Reviewerly/exHarmony huggingface dataset:
Vulnerabilities Assessment of Financial and Government Websites: A Developing Country Perspective

The growing number of web applications in a developing country like Bangladesh has led to an increase in cybercrime activities. This study focuses on measuring the vulnerabilities present in financial and government websites of Bangladesh to address the rising security concerns. We reviewed related works on web application vulnerability scanners, comparative studies on web application security parameters, surveys on web application penetration testing methodologies and tools, and security analyses of government and financial websites in Bangladesh. Existing studies in the context of developing countries have provided limited insight into web application vulnerabilities and their solutions. These studies have focused on specific vulnerabilities, lacked comprehensive evaluations of security parameters, and offered a limited comparative analysis of vulnerability scanners. Our study aims to address these gaps by conducting an in-depth analysis using the OWASP ZAP tool to scan and analyze risk alerts, including risk levels such as high, medium, low, and informational. Our investigation unveiled eight key vulnerabilities, including Hash Disclosure, SQL injection (SQLi), Cross-Site Request Forgery (CSRF), missing Content Security Policy (CSP) headers, Cross-Domain JavaScript File Inclusion, absence of X-Content-Type-Options headers, Cache-related concerns, and potential Cross-Site Scripting (XSS), which can lead to revealing hidden information, enabling malicious code, and failing to protect against specific types of attacks. In essence, this study does not only reveal major security weaknesses but also provides guidance on how to mitigate them, thereby playing a vital role in promoting enhanced cybersecurity practices within the nation.
A Novel Algorithm for Stacked Generalization Approach to Predict Neurological Disorder over Digital Footprints

Digital footprints track online behaviors of an individual when communicating over social media platforms. In this paper, sentiment classification is carried out over online posts and tweets to pre detect whether a person is having neurological disorder or not. This study proposed a Hybrid Optimized Model Ensemble STACKed (HOMESTACK) algorithm built on stacked generalization approach that uses stacking and blending ensemble learning technique. The model is then evaluated over two datasets (Reddit Dataset1 & Twitter Dataset2) that include varied number of tweets. The pre-processing of the data and feature extraction is carried out to get cleaned text and vector corpus. The proposed HOMESTACK algorithm is then applied over training data using four base classifiers as Support Vector, Random Forest, K-Nearest Neighbor and CatBoost along with a Meta classifier as Logistic Regression. The testing data is then fed to the tuned model to compare the classification results and analysis. Also, Stacking and Blending ensemble frameworks and algorithms are proposed in this study. Execution time and metric evaluation are calculated in respect of Accuracy, Precision, Recall and F1-score. The experimental results clearly show that the proposed HOMESTACK algorithm performed better over chosen datasets as compared to blending ensemble and standalone machine learning classifiers.
Using xAPIs for Monitoring Behavioral Lessons in Augmented Reality

For the first time, augmented reality is introduced into a behaviour lesson according to the framework of positive behaviour intervention and support (PBIS) and the first interaction data between the user and the teaching resources are investigated. The xAPI statements reflect the actions that commonly compose a behavioural lesson; that is, teach, practice, and reinforce. With a specially developed application to access AR behavioural learning content, the student is able to perform actions within the three phases of a lesson. The xAPIs make it possible to monitor users in action within their learning activity. This work introduces the use of xAPIs in the new context of monitoring interactions with augmented reality objects used for behavioural teaching.
From the Cloud to the Landfill: The Case of the Agbogbloshie Makerspace Platform and Akwasi Bediako Afrane’s TRONS

This contribution sheds light on discarded electronics repair in Accra, Ghana. After placing these practices in dialogue with the Western and Eurocentric narratives around the materiality of digital interactions and infrastructure, it delves into two arts and design contexts that gravitate around the electronic waste landfill and processing site of Agbogbloshie (Ghana). The first case study is the Agbogbloshie Makerspace (AMP), a critical making (and unmaking) platform empowering local repairers and dismantlers through open-source collaborative design methods. The contribution then focuses on the work of Akwasi Bediako Afrane, a Ghanaian media artist who re-appropriates discarded computers to critique and speculate on our sociotechnical condition. Situating these initiatives in the light of our broader dominant internet and computing narratives, the article situates the importance of these practices in order to tackle and raise awareness about the planetary electronic waste condition we live in.
Data Mining and Fusion Framework for In-Home Monitoring Applications

Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework. It compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. A total of 574 privacy-friendly (binary) images and 1722 datasets gleaned from thermal and Radar sensing solutions, respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets, respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, and CN2 Induction. Further evaluation of the Sensor Data Fusion framework based on cross-validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% for Precision, and 96.4% for Recall. The novelty of the proposed framework includes cost and timesaving advantages for data labelling and preparation, and feature extraction.
SiamHSFT: A Siamese Network-Based Tracker with Hierarchical Sparse Fusion and Transformer for UAV Tracking

Due to high maneuverability as well as hardware limitations of Unmanned Aerial Vehicle (UAV) platforms, tracking targets in UAV views often encounter challenges such as low resolution, fast motion, and background interference, which make it difficult to strike a compatibility between performance and efficiency. Based on the Siamese network framework, this paper proposes a novel UAV tracking algorithm, SiamHSFT, aiming to achieve a balance between tracking robustness and real-time computation. Firstly, by combining CBAM attention and downward information interaction in the feature enhancement module, the provided method merges high-level and low-level feature maps to prevent the loss of information when dealing with small targets. Secondly, it focuses on both long and short spatial intervals within the affinity in the interlaced sparse attention module, thereby enhancing the utilization of global context and prioritizing crucial information in feature extraction. Lastly, the Transformer’s encoder is optimized with a modulation enhancement layer, which integrates triplet attention to enhance inter-layer dependencies and improve target discrimination. Experimental results demonstrate SiamHSFT’s excellent performance across diverse datasets, including UAV123, UAV20L, UAV123@10fps, and DTB70. Notably, it performs better in fast motion and dynamic blurring scenarios. Meanwhile, it maintains an average tracking speed of 126.7 fps across all datasets, meeting real-time tracking requirements.
High-Precision DOA Estimation Based on Synthetic Aperture and Sparse Reconstruction

The direction-of-arrival (DOA) estimation is predominantly influenced by the antenna’s aperture size. However, space constraints on flight platforms often necessitate the use of antennas with smaller apertures and fewer array elements. This inevitably imposes limitations on the DOA estimation’s resolution and degrees of freedom. To address these precision constraints, we introduce an accurate DOA estimation method based on spatial synthetic aperture model. This method adopts a two-stage strategy to ensure both efficiency and precision in DOA estimation. Initially, the orthogonal matching pursuit (OMP) reconstruction algorithm processes the original aperture data, providing a rough estimate of target angles that guides the aircraft’s flight direction. Subsequently, the early estimations merge with the aircraft’s motion space samples, forming equivalent spatially synthesized array samples. The refined angle estimation then employs the OMP-RELAX algorithm. Moreover, with the off-grid issue in mind, we devise an estimation method integrating Bayesian parameter estimation with dictionary sequence refinement. The proposed technique harnesses the spatial synthetic aperture for pinpoint estimation, effectively addressing the challenges of atomic orthogonality and angular off-grid on estimation accuracy. Importantly, the efficiency of deploying sparse reconstruction for angle estimation is bolstered by our phased strategy, eliminating the necessity for fine grid analysis across the entire observation scene. Moreover, the poor estimation accuracy caused by coherent source targets and angular-flickering targets is improved by sparse reconstruction. Through simulation and experiment, we affirm the proposed method’s efficacy in angle estimation. The results indicate that target angle estimation errors are limited to within 1°. Furthermore, we assess the impact of variables such as target state, heading angle, spatial sampling points, and target distance on the estimation accuracy of our method, showcasing its resilience and adaptability.
Android-Based Bus Vehicle Maintenance Application Design at PT. Sumber Jaya Trans Tangerang

Sumber Jaya Trans is a tourism bus transportation company whose goal is to make tourism easier. Maintenance of bus vehicles is required because of their long-term use. At PT. Sumber Jaya Trans Tangerang has problems, namely vehicle maintenance in terms of data reporting which is still manual, maintenance reports are carried out on a paper form filled in by mechanics who have repaired bus vehicles and recapitulated into the ledger, managers have difficulty viewing or checking maintenance reports, because lots of paper forms and disorganized ledgers. Due to the issues at hand, the author suggests an Android-based bus vehicle maintenance app. utilizing the PIECES technique of analysis. The programming languages for Web Services and Android are PHP and the Laravel framework, respectively, and object-oriented analysis techniques are used in the system architecture together with the Unified Modeling Language (UML). Android was selected as the mobile operating system because it is well-liked in Indonesia and felt capable of replacing manual labor. In order to improve the effectiveness and efficiency of reporting vehicle maintenance data, we require an Android-based solution
Artificial Intelligence in Digital Marketing: Descriptive Analysis of Companies’ Artificial Intelligence Strategies with Random Sampling

