Reviewerly/exHarmony huggingface dataset:
The Relationship between Human-centric Cybersecurity and Cybercrime

The aim of this study is to propose a model of human-centric cybersecurity and its relationship tocurbing cybercrime in Zimbabwean commercial banks. The effects of human-centric cyber-crimes (e.g.email phishing, identity theft, hacking and malware) are increasing irrespective of the use of cyber securitytechniques (such as firewalls, antispyware and antivirus). In order to prevent security breaches, the role ofthe human element should always be emphasized. The study proposed a model of human-centric cybersecurity solutions in Zimbabwean commercial banks to counter identity theft. Item frameworks were modifiedto illustrate how human cyber security measures would effectively curb cybercrime from the theory ofprotection motivation and the theory of routine activity. Solutions to cybercrime centered on human beingsinclude raising aware- ness about cyber security, top management support and cyber security policies.Quantitative data collected through survey questionnaires which were distributed to 118 key informantsacross thirteen commercial banks in Zimbabwe. Gathered data were analyzed using SPSS 23 and Smart PLSVersion 3.2.8 for partial list structural equation modeling. The findings were interesting, the human solutionssuch as raising awareness, top management support, and cybersecurity policy indicated a negative andmoderate effect on identity theft, R2 value was of 0.64. The standardized root mean square residual value forthe human structural model was 0.08 indicating a good model fit. The findings show that the human factoris key for the successful cybersecurity of any banking institution.
Gesture Recognition in Virtual reality

Virtual Reality (VR) provides users with a sensory experience that is close to reality, creating a sense of interaction. It is widely used, and the gesture recognition in VR also has a great effect. Gesture recognition enriches VR using experience and promotes a more direct and natural interaction. Gesture recognition usually employs sensors to collect data from users and machine leaning algorithms to interpret and respond to human activities. Complex gestures need more complex algorithms and more rigorous operations. The reason is that complex gestures mean larger quantity of data. If data is larger, the harder to get robust and effective datasets. Then, features can also become difficult to extract, contributing to misrecognition or unrecognizable. Though machine leaning algorithms are widely used in gesture recognition, there are still some important challenges need to be addressed, like lack of standardization and limitations of availability of diverse and large datasets. However, VR, gesture recognition and machine leaning algorithms all have excellent prospect, because they are in line with the development of the Times and show the progress of science and technology. This paper not only focuses on their advantages but also does not ignore their shortcomings, and looks at them comprehensively.
Improving Critical Thinking Skills OF High School Students THROUGH Gamification-Based Learning Media Development

This study aims to develop gamification-based learning media and test the effectiveness of the media on students' critical thinking skills. This study uses the Research and Development methodology, the ADDIE development model (Analysis, Design, Development, Implementation and Evaluation). The subjects of this research trial were class X geography students at the Pasanggaran High School in East Java, which consisted of an experimental class and a control class. Sampling using a purposive sampling technique. The type of data used is quantitative data. Collecting data using questionnaires and tests using descriptive analysis techniques. The respondent's due diligence resulted in 8326 and has been validated. The results showed that the use of gamification-based learning media had a positive effect on students’ critical thinking skills using sig. 0.000 &lt; 0.05. Thus, gamification-based learning media is feasible to use and influences students' critical thinking skills.
Implementation of SIC Algorithms in the 5G DL

In this paper, we have implemented successive interference cancellation algorithms in the 5G downlink. We have calculated the maximum throughput in Frequency Division Duplex (FDD) mode in the downlink, where we have obtained a value equal to 836932 b/ms. The transmitter is of type Multiple Input Multiple Output (MIMO) with eight transmitting and receiving antennas. Each antenna among eight transmits simultaneously a data rate of 104616 b/ms that contains the binary messages of the three users. In this case, the cyclic redundancy check CRC is negligible, the Block error rate BLER is null, the MIMO category is the spatial diversity. The technology used for this is called Non-Orthogonal Multiple Access (NOMA) with a Quadrature Phase Shift Keying (QPSK) modulation. The transmission is done in a Rayleigh fading channel with the presence of obstacles. The MIMO Successive Interference Cancellation (SIC) receiver with two transmitting and receiving antennas recovers its binary message without errors for certain values of transmission power such as 50 dBm, with 0.054485% errors when the transmitted power is 20 dBm and with 0.00286763% errors for a transmitted power of 32 dBm(in the case of user 1) as well as with 0.0114705% errors when the transmitted power is 20 dBm also with 0.00286763% errors for a power of 24 dBm(in the case of user2) by applying the steps involved in SIC.
A Novel Meta Heuristic Approach with Optimal Deep Learning Neural Network Based Oral Cancer Detection Model

