Reviewerly/exHarmony huggingface dataset:
Hybrid deterministic non-deterministic data-parallel algorithm for real-time unmanned aerial vehicle trajectory planning in CUDA

The development of autonomous Unmanned Aerial Vehicles (UAVs) is a priority to many civilian and military organizations. Real time optimal trajectory planning is an essential element for the autonomy of UAVs. The use of metaheuristic algorithms for solving such complex optimization problems with non-linearity and multimodality has gained popularity recently. In this paper, we use a non-deterministic Flower Pollination Algorithm (FPA) to deal with the problem's complexity and compute feasible and quasi-optimal trajectories for fixed-wing UAVs in complex 3D environments, taking into account the vehicle's flight properties. The global optimization algorithm is improved with the addition of a deterministic 2-opt local search providing a significant improvement. To achieve real-time performance, the proposed trajectory planner in implemented and parallelized following the data-parallel paradigm on a Graphics Processing Unit (GPU) using the Compute Unified Device Architecture (CUDA) resulting in a 253.4x speedup compared to the sequential implementation on CPU. The parallel implementation is able to compute quasi-optimal trajectories in just 0.369 s.
The Impact on Multicultural Acceptability of University Student’s Happiness: Mediated Effect of Human Rights Awareness

본 연구의 목적은 대학생의 행복감이 다문화수용성에 미치는 영향에서 인권의식의 매개효과를 살펴보고자 연구되었다. 연구방법은 전라남도 지역에 위치한 대학생 257명을 대상으로 설문조사를 실시하였다. 수집된 자료는 설문 문항의 신뢰도를 검증하기 위하여 Cronbach 계수로 산출하였고, 대학생의 행복감, 인권의식, 다문화수용성 간의 관계를 살펴보기 위하여 Pearson 적률상관분석을 실시하였으며, 대학생의 행복감이 다문화수용성에 미치는 영향에서 인권의식의 매개효과를 살펴본기 위하여 Baron과 Kenny의 3단계매개회귀분석을 실시하였다. 연구결과는 첫째, 대학생의 행복감, 인권의식, 다문화수용성 간의 유의한 정적상관이 나타났다. 둘째, 대학생의 인권의식은 행복감이 전체 다문화수용성에 미치는 영향에서 완전매개효과가 행복감이 다문화수용성의 하위 요인인 다양성과 관계성에 미치는 영향에서는 완전매개효과가 보편성에 미치는 영향에서는 부분 매개효과가 나타났다. 결론적으로 본 연구는 대학생의 인권의식에 관한 교육 및 프로그램 개발에 기초자료를 제공할 것으로 판단한다.The purpose of this study is to analyze the mediated effect of human rights awareness in the impact on multicultural acceptability of university student’s happiness.. The survey target was two hundred fifty-seven university students in Jeollanam-do. Cronbach Coefficient was calculated to test reliability of the survey questionnaires for the collected data, and Pearson’s Ratio Correlation Analysis was applied to see the relationship between university student’s happiness, human rights awareness, and multicultural acceptability. The third state of mediating effect analysis from Baron and Kenny(1986) was implemented to examine the mediated effect of human rights awareness in the impact on multicultural acceptability of university student’s happiness. First, there was significant static correlation between university student’s happiness, human rights awareness, multicultural acceptability. S Second, university student’s human rights awareness had a complete mediating effect in the impact on the overall multicultural acceptability of happiness and in the impact on multicultural acceptability’s subfactors of happiness, which are diversity and relationship, and a partial mediating effect in the impact on universality. This study would provide as basic data for developing the education and program about university student’s human rights awareness.
Popping the chatbot hype balloon

ABSTRACTSince ChatGPT’s release in November 2022, artificial intelligence has come into the spotlight. Inspiring both fascination and fear, chatbots have stirred debates among researchers, developers, and policy makers. The concerns range from concrete and tangible ones—which include replication of existing biases and discrimination at scale, harvesting personal data, and spreading misinformation—to more existential fears that their development will lead to machines with human-like cognitive abilities. Understanding how chatbots work and the human labor and data involved can better help evaluate the validity of concerns surrounding these systems, which although innovative, are hardly the stuff of science fiction.KEYWORDS: Chatbotsartificial intelligenceAIlarge language modelsChatGPT Disclosure statementNo potential conflict of interest was reported by the author(s).FundingThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.FundingThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.Additional informationFundingThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.Notes on contributorsSara GoudarziSara Goudarzi is associate editor, disruptive technologies at the Bulletin. Her work has appeared in Scientific American, The New York Times, and National Geographic News, among others. She is the author of the debut novel The Almond in the Apricot and several children’s titles including Leila’s Day at the Pool.
Data Glove for the Recognition of the Letters of the Polish Sign Language Alphabet

This article presents the results of a study on the classification of 36 letters of the Polish Sign Alphabet (PSL) using a data glove. The data glove includes a Raspberry Pi, a PCB with four 16-bit ADS1115 ADCs, and ten piezoresistive sensors. The sensors function as potentiometers that respond to finger bending. A neural network consisting of LSTM and convolutional layers was used for classification, achieving an efficiency of 99% on data from a single subject, which underwent prior augmentation. The study suggests that the device could potentially be calibrated solely from the target user. This is the first study of its kind on PSL.
A Secure Deduplication Technique for Data in the Cloud

The tremendous growth of digital data in cloud storage systems is a critical issue, as many duplicate data in storage systems cause extra load. Cloud Service Providers (CSPs) often employ Data Deduplication techniques to eliminate redundant data and store only one copy of data to save storage space and reduce transmission costs. Data Deduplication is mostly effective when multiple clients outsource the same data to cloud storage, but it raises security and ownership issues. This paper proposes a secure, Proof of Ownership (PoW)-based Data Deduplication scheme that has a low communication overhead and ensures that only valid cloud clients can download and decrypt ciphertext from cloud storage. The Advanced Encryption Standard (AES) is used as the encryption algorithm in the proposed scheme. It utilizes two modes of AES encryption, namely, Cipher Block Chaining (CBC) and Galois Counter Mode (GCM), with single-threading and multi-threading to upload and download ciphertext between the client and the server to measure the effect of upload and download times. We present a new approach for PoW to reduce communication overhead. PoW enables owners of the same data to prove to the cloud server that they own the data in a robust way. The comparison between CBC and GCM is implemented in a Java environment with two scenarios: single-threading and multi-threading. The simulation results show that AES-GCM with multi-threading is better during the uploading and downloading times
Dynamic Obstacle Avoidance Technique for Mobile Robot Navigation Using Deep Reinforcement Learning

In the realm of mobile robotics, navigating around obstacles is a fundamental task, particularly in constantly changing situations. Although deep reinforcement learning (DRL) techniques exist that utilize the positional information of robot’s, environmental states, and input dataset for neural networks. Although, the positional information alone does not provide sufficient insights into the motion trends of obstacles. To solve this issue, this paper presents a dynamic obstacle mobility pattern approach for mobile robots (MRs) that rely on DRL. This method employs the positional details of dynamic obstacles dependent upon time for establishing a movement trend vector. This vector, in conjunction with another mobility state attribute, forms the MR mobility guidance matrix, that essentially conveys the pattern variation of dynamic obstacles trend over a specified interval. Using this matrix, the robot can choose its avoidance action. Also, this methodology uses the DRL-based dynamic policy algorithm for the testing and validation of the proposed technique through Python programming. The experimental outcomes demonstrate that this technique substantially improves the safety of avoiding dynamic obstacles
Advertising with Anonymity and Impunity: The Harmful Consequences of Counterfeit PPE Supply Chains

The rapid growth of illicit supply chains during and after the Covid-19 pandemic reveals a need for effectively combating and preventing the cross-border movement of contraband, including but not limited to counterfeit goods. A proactive approach by companies along with public stakeholders, such as government agencies and individual consumers, toward disrupting illicit supply chains operating across borders is especially important during moments of global crisis when consumers are more susceptible to unknowingly purchasing substandard counterfeit products such as respirators. While marketplaces, platforms, and other legitimate businesses have worked to prevent movement of counterfeits and illicit goods through their services, the high adaptability and sophistication of counterfeiters requires more preventative and multistakeholder approaches. This article outlines a multidisciplinary and multilayered approach to detecting and disrupting illicit supply chains of counterfeit personal protective equipment (PPE) with a focus on respirators. It utilizes research conducted for a National Science Foundation (NSF) grant on Covid-19 related crime, including the advertising and sale of counterfeit respirators. One layer examines online content as seen by the end user and the activity of vendors or sellers used to advertise and sell counterfeit products. The research is also informed by data on the information, financial, and physical flows of counterfeit respirators obtained through a public-private partnership with George Mason University’s Terrorism, Transnational Crime and Corruption Center (TraCCC-GMU) and 3M, one of the largest manufacturers of respirators in the world. The article examines an important and relatively recent trend - how emerging technological shifts in the marketplace are affecting global security. Research from the TraCCC-GMU and 3M partnership, including a data sharing agreement, revealed that counterfeiters constantly change their modus operandi to continue selling illicit goods with impunity, facilitating illicit activity with the use and abuse of legitimate companies such as ecommerce marketplaces and social media. The article presents an overview of the current state of counterfeit supply chains and provides concrete policy recommendations on how legitimate companies can move beyond just removing listings but must also actively prevent these transnational crimes through innovative multidisciplinary approaches, advanced data analytics, and public awareness campaigns. The research also seeks to connect the dots to broader policy implications in terms of the legitimate economy and environmental sustainability.
Project Tracker