Brands are constantly looking for strategies to meet customer needs in the marketplace and to promote and market their products more effectively. As the new world of technology replaces traditional TV commercials and outdoor billboards, different approaches have emerged. At this point, with the development of computer software and machine learning, Artificial Intelligence (AI) has become an indispensable option for those companies that want to expand their sales and marketing strategies to different dimensions. Artificial Intelligence technologies, deep machine learning and appropriate software can support companies on a wide range of scales, from providing brands with product options suitable for the target audience to offering customers special discounts, or from managing a complete process of collecting marketing data to ensuring that company information is accurately archived in the digital environment. The purpose of this article is to provide a descriptive analysis of the leading companies in today's market, with annual revenues of more than $10 million, that were included in the research through random sampling. At this point, the article draws attention to the importance of AI in the development of brands' marketing strategies and gives several examples of campaigns and applications in which brands have used AI. Finally, as a result of the descriptive analysis, the increase in revenue following the use of AI in marketing strategies and advertising is also considered.
Comparative Analysis of Thresholding Methods to Facilitate Decision Making over Microscopic Skin Image

It is a useful technique used in many image processing applications to distinguish objects from the background. In this work, we examine the thresholding techniques that may be applied to make microscopic skin images easier to grasp in terms of collagen intensity, which can disclose the majority of skin illnesses. On the provided data set, 15 global thresholding techniques that are commonly recommended in the literature were looked at and tried. 10 histopathology images stained with Masson's Trichrome were used to build the dataset. The data set was used to evaluate several thresholding techniques, and ME was computed to choose the best one. The experiments revealed that the Otsu and IsoData approaches produced the best outcomes, with a ME value of 0.063 and 0.073 respectively.
An Efficient and Scalable Consensus for Main Blockchain in the Multi-Chain Network

While blockchain technology has gained significant popularity and adoption, auditing the legitimacy of on-chain transactions continues to present complex technical challenges. To address the challenge of auditing the legitimacy of on-chain transactions in multi-chain systems, this paper proposes an Auditing-Service Multi-Chain architecture that utilizes a new Auditing Chain to facilitate cross-chain auditing of transactions across multiple Service Chains. However, the performance of the consensus mechanism of the Auditing Chain is limited under the increasing number of Service Chains and transaction volumes. To overcome the shortcomings of the Auditing Chain's consensus mechanism, this paper introduces a new consensus that employs a sliding window approach to determine the consensus nodes' membership and automatically selects the leader node. Moreover, the communication tree structure is utilized in the broadcast phase to optimize the network overhead in the consensus stage. Additionally, this paper applies the CoSi collective signature protocol to streamline the consensus process and enhance the throughput and scalability of the Auditing Chain. The experimental results demonstrate that the proposed consensus mechanism satisfies the performance requirements of the Auditing-Service Multi-Chain architecture in a local test network environment.
Anomaly Detection for Multi-Zone Buildings Using Cluster-Trained LSTM Autoencoders

The optimal energy performance of building operations is affected by component faults, which may go unnoticed for long periods of time. Significant energy savings can be achieved if faulty behaviors are detected and rectified in a timely manner. In this work, we adopt an unsupervised approach for anomaly detection that combines automatic data engineering using clustering methods with Long-Short Term Memory (LSTM)-based Autoencoders. First, data engineering is used to extract multiple operating modes from nominal data of building operations. Then, an LSTM-based Autoencoder is trained to capture the characteristics of non-linear and temporal dynamics for each operating mode. Finally, the ensemble of models can be used for anomaly detection after training has been completed. We benchmark variants of our approach against state-of-the-art Autoencoders for anomaly detection by using a recently developed experimental dataset provided by the ASHRAE Research Project RP-1312. Unsupervised anomaly detection is a challenging task due to the lack of faulty labels and the need to identify faults while avoiding false alarms. Our novel approach improves the average true positive rate for fault detection by 11.4% against a state-of-the-art plain LSTM Autoencoder while keeping the false alarm rate around 5 % without having to use labeled fault data.
DISTRIBUTIVE JUSTICE, INDIVIDUAL SPIRITUALITY, AND DEVIANT BEHAVIOUR AMONG ACADEMICS AT TARABA STATE UNIVERSITY

