Reviewerly/exHarmony huggingface dataset:
The teaching electromagnetism using digital technologies

The Physics of Electromagnetism began in ancient Greece and after centuries the studies of Electricity and Magnetism became unified. Although Electromagnetism is present in the curricular components of Brazilian education and in the daily lives of students, the difficulties that students have in assimilating the basic concepts related to this topic are notable. In this context, a didactic and practical mini-course was proposed where Digital Information and Communication Technologies (DICT) were used, conveniently to teach students some concepts of Electromagnetism. During the activity, two questionnaires were administered, one before the mini-course and the other after it. The first composed of seven questions (objective and discursive) aimed to obtain students' prior knowledge in relation to Electromagnetism. The second questionnaire consisted of four questions, three of which were paired with questions from the first questionnaire. It was applied with the aim of evaluating whether the mini-course carried out favored student learning, thus revealing possible signs of significant learning. The post-questionnaire also presented a discursive question that asked students' opinions about the mini-course developed based on DICT. This research can be characterized as qualitative and quantitative and from the data analysis, it was noticed that the activities developed with high school students favored their learning as observed in the best results revealed in the post-questionnaire. It is concluded that the use of DICT should be encouraged in schools, as they constitute tools that alleviate difficulties and help students learn.
Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest

Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models—such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier—is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively.
Advancing weakly supervised semantic segmentation using class activation map augmented with graph attention module

In the field of computer vision, Weakly Supervised Semantic Segmentation (WSSS) at the image level poses a significant research challenge. Existing WSSS methods primarily rely on class activation maps (CAM). Due to the disparity between fully-supervised and weakly-supervised approaches, CAM often yields imprecise and coarse semantic information in generating target masks. To address the issues of semantic coarseness and detail loss in image-level WSSS, this paper introduces a novel approach that utilizes pixel-level relationships to optimize and refine the features extracted by the network. Maintaining the foundation of image-level class label weak supervision for semantic segmentation, this study forgoes extensive modifications to the model structure in favor of incorporating a Graph Attention Module (GAM). This module, by constructing a weighted undirected graph with pixels as vertices and pixel affinity as edges, simulates the interactions among neighboring pixels in graph representation learning. Such an arrangement allows for the effective propagation and integration of relational information between pixels, thereby enhancing the overall representational quality of the image. By employing graph constraints to facilitate semantic dissemination, our method optimizes the network's localization map, resulting in more precise CAMs and more accurate pseudo-labels for subsequent semantic segmentation network training. To evaluate our approach, we conducted quantity and quality experiments on the PASCAL VOC 2012 and MS COCO 2014 datasets. Compared to existing advanced weakly-supervised semantic segmentation methods, our approach shows notable improvements in performance.
Blockchain Applications in Retail Cybersecurity: Enhancing Supply Chain Integrity, Secure Transactions, and Data Protection

Blockchain technology has proven a powerful tool for reinforcing cybersecurity in the retail sector. This research offers an extensive overview of the applications of blockchain in retail cybersecurity, particularly, underscoring supply chain integrity, data protection, and transaction security. The research explored how blockchain can facilitate provenance and traceability well as prevent counterfeiting and enhance vendor management in the supply chain. It also explores how blockchain-based payment frameworks and fraud detection systems can boost transaction security. Moreover, the study assesses the capability of blockchain to safeguard data via privacy and consent management, and secure and immutable data storage. The findings outline the capability of blockchain technology to diminish risks, enhance transparency, and affirm trust in retail cybersecurity. While challenges are inevitable, such as regulatory and scalability considerations, the research infers that blockchain technology presents noteworthy opportunities for innovation and advancement in the retail industry's cybersecurity landscape.
Classification and Regression Based on Decision Tree Algorithm for Machine Learning

Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed. Keywords: Machine Learning, Supervised, Classification, Regression, Decision Tree.
A comprehensive and reliable feature attribution method: Double-sided remove and reconstruct (DoRaR)

