Reviewerly/exHarmony huggingface dataset:
Controllers or Bare Hands? A Controlled Evaluation of Input Techniques on Interaction Performance and Exertion in Virtual Reality

Virtual Reality (VR) systems have traditionally required users to operate the user interface with controllers in mid-air. More recent VR systems, however, integrate cameras to track the headset's position inside the environment as well as the user's hands when possible. This allows users to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directly</i> interact with virtual content in mid-air just by reaching out, thus discarding the need for hand-held physical controllers. However, it is unclear which of these two modalities—controller-based or free-hand interaction—is more suitable for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">efficient</i> input, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accurate</i> interaction, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">long-term</i> use under <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reliable</i> tracking conditions. While interacting with hand-held controllers introduces weight, it also requires less finger movement to invoke actions (e.g., pressing a button) and allows users to hold on to a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">physical</i> object during virtual interaction. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In this paper, we investigate the effect of VR input modality (controller vs. free-hand interaction) on physical exertion, agency, task performance, and motor behavior across two mid-air interaction techniques (touch, raycast) and tasks (selection, trajