Reviewerly/exHarmony huggingface dataset:
Students’ Attitudes Towards Mobile Applications in Learning English Listening Skills at Ho Chi Minh University of Foreign Languages and Information Technology

Technology continues to create new avenues for inspiring language learners. Unlocking the vast potential of technology requires institutions, teachers, students, and communities to collaborate to determine how it can be optimally implemented in a particular setting. This study aimed to determine how sophomores majoring in English at Ho Chi Minh University of Foreign Languages and Information Technology (HUFLIT) thought about using mobile applications through mobile devices to improve their English listening skills. 101 English major students at HUFLIT participated in the mixed-methods study. Using the mixed-methods research design with a survey questionnaire as the primary data collecting instrument, the study obtained significant findings and accomplished its objectives. The findings from both quantitative and qualitative data indicate that the majority of participants had positive sentiments regarding the use of mobile applications inside and outside the classroom. This study also revealed that most English majors agreed that listening practice through mobile applications could improve their listening skills. In addition, although the participants accepted that the mobile devices contained a lot of distractions while using, they still held positive attitudes towards the use of mobile applications and continued using them to practice their English listening skills.
Feature-Guided Analysis of Neural Networks

Abstract Applying standard software engineering practices to neural networks is challenging due to the lack of high-level abstractions describing a neural network’s behavior. To address this challenge, we propose to extract high-level task-specific features from the neural network internal representation, based on monitoring the neural network activations. The extracted feature representations can serve as a link to high-level requirements and can be leveraged to enable fundamental software engineering activities, such as automated testing, debugging, requirements analysis, and formal verification, leading to better engineering of neural networks. Using two case studies, we present initial empirical evidence demonstrating the feasibility of our ideas.
Efficient random subspace decision forests with a simple probability dimensionality setting scheme

Random subspace decision forests are commonly used machine learning methods in a wide range of application domains. How to set the random subspace dimensionality ds in decision forests is a considerable issue that impacts classification quality and efficiency, especially for high dimensional cases. To obtain effective and efficient decision forests that are generally suitable for various classification cases, this paper proposes a novel framework, named Efficient Random Subspace decision forest (ERS). A Half-Range Discrete Uniform distribution-based Varied Dimensionality setting (HRDUVD) method is provided for determining the random subspace dimensionality, and the ERS is formed based on the HRDUVD method. In more detail, a simple discrete uniform distribution in a specific range is employed to set with a given probability the number of randomly selected features for each tree in random subspace decision forests. The HRDUVD method removes the hesitation which appropriate ds value one should preset for different datasets, while also achieving adequate classification performance along with a relatively short running time. Therefore, setting ds using the discrete uniform distribution is a highly useful strategy for the proposed ERS.
Exploring Location-Based AR Narrative Design for Historic Site

Abstract The Pickrick Protests is a project exploring the use of Augmented Reality (AR) to recreate a sequence of Civil Rights--era incidents that occurred at the Pickrick restaurant, in Atlanta, over a period of 8 months in 1964–1965. This project gives a connected and chronological narrative of the historic events to form a cohesive experience, with AR projections of computer graphic depictions of the 1960s buildings, and events on the site where they happened. The objective of this project is to make the history of the Civil Rights Movement visible by privileging the perspective of the African Americans who fought for equality. This paper focuses on introducing the narrative design for this location-based AR application (Bowman et al., 1998). We will discuss our approach to creating a meaningful narrative within the technical affordances and environmental constraints of the AR medium and the specific location. The design challenges posed by this project were how to merge the virtual content with the existing, albeit changed, physical space; how to recenter the story on the activists rather than on the segregationist restaurant owner who went on to become a governor of Georgia; and how to direct attention in AR in order to allow interactors to follow a multiepisode narrative, and to focus on the nonviolent activists rather than the aggressive segregationists.
Filtering Crosstalk from Bath Non-Markovianity via Spacetime Classical Shadows

From an open system perspective non-Markovian effects due to a nearby bath or neighboring qubits are dynamically equivalent. However, there is a conceptual distinction to account for: neighboring qubits may be controlled. We combine recent advances in non-Markovian quantum process tomography with the framework of classical shadows to characterize spatiotemporal quantum correlations. Observables here constitute operations applied to the system, where the free operation is the maximally depolarizing channel. Using this as a causal break, we systematically erase causal pathways to narrow down the progenitors of temporal correlations. We show that one application of this is to filter out the effects of crosstalk and probe only non-Markovianity from an inaccessible bath. It also provides a lens on spatiotemporally spreading correlated noise throughout a lattice from common environments. We demonstrate both examples on synthetic data. Owing to the scaling of classical shadows, we can erase arbitrarily many neighboring qubits at no extra cost. Our procedure is thus efficient and amenable to systems even with all-to-all interactions.
A Simple Model for Describing Non-Adherence to Medication in Tuberculosis Patients in a Tropical Area, Indonesia

