarding school and traditional buffet. With an input of 7.6 kg/day, the results showed that organic waste used had a pH, total solid (TS), volatile solid (VS), chemical oxygen demand (COD), and temperature of 6.1±0.38, 25±0.092%, 95±0.0054%TS, 453±188 g/L, and 29.5±1.12°C, respectively. The digester reported a volatile solid destruction (VSD) and chemical oxygen demand (COD) reduction of 91±0.015% and 89±0.081%, respectively. This produced biogas volume and methane yield of 805±219 L/day and 292±130 L.methane/kg.VS. So, anaerobic digestion with a Toren Biogas reactor is the recommended technology to manage organic waste at traditional boarding schools, with a potential reduction factor of 54%.