Reviewerly/exHarmony huggingface dataset:
Exploring the Impact of Mobile-Assisted Teaching on English Vocabulary Learning among High School Students

The demand for English language proficiency in the globalized economy has fueled the adoption of mobile learning as a popular method of autonomous learning. Enabled by technological advancements and the widespread use of smart devices, this approach offers promising opportunities. However, the teaching of English vocabulary in senior high schools faces numerous challenges. To address these challenges, integrating vocabulary teaching software as an auxiliary tool in high school English classrooms represents a significant innovation in traditional vocabulary instruction.This study aims to investigate the use of vocabulary learning apps as auxiliary tools in high school English vocabulary teaching. Specifically, it seeks to answer the following research questions: 1) What are the observed differences in English vocabulary proficiency after the implementation of mobile-assisted teaching? 2) How does mobile-assisted teaching impact students' vocabulary strategies? Through a comparative study between experimental and control classes, this research explores the integration of vocabulary software in classroom teaching and examines its effects on student learning outcomes. The findings of this study have substantial implications for teaching practices, emphasizing the effective utilization of mobile-assisted tools and their influence on students' vocabulary proficiency. By shedding light on the potential benefits of incorporating vocabulary learning apps in high school English classrooms, this research contributes to the ongoing efforts in optimizing vocabulary instruction and enhancing language learning outcomes.
InsectVR: Simulating Crawling Insects in Virtual Reality for Biology Edutainment

In this paper, we propose InsectVR, a Virtual Reality (VR) platform designed for biology edutainment that simulates crawling insects in realistic and immersive virtual environments. InsectVR offers a unique opportunity for educators to teach biology concepts related to insects and their behavior in an engaging way. InsectVR is designed to replicate the real-world behavior of crawling insects using a realistic mathematical model called the random walk algorithm. Through InsectVR, users can observe the movement of the insects and their behavior in immersive virtual environments which can help foster users' understanding of these amazing creatures. InsectVR uses state-of-the-art VR technology to create a highly realistic insect world, which includes different types of insects and various environmental conditions. After being tested through a series of numerical experiments and preliminary user studies, InsectVR demonstrates the potential to revolutionize biology edutainment by providing users with immersive virtual experiences.
Analysis of Sentiments and Emotions Attributes of COVID-19-related tweets in the Philippines Using time-Series Analysis

Twitter has become a host of individuals' expressions of sentiments and emotion given the effect and consequences of the COVID-19 pandemic. Supported by the appraisal theory of emotion [1], this descriptive cross-sectional (Type 2) study described the pattern of sentiments and emotions over the course of COVID-19 outbreak in accordance to gender. In the following analysis, Gupta et al.'s (2020) COVID-19 twitter dataset and the additional scraped data from September 2021 – December 2021 was used in the present study. The methodologies heavily relied on topic modeling techniques and pre-trained machine learning-based emotion analytic algorithms. Results showed that the negative sentiments surrounding the media is caused by using keywords like covid19, coronavirus, pandemic, and cases but, it appeared to be in a joyful emotion as tweets are expressed in prayers and social solidarity. Furthermore, an increase in the number of reported COVID-19 cases also increases the intensity of fear in females. Implementation of lockdown, quarantines and strict measures lead to higher anger for males. Lastly, with the continuous vaccine rollouts, positivity and optimism are being more expressed across threads. Given the rise in COVID-19 cases, these findings provide sentiment analysis in a population which can be used to improve COVID-19 management through policies and projects in supporting the well-being of the society.
Postdigital Collective Intelligence

This entry begins with a description of collective intelligence as developed within the physical and natural sciences, which leads to an anthropomorphized accounting of this concept as having been directed by techno-deterministic visions of human advancement through networked information, collaboration, and exchange. This understanding is then directed towards a contemporary transdisciplinary concern for collective intelligence as an avenue for addressing complexity and crisis. Finally, this leads to a descriptive accounting of postdigital collective intelligence as a project that challenges these utopic visions within a process of biodigital coevolution driven by collaborative ecologies of knowledge aimed at overcoming crisis through collective systemic adaptation/transgression.
Postdigital Knowledge Ecologies

This entry first provides a foundational understanding of various field-specific ecological metaphors as having been developed toward better understanding, managing, capturing, or catalyzing the socially contingent and evolutionary nature of knowledge. This is followed by a descriptive accounting of how these metaphors have both diverged semantically within discrete disciplinary aims, as well as converged within a transdisciplinary concern for epistemic justice. Finally, a concrete definition of postdigital ecologies of knowledge is provided that coalesces with the abovementioned convergence through a co-evolutionary process of knowledge collaboration, production, dissemination, and consumption aimed at ushering forth a more just biodigital future.
Using AI Tool to Diagnose Cancer on Breast

Breast cancer is a significant global health concern affecting women worldwide. Timely detection of this disease is crucial for effective treatment and a positive prognosis. Radiologists rely on mammography images to diagnose the presence or absence of breast cancer. In recent years, the utilization of machine learning techniques in healthcare has gained considerable attention. This research paper aims to provide a comprehensive analysis of the implementation of the K Nearest Neighbour (KNN) classifier for breast cancer diagnosis. The application of any machine learning algorithm poses challenges, such as handling missing or erroneous data, and potentially generating new data.
Task-Aware Dynamic Model Optimization for Multi-Task Learning

Multi-task learning (MTL) is a field in which a deep neural network simultaneously learns knowledge from multiple tasks. However, achieving resource-efficient MTL remains challenging due to entangled network parameters across tasks and varying task-specific complexity. Existing methods employ network compression techniques while maintaining comparable performance, but they often compress uniformly across all tasks without considering individual complexity. This can lead to suboptimal solutions due to entangled network parameters and memory inefficiency, as the parameters for each task may be insufficient or excessive. To address these challenges, we propose a framework called Dynamic Model Optimization (DMO) that dynamically allocates network parameters to groups based on task-specific complexity. This framework consists of three key steps: measuring task similarity and task difficulty, grouping tasks, and allocating parameters. This process involves the calculation of both weight and loss similarities across tasks and employs sample-wise loss as a measure of task difficulty. Similar tasks are grouped based on similarity, and parameters are allocated with dynamic pruning according to task difficulty within their respective groups. We apply the proposed framework to MTL with various classification datasets. Experimental results demonstrate that the proposed approach achieves high performance while taking fewer network parameters than other MTL methods.
Transformer-based Discriminative and Strong Representation Deep Hashing for Cross-Modal Retrieval

Cross-modal hashing retrieval has attracted extensive attention due to its low storage requirements as well as high retrieval efficiency. In particular, how to more fully exploit the correlation of different modality data and generate a more distinguished representation is the key to improving the performance of this method. Moreover, Transformer-based models have been widely used in various fields, including natural language processing, due to their powerful contextual information processing capabilities. Based on these motivations, we propose a Transformer-based Distinguishing Strong Representation Deep Hashing (TDSRDH). For text modality, since the sequential relations between words imply semantic relations that are not independent relations, we thoughtfully encode them using a transformer-based encoder to obtain a strong representation. In addition, we propose a triple-supervised loss based on the commonly used pairwise loss and quantization loss. The latter two ensure the learned features and hash-codes can preserve the similarity of the original data during the learning process. The former ensures that the distance between similar instances is closer and the distance between dissimilar instances is farther. So that TDSRDH can generate more discriminative representations while preserving the similarity between modalities. Finally, experiments on the three datasets <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MIRFLICKR-25K</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IAPR TC-12</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NUS-WIDE</i> demonstrated the superiority of TDSRDH over the other baselines. Moreover, the effectiveness of the proposed idea was demonstrated by ablation experiments.
Blockchain-Enabled Fintech Innovation: A Case of Reengineering Stock Trading Services

As it plays a key role in industrial and commercial development, the stock market has significant impacts on a country’s economic growth; the stability of the stock market is therefore very important. However, the current stock-trading system is centralized, and investors have to participate in trading stocks through intermediaries/dealers, consequently causing problems such as high transaction fees, potential manipulation by intermediaries/dealers, long settlement times, and data protection issues. The advantages of blockchain technology, such as immutability, improved security, transparency and efficiency, not only make blockchain and fintech inextricably linked, but also shed light on innovations of existing stock-trading systems. This research aimed to alleviate the transaction costs, data storage security, and centralized system failures caused by the original centralized stock trading system through the blockchain-based stock trading prototype innovation/renovation of stock trading services. Further, it explored the application of blockchain in the field of financial technology. In order to prove the feasibility of a decentralized blockchain-based stock trading service, in the research methodology, this study first drew the architecture of the system through object-oriented analysis, followed by the flowchart of each function and smart contract. Finally, based on each flowchart, a blockchain stock system was developed to replace the centralized stock trading service in a decentralized, transparent, tamper-proof, and secure manner. After proposing the design and implementation details of a blockchain-based system with required service functions, we performed functional/performance tests and theoretical analysis of the system, confirming its feasibility, applicability, and value. The research results showed that our system has the characteristics of blockchain decentralization, which can alleviate the problems of transaction fees, broker manipulation, and data storage security encountered by existing stock trading services.
Semi-supervised Bootstrapped Syntax-Semantics based Approach for Agriculture Relation Extraction for Knowledge Graph Creation and Reasoning

