Reviewerly/exHarmony huggingface dataset:
Extreme Gradient Boosting Model-based Forecasting of Big Data Online Sales Record

Nowadays, big data plays a crucial role for many online e-commerce businesses to generate more sales. Big data is a huge collection of data and information which are utilized by many organizations to forecast which products, costs, and advertisements are better to maximize their business profits. This paper aims to apply the extreme gradient boosting (XGBoost) based model to forecast sales growth of online products, specifically books and magazines, from massive datasets present in online shopping. PySpark, as the best suitable and compatible framework, is used for data analysis. The result shows that the proposed model has higher forecasting accuracy with a minimum error rate than other models. A comparative visualization and conclusion are presented in terms of the proposed system's prediction accuracy, error rate, and efficiency.
Locus of control, personality temperaments, and coping strategies of marine transportation students

The globalization of the shipping industry necessitates improving maritime students' ability to operate under pressure. This descriptive-comparative-correlational study aims to determine 239 marine students' locus of control, personality temperaments, and stress-coping strategies. It examines correlations among the aforementioned variables, and differences in coping in terms of year level, family structure, religion, locus of control (LOC) and temperament. The findings indicate that there is a plenitude of externals and phlegmatics among maritime students. They used coping strategies moderately across all three coping categories, namely avoidance, emotion-oriented, and task-oriented approaches, which is most preferred. Family structure, religion, and LOC do not affect their coping abilities. Choleric students coped better than sanguine and melancholic students; second-year students coped better than first-year students. Moreover, there is a link between temperament and coping, but not between temperament and LOC, nor between LOC and coping. As for externals, students would tend to believe that life circumstances or outcomes in school or elsewhere are influenced by external forces. As restrained, and sensitive phlegmatics, suppressing feelings of stress leads to more susceptibility to physical and mental stress, requiring effective coping techniques. Thus, recognizing and understanding their locus of control and temperaments will help them cope with stress more successfully.
A Virtual Reality Study Investigating The Effect of Cybersickness On The Relationship Between Vection and Presence Across Environments With Varying Levels of Ecological Relevance

In the absence of physical motion, people sometimes experience the illusory sensation of self-motion which is known as vection. Vection and presence are positively related and vection research could contribute to the improvement of the fidelity of motion simulators. However, when utilizing Virtual Reality technology for motion simulators, visually-induced motion sickness (VIMS) in the form of Cybersickness (CS) sometimes co-occurs when one experiences vection. However, the relationship between vection and CS is not evident as, to the best of our knowledge, past research mainly investigated the relationship between the vection and CS with environments with a certain level of ecological relevance. Herein we aimed to investigate whether CS negatively affects the relationship between vection and presence across different virtual environments with varying levels of ecological relevance. We immersed twenty-nine participants visually and audibly in virtual environments and after each trial, participants reported their vection intensity, CS and presence. Our results showed that the relationship between vection intensity and presence was unaffected by CS. We conclude that the relationship between vection and presence is unaffected by CS with low levels of discomfort.
Team Formation and Project Assignment − the dilemma of assigning students to project groups

To meet the needs of globalization and change in higher education group work projects are increasingly used. However, there is many methods mentioned in literature to assign student teams but little guidance on how best to allocate students to groups with reference to the project itself.The experience over a decade of teaching an IT project course is shared with different team composition methods in the context of various project settings: first: teams and projects are self-selected, second: randomly assigned groups select the projects, and third: projects are selected individually followed by assigned team formation. The different methodologies are outlined in detail and the impact of the composition and assignment method on project results and individual learning progress and success is discussed.
Impacts of English Reading Attitudes on L2 Achievements

Reading is an acquired skill needed for obtaining knowledge. To be successful in reading, one should adopt certain attitudes to ensure the reading activity's fruitfulness. This correlational study investigates the possible significant relationship between reading attitude and L2 achievement. The subjects of this study were 47 vocational school students in Yogyakarta. Samples were chosen using purposive sampling with X graders and XI graders of a vocational school. The data were collected through a questionnaire, interview sessions, and an English exam. The hypothesis of this research was the more positive the students’ attitude was, the higher were L2 achievements of the students. The writers conducted a Pearson correlation computation to analyze the correlation of the obtained data. The findings showed that the students' reading attitude was positive and the L2 achievements were high, but the reading attitude correlated negatively and weakly to L2 achievements. Other factors affected L2 achievements as well. ESL teachers should be concerned about their students’ reading attitudes
Software Defect Prediction: State of the Art Survey

