Reviewerly/exHarmony huggingface dataset:
Mobile Learning Integration into Teaching Kinematics Topic in English in Vietnam High School

In the Vietnam National Education Program (2018) the chapter “Kinematics” has been constructed as fundamental part for 10th grade Physics curriculum. The understanding of concepts and laws, practice with experiments should be a serious target and essential subject of study and competence development for students. The application mobile devices and solution support the flexibility and effectiveness in the learning process. Using English as a medium of instruction is a phenomenon and is of significant challenge for both teachers and students in Vietnam schools today. Teaching Physics for high school students requires the balance between the teaching experiential science (subjects in specialization), foreign languages (English in particular), and new technology integrated learning approaches. This paper examined the relationship between motivation and meaningful learning for high school students with mobile App and mobile learning approaches implemented in Kinematics studying in English.
Cryptocurrency and Application of Blockchain Technology: An Innovative Perspective

Cryptocurrency has been a moving point over the course of the last 10–15 years due to the increasing demand of Digital Currency many people are using it which makes it a useful investment. Efficiency, adaptability and data dense qualities are determined by its unique design and technological innovation. The technology used for cryptocurrency is Blockchain. Moreover, this paper deals with systematic interaction between Block Chain and cryptocurrency. The present work shows and summarizes the interplay connection of two key ideas in today’s digitalized society both cryptocurrency and blockchain are at the forefront of technical study, and this article focuses on their most current applications and advances. The main aim of this study is to investigate cryptocurrencies and its legal status in India, as well as suggesting ways to regulate cryptocurrency. This paper also deals with primary source of data for analysing and to generate result about the awareness of digital currency and the requirement of regulation.
Graph Embedded Representation Learning in Skeleton-based Action Classification

The wide usage of GCN and GNN on the tasks of action classification tasks has made great improvement since the first proposed ST-GCN model. Plenty of works proposed methodologies based on the classification task requirements. As a result of them, we manipulated similar skeleton structures which are extracted from images and videos by analyzing intra and inter-class to represent the behaviour isomerism graphs. Our methodology manipulated the unsupervised graph embedding methodology to solve the classification downstream tasks based on the collected large-scale 2-dimensional datasets. We apply our proposed methodology on top of 4 pose estimation datasets to verify the effectiveness of the results. To solve the unsuper-vised classification problem, we are focusing on the property of skeleton data which is view-invariant through manipulating the attention-based and encoder-decoder structure to generate the corresponding embeddings and compare them through the contrastive learning methodology.
Robust and Secure Zero-Watermark Architecture With Arnold and Phase Transformation

This paper propose a secure watermarking method based on the zero-watermarking framework, combining Arnold transformation, phase transformation, and advanced encryption techniques. The proposed method aims to enhance the concealment and security of the watermark. By applying Arnold scrambling to the images, the hiding capability of the watermark is improved, preventing unauthorized extraction. The image data is decomposed into overlapping blocks in the vertical and horizontal directions, and these blocks are extracted as the image features and transformed into the phase domain to ensure robustness against various image operations. To enhance security, the watermark embedding is encrypted using the Rivest-Shamir-Adleman (RSA) asymmetric encryption algorithm with a Golden Key. Experimental results demonstrate that the proposed method maintains excellent watermark quality and visibility under different noise attacks, while providing a high level of tamper resistance. The effectiveness of the method is evaluated using metrics such as Bit Error Rate (BER) and Normalized Correlation (NC). Overall, the proposed secure watermarking method exhibits strong performance in terms of robustness and security.
Optimizing LIME Explanations Using REVEL Metrics

Explainable artificial intelligence (XAI) has emerged as a crucial topic in the field of machine learning to provide insights into the reasoning performed by artificial intelligence (AI) systems. However, the lack of a clear definition of explanation and a standard methodology for evaluating the quality of explanations has made it challenging to develop effective XAI systems. One commonly used approach is Local Linear Explanations, but the evaluation of their quality is still unclear due to theoretical inconsistencies. This issue is even more challenging in image recognition, where visual explanations often detect edges rather than providing clear explanations for decisions. To address this issue, several metrics that quantitatively measure different aspects of explanation quality in a robust and mathematically consistent manner has been proposed. On this work, we apply the REVEL framework approach, which standardizes the concept of explanation and allows for the comparison of different explanations and the absolute evaluation of individual explanations. We provide a guide of the REVEL framework to perform an optimization process that aims to improve the explainability of machine learning models. We apply the proposed five metrics on the CIFAR 10 benchmark and demonstrate their descriptive, analytical and optimization power. Our work contributes to the development of XAI systems that provide reliable and interpretable explanations for AI reasoning.
An Evidence Logic Perspective on Schotch-Jennings Forcing