Indonesia has the second-highest number of tuberculosis (TB) cases in the world. Many TB patients did not take their medicine at health care centers causing an increase in taking medication. This study aimed to develop a simple model for describing non-adherence to medication in TB patients. This study used an analytical with a cross-sectional approach. The following criteria were used in selecting the respondent: adolescent, able to read and write, and willingness to be a respondent. A total of 93 participants were recruited randomly. Data were collected through questionnaires and analyzed using logistic regression. The predisposing factors were knowledge, attitude, belief (p = 0.000), trust (p = 0.013), and values (p = 0.001). Family support (p = 0.034) and healthcare personnel support (p = 0.022) were reinforcing factors. The enabling factor was the healthcare facility (p = 0.000). This study found that the most dominant factor is knowledge (B = 56.4). The model was Logit (Pi) = 22.363 + 56.4 knowledge (1) + 22.56 belief (1) + 2.9 family support (1) + 0.577 healthcare support (1) + 0.061 healthcare facility (1). This study suggests that TB patients’ knowledge must be increased to improve their TB treatment and management.
Hybrid Consensus Mechanism (HCM) : Achieving Efficient and Secure Consensus in Blockchain Networks

Consensus mechanisms play a critical role in blockchain networks, ensuring that all participants agree on the state of the distributed ledger. However, existing consensus mechanisms have limitations in terms of efficiency, security, and decentralization. In this research paper, we propose a Hybrid Consensus Mechanism (HCM) that combines the advantages of Delegated Proof of Stake (DPoS), Practical Byzantine Fault Tolerance (PBFT), and Proof of Elapsed Time (PoET) to address these limitations. HCM uses a set of trusted validators (delegates) similar to DPoS, who participate in a PBFT-like consensus protocol to reach agreement on the state of the ledger. PoET is used to randomly select the leader for each consensus round, preventing the concentration of power. HCM also introduces a dynamic delegation mechanism and sharding technique to enhance fairness, resilience, and scalability in blockchain networks. We provide detailed technical insights, backed by underlying data, to illustrate the effectiveness of HCM in achieving efficient and secure consensus.
Integrity Perceptions and Behavior Triggered by the Hand-Over-Chest Gesture: A Semiotic Perspective

The current study, based on Semiotic Theory, Conceptual Metaphor Theory, and a situational induced research method, explores the influence of the hand-over-chest gesture when it comes to perceptions and behaviors of integrity within the Chinese cultural context.Three different experiments consistently prove that the hand-over-chest gesture is associated with the concept of integrity in the Chinese culture, and helps to increase individuals' sense and behavior of integrity.In Experiment 1, the target person performing the hand-over-chest gesture was regarded as an integrity person.In Experiment 2, the statements in the self-introduction from an interviewee who performed the hand-overchest gesture were considered more credible than the statements made by the interviewee with the control gesture.In Experiment 3, compared with those performing the control gesture, the participants with the hand-over-chest gesture gave more honest ratings to the least unattractive women and refrained from telling white lies.All the three experiments achieved statistically significant results.The results in this study indicate that the handover-chest gesture is the symbolic body language for Chinese college students to express integrity, triggering more trust in gesture makers and less white lies from the gesture makers themselves.This paper raises suggestions for further research on this topic as well.
A Recommender System for Recovering Relevant JavaScript Packages from Web Repositories

When developing JavaScript (JS) applications, the assessment of JS packages has become a difficult and time-consuming task for developers, due to the growing number of technology options available. Given a technology need, a common developers’ strategy is to browse software repositories via search engines (e.g., NPM, Google) and identify candidate JS packages. However, these engines might return a long list of results, which often causes information overloading issues in the developer. Furthermore, the results should be ranked according to the developer’s criteria, but weighting the available criteria to choose a JS package is not straightforward. To address these problems, we propose a two-phase recommender system for assisting developers in retrieving and ranking JS packages in a semi-automated fashion. The first phase uses a meta-search technique for collecting JS packages that meet the developer’s needs. Based on criteria used by other projects on the Web, the second phase applies a machine learning technique to infer a ranking of relevant packages for the output of the first phase. We performed an initial evaluation of our approach with the NPM package repository and obtained satisfactory results in terms of both the accuracy of the retrieved packages and the quality of the ranking for the developers.
Feature level fine grained sentiment analysis using boosted long short-term memory with improvised local search whale optimization

In the modern era, Internet-based e-commerce world, consumers express their thoughts on the product or service through ranking and reviews. Sentiment analysis uncovers contextual inferences in user sentiment, assisting the commercial industry and end users in understanding the perception of the product or service. Variations in textual arrangement, complex logic, and sequence length are some of the challenges to accurately forecast the sentiment score of user reviews. Therefore, a novel improvised local search whale optimization improved long short-term memory (LSTM) for feature-level sentiment analysis of online product reviews is proposed in this study.The proposed feature-level sentiment analysis method includes 'data collection', 'pre-processing', 'feature extraction', 'feature selection', and finally 'sentiment classification'. First, the product reviews given from different customers are acquired, and then the retrieved data is pre-processed. These pre-processed data go through a feature extraction procedure using a modified inverse class frequency algorithm (LFMI) based on log term frequency. Then the feature is selected via levy flight-based mayfly optimization algorithm (LFMO). At last, the selected data is transformed to the improvised local search whale optimization boosted long short-term memory (ILW-LSTM) model, which categorizes the sentiment of the customer reviews as 'positive', 'negative', 'very positive', 'very negative', and 'neutral'. The 'Prompt Cloud dataset' is used for the performance study of the suggested classifiers. Our suggested ILW-LSTM model is put to the test using standard performance evaluation. The primary metrics used to assess our suggested model are 'accuracy', 'recall', 'precision', and 'F1-score'.The proposed ILW-LSTM method provides an accuracy of 97%. In comparison to other leading algorithms, the outcome reveals that the ILW-LSTM model outperformed well in feature-level sentiment classification.
Detecting Favorite Topics in Computing Scientific Literature via Dynamic Topic Modeling

Topic modeling comprises a set of machine learning algorithms that allow topics to be extracted from a collection of documents. These algorithms have been widely used in many areas, such as the identification of dominant topics in scientific research. However, works addressing such problems often focus on identifying static topics, providing snapshots that are unable to show how those topics evolve over time. Aiming to close this gap, in this article, we describe an approach for dynamic article set analysis and classification. This is accomplished by querying open data of notable scientific databases via representational state transfers and subsequently enforcing data management practices with a dynamic topic modeling approach on the associated metadata available. As a result, we identify research trends for a given field at specific instants and the referred terminology trends evolution throughout the years. It was possible to detect the associated lexical variation over time in published content, ultimately determining the so-called “hot topics” in arbitrary instants and also how they are correlated to each other.
Artificial Intelligence based Security Solution for Data Encryption using AES Algorithm

The Internet of Things (IoT) has a significant impact on the transportation industry. Autonomous vehicles (AVs) were created to make daily activities easier by hauling goods, distributing packages, and easing traffic. The AVs had a wide range of uses and comprised land vehicles, aerial vehicles, and maritime vehicles. The Cyber Security (CS) enabled data transfer autonomous driving was set up by them to facilitate the solution of this challenge. A network acts as the mediator, downloading data of the transmitter to the autonomous car. For additional safety, the CS -based method Advanced Encryption Standard (AES) is involved to decrypt the data, which is transferable to cypher text. The encryption content could be deciphered by the secret key which is given by the transmitter to the peculiar AV. Customized particle swarm efficiency would be used to modify a conventional neural network. The researchers proposed product’s final stage should be to decrypt the document using dual encryption technology. After the dual cryptography, steganography techniques are used to improve the retention safety of the proposed solution. Their proposed approach was implemented in the Java work area using Internet simulation.
The Effect of Problem-Based Flipped Classroom Learning Accompanied by Digital Literacy Using Edpuzzle on Spatial Problem-Solving Abilities for 7th-Grade Students at Pangudi Luhur Ambarawa Junior High School

Indonesian education is transforming to respond to future challenges in which a new education paradigm focuses learning activities on students and optimizes TPACK (Technological, Pedagogical, and Content Knowledge) learning. Kurikulum Merdeka explains the needs of students in the 21st century that can be taught through Social Studies learning with a Geography perspective. The purpose of this study was to determine the effect of the implementation of the Problem Based - Flipped Classroom Learning (PBFCL) model accompanied by Edpuzzle-based digital literacy on spatial problem-solving abilities in the material of spatial relations between geographical conditions of the area and the characteristics and ways of community activities in social studies learning. This research is already implemented at Pangudi Luhur Ambarawa Junior High School in 2022/2023. The results of statistical analysis are known that (1) Learning with the PBFCL model accompanied by digital literacy using Edpuzzle has a better effect on the spatial problem-solving ability with an average score of 72.40 compared to PBFCL learning accompanied by digital literacy using Canva with an average score 68.93 both learning with Expository accompanied by digital literacy using Edpuzzle with an average score of 68.53 and learning with Expository accompanied by digital literacy using Canva with an average score of 65.23; (2) the factor of the PBFCL learning model has a significance of 0.001 &lt; 0.05 so that it can be concluded that there is an influence of the PBFCL learning model on the spatial problem-solving ability; (3) the Edpuzzle-based digital literacy factor has a significance of 0.003 &lt; 0.05 so that it means that there is an influence of digital Edpuzzle-based literacy on the spatial problem- solving.
Multi-stage warm started optimal motion planning for over-actuated mobile platforms

Abstract This work presents a computationally lightweight motion planner for over-actuated platforms. For this purpose, a general state-space model for mobile platforms with several kinematic chains is defined, which considers dynamics, nonlinearities and constraints. The proposed motion planner is based on a sequential multi-stage approach that takes advantage of the warm start on each step. Firstly, a globally optimal and smooth 2D/3D trajectory is generated using the Fast Marching Method. This trajectory is fed as a warm start to a sequential linear quadratic regulator that is able to generate an optimal motion plan without constraints for all the platform actuators. Finally, a feasible motion plan is generated considering the constraints defined in the model. In this respect, the sequential linear quadratic regulator is employed again, taking the previously generated unconstrained motion plan as a warm start. The motion planner has been deployed into the Exomars Testing Rover of the European Space Agency. This rover is an Ackermann-capable planetary exploration testbed that is equipped with a robotic arm. Several experiments were carried out demonstrating that the proposed approach speeds up the computation time and increases the success ratio for a martian sample retrieval mission, which can be considered as a representative use case of goal-constrained trajectory generation for an over-actuated mobile platform.
Using Socket.io Approach for Many-to-Many Bi-Directional Video Conferencing

Video conferencing has become a critical need in today’s world due to its importance in education and business to mention a few; also, recent years have witnessed a great revolution in communication technologies. However, there still exist limitations in these technologies in terms of the quality of communication established between two peers. Therefore, many solutions have been suggested for a variety of video conferencing applications. One of these technologies is Web Real-Time Communication technology (WebRTC). WebRTC provides the ability to efficiently perform peer-to-peer communication, which improves the quality of the communication. This work tries to propose a WebRTC bi-directional video conferencing for many-to-many (mesh topology) peers. In this work, signaling was obtained using Socket.io Library. The performance evaluation of the proposed approach was performed in terms of CPU performance, and Quality of Experience (QoE). Moreover, to validate the simulations results, a real implementation was achieved based on the following scenarios a) involving several peers, b) at the same time, opening several video rooms, c) a session will still be active even when the room initiator leaves, and d) new users can be shared with currently involved participants.
Blockchain-Based Access and Timeliness Control for Administrative Punishment Market Supervision

Administrative punishment is one of the most important ways of enforcing administrative law in the field of market supervision in China. However, at the current stage, the abuse of data access permission and the difficulty in managing timeliness in administrative punishment still remain unresolved, which hinders the legalization and standardization of the administrative punishment system. Inspired by blockchain, which is inherently traceable, tamper-proof, and transparent, we design a system, punishment supervisor (PEATS), which is suitable for administrative punishment and technically overcomes the defects of the traditional administrative punishment procedure. To prevent the abuse of data access permission, we innovatively introduce the ACG (authorization control gateway) to verify the access permission of users based on the records in the MSD (market supervision department) contract. To ensure the timeliness of the administrative punishment procedure, we design a special case contract that has the same status as the general case processing state of administrative punishment to track case progress on the blockchain. We experiment and evaluate PEATS in terms of functionality and performance and find that PEATS provides traceability, transparency, and timeliness assurance. In addition, PEATS has 80.9% of the throughput of a traditional server with no more than an additional 3% latency and at most 60 KB additional storage space per case.
Comparing K-means and OPTICS clustering algorithms for identifying vowel categories

The K-means algorithm is the most commonly used clustering method for phonetic vowel description but has some properties that may be sub-optimal for representing phonetic data. This study compares K-means with an alternative algorithm, OPTICS, in two speech styles (lab vs. conversational) in English to test whether OPTICS is a viable alternative to K-means for characterizing vowel spaces. We find that with noisier data, OPTICS identifies clusters that more accurately represent the underlying data. Our results highlight the importance of choosing an algorithm whose assumptions are in line with the phonetic data being considered.