Reviewerly/exHarmony huggingface dataset:
Towards a Framework for Intelligent Urban Traffic Routing

Intelligent traffic routing is one of the key techniques that can be used to optimise traffic, especially in urban areas. Deliberative reasoning techniques such as Automated Planning have shown their potential since they can take a global and longer-term perspective on the traffic situations. Such techniques have to be embedded in a urban traffic control framework such that they can generate and assign routes to the vehicles on the fly while considering the current traffic situation in the area.&#x0D; This paper presents an ongoing work on a framework that, in a nutshell, integrates an automated planning component, responsible for intelligent traffic routing, in the well known SUMO simulator in order to evaluate and study its impact in realistic traffic settings. In particular, the framework has to simplify the representation of the road network provided by SUMO, translate it into PDDL, a language for describing planning problems, and then interpret plans and fed them in form of vehicle routes into SUMO.
Counterfeited Product Identification in a Supply Chain using Blockchain Technology

Since the invention of the Blockchain technology in 2008, it has been used in many domains to ensurehigh security and reliability of data, like from the use of Bitcoin to BaaS (Blockchain as a Service)which is a new blockchain trend and is a sort of cloud-based network for the organizations in thebusiness of building blockchain-based applications. This paper implements the combined approachof the decentralized Blockchain technology and the Supply Chain to establish that the end-users ina supply chain do not completely rely on the trader to establish that the product is counterfeited ornot and this can be done by authenticating the product at every stage in the Supply Chain by usingOne Time Passwords on the receiver’s mobile phone along with a deployed personnel who willbe responsible for assuring the quality of products. Furthermore, using this combined technical approachcan considerably lower down the cost of product quality assurance and this proposed systemwill track the authenticity of the product from its origin from the manufacturer to the end-user as well.
Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review, Taxonomy, Issues, and Future Directions

Due to the rise of social media, a vast volume of information is shared daily. Finding relevant and acceptable information has become more challenging as the Internet’s information flow has changed and more options have been available. Various recommendation systems have been proposed and successfully used for different applications. This paper presents a taxonomy of deep learning algorithms for social recommendation by examining selected papers using a systematic literature review approach. Fortysix publications were chosen from research published between 2016 and 2022 in six major online libraries. The main purpose of this research is to provide a brief review of published studies to assist future researchers in establishing new strategies in this field. The implantation of deep learning in recommender systems proved to be very effective and achieved competitive performance. Different methods and domains have been summarized to find the most appropriate method and domain.
Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering

Generalized Labeled Multi-Bernoulli (GLMB) densities arise in a host of multi-object system applications analogous to Gaussians in single-object filtering.However, computing the GLMB filtering density requires solving NP-hard problems.To alleviate this computational bottleneck, we develop a linear complexity Gibbs sampling framework for GLMB density computation.Specifically, we propose a tempered Gibbs sampler that exploits the structure of the GLMB filtering density to achieve an O(T (P + M )) complexity, where T is the number of iterations of the algorithm, P and M are the number hypothesized objects and measurements.This innovation enables the GLMB filter implementation to be reduced from an O(T P 2 M ) complexity to O(T (P + M + log T ) + P M).Moreover, the proposed framework provides the flexibility for trade-offs between tracking performance and computational load.Convergence of the proposed Gibbs sampler is established, and numerical studies are presented to validate the proposed GLMB filter implementation.
Unraveling the Mysteries of AI Chatbots

This primer provides an overview of the rapidly evolving field of generative artificial intelligence, specifically focusing on large language models like ChatGPT (OpenAI) and Bard (Google). Large language models have demonstrated unprecedented capabilities in responding to natural language prompts. The aim of this primer is to demystify the underlying theory and architecture of large language models, providing intuitive explanations for a broader audience. Learners seeking to gain insight into the technical underpinnings of large language models must sift through rapidly growing and fragmented literature on the topic. This primer brings all the main concepts into a single digestible document. Topics covered include text tokenization, vocabulary construction, token embedding, context embedding with attention mechanisms, artificial neural networks, and objective functions in model training. The primer also explores state-of-the-art methods in training large language models to generalize on specific applications and to align with human intentions. Finally, an introduction to the concept of prompt engineering highlights the importance of effective human-machine interaction through natural language in harnessing the full potential of artificial intelligence chatbots. This comprehensive yet accessible primer will benefit students and researchers seeking foundational knowledge and a deeper understanding of the inner workings of existing and emerging artificial intelligence models. The author hopes that the primer will encourage further responsible innovation and informed discussions about these increasingly powerful tools.
Automated Knowledge Organization AI ML based Subject Indexing System for Libraries