Software has evolved into a critical component in today's world. The quantity of faults in a software product is connected to its quality, which is also restricted by time and cost. In terms of both quality and cost, software faults are costly. The practice of tracing problematic components in software prior to the product's launch is known as software defect prediction. Defects are unavoidable, but we should strive to keep the number of defects to a bare minimum. Defect prediction results in shorter development times, lower costs, less rework, higher customer satisfaction, and more dependable software. As a result, defect prediction procedures are critical for achieving software quality and learning from prior errors.In this study, we conduct a review of the literature from the last two decades and look into recent advancements in the field of defect prediction.
stabiliser: Variable Selection using a Permutation-based Objective Stability Threshold (POST)

Abstract Identification of variables truly associated with an outcome, whilst minimising false positives,is critical to any data analysis. This is particularly challenging in wider datasets, where the proportionof false positives tends to increase as the number of variables increases. Whilst stability selection hasbeen demonstrated to improve model performance, arbitrary stability thresholds mean performanceimprovements are model and dataset dependent. We propose a novel permutation-based objectivestability threshold (POST) to enhance the stability selection procedure. The performance of POSTis evaluated through simulation analysis, and performance benefits of POST are demonstrated incomparison with conventional approaches. Furthermore, the advantages of statistical triangulation,the use of multiple models simultaneously, alongside POST are highlighted. To facilitate the implementationof these methods, we present the R package stabiliser, which is designed to improve inferentialmodel performance regardless of model type or dataset structure.
Automated Dynamic Algorithm Configuration

The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve users from the tedious and error-prone task of manually tuning parameters, there is still a lot of untapped potential as the learned configuration is static, i.e., parameter settings remain fixed throughout the run. However, it has been shown that some algorithm parameters are best adjusted dynamically during execution, e.g., to adapt to the current part of the optimization landscape. Thus far, this is most commonly achieved through hand-crafted heuristics. A promising recent alternative is to automatically learn such dynamic parameter adaptation policies from data. In this article, we give the first comprehensive account of this new field of automated dynamic algorithm configuration (DAC), present a series of recent advances, and provide a solid foundation for future research in this field. Specifically, we (i) situate DAC in the broader historical context of AI research; (ii) formalize DAC as a computational problem; (iii) identify the methods used in prior-art to tackle this problem; (iv) conduct empirical case studies for using DAC in evolutionary optimization, AI planning, and machine learning.
Ethisch verantwoord onderzoek aan de hand van posts op social media

Ethically responsible research of posts on social media: How to discuss it with the users of a platform? In discourse analysis, it is common to examine social media data without informing users and without asking for their consent. However, ethics are increasingly considered to be important. In this paper, we reflect on a method to develop more ethically sensitive approaches in discourse analysis by taking the users’ opinions and views into consideration. We approached Facebook, Reddit, Twitter and Instagram users in order to start a conversation with them about their views on research into their posts. In this paper, we reflect on our attempts to involve users in a conversation and what this might imply for discourse analysis of social media.
CcHarmony: Color-checker based Image Harmonization Dataset

Image harmonization targets at adjusting the foreground in a composite image to make it compatible with the background, producing a more realistic and harmonious image. Training deep image harmonization network requires abundant training data, but it is extremely difficult to acquire training pairs of composite images and ground-truth harmonious images. Therefore, existing works turn to adjust the foreground appearance in a real image to create a synthetic composite image. However, such adjustment may not faithfully reflect the natural illumination change of foreground. In this work, we explore a novel transitive way to construct image harmonization dataset. Specifically, based on the existing datasets with recorded illumination information, we first convert the foreground in a real image to the standard illumination condition, and then convert it to another illumination condition, which is combined with the original background to form a synthetic composite image. In this manner, we construct an image harmonization dataset called ccHarmony, which is named after color checker (cc). The dataset is available at https://github.com/bcmi/Image-Harmonization-Dataset-ccHarmony.
Emergent organization of receptive fields in networks of excitatory and inhibitory neurons