The study examined the effect of distributive justice on deviant behaviour, and the moderating role of individual spirituality among university academics at Taraba State University. Relevant literatures on distributive justice, deviant behaviour and spirituality were reviewed under conceptual, theoretical, and empirical review. The study was anchored on social exchange theory and person organisational fit theory. A descriptive survey research design method was used. The target population for the study is 586 academics at Taraba State University. The sample size was 226. The study employed a purposive sampling technique. The structural questionnaire was used to source data from the respondents. The researcher distributed 300 questionnaires, but only 190 copies were retrieved, and used for the analysis. Structural equation modeling specifically Partial least squares (PLS) software version 4 was used to test the hypotheses formulated. The findings of the study revealed that distributive justice has a negative and significant effect on the deviant behaviour of academics at Taraba State University. Individual spirituality does not moderate the relationship between distributive justice and deviant behaviour. The study recommends that the government and management of the university should ensure that academics’ salaries, benefits, allowances, and promotions are given to them at the right time to reduce deviant behaviour.
Multi-Level Precues for Guiding Tasks Within and Between Workspaces in Spatial Augmented Reality

We explore Spatial Augmented Reality (SAR) precues (predictive cues) for procedural tasks within and between workspaces and for visualizing multiple upcoming steps in advance. We designed precues based on several factors: cue type, color transparency, and multi-level (number of precues). Precues were evaluated in a procedural task requiring the user to press buttons in three surrounding workspaces. Participants performed fastest in conditions where tasks were linked with line cues with different levels of color transparency. Precue performance was also affected by whether the next task was in the same workspace or a different one.
KONDISI LINGKUNGAN PEMBELAJARAN BAHASA INDONESIA DALAM KELUARGA SISWA KELAS XI IPS 3 SMA N 1 PURWANEGARA BANJARNEGARA (Suatu Kajian Fenomenologi)

The purpose of this study was to find out about the form of the Indonesian language learning environment in the family for student of class XI IPS 3 SMA N 1 Purwanegara Banjarnegara. To gain an understanding of the student learning environtment in the family based on student experience, the researcher used a qualitative approach with the phenomenological method. From the result of the study, it can be explained that to create a conducive and comfortable learning environment in the family needed the availability of several learning facilities. Those are: (1) student have a room to study; (2) the room has good lighting; (3) the room has a room temperature controller; (4) have a comfortable and conducive learning atmosphere; (5) not all have study chairs and desks, but still comfortable studying on the carpet, guest chairs, on the floor or on the bed; (6) has cupboards and bookshelves; (7) have all the necessary study equipment; and (8) most of the students stated that they felt comfortable studying in a family environment. Based on these data, the researcher concluded that the support of a complete and conducive learning environment makes students able to study comfortably so that they can make learning more effective and efficient. Keywords: Learning Environment, Phenomenology
BUKU POP UP DAN IMPLEMENTASINYA SEBAGAI SARANA MEDIA PENGEMBANGAN LITERASI ANAK USIA DINI BAGI GURU PAUD DI KABUPATEN BANYUMAS

A pop up book is a book with an embossed, two-dimensional or three-dimensional image display. Pop-up books can be used as a learning medium to develop literacy in early childhood. Based on a survey of 103 ECCE teachers in Banyumas District, data related to teachers' responses to the use of pop-up books was obtained. The data showed that respondents had an understanding that literacy recognition could be conveyed to learners through pop-up books. The basis for consideration of using pop up books can be used as a learning medium, because pop up books:(1) can be used to show the ability to recognize several symbols and signs including language (35%), (2) able to communicate learning messages with more concrete thoughts/ideas (65%). Respondents stated that pop up books can be used in learning activities, because pop up books can serve to support literacy: 1) reading literacy with the introduction of letters, words, and sentences (68.9%), 2) knowing stories (2%) and numeracy literacy, knowing numbers (29.1%). Although respondents mostly understand that pop-up books can be a learning medium to improve children's literacy, not many respondents have used them in learning activities. Based on respondents' answers, only 35% claimed to have used pop up books in learning activities, the remaining 65% of respondents admitted that they had never used pop up books in learning activities. Respondents who have not used pop up books, stated that it is not easy to find pop up books in bookstores. Another respondent stated that the price of pop up books is quite expensive, and hoped that the government would provide assistance in providing pop up book packages to schools. Keywords: pop up books, learning media, literacy
Online assessment in English language teaching in Vietnamese universities

Teaching and learning online has long been a popular mode in the world. However, for foreign language teaching and learning, which requires a lot of practice, online teaching and learning is not the best option. In recent years, with the spread of the coronavirus pandemic, online teaching and learning is no longer an optional mode but an obligatory one. This requires all educational policymakers and school administrators to apply appropriate methods of online assessments so that they can meet such requirements. This paper reports the results of a survey study of 94 English lecturers who experienced online assessment in universities in the South of Vietnam. Based on the analyses of the advantages and disadvantages of using online assessment, this paper suggests ways of selecting suitable methods and appropriate tools for online assessment. The research findings may contribute to the application of technology in language teaching and learning and suggest practical criteria for selecting appropriate assessment tools for online teaching and learning.