The research study as reported here is an attempt to explore the possibilities of an AI/ML-based semi-automated indexing system in a library setup to handle large volumes of documents. It uses the Python virtual environment to install and configure an open source AI environment (named Annif) to feed the LOD (Linked Open Data) dataset of Library of Congress Subject Headings (LCSH) as a standard KOS (Knowledge Organization System). The framework deployed the Turtle format of LCSH after cleaning the file with Skosify, applied TF-IDF as a language model (backend algorithm), and selected Snowball as an analyzer. The training of Annif was conducted with a large set of bibliographic records populated with subject descriptors (MARC tag 650$a) and indexed by trained LIS professionals. The training dataset is first treated with MarcEdit to export it in a format suitable for OpenRefine, and then in OpenRefine it undergoes many steps to produce a bibliographic record set suitable to train Annif. The framework, after training, has been tested with a bibliographic dataset to measure indexing efficiencies, and finally, the automated indexing framework is integrated with a data wrangling software (OpenRefine) to produce suggested headings on a mass scale. The entire framework is based on open source software, open datasets, and open standards.
Voxel-Wise Medical Imaging Transformation and Adaption Based on CycleGAN and Score-Based Diffusion

Supervised methods, such as those utilized in classification, prediction, and segmentation tasks for medical images, experience a decline in performance when the training and testing datasets violate the i.i.d (independent and identically distributed) assumption. Hence we adopted the CycleGAN(Generative Adversarial Networks) method to cycle training the CT(Computer Tomography) data from different terminals/manufacturers, which aims to eliminate the distribution shift from diverse data terminals. But due to the model collapse problem of the GAN-based model, the images we generated suffer serious radiology artifacts. To eliminate the boundary marks and artifacts, we adopted a score-based generative model to refine the images voxel-wisely. This novel combination of two generative models makes the transformation between diverse data providers to a higher fidelity level without sacrificing any significant features. In future works, we will evaluate the original datasets and generative datasets by experimenting with a broader range of supervised methods.
Insider Threat Mitigation in High Security Facilities

The biggest challenge for the security in high security facilities is the insider threat, humans as the weakest link of the system. The insider is an invisible enemy of the security, because it has unique capabilities. Although perfect security cannot exist, the aim of the present study – besides showing the threat represented by insider offenders – is to introduce the measures for risk mitigation.
Using Shiny apps for statistical analyses and laboratory workflows

Abstract In many areas of healthcare, digitization has progressed only slowly so far. The SARS-CoV-2 pandemic in particular has shown how valuable software solutions that are deployable at short notice, can be. In this review we present some selected possibilities of the easy-to-learn programming language R and demonstrate potential applications of the package Shiny in the fields of statistical analysis and laboratory medicine. In addition to a brief tabular overview of published applications, we present two examples of their use in routine laboratory workflows. The first example demonstrates how a Shiny app can be used to estimate the minimal difference (MD) of laboratory analytes, while the second example illustrates how pre- and post-analytical processing steps can be integrated into a fully automated workflow using R and the Shiny package.
Auto-labelling of Bug Report using Natural Language Processing

The exercise of detecting similar bug reports in bug tracking systems is known as duplicate bug report detection. Having prior knowledge of a bug report’s existence reduces efforts put into debugging problems and identifying the root cause. Rule and Query-based solutions recommend a long list of potential similar bug reports with no clear ranking. In addition, triage engineers are less motivated to spend time going through an extensive list. Consequently, this deters the use of duplicate bug report retrieval solutions. In this paper, we have proposed a solution using a combination of NLP techniques. Our approach considers unstructured and structured attributes of a bug report like summary, description and severity, impacted products, platforms, categories, etc. It uses a custom data transformer, a deep neural network, and a non-generalizing machine learning method to retrieve existing identical bug reports. We have performed numerous experiments with significant data sources containing thousands of bug reports and showcased that the proposed solution achieves a high retrieval accuracy of 70% for recall@5.
A CRYPTO-TWIN FRAMEWORK FOR THE AEC INDUSTRY - ENABLING DIGITAL TWINS WITH BLOCKCHAIN TECHNOLOGIES