A digital application called a project management platform aids teams in planning and monitoring their projects, tasks, and resources. It has features like project planning, task delegation, tracking and reporting, and collaboration tools to make teamwork easier. Large-scale projects need effective management and technical assistance to be successful. Organizing and managing team projects online with a project management software creates a virtual meeting room for team members, project managers, and clients. It provides a selection of strong features and tools that are intended to make project planning, execution, and monitoring easier. Employing such a platform enables businesses to improve productivity, boost project success rates, and ultimately accomplish their objectives more quickly
Students’ Digital Literacy Skill at Universitas PGRI Palembang

Developing digital literacy skills is necessary for students to survive in industrial revolution 4.0. The objective of this study aimed to find out the digital literacy skills of students in the English Education Study Program at Universitas PGRI Palembang. The sample of this study were 25 students enrolled in the second semester of the English education study program at Universitas PGRI Palembang. The survey used a Likert scale to score responses, with Strongly Disagree (SDA) equaling 1, Disagree (DA) equaling 2, Agree (A) equaling 3, and Strongly Agree (SA) equaling 4. Every single statement in the questionnaire was related to the data collected by percentage analysis. Based on The results of data analysis indicated that students of English Education Study Program at University of PGRI Palembang have digital literacy skill. They can use and utilize technology in their daily lives.
Multi‐cues underwater image restoration algorithm combined with light field technology

Abstract Underwater images suffer from color distortion, low clarity and halos problems due to light absorption, particle scattering and non‐uniform illumination. To address these degradation issues, a multi‐cues underwater image restoration algorithm combined with light field technology is proposed. First, based on Epipolar Plane Image, the light field cue transmittance containing depth information is calculated. Then, according to the turbidity of the underwater image, the light field cue transmittance and the polarization cue transmittance are fused to obtain the multi‐cues transmittance, which can effectively reduce the effect of particle scattering and color bias. Finally, the background light is estimated through the all‐focus operation, which can effectively overcome the distortion of an underwater single image and simultaneously reduce the halo phenomenon. Experimental results show that the method achieves the best results evaluated by UCIQE, UIQM, PSNR, and SSIM, and the restored color under the method is closer to the actual image than other underwater restoration methods.
The Use of Kahoot to Improve Students' Narrative Text Reading Comprehension

This research discusses the usage of a game-based learning media to solve the problem of reading comprehension score poorly, namely Kahoot!. The objective of this research was to improve the students’ narrative text reading comprehension by using Kahoot as a media in the teaching and learning process. This research was classroom action research. The research was conducted to 25 students of eighth graders of Nusantara Krian junior high school. The research showed that by using Kahoot as media in the classroom learning could improve the students' narrative text reading comprehension. The students’ mean score on the pre-test was 64 and only 9 (36%) students were able to pass the minimum completeness criteria (KKM). The result score was under the KKM set by the school 75. The learners' mean score improved after the first cycle, rising to 71.2 and 13 (52%) students passed KKM. However, it showed still under minimum completeness criteria. It was therefore necessary for the second cycle, during which the score rose to 79,6 and 20 (80%) students passed the KKM. It was concluded that using Kahoot can improve the student’s narrative text reading comprehension. The students’ positive response to the usage of Kahoot in teaching and learning reinforced the improvement in the students' achievement.
English speaking practice with conversational AI: Lower secondary students' educational experiences over time

Conversational artificial intelligence enables opportunities for practicing speaking the target language while giving individualized feedback in a low-anxiety environment offered in spoken dialogue systems with conversational agents. In this paper, we present results from a longitudinal study conducted on Swedish lower-secondary students who used a spoken dialogue system as an integrated part of their ordinary English lessons. They interacted orally with embodied conversational agents to solve given tasks in everyday-life scenarios and self-reported their experiences in questionnaires and systematic logbook reflections. Analytical methods were mainly non-parametric tests. Results revealed that the students sustained practicing, socially and emotionally engaged with a slightly positive trend in their educational experience. These insights can inspire teachers and stakeholders in the integration of conversational artificial intelligence in language education and designers in the development of such systems for this age group.
A Deep LSTM-RNN Classification Method for Covid-19 Twitter Review Based on Sentiment Analysis