We propose a novel approach that uses semi-supervised learning to extract triplets from domain-specific texts and create a Knowledge Graph (KG), with a focus on the agricultural domain. Building domain specific knowledge graphs can be challenging due to several factors such as domain specific vocabulary, data integration challenges, dynamic data, and the need for domain expertise. Our approach primarily focuses on triplet extraction for the creation of knowledge graph. We employ dependency parsing techniques to extract relationships between entities, and utilize an extended version of BERT, combined with Latent Dirichlet Allocation (LDA) for Named Entity Recognition (NER). The proposed Agriculture knowledge graph covers significant areas of the agricultural domain by focusing on six major entities: soil, place, disease, pathogen, pesticide, and crops, along with their intra and inter-relationships. There is no benchmark dataset in the agriculture domain encompassing all the major entities. Hence we create our own corpus comprises 30k sentences sourced from reputable agriculture websites. To evaluate the effectiveness of our triplet extraction model, we utilized a test corpus containing 3500 agriculture triplets. Based on the experimental results, we were able to achieve an average macro F-score of 87% for relation extraction, indicating the efficacy of our approach. Additionally, we created an Agriculture knowledge graph using a triplet corpus of 6236 triplets. We also analyzed various knowledge graph reasoning models that improve the discovery of implicit knowledge that is not explicitly represented in the knowledge graph. Experimental results indicate that our approach is effective in creating triplets and reasoning knowledge graphs for the agricultural domain.
Antifragile Control Systems: The Case of Mobile Robot Trajectory Tracking Under Uncertainty and Volatility

Mobile robots are ubiquitous.Such vehicles benefit from well-designed and calibrated control algorithms ensuring their task execution under precise uncertainty bounds.Yet, in tasks involving humans in the loop, such as elderly or mobility impaired, the problem takes a new dimension.In such cases, the system needs not only to compensate for uncertainty and volatility in its operation but at the same time to anticipate and offer responses that go beyond robust.Such robots operate in cluttered, complex environments, akin to human residences, and need to face during their operation sensor and, even, actuator faults, and still operate.This is where our thesis comes into the foreground.We propose a new control design framework based on the principles of antifragility.Such a design is meant to offer a high uncertainty anticipation given previous exposure to failures and faults, and exploit this anticipation capacity to provide performance beyond robust.In the current instantiation of antifragile control applied to mobile robot trajectory tracking, we provide controller design steps, the analysis of performance under parametrizable uncertainty and faults, as well as an extended comparative evaluation against state-of-the-art controllers.We believe in the potential antifragile control has in achieving closed-loop performance in the face of uncertainty and volatility by using its exposures to uncertainty to increase its capacity to anticipate and compensate for such events.
Shapelet-based Fault Sensor Detection and Human-centered Explanations in Industrial Control System

With the development of information and communication technology, industrial control systems (ICSs) that operate in a closed environment are now operating in a smart environment, and external threats are increasing. To predict failure and respond to threats, anomaly detection and fault detection using artificial intelligence (AI) are being introduced, but the issue of the reliability of AI prediction is emerging. In the case of anomaly detection, the operator must check thousands of sensors. In addition, since AI predictions are not always accurate, there are practical operational constraints, so this paper proposes shapelet-based anomaly detection and automatic failure sensor descriptions. When an abnormal situation occurs, the operator can immediately know which sensors caused the problem and how different the sensors appear from the normal patterns. This paper verified it with the HIL-based Augmented ICS Security Dataset (HAI) and Secure Water Treatment (SWaT) Dataset, widely used in the ICS field. In the case of the HAI Dataset, it was confirmed that 95.12% of the failed sensors could be analyzed by extracting and inspecting only 4% of the total sensors. In the case of the SWaT Dataset, only 7% of the total sensors were extracted and inspected, confirming that 84% of the failed sensors could be analyzed. We expect that intuitive explanations and anomaly detection will enable more effective technology operations in industrial environments.
SBVQA 2.0: Robust End-to-End Speech-Based Visual Question Answering for Open-Ended Questions