The limited transparency of the inner decision-making mechanism in deep neural networks (DNN) and other machine learning (ML) models has hindered their application in several domains. In order to tackle this issue, feature attribution methods have been developed to identify the crucial features that heavily influence decisions made by these black box models. However, many feature attribution methods have inherent downsides. For example, one category of feature attribution methods suffers from the artifacts problem, which feeds out-of-distribution masked inputs directly through the classifier that was originally trained on natural data points. Another category of feature attribution method finds explanations by using jointly trained feature selectors and predictors. While avoiding the artifacts problem, this new category suffers from the Encoding Prediction in the Explanation (EPITE) problem, in which the predictor's decisions rely not on the features, but on the masks that selects those features. As a result, the credibility of attribution results is undermined by these downsides. In this research, we introduce the Double-sided Remove and Reconstruct (DoRaR) feature attribution method based on several improvement methods that addresses these issues. By conducting thorough testing on MNIST, CIFAR10 and our own synthetic dataset, we demonstrate that the DoRaR feature attribution method can effectively bypass the above issues and can aid in training a feature selector that outperforms other state-of-the-art feature attribution methods. Our code is available at https://github.com/dxq21/DoRaR.
Longitudinal attacks against iterative data collection with local differential privacy

Local differential privacy (LDP) has recently emerged as an accepted standard for privacy-preserving collection of users' data from smartphones and IoT devices. In many practical scenarios, users' data needs to be collected repeatedly across multiple iterations. In such cases, although each collection satisfies LDP individually by itself, a longitudinal collection of multiple responses from the same user degrades that user's privacy. To demonstrate this claim, in this paper, we propose longitudinal attacks against iterative data collection with LDP. We formulate a general Bayesian adversary model, and then individually show the application of this adversary model on six popular LDP protocols: GRR, BLH, OLR, RAPPOR, OUE, and SS. We experimentally demonstrate the effectiveness of our attacks using two metrics, three datasets, and various privacy and domain parameters. The effectiveness of our attacks highlights the privacy risks associated with longitudinal data collection in a practical and quantifiable manner and motivates the need for appropriate countermeasures.
Interpretable Artificial Intelligence in Cardiovascular Health: An In-depth Analysis of Heart Disease Data

With the escalating abundance of structured and unstructured data and the rapid advancements in analytical techniques, Artificial Intelligence (AI) is catalyzing a revolution in the healthcare industry. However, as AI becomes increasingly indispensable in healthcare, concerns are mounting regarding the lack of transparency, explainability, and potential bias in model predictions. Addressing these issues, Explainable Artificial Intelligence (XAI) emerges as a pivotal solution. XAI plays a crucial role in fostering trust among medical practitioners and AI researchers, thereby paving the way for the broader integration of AI in healthcare. This paper aims to introduce diverse interpretability techniques, shedding light on the comprehensibility and interpretability of XAI systems. These techniques, when applied judiciously, offer significant advantages in the healthcare domain. Given that medical diagnosis models directly impact human life, it is imperative to instill confidence in treating patients based on instructionsfrom seemingly opaque models. The content of this paper includes illustrations grounded in the heart disease dataset, demonstrating how explainability techniques should be prioritized to establish trustworthiness when utilizing AI systems in healthcare. Keywords: Explainable AI, Healthcare, Heart disease, Programming frame- works, LIME, SHAP, Example-based Techniques, Feature-based Techniques.
Analisis Kesalahan Tata Tulis Bahasa Jawa Pada Siswa Kelas VII MTs AL Muslihun Tlogo Kanigoro

The purpose of this research is to describe grammatical errors in writing Javanese for grade 7 students and to describe what are the factors that cause errors in writing Javanese letters. This study uses a type of qualitative descriptive research. Test and interview methods were used to collect data. Data analysis in this study used descriptive data analysis and the results showed that there were several grammatical errors in the Javanese language in the form of errors in the use of vowels. Factors causing errors in writing are due to a lack of learning and understanding of Javanese vowels
A Hybrid Cryptosystem Incorporating a New Algorithm for Improved Entropy