Traditional epistemic and doxastic logics cannot deal with inconsistent beliefs nor do they represent the evidence an agent possesses. So-called ‘evidence logics’ have been introduced to deal with both of those issues. The semantics of these logics are based on neighbourhood or hypergraph frames. The neighbourhoods of a world represent the basic evidence available to an agent. On one view, beliefs supported by evidence are propositions derived from all maximally consistent collections evidence. An alternative concept of beliefs takes them to be propositions derivable from consistent partitions of one’s inconsistent evidence; this is known as Schotch-Jennings Forcing. This paper develops a modal logic based on the hypergraph semantics to represent Schotch-Jennings Forcing. The modal language includes an operator $$U(\varphi _1, \ldots , \varphi _n;\psi )$$ which is similar to one introduced in Instantial Neighbourhood Logic. It is of variable arity and the input formulas enjoy distinct roles. The U operator expresses that all evidence at a particular world that supports $$\psi $$ can be supported by at least one of the $$\varphi _i$$ s. U can then be used to express that all the evidence available can be unified by the finite set of formulas $$\varphi _1,\ldots , \varphi _n$$ if $$\psi $$ is taken to be $$\top $$ . Future developments will then use that semantics as the basis for a doxastic logic akin to evidence logics.
A BI-RADS-based Approach for Tumor Classification in Breast Ultrasound Using Binary Decomposition Strategies

This article presents a BI-RADS-based method for classifying breast tumors on ultrasound. The multiclass classification problem into BI-RADS categories 2 to 5 is addressed by two binary decomposition schemes: one-vs.-all (OVA) and one-vs.-one (OVO). Furthermore, the base classifiers of these strategies are trained with local features that are selected using a search method based on the genetic algorithm. The experimental results show that the proposed approach outperforms the classification performance of traditional OVO and OVA approaches that use a global feature space shared by all base classifiers. Therefore, using local features allows the specialization of the base classifiers to distinguish between two specific classes.
GEO-based Quantum Key Distribution for Secure Regional Services: A Feasibility Study

This paper presents a feasibility analysis of GEO-based quantum key distribution (QKD) particularly amenable for regional services. Also the factors impacting link performance with optimum choice of parameters and the challenges associated with such an implementation are discussed. The major factors limiting the utility of GEO-based $QKD$ solution are requirement of large apertures, pointing capabilities for very narrow beams $\sim\mu rad$ and a tight filtering mechanism to limit background noise. Based on an evaluation / trade-off of determining factors, it is found that GEO link can be sustained using available technology in terms of optics & detector efficiencies, even with moderate atmospheric transmittance. In an example use-case over the Indian region, it could potentially provide key rates up to 216 Mb keys per day (vs 180 Mb keys per day in LEO case) which are comparable. Whereas a single GEO based satellite can cater to a large regional footprint, a constellation of LEO satellites would be required to meet similar functional demands with obvious under-utilization of on-board capacities. This feasibility study thus validates GEO-based QKD as a technically viable and preferable option for secure regional services.
AHP-PCP: AHP based Probabilistic Consensus Protocol for Probabilistic Knowledge-based Blockchains

Knowledge-based blockchain, capable of expressing opinion on events and performing collaborative decision-making, is a recent research development in the area of blockchain network technology. An implementation of knowledge-based blockchain is probabilistic blockchain where opinion on events is stored in the form of subjective judgement values such as probability of occurrence of an event. However, a more democratic approach where decision-makers can express their opinions on block creation as well as acceptance during consensus, is missing in existing probabilistic blockchain platform. The paper presents a solution to fulfil this research gap by combining a group decision-making technique with a consensus protocol. A highly successful centralized group decision-making mechanism from the field of psychology, called Analytic Hierarchy Process (AHP), has been fit onto a decentralized communication protocol Practical Byzantine Fault Tolerant (PBFT) resulting into AHP-based Probabilistic Consensus Protocol (AHP-PCP). The proposed new protocol has been analyzed for both crash and Byzantine fault tolerance. Extensive simulation experiments validate the feasibility of AHP-PCP and also evaluate its performance.
Efficient Dissimilarity Detection in Time Series With Application to Side-Channel Analysis