The paper describes a conceptual framework and the smart contract architecture for Crypto-Twins (CT) in the AEC industry, i.e. blockchain (BC) enabled digital twins.We describe the background, terminology of technologies involved, while the methodology follows design science research patterns to construct the framework and architecture of the Crypto-Twin.Further avenues for prototype development and validation of the framework are proposed in the conclusion.
Sentiment analysis of weather news in British online newspapers

The advancement of modern technologies has influenced the way news is presented and consumed, particularly online. Weather is an important topic for the public as it relates to the human experience and addresses current societal issues. In this paper, we introduce a systematic approach to conduct sentiment analysis of weather news stories, to specify the emotional tone and examine the role of subjectivity in online news reporting. This research falls within the scope of a lexicon-based (unsupervised) approach to sentiment analysis, which involves finding the sentiment polarity of words. The analysis is predominantly based on sentence-level sentiment analysis. Two popular online web services, MonkeyLearn and SentiStrength, were applied to automatically detect human emotions. We compared the efficiency of each tool and found that MonkeyLearn provided better final results in comparison to SentiStrength, which tended to misclassify negative sentiments into neutral ones. The final results of frequency calculation showed the dominance of weather news stories with negative sentiment polarity over positive and neutral ones, with neutral sentiments being in the minority. Based on the empirical findings, we observed an objectivity-to-subjectivity shift in online news reporting.
A Systematic Review of Voice-based Intelligent Virtual Agents in EFL Education

Since its debut in the field of education nearly three decades ago, Artificial Intelligence (AI) has been considered as a powerful tool to facilitate new paradigms for instructional design and innovative educational practice in the form of intelligent tutoring systems, chatbots, teaching robots and adaptive learning systems among others. Recent technological advances in the adjacent areas of natural language processing, machine learning and computer graphics focusing primarily on design features that can improve their human-like qualities of naturalness and believability as human interlocutors have also amplified new application opportunities for Intelligent Virtual Agents (IVAs) or Animated Pedagogical Agents (APAs) within the area of Intelligent Computer-Assisted Language Learning (ICALL). Although AI-powered IVAs hold the potential to enhance the learning process in nearly any knowledge domain and personalize automation in teaching by embodying different roles in the learning environment, strikingly few studies have empirically attempted to assess IVAs impact on L2 learners’ academic achievement when learning English as a Foreign (EFL) so far. This study addresses this issue via a systematic review of relevant interventionist IVA studies that were conducted in EFL settings and published within the 2015-2020 timeframe examining IVAs key affordances, major barriers in their adoption for language learning purposes, and the CALL research trends currently prevalent on the topic. Pedagogical implications for the effective implementation of IVA technology in L2 contexts are discussed and future research avenues in the area are highlighted.
Research Trend of Big Data in Education During the Last 10 Years

Big Data research has rapidly developed in the last 10 years, making it an interesting topic to investigate to understand the trends and developments of Big Data in Education. This study aims to analyze source documents and state contributions, and visualize research trends on the subject of Big Data in education during the last 10 years and identify potential research topics related to Big Data in Education in the future. This study uses a literature review and Meta-Analyses (PRISMA) method associated with bibliometric analysis using the Scopus and VOSViewer databases, through which 1,076 documents were obtained. The results of the bibliometric analysis show that Big Data research in education has increased significantly in the last 10 years. However, in the last year, it has decreased, this is the next challenge, and an opportunity for future research. The most common types of documents are conference papers, the source of most documents is conference proceedings, and the country that contributes the most is China. English is the most widely spoken language, and the authors with the highest contribution are Daniel, B. K. Further research related to Big Data can be used and implemented in the business world for Big Data analysis in education. Big Data can also be integrated with Science, technology, engineering, and mathematics (STEM) which can be a further research opportunity that can be applied to education because analyzing Big Data requires STEM learning skills. Thus, this research recommends finding updates in the study of Big Data in education by integrating STEM in education because analyzing Big Data again requires STEM learning expertise. This study has limitations in using only one database, namely Scopus, to obtain research data. Therefore, it is recommended that Big Data in Education research be conducted using other databases besides Scopus to obtain more extensive data.
Application of Analytic Hierarchy Process to Rank Fire Safety Factors for Assessing the Fire Probabilistic Risk in School for the Blind Building