Objectives: This study proposes a new approach to improve oral cancer detection in medical images by utilizing a Deep Convolutional Neural Network (DCNN) and an optimized Long Short-Term Memory (LSTM) technique. Methods: First, the input oral squamous cell carcinoma images are pre-processed using median filtering as well as CLAHE. Next, feature extraction is performed using the Local Tetra Pattern (LTrP) to extract different features. The HHHLO algorithm is then applied to select the optimal features for the subsequent feature selection process. Finally, the selected features are classified using a hybrid classifier called DCNN-LSTM, which predicts the diagnosis of patients with oral cancer. The investigation of the DCNN-LSTM model involves conducting experiments on a commonly used biomedical image dataset that is readily accessible through the Kaggle repository. Findings: The proposed method was implemented on the MATLAB platform, and its performance was evaluated using various metrics. The results demonstrated the superiority of the DCNN-LSTM model over existing methods, achieving a maximum accuracy of 0.975. Novelty: Oral cancer is a common and formidable type of cancer associated with a significant mortality rate. Keywords: Oral cancer, Improved Squirrel Search Algorithm (ISSA), Deep Convolutional Neural Network (DCNN), Contrast Limited Adaptive Histogram Equalization (CLAHE), Hybrid Horse herd lion optimization (HHHLO)
Accumulation and Risk Assessment of Heavy Metals in Sediments from Dredged Tributaries and Creeks of River Ethiope, South-South, Nigeria

The presence of heavy metals in rivers in the Niger Delta region has become a source of concern due to its associated health challenges. The present study was conducted to assess the risk of heavy metal accumulation in surface sediments obtained from creeks and dredged tributaries of the River Ethiope, Delta State, South-South, Nigeria. Heavy metals in the sediments were extracted using the three-step sequential extraction method of the European Commission Standard Measurement and Testing Program. The heavy metals; magnesium (Mn), iron (Fe), zinc (Zn), lead (Pb), copper (Cu), cobalt (Co), arsenic (As), chromium (Cr), cadmium (Cd) and barium (Ba) were quantified by employing inductively coupled plasma-mass spectrometry (ICP-MS). Assessment of the extent of sediment contamination was carried out by determining the contamination factor (CF), degree of contamination (Cd), modified degree of contamination (mCd), pollution load index (PLI), ecological risk factor (Er), potential ecological risk index (PI) and geo accumulation index (1 geo). Pearson’s correlation coefficient and principal component analysis (PCA) were used to determine the sources and the relationship between pollutants across sediments. The values of heavy metals ranged from 12.5 mg kg–1–116 mg kg–1 and 21.6 mg kg–1–71.1 mg kg–1 in the wet and dry seasons, respectively. The trend of heavy metals for risk index (RI) in this study is Cd &gt; Pb &gt; Cr &gt; Co &gt; Zn &gt; Cu &gt; Mn (wet season) and Cu &gt; Cd &gt; Pb &gt; Zn &gt; Cr &gt; Mn &gt; Co = As (dry season). It showed that heavy metal pollution was a result of Cd for extreme contamination, while moderate to high contamination levels were due to Pb and Cu. The Pearson’s correlation coefficient analysis and PCA displayed strong positive loadings for Mn, Fe, Zn, Pb, Cu and Cd across seasons as a result of high contamination levels in the study sites. The pollution load index revealed that the sediments were polluted by the metals, and the mean and median analyses revealed that the metals datasets were normally distributed, except for Cu with an irregular distribution.
Swin-APT: An Enhancing Swin-Transformer Adaptor for Intelligent Transportation

Artificial Intelligence has been widely applied in intelligent transportation systems. In this work, Swin-APT, a deep learning-based approach for semantic segmentation and object detection in intelligent transportation systems is presented. Swin-APT includes a lightweight network and a multiscale adapter network designed for image semantic segmentation and object detection tasks. An inter-frame consistency module is proposed to extract more accurate road information from images. Experimental results on four datasets: BDD100K, CamVid, SYNTHIA, and CeyMo, demonstrate that Swin-APT outperforms the baseline by 13.1%. Furthermore, experiments on the road marking detection benchmark show an improvement of 1.85% of mAcc.
Empirical Analysis of Benchmark Generation for the Verification of Neural Network Image Classifiers

Deep Learning success in a wide range of applications, such as image recognition and natural language processing, has led to the increasing usage of this technology in many domains, including safety-critical applications such as autonomous cars and medicine. The usage of the models, e.g., neural networks, in safety critical applications demands a thorough evaluation from a component and system level perspective. In these domains, formal methods have the ability to guarantee the correct operation of these components. Despite great efforts in the formal verification of neural networks in the past decade, several challenges remain. One of these challenges is the development of neural networks for easier verification. In this work, we present an empirical analysis, presented as a Latin Hypercube experiment design, in which we evaluate how regularization and initialization methods across different random seeds on two datasets affect the verification analysis of a reachability analysis technique for the verification of neural networks. We show that there are certain training routines that simplify the formal verification task. Lastly, a discussion on how these training approaches impact the robustness verification and reachability computation of the method utilized is included.
Benchmarks: Semantic Segmentation Neural Network Verification and Objection Detection Neural Network Verification in Perceptions Tasks of Autonomous Driving

The verification of the security of neural networks is cruicial, especially for the field of autonomous driving. Although there are currently benchmarks for the verification of the robustness of neural networks, there are hardly any benchmarks related to the field of autonomous driving, especially those related to object detection and semantic segmentation. Thus, a notable gap exists in formally verifying the robustness of semantic semantic segmentation and object detection tasks under complex, real-world conditions. To address this, we present an innovative approach to benchamark formal verification for autonomous driving perception tasks. Firstly, we propose robust verification benchmarks for semantic segmentation and object detection, supplementing existing methods. Secondly, and more significantly, we introduce a novel patch-level disturbance approach for object detection, providing a more realistic representation of real-world scenarios. By augmenting the current verification benchmarks with our novel proposals, our work contributes towards developing a more comprehensive, practical, and realistic benchmarking methodology for perception tasks in autonomous driving, thereby propelling the field towards improved safety and reliability. Our dataset and code used in this work are publicly available.
ViTSTR-Transducer: Cross-Attention-Free Vision Transformer Transducer for Scene Text Recognition

Attention-based encoder–decoder scene text recognition (STR) architectures have been proven effective in recognizing text in the real world, thanks to their ability to learn an internal language model. Nevertheless, the cross-attention operation that is used to align visual and linguistic features during decoding is computationally expensive, especially in low-resource environments. To address this bottleneck, we propose a cross-attention-free STR framework that still learns a language model. The framework we propose is ViTSTR-Transducer, which draws inspiration from ViTSTR, a vision transformer (ViT)-based method designed for STR and the recurrent neural network transducer (RNN-T) initially introduced for speech recognition. The experimental results show that our ViTSTR-Transducer models outperform the baseline attention-based models in terms of the required decoding floating point operations (FLOPs) and latency while achieving a comparable level of recognition accuracy. Compared with the baseline context-free ViTSTR models, our proposed models achieve superior recognition accuracy. Furthermore, compared with the recent state-of-the-art (SOTA) methods, our proposed models deliver competitive results.
Formal Verification of a Neural Network Based Prognostics System for Aircraft Equipment

We demonstrate the use of formal methods to verify properties of a deep convolutional neural network that estimates remaining useful life of aircraft mechanical equipment. We provide mathematical formalizations of requirements of the estimator, such as stability and monotonicity, as properties. To efficiently apply existing tools for verification of neural networks, we reduce the verification of global properties to a representative set of local properties defined for the points of the test dataset. We encode these properties as linear constraints and verify them using a state-of-the-art tool for neural network verification. To increase the completeness and the scalability of the analysis, we develop a two-step verification method involving abstract interpretation and simulation-based falsification. Numerical results confirm the applicability of the approach.
Revisiting the viability of shaming trademark bullies*

Trademark bullying, whereby well-resourced entities attempt to enforce an unreasonable interpretation of its trademark rights against small businesses or individuals through the use of intimidation tactics, has continued to be problematic in the decade since this author published an article titled, “Shaming Trademark Bullies.” This article detailed efforts by small businesses and individuals to counter trademark bullying through shaming their bullies on social media, and also identified the factors that lead to a successful and effective shaming campaign. The driving concern behind this article was that small businesses and individuals needed low-cost, extra-judicial tools to fight back against abusive pre-litigation tactics. Trademark bullying had (and continues to have) the ability to push a small business or an individual into bankruptcy or economic insecurity, given the expense of hiring an attorney or even complying with demands of the bully (such as changing a company name or destroying inventory containing the mark in question). With the COVID-19 global pandemic, and its resulting economic harms, small businesses and individuals are suffering to make ends meet. The need for free or low-cost, extra-judicial tools to combat trademark bullying appears more resonant than ever. Therefore, this book chapter revisits whether shaming has retained its viability as an effective tool for small businesses or individuals to combat trademark bullying.
AlphaML: A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data

Leveraging the potential of machine learning and recognizing the broad applications of binary classification, it becomes essential to develop platforms that are not only powerful but also transparent, interpretable, and user friendly. We introduce alphaML, a user-friendly platform that provides clear, legible, explainable, transparent, and elucidative (CLETE) binary classification models with comprehensive customization options. AlphaML offers feature selection, hyperparameter search, sampling, and normalization methods, along with 15 machine learning algorithms with global and local interpretation. We have integrated a custom metric for hyperparameter search that considers both training and validation scores, safeguarding against under- or overfitting. Additionally, we employ the NegLog2RMSL scoring method, which uses both training and test scores for a thorough model evaluation. The platform has been tested using datasets from multiple domains and offers a graphical interface, removing the need for programming expertise. Consequently, alphaML exhibits versatility, demonstrating promising applicability across a broad spectrum of tabular data configurations.
The Professional Activity of the Head of the Educational Institution as a Sphere of Formation of his/her Professional Creativity

The aim of our research is to show the professional activity of the head of the educational institution as a sphere of formation of his/her professional creativity. Methods of the research. The following theoretical methods of the research were used to solve the tasks formulated in the article: a categorical method, structural and functional methods, the methods of the analysis, systematization, modeling and generalization. The results of the research. We believe that the professional creativity of a manager is a process of carrying out creative activity, because it is professional creativity aimed at solving tasks that are characterized by the absence of a solution method in a given subject area, or the absence of such a method in a given management subject. The latter can be explained by the lack of subjectspecific knowledge necessary for a specialist to solve this problem. In the process of implementing professional creativity, the manager becomes aware of new knowledge for him/her as a kind of indicative basis for further development of a method of solving managerial and professional problems and tasks. In professional creativity, at first, the manager is in some uncertainty: there is no clearly formed opportunity to develop new subjects of professional activity and, on their basis, to invent a way to solve the task; this kind of uncertainty is due to the absence of any other knowledge that will clearly determine the development in asking questions. Conclusions. The practice of the manager’s creative professional activity, its reflection in theoretical studies, allows us to conclude that professional creativity is a specific activity, which is characterized by a professional direction with the aim of finding new, non-standard, original and unique rational solutions, which facilitate a new solution to use creative problem tasks by individual tasks in the professional activity and their implementation into practice.
Improving Students’ Social Studies Learning Outcomes Using the Group Investigation Model

This study aims to explore how the group investigation model could be used to improve student learning results in social studies courses. This study was a two-cycle classroom action research study. Six students of class IV from SDN Inti Sungai Miai 11 Banjarmasin participated in this study for the 2020/2021 academic year. Data were collected using observation techniques on implementing lesson plans and student activities and testing on student learning outcomes. The student standard score was ≥ 70, and the expected classical success indicator was 81%-99%. According to the research findings, teacher activity received a score of 90.625 on very good criteria. With practically all active criteria, student activity improved to 85.7%. Individual learning outcomes showed that five students with a classical completeness of 83.33% were nearly entirely complete. Based on these findings, it was possible to conclude that using the Group Investigation model could increase student learning outcomes in social studies courses.
School, family and the community in cooperation: the model of syneducation

School, family and the community constitute three distinct but at the same time interdependent systems. The ideas, notions and research which are related to the necessity of the functional communication of those systems have recently flourished internationally, creating a substantial “heritage” in principles, acceptances, theoretical models, application proposals, research programs as well as new orientations and perspectives. This paper proposes the model of syneducation. As syneducation (synergy + education) we define the acquisition of a common educational experience, simultaneously and in cooperation, of persons differing in age and cognitive infrastructure and often in social and cultural level. Syneducation is a new emerging research field. With its application, we aim at the change of attitudes and behaviours of the participants (parents, educators, students, policy makers and the representatives of the community) from an initial state of possible indifference- disinterest to an active and effective participation in common syneducational actions in order to face specific issues, and even further to being multipliers of the above mentioned actions. Furthermore, the application of this model creates, develops and accrues social capital.In this paper we will present certain research applications which took place according to the model of syneducation based on the methodology of collaborative action research (for example, a transdisciplinary research program, a research program in Literacy in Information Technology, a research program focusing on problem management within the family etc.) The research findings and the prospects of the syneducation model are still being discussed. This model is proposed as a realistic way of thought and action adapted to the contemporary conditions of our technological era and the foreseen needs of social computing.