Today, safeguarding sensitive content through encryption is crucial. This work presents a hybrid cryptosystem for images that employs both asymmetric and symmetric encryption. The asymmetric component involves applying the Diffie-Hellman protocol and the ElGamal cryptosystem to securely transmit two constants. These constants are necessary for the symmetrical aspect to generate dynamic permutations, substitution boxes, and round keys. Following an encryption process with fourteen rounds, the encrypted images are processed by an algorithm proposed to enhance entropy, a critical metric for assessing encryption quality. It increases the frequencies of the basic colors to achieve a histogram closely resembling a uniform distribution, but it increases the image size by approximately 8%. This improves the entropy values achieved by the hybrid cryptosystem, bringing them remarkably close to the ideal value of 8.0. In specific instances, the entropy values were elevated from 7.99926 to 8.0. The proposed method exhibits resilience against various attacks, including differential, linear, brute force, and algebraic attacks, as evaluated through the entropy, correlation, goodness of fit, Discrete Fourier Transform (DFT), Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Avalanche Criteria (AC), contrast, energy, and homogeneity. Further, encrypted images are subjected to noise attacks ranging from 20% to 50% noise, including additive, multiplicative, occlusion noise, as well as the newly introduced χ2 noise. The noise damage is quantified using the proposed Similarity Parameter (SP), and a 3 × 3 median filter is employed to enhance the visual quality.
INSANet: INtra-INter Spectral Attention Network for Effective Feature Fusion of Multispectral Pedestrian Detection

Pedestrian detection is a critical task for safety-critical systems, but detecting pedestrians is challenging in low-light and adverse weather conditions. Thermal images can be used to improve robustness by providing complementary information to RGB images. Previous studies have shown that multi-modal feature fusion using convolution operation can be effective, but such methods rely solely on local feature correlations, which can degrade the performance capabilities. To address this issue, we propose an attention-based novel fusion network, referred to as INSANet (INtra-INter Spectral Attention Network), that captures global intra- and inter-information. It consists of intra- and inter-spectral attention blocks that allow the model to learn mutual spectral relationships. Additionally, we identified an imbalance in the multispectral dataset caused by several factors and designed an augmentation strategy that mitigates concentrated distributions and enables the model to learn the diverse locations of pedestrians. Extensive experiments demonstrate the effectiveness of the proposed methods, which achieve state-of-the-art performance on the KAIST dataset and LLVIP dataset. Finally, we conduct a regional performance evaluation to demonstrate the effectiveness of our proposed network in various regions.
Merge-and-Split Graph Convolutional Network for Skeleton-Based Interaction Recognition

We introduce an innovative approach to address a significant challenge in interaction recognition, specifically the capture of correlation features between different interaction body parts. These features are often overlooked by traditional graph convolution networks commonly used in interaction recognition tasks. Our solution, the Merge-and-Split Graph Convolutional Network, takes a unique perspective, treating interaction recognition as a global problem. It leverages a Merge-and-Split Graph structure to effectively capture dependencies between interaction body parts. To extract the essential interaction features, we introduce the Merge-and-Split Graph Convolution module, which seamlessly combines the Merge-and-Split Graph with Graph Convolutional Networks. This fusion enables the extraction of rich semantic information between adjacent joint points. In addition, we introduce a Short-term Dependence module designed to extract joint and motion characteristics specific to each type of interaction. Furthermore, to extract correlation features between different hierarchical sets, we present the Hierarchical Guided Attention Module. This module plays a crucial role in highlighting the relevant hierarchical sets that contain essential interaction information. The effectiveness of our proposed model is demonstrated by achieving state-of-the-art performance on 2 widely recognized datasets, namely, the NTU60 and NTU120 interaction datasets. Our model's efficacy is rigorously validated through extensive experiments, and we have made the code available for the research community at https://github.com/wanghq05/MS-GCN/.
Motivations for Artificial Intelligence, for Deep Learning, for ALife: Mortality and Existential Risk

We survey the general trajectory of artificial intelligence (AI) over the last century, in the context of influences from Artificial Life. With a broad brush, we can divide technical approaches to solving AI problems into two camps: GOFAIstic (or computationally inspired) or cybernetic (or ALife inspired). The latter approach has enabled advances in deep learning and the astonishing AI advances we see today-bringing immense benefits but also societal risks. There is a similar divide, regrettably unrecognized, over the very way that such AI problems have been framed. To date, this has been overwhelmingly GOFAIstic, meaning that tools for humans to use have been developed; they have no agency or motivations of their own. We explore the implications of this for concerns about existential risk for humans of the "robots taking over." The risks may be blamed exclusively on human users-the robots could not care less.
Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors

Assessment and proper management of sites contaminated with heavy metals require precise information on the spatial distribution of these metals. This study aimed to predict and map the distribution of Cd, Cu, Ni, Pb, and Zn across the conterminous USA using point observations, environmental variables, and Histogram-based Gradient Boosting (HGB) modeling. Over 9180 surficial soil observations from the Soil Geochemistry Spatial Database (SGSD) (n = 1150), the Geochemical and Mineralogical Survey of Soils (GMSS) (n = 4857), and the Holmgren Dataset (HD) (n = 3400), and 28 covariates (100 m × 100 m grid) representing climate, topography, vegetation, soils, and anthropic activity were compiled. Model performance was evaluated on 20 % of the data not used in calibration using the coefficient of determination (R2), concordance correlation coefficient (ρc), and root mean square error (RMSE) indices. Uncertainty of predictions was calculated as the difference between the estimated 95 and 5 % quantiles provided by HGB. The model explained up to 50 % of the variance in the data with RMSE ranging between 0.16 (mg kg−1) for Cu and 23.4 (mg kg−1) for Zn, respectively. Likewise, ρc ranged between 0.55 (Cu) and 0.68 (Zn), respectively, and Zn had the highest R2 (0.50) among all predictions. We observed high Pb concentrations near urban areas. Peak concentrations of all studied metals were found in the Lower Mississippi River Valley. Cu, Ni, and Zn concentrations were higher on the West Coast; Cd concentrations were higher in the central USA. Clay, pH, potential evapotranspiration, temperature, and precipitation were among the model's top five important covariates for spatial predictions of heavy metals. The combined use of point observations and environmental covariates coupled with machine learning provided a reliable prediction of heavy metals distribution in the soils of the conterminous USA. The updated maps could support environmental assessments, monitoring, and decision-making with this methodology applicable to other soil databases, worldwide.
Extended Reality—New Opportunity for People With Disability? Practical and Ethical Considerations

Since the introduction of virtual environments in the 70s, technologies have moved through virtual reality, mixed reality, and augmented reality into extended reality (XR). This development is promising for various groups. Previous research has shown people with disability benefiting from using technology in social and professional settings. Technology has offered people with disability the opportunity to communicate, interact, participate, and build new relationships. However, we do not know what impact XR has or will have and whether it will offer new opportunities for people with disability. This paper aims to indicate potential opportunities and challenges afforded by XR to people with disability. We offer reflections on the opportunities as well as the ethical considerations needed when introducing immersive technologies to a marginalized group.
REDISCOVER PARADISE: ELEVATING BALI’S MARINE TOURISM THROUGH SUSTAINABLE SOLID WASTE MANAGEMENT (SSWM)

As a global tourist destination, Bali’s marine ecosystem holds significant value for the leisure industry. However, along with the enormous growth of the industry, the island also faces an imminent waste crisis. As of 2022, 1,085,507 metric tonnes of waste are produced by the island, which 18% of it or 190,726 metric tonnes of waste left unmanaged. Coupled with monsoon and wet season weather that delivers trash ashore, effective waste management holds a pivotal role to preserve the marine ecosystem of the island and its tourism industry. By conducting a qualitative descriptive method, this research proposes sustainable solid waste management (SSWM) as an attempt to combat the waste crisis on the island. This research aims to depict the feasibility – cost, and benefit – of implementing the SSWM. The findings of this research conclude that the implementation of SSWM will benefit the marine ecosystem and further sustain tourism in Bali.
INVESTIGATING THE IMPACT OF INNOVATION TECHNOLOGIES IN LOGISTICS (CROSSING TRANSPORTATION MODEL: CASE STUDY OF THE LANGARA - KENDARI ROUTE)

Konawe Islands is a regency in the Southeast Sulawesi Province. This area heavily relies on Kendari City, the provincial capital, to support daily necessities, recreational activities, and higher education pursuits for the people of Konawe Islands. Wooden boats and ferries serve as the primary means of transportation for the locals. However, during harsh weather, only ferries is operational. Yet, there have been loading violations on ferries, increasing the risk to maritime safety. This research aims to formulate ways to address loading issues on ro-ro ferries along the Langara-Kendari route, ensuring compliance with laws and facilitating smooth cargo distribution. The distribution system is structured based on the method persona user and empathy mapping. Research findings indicate that distribution service users require a freight forwarder for large-scale cargo and consolidation for smaller-scale loads. Therefore, the author recommends the use of applications like Uber, Grab, and Uber to resolve this issue.