Speech-based Visual Question Answering (SBVQA) is a challenging task that aims to answer spoken questions about images. The challenges of this task involve the variability of speakers, the different recording environments, and the various objects in the image and their locations. This paper presents SBVQA 2.0, a robust multimodal neural network architecture that integrates information from both the visual and the speech domains. SBVQA 2.0 is composed of four modules: speech encoder, image encoder, features fusor, and answer generator. The speech encoder extracts semantic information from spoken questions, and the image encoder extracts visual information from images. The outputs of the two modules are combined using the features fusor and then processed by the answer generator to predict the answer. Although SBVQA 2.0 was trained on a single-speaker with clean background dataset, we show that our selected speech encoder is more robust to noise and it is speaker-independent. Moreover, we demonstrate that SBVQA 2.0 can be further improved by finetuning in an end-to-end manner since it uses fully differentiable modules. We open-source our pretrained models, source code, and data for the research community.
Blockchain and Artificial Intelligence: Scientometric Analysis and Visualization

Integrating Artificial Intelligence (AI) with Blockchain Technology (BT) is deemed the fourth generation of BT applications (Blockchain 4.0). This generation has gained considerable attention from the research community. Such attention has led to a vast amount of scientific literature. However, a comprehensive quantitative analysis of this literature is still missing. The present study conducts a scientometric analysis to explore and characterize the development track and trends of BT-AI research. Using the Web of Science (WoS) Core Collection database, a total of 2615 peer-reviewed journal articles were identified between 2017-2023 and extracted for analysis, while employing VOSviewer and Biblioshiny as software tools. First, the publication trend was analyzed, and the pivotal articles were identified. Second, the scientific collaboration networks were analyzed and mapped to identify the key researchers, countries, and organizations. Third, the sources’ productivity and citation were analyzed and mapped to identify the dependable sources of information and the best-fit sources for publishing the BT-AI studies. Fourth, the conceptual structure for the BT-AI literature was analyzed and visualized using keywords co-occurrence and keywords thematic evolution to explore and identify the research hotspots and emerging themes. The findings of this study can help in further familiarizing new researchers with BT-AI literature and assist practitioners, policy-makers, and editors to focus on the promising and arising BT-AI trends for further development.
SCGAN: Extract Features from Normal Semantics for Unsupervised Anomaly Detection

Anomaly detection within the realm of industrial products seeks to identify regions of image semantics that deviate from established normal patterns. Given the inherent challenges associated with collecting anomaly samples, we exclusively extract features from normal semantics. Our proposed solution involves a Semantic CopyPaste based Generative Adversarial Network (SCGAN) for unsupervised anomaly detection. To enable the comprehensive acquisition of semantic features within intricate real-world images, we embrace an encoder-decoder-encoder as the fundamental network structure. In practical terms, our approach commences with the input image being subjected to the CopyPaste augmentation module. Here, we strategically copy N patches, each constituting 1% of the image’s area, from normal samples. These patches are then randomly pasted into different regions of the original image. Subsequently, a generative adversarial network is trained to facilitate sample reconstruction. A noteworthy augmentation to the network’s channel attention capabilities entails the incorporation of a multi-scale channel attention module within the first encoder. This module serves to emphasize contextual features across varying scales within the image. During the test, we detect anomalous regions by meticulously comparing residuals between the input image and its reconstructed counterpart. Our methodology is rigorously validated through diverse experiments conducted on challenging MVTec and BTAD public datasets. The results conclusively affirm the state-of-the-art performance achieved by our proposed method in the domain of anomaly detection.
A Novel Semi-supervised Adversarially Learned Meta-classifier for Detecting Neural Trojan Attacks

Deep neural networks (DNNs) are highly vulnerable to neural Trojan attacks. To carry out such an attack, an adversary retrains a DNN with poisoned data or modifies its parameters to produce incorrect output. These attacks can remain unnoticed until triggered by a specific pattern in the input, making detection challenging. In this article, we propose a novel semi-supervised adversarially learned meta-classifier (SESALME) to detect if a target model has been trojaned. Unlike previous Trojan detection methods, SESALME assumes that the defender has no knowledge of the attack mechanisms, and no access to training data, poisoned data, or parameters/layers of a target model. In the absence of poisoned data and knowledge of the attack mechanisms, we use a set of shadow models to emulate normal behavior of the target model. Having learned the normal behavior of the target model, SESALME then uses one-class learning, implemented within a semi-supervised generative adversarial network (GAN), to detect abnormal behavior of a model to be investigated, if any. Behavior that deviates from the learned normal behavior indicates a high likelihood that the model is trojaned. We compare the performance of SESALME with that of state-of-the-art neural Trojan detectors using popular datasets such as MNIST, CIFAR-10, and SC. Experimental results show that SESALME outperforms state-of-the-art Trojan detection methods in terms of detection performance and inference time in almost all cases, while being attack-agnostic and requiring no access to training data, poisoned data, or parameters of the target model.