In today’s world, advanced internet technologies have significantly increased people’s affinity towards social networks to stay updated on current events and communicate with others residing in different cities. Social opinion analyses helped determine the optimal public health response during the COVID-19 pandemic. Analysis of articulating tweets from Twitter can reveal the public’s perceptions of social distance. Sentiment Analysis is used for classifying text data and analyzing people’s emotions. The proposed work uses LSTM-RNN with the SMOTE method for categorizing Twitter data. The suggested approach uses increased characteristics weighted by attention layers and an LSTM-RNN-based network as its foundation. This method computes the advantage of an improved information transformation framework through the attention mechanism compared to existing BI-LSTM and LSTM models. A combination of four publicly accessible class labels such as happy, sad, neutral, and angry, is analyzed. The message of tweets is analyzed for polarization and subjectivity using TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet. The model has been successfully built and evaluated using two feature extraction methods, TF-IDF (Term Frequency-Inverse Document Frequency) and Bag of Words (BoW). Compared to the previous methodologies, the suggested deep learning model improved considerably in performance measures, including accuracy, precision, and recall. This demonstrates how effective and practical the recommended deep learning strategy is and how simple it is to employ for sentiment categorization of COVID-19 reviews. The proposed method achieves 97% accuracy in classifying the text whereas, among existing Bi-LSTM, achieves 88% maximum in the text classification.
PRONUNCIATION PROBLEM OF INDONESIAN LEARNERS IN PRONOUNCING /G/ SOUND AT BHAYANGKARI 01 SENIOR HIGH SCHOOL IN MEDAN

Pronunciation is the production of sounds that are employed to convey meaning. Pronouncing English is a little bit difficult for Indonesian students since they use their mother tongue for their communication. This study was focused on the problem of pronunciation of the /g/ sound in English. The researcher used descriptive qualitative for the research design. The subject of research was the students in grade X MIA 3 that consisted of 27 students. The researcher used the purposive technique to take the sample of the research. The data collection of research was through observation, documentation, and diary notes. The researcher obtained five (5) types of problems in pronouncing /g/ sound, such as /g/ sound before /h/ at the end of words, /g/ sound before /n/ in the middle of words, /g/ sound before /n/ at the beginning of words, /g/ sounds before /e/ at the beginning of words, and /g/ sounds before /i/ at the beginning of words. The mean of data was 14.6, which suggested that there were about 15 students who had a good pronunciation. And the rest students were still had incorrect pronunciation.
Professionalization of Research Computing and Data: An Expanded Agenda

There is considerable debate around modes of professionalization for the research computing and data (RCD) community. In 2018, credentialing was recommended around three roles (systems facing, software facing, and researcher facing). Reviewing the experience in the profession since 2018 and with new survey data collected in 2022, we provide an expanded set of findings and recommendations addressing career paths, inclusion, professional networks, burnout, community building, and other challenges facing research computing and data professionals. Although there is support for this broader view of professionalization via a professional society, there is also variation in views across the many types of RCD professionals with regard to the desired function of such a society.
Performance of Distributed Deep Learning Workloads on a Composable Cyberinfrastructure

The next generation of computing systems are likely to rely on disaggregated resources that can be dynamically reconfigured and customized for researchers to support scientific and engineering workflows that require different cyberinfrastructure (CI) technologies. These resources would include memory, accelerators, co-processors among other technologies. This would represent a significant shift in High Performance Computing (HPC) from the now typical model of clusters that have these resources permanently connected to a single server. While composing hardware frameworks with disaggregated resources holds promise, we need to understand how to situate workflows on these resources and evaluate the impact of this approach on workflow performance against “traditional” clusters. Toward developing this knowledge framework, we study the applicability and performance of deep learning workloads on GPU-enabled composable and traditional HPC computing platforms. Results from tests performed using the Horovod framework with TensorFlow and PyTorch models on these HPC environments are presented here.
Immersive OSPRay: Enabling VR Experiences with OSPRay

We present an extension to OSPRay Studio, Intel’s open-source ray tracing application, to support immersive virtual reality experiences in public exhibition settings. This extension enables one to display a single, coherent 3D virtual environment on tiled display walls and use gesture-based interaction techniques to navigate the environment. Additional mechanisms are provided to configure the application for different display arrangements and integrate other motion-tracking technologies.
FASTQ File Compression Benchmarking Using Lossless General Purpose Algorithms

FASTQ format is a text-based format for storing a biological sequence (usually nucleotide sequence) and its corresponding quality scores used for genome sequencing. While most text-based formats compress well using traditional methods such as tar and gzip, FASTQ files are generally quite large and don’t compress well using these methods, leading to much of a file system’s space being used to store these data sets. As most computing platforms are shared resources, balancing compression and resource allocation is vital. This paper investigates the best general use compression software for FASTQ files to run at the end of a job in a mixed-use throughput high-performance compute cluster. As most computing platforms are shared resources, balancing compression and resource allocation is vital. It was found that zpaq high compression delivers the highest compression ratios of the fifty-seven methods tested in this paper. However, for more real-world scenarios where system resources are shared or limited, we recommend pzstd medium as a good all-around compression method for FASTQ files. It delivers high compression ratios at fast speeds while performing well on CPU and memory efficiency.