Local patterns of excitation and inhibition that can generate neural waves are studied as a computational mechanism underlying the organization of neuronal tunings. Sparse coding algorithms based on networks of excitatory and inhibitory neurons are proposed that exhibit topographic maps as the receptive fields are adapted to input stimuli. Motivated by a leaky integrate-and-fire model of neural waves, we propose an activation model that is more typical of artificial neural networks. Computational experiments with the activation model using both natural images and natural language text are presented. In the case of images, familiar "pinwheel" patterns of oriented edge detectors emerge; in the case of text, the resulting topographic maps exhibit a 2-dimensional representation of granular word semantics. Experiments with a synthetic model of somatosensory input are used to investigate how the network dynamics may affect plasticity of neuronal maps under changes to the inputs.
Boundary between noise and information applied to filtering neural network weight matrices

Deep neural networks have been successfully applied to a broad range of problems where overparametrization yields weight matrices which are partially random. A comparison of weight matrix singular vectors to the Porter-Thomas distribution suggests that there is a boundary between randomness and learned information in the singular value spectrum. Inspired by this finding, we introduce an algorithm for noise filtering, which both removes small singular values and reduces the magnitude of large singular values to counteract the effect of level repulsion between the noise and the information part of the spectrum. For networks trained in the presence of label noise, we indeed find that the generalization performance improves significantly due to noise filtering.
A Cyber Counterintelligence Competence Framework

The increased use of cyberspace and technological advancement are fundamentally changing the cyber threat landscape. Cyberattacks are becoming more sophisticated, frequent, and destructive. Internationally, there is a growing acceptance that Cyber Counterintelligence (CCI) is essential to counter cyber-attacks optimally. Therefore, in addition to government intelligence and security agencies, more companies are incorporating a CCI approach as a critical element of their posture for engaging cyber threats. However, the successful adoption of a CCI approach depends on the availability of skilled CCI professionals equipped with the requisite competences. The creation of such CCI professionals, in turn, requires a framework for developing the necessary CCI competences. At least in as far as reviewed academic literature is concerned, there is no existing postulation on a framework to develop the CCI competences, specifically for developing countries. Given the complexity and multi-disciplinary nature of the emerging CCI field, such a framework needs to provide two distinctive skillsets linked to CCI’s two distinct areas of expertise, namely cyber (security) and counterintelligence. The paper presents a high-level Cyber Counterintelligence Competence Framework (CCIC Framework) that outlines dimensions of CCI, functional areas, job roles and requisite competences (knowledge, skills, and abilities), and tasks for each CCI job role. The CCI framework also outlines five levels of proficiency expected for each job role. The identification of competences and levels of proficiency are integral to the successful implementation of the framework and workforce development. The CCIC Framework is intended to be used as a tool to retain, assess, and monitor knowledge, skills, and abilities for CCI workforce development. In addition, the CCIC Framework can be used to assist in providing the basis for individual performance management, education, training, and development pathway, as well as career progression. Therefore, this paper presents a CCIC Framework which is an overarching, integrative construct that synergistically combines different components required to develop a competent workforce for the emerging field of CCI.
Evidence and investigation of Internet crimes in UAE

This study talked about the Internet and what distinguishes it as a boom in our current and modern era, information crime or Internet crime and its risks to society. It also talked about the role of the police in inspecting, controlling crime, and seizing the tools used in the crime, as it is a form of evidence gathering. There are many difficulties and obstacles that stand as a barrier for investigation authorities in gathering evidence and investigations into this type of serious crime ,Where this study raises a problem and opinions that require exposure to it in order to solve it and focus on judicial jurisprudence and its inability or weakness in the face of this type of crime due to its secrecy and the difficulty of proving and gathering evidence with it, given the evaporation of information or programs that those responsible for this crime can use to erase data or any evidence against them. This study aimed to clarify what is meant by the Internet and what the information crime is, and to highlight the role of the police in confronting these crimes, the difficulty of their work, and the problems they face, especially in these crimes, as they are classified information crimes. In this study, the descriptive-analytical approach was followed in arriving at the meaning of the Internet and what is meant by the information crime and its analysis, as well as reaching the role of the police in proving and collecting forensic evidence about Internet crime and the difficulties it faces and obstacles, as well as the analytical approach in explaining the difficulties and problems that arise before the policemen in Confronting information crimes and the difficulty of proving and investigating them.
Violations of Gricean Maxims in the TV Series &lt;em&gt;You&lt;/em&gt;

This research investigates the violations of Gricean maxims committed by the main characters in the TV series You Season 1, especially Joe Goldberg and Guinevere Beck. It focuses on how the two characters violate the maxims and the possible purposes behind each violation. Quantitative and qualitative methods were applied to the data analysis. The results show that 100 maxim violations were found in the first season of the series. The most frequently occurring violation is the violation of the quality maxim with 56 occurrences (56%), followed by the relation maxim (28%) and the manner maxim with (10%). The least occurring violation is the violation of the quantity maxim (6%). Joe Goldberg and Guinevere Beck tend to violate the maxims by being dishonest, saying irrelevant things, giving vague or obscure responses, and providing insufficient or unnecessary information. Several purposes of violations can be identified throughout the data, namely to avoid unwanted responses, avoid suspicion, make certain impressions, deceive, divert, persuade others, and relieve worries.
Efficiency of lattice-based security mechanisms supporting public tasks on digital integration platforms

Integration platforms are the solution increasingly used by the public administration in Poland in order to make public tasks available and implement the tasks on these platforms (in the form of electronic services). In earlier publications, the author proposed a mathematical model based on lattice mechanisms that ensures the secure provision of these services (from a confidentiality perspective). In the following publication the author's model was cited (its main elements) and then the analysis of a time efficiency of the approach with using lattice mechanisms with the general model was performed. The efficiency examined concerned both the implementation of public tasks – and more precisely the verification of their correctness from the security perspective before the implementation as well as concerned an expansion of the process of handling the public task. The author has demonstrated that in both cases it is appropriate to use the lattice solution, as its time efficiency is better.
Penerapan Metode Trend Moment Untuk Memprediksi Jumlah Pertumbuhan Penduduk

Population growth is caused by three components, namely birth (fertility), death (mortality), and migration. These three components greatly affect the process of population growth that occurs. The development of population growth in one sub-district is very important to be detailed so that the development of the subdistrict can be improved. Forecasting is an important tool in effective and efficient planning. An important step after forecasting is done is verification of forecasting in such a way that it reflects past data and the underlying causal systems of such growth. As long as the forecasting representation is reliable, forecasting results can continue to be used. The purpose of this study is to apply the Trend Moment method to predict the population growth in Joman Water District in the next few years based on three components, namely birth (fertility), death (mortality), and migration. The results of this study can predict the number of population growth using the Trend Moment Method in 2022 with birth rates of 1573, 641 arrivals, 601 arrivals, and 235 displacements with errors below 10%
Improvement of Clustering Classification Performance Using Row-Based Filtering for health care application

Abstract A combination of unsupervised and supervised machine learning with an outlier-based row filtering technique is proposed in this paper. Our idea is based upon the concept of row-based filtering technique, a novel approach of records selection for a given structured dataset. Initially, dropping the class label, dataset is divided into groups by using clustering algorithm and class labels are assigned to the clusters. The cluster labels are compared with the original class labels. Records having matching class label and cluster label are extracted to frame a dataset having matching record. Remaining records after filtering matching records are treated as outliers. UCI repository datasets such as Breast cancer, Pima Indian diabetes dataset, Hungarian dataset, Indian liver patient dataset and Cleveland heart dataset are used to analyze the performance of proposed method. For clustering K means clustering algorithm is used and the classification performance is measured by applying various classification algorithm such as K Nearest Neighbour, J48, Naïve Bayes and Logistic regression.
MDMLP: Image Classification from Scratch on Small Datasets with MLP

The attention mechanism has become a go-to technique for natural language processing and computer vision tasks. Recently, the MLP-Mixer and other MLP-based architectures, based simply on multi-layer perceptrons (MLPs), are also powerful compared to CNNs and attention techniques and raises a new research direction. However, the high capability of the MLP-based networks severely relies on large volume of training data, and lacks of explanation ability compared to the Vision Transformer (ViT) or ConvNets. When trained on small datasets, they usually achieved inferior results than ConvNets. To resolve it, we present (i) multi-dimensional MLP (MDMLP), a conceptually simple and lightweight MLP-based architecture yet achieves SOTA when training from scratch on small-size datasets; (ii) multi-dimension MLP Attention Tool (MDAttnTool), a novel and efficient attention mechanism based on MLPs. Even without strong data augmentation, MDMLP achieves 90.90% accuracy on CIFAR10 with only 0.3M parameters, while the well-known MLP-Mixer achieves 85.45% with 17.1M parameters. In addition, the lightweight MDAttnTool highlights objects in images, indicating its explanation power. Our code is available at https://github.com/Amoza-Theodore/MDMLP.