This paper proposes a dissimilarity detection algorithm that can find different regions between similar signals. In particular, we address the detection problem of branching statements in program control flow using electromagnetic (EM) side-channels, where we have shown that such statements can be detected in emanating side-channels. Based on the findings, we have proposed a generalized approach for dissimilarity detection that can be efficiently applied to various real-world applications. In the proposed method, symbolic representation of the signals is used for efficient processing, where each signal frame is transformed into a string. The codebook of observed patterns is constructed with the reference signal. Then, the sequence of strings that is obtained from the main signal is compared with the codebook to find the newly observed patterns. Finally, the presented method outputs the samples of dissimilar regions in the signal compared to the reference. In the experiments, various EM side-channel signals are collected from different devices and different control flow examples to show the applicability and efficiency of the proposed method and the results show that dissimilarities can be detected with >98% accuracy.
Leveraging Transfer Learning for Long Text Classification with Limited Data

Natural language processing (NLP) has emerged as a significant area of research within the field of artificial intelligence, receiving increased attention in recent years, which has prompted the Brazilian Ministry of Science, Technology, and Innovation to launch a project aimed at finding international funding opportunities for Brazilian researchers through its Research Financing Products Portfolio. However, the challenge of classification in this context is exacerbated by the scarcity of high-quality labeled data, which is a requirement for state-of-the-art NLP implementations. In this study, we employ machine learning strategies to classify long, unstructured, and irregular texts obtained by scraping funding institutions’ websites. Given the limited availability of labeled training data, we adopt an incremental approach to identify a suitable method with optimal performance. In order to alleviate the challenge of data scarcity, we use pre-training technology to learn word context from other data sets with significant similarities and larger scales. Then, we combine transfer learning with deep learning models to enhance sentence comprehension. We also conduct pre-processing experiments to address text irregularities. Comparative analysis with the baseline model reveals that our proposed approach yields promising results, with most trained models achieving over 90% accuracy. Our Longformer + CNN model has achieved 94% accuracy with 100% precision, while the Word2Vec + CNN model has achieved 93.55% accuracy. These findings highlight the successful application of artificial intelligence in public administration.
Decidability of Modal Logics of Non-k-Colorable Graphs

We consider the bimodal language, where the first modality is interpreted by a binary relation in the standard way, and the second is interpreted by the relation of inequality. It follows from Hughes (1990), that in this language, non-k-colorability of a graph is expressible for every finite k. We show that modal logics of classes of non-k-colorable graphs (directed or non-directed), and some of their extensions, are decidable.
Image Reconstruction Using Cellular Automata and Neural Networks

Combining cellular automata (CA) with convolutional neural networks (CNN) has been overtime an important topic of scientific interest in the field of computer vision. This has been due to the high number of common aspects between the two models, such as extracting information and making decision based on pixel neighbourhoods. While there are many recent papers on the subject, they are mainly focusing on the visual aspects of the problem and ignore the use of numerical evaluations for the obtained results. In this articles we present a numerical evaluation of a neural cellular automaton (NCA) with the task of reconstructing images. For this evaluation we employed several image quality metrics and the use of multiple training algorithms for the presented model. As such, we determined that cellular automata could be used as a method of restoring images, starting from a damaged state of the image, with a high accuracy. We also concluded that the algorithm presents certain weaknesses when it comes to images with very few details in them, where almost the entirety of the image is predominated by one colour.
Revisiting Histogram Based Outlier Scores: Strengths and Weaknesses

Anomaly detection is a crucial task in various domains such as finance, cybersecurity or medical diagnosis. The demand for interpretability and explainability in model decisions has revived the use of traceable models, with Histogram Based Outlier Scores being a notable option due to its fast speed and commendable performance. Histogram Based Outlier Scores is a well-known and efficient unsupervised anomaly detection algorithm. Despite its popularity, it suffers from several limitations, including the inability to update its internal knowledge, model complex distributions, and consider feature relations. This work aims to provide a comprehensive analysis of the Histogram Based Outlier Scores algorithm status and its limitations. We conduct a comparative analysis of Histogram Based Outlier Scores with other state-of-the-art anomaly detection algorithms to identify its strengths and weaknesses. Our study shows that while Histogram Based Outlier Scores is efficient and computationally inexpensive, it may not be the best option in scenarios where the underlying data distribution is complex or where variable relations play a significant role. The presented alternatives and extensions to Histogram Based Outlier Scores provide valuable insights into the development of future anomaly detection methods.
Multimodal Biological Knowledge Graph Completion via Triple Co-Attention Mechanism

Biological Knowledge Graphs (BKGs) can help to model complex biological systems in a structural way to support various tasks. Nevertheless, the incompleteness problem may limit the performance of existing BKGs, which still deserves new methods to reveal the missing relations. Though great efforts have been made to knowledge graph completion, existing methods are not easy to be adapted to the multimodal biological information such as molecular structures and textual descriptions. To this end, we propose a novel co-attention-based multimodal embedding framework, named CamE, for the multimodal BKG completion task. Specifically, we design a Triple Co-Attention (TCA) operator to capture and highlight the same semantic features among different modalities. Based on TCA, we further propose two components to handle multimodal fusion and multimodal entity-relation interaction, respectively. One is the multimodal TCA fusion module to achieve a multimodal joint representation for each entity in the BKG. It aims to project different modal information into a common space by capturing the same semantic features and overcoming the modality gap. The other is the relation-aware interactive TCA module to learn interactive representation by modelling the deep interaction between multimodal entities and relations. Extensive experiments on two real-world multimodal BKG datasets demonstrate that our method significantly outperforms several state-of-the-art baselines, including 10.3% and 16.2% improvement w.r.t MRR and Hits@1 metrics over its best competitors on public DRKG-MM dataset.
Heuristic-based Resource Allocation for Cloud-native Machine Learning Workloads

As machine learning workloads become computationally demanding, there is an increased focus on distributed machine learning to train and deploy models across multiple machines in a cloud-native cluster. However, optimizing a machine learning model’s lifecycle to facilitate efficient resource utilization is still an active area of research. The approach typically involves a manual effort to partition the models into distinct layers and decide how to store these distinct layers on a distributed computing framework. However, distributing distinct layers across nodes can induce a network latency bottleneck in the machine learning pipeline. Further, the above process becomes more inefficient as models become increasingly complex. In this paper, we present a heuristic-based approach to distributed model training. Further, we analyze the resource utilization metrics from a sample machine learning pipeline deployed on a KubeFlow MLOps framework testbed.
Threshold-Learned CNN for Multi-Label Text Classification of Electronic Health Records

Text data in the form of natural language is a valuable resource that contains domain-specific information applicable to various applications. An example are electronic health records (eHR) offering comprehensive insights into patients’ health histories, enabling knowledge extraction for clinical diagnosis and treatment. In this paper, we study multi-label text classification (MLTC) of eHR data by introducing two novel MLTC methods based on a threshold-learned convolutional neural network (CNN). We conduct comprehensive comparisons with other multi-label models and binary relevance (BR). Importantly, we do not only optimize the architecture of multi-label classifiers but also of the baseline BR model. As a result, our findings indicate that the adaptive-threshold CNN (AT-CNN) and implicit-threshold CNN (IT-CNN) provide a favorable approximation of a binary CNN (B-CNN) with the added benefit of improved runtime efficiency. The latter is crucial when the number of classes grows larger because the runtime of classifiers based on one-vs-rest mappings becomes increasingly prohibitive for such configurations.
Financial Distress Prediction in an Imbalanced Data Stream Environment

Corporate bankruptcy predictions are crucial to companies, investors, and authorities. However, most bankruptcy prediction studies have been based on stationary models, and they tend to ignore important challenges of financial distress like data non-stationarity, concept drift and data imbalance. This study proposes methods for dealing with these challenges and uses data collected from financial statements quarterly provided by companies to the Securities and Exchange Commission of Brazil (CVM). It is composed of information from 10 years (2011 to 2020), with 905 different corporations and 23,834 records with 82 indicators each. The sample majority have no financial difficulties, and only 651 companies have financial distress. The empirical experiment uses a sliding window, a history and a forgetting mechanism to avoid the degradation of the predictive model due to concept drift. The characteristics of the problem, especially the data imbalance, the performance of the models is measured through AUC, Gmean, and F1-Score and achieved 0.95, 0.68, and 0.58, respectively.
Application of Anomaly Detection Models to Malware Detection in the Presence of Concept Drift

Machine learning is one of the main approaches to malware detection in the literature, since machine learning models are more adaptive than signature based solutions. One of the main challenges in the application of machine learning to malware detection is the presence of concept drift, which is a change in the data distribution over time. To tackle drift, online models that can be dynamically updated passively or by actively detecting change are applied. However, these models require new instances to be labelled to update the model. Usually, labels are scarce, cannot be obtained immediately and the presence of imbalance in the data make the construction of an effective model difficult. It has been studied that concept drift has a lower impact on benign instances, so we test the effectiveness of anomaly detection models to detect malware in the presence of concept drift. Anomaly detection models only need benign instances for training, and therefore may be less affected by the scarcity of labelled malicious instances. The results show that anomaly detection models achieve better results than supervised online models in conditions of heavy data imbalance and label scarcity.