Fires are the leading cause of death, serious injury and property damage. In the past, schools, temples and government offices had more frequent fires than they should. Statistics showed that the number of fires between 2017 to 2022 amounted 13,593 cases which mostly occurred in the school, temple and government offices (40.0% of all buildings). Moreover, it causes more damage among disabled especially the blinds who has a limited vision. Therefore, this cross-sectional purpose of this study was to assess fire risk including management model in school for the blind. The fire checklists, brainstorming and Analytic Hierarchy Process (AHP) were applied to estimate the fire risk in school for the blind building. The findings revealed an inherent fire hazard factors with a risk score equal to 3.2830 and evacuation factors with a risk score equal to 3.3178 were acceptable risk except the fire control factors with a risk score equal to 1.4320 was unacceptable risk may cause an impact on life, health, property and public communities. Eventually, efforts should be made to supervise those risk factors by designing suitable activities to reduce undesirable conditions in school for the blind.
A Security Enhanced Android Unlock Scheme based on Pinch-to-Zoom for Smart Devices

With the rapid development of information technology, mobile phones especially smartphones have become a basic and important personal device to assist and facilitate an individual’s daily life. Due to the convenience, an individual is very likely to store various personal information on smartphones, including healthcare record, ID card manifold, family photos, private video clips, etc. This may pose a big challenge on data security and privacy. To mitigate such risk, one commonly used security solution is phone unlock pattern, where Android unlock pattern should be the most widely used one on Android devices. However, Android unlock scheme is known to be not secure under various adversarial scenarios. In the literature, there is a trend of combining the unlock scheme with touch biometric. In this work, we follow this trend and develop ZoomPass, a security-enhanced Android unlock scheme by analyzing solely zoom behavior from individuals. It is a two-step scheme that requires an individual to select two dots and perform a pinch-to-zoom action for each dot. To evaluate the scheme performance, we conduct a user study with 100 participants and found that our scheme can reach good performance in both usability and security.
Investigating how immersive virtual reality and active navigation mediate the experience of virtual concerts

Abstract We conducted an experiment to examine how virtual reality (VR) and active navigation interact to improve audience experience in virtual concerts. To manipulate the medium, the participants were presented with concert-related audiovisual stimuli via a head-mounted VR device or a computer. To manipulate the participants’ access to different perspectives (navigation mode), they were allowed to actively switch, or were passively guided, between the audience’s perspective and the performer’s perspective. According to the results, VR and active navigation induced a higher sense of presence (feeling of being somewhere else) than did computer and passive navigation, and thus, they increased the audience’s state of flow and gave them higher degrees of satisfaction and concert-attending intention. VR and active navigation also increased the participants’ role identification (feeling of being someone else), which again gave them higher degrees of satisfaction and concert-attending intention. This research contributes to the literature supporting VR’s enhancement of concert experiences and further highlights the important relationship between action, perception, and experience satisfaction.
Counting Activities Using Weakly Labeled Raw Acceleration Data: A Variable-Length Sequence Approach with Deep Learning to Maintain Event Duration Flexibility

This paper presents a novel approach for counting hand-performed activities using deep learning and inertial measurement units (IMUs). The particular challenge in this task is finding the correct window size for capturing activities with different durations. Traditionally, fixed window sizes have been used, which occasionally result in incorrectly represented activities. To address this limitation, we propose segmenting the time series data into variable-length sequences using ragged tensors to store and process the data. Additionally, our approach utilizes weakly labeled data to simplify the annotation process and reduce the time to prepare annotated data for machine learning algorithms. Thus, the model receives only partial information about the performed activity. Therefore, we propose an LSTM-based architecture, which takes into account both the ragged tensors and the weak labels. To the best of our knowledge, no prior studies attempted counting utilizing variable-size IMU acceleration data with relatively low computational requirements using the number of completed repetitions of hand-performed activities as a label. Hence, we present the data segmentation method we employed and the model architecture that we implemented to show the effectiveness of our approach. Our results are evaluated using the Skoda public dataset for Human activity recognition (HAR) and demonstrate a repetition error of ±1 even in the most challenging cases. The findings of this study have applications and can be beneficial for various fields, including healthcare, sports and fitness, human–computer interaction, robotics, and the manufacturing industry.
Virtual reality technology in physical education: a systematic review in instructional design &amp; implementation

Virtual reality is an immersive technology that is utilized in education. The increasing growth of physical education research led to the need for a comprehensive examination. Furthermore, due to the rapid growth of VR research, this systematic analysis intends to investigate the instructional design used to develop virtual reality in physical education over the previous five years. As a result, 31 previous research articles on virtual reality technology and physical education were analyzed for this paper. The most instructional design used for VR development is ADDIE followed by Bloom’s Taxonomy, while for the implementation of VR, undergraduate students are not the only option for using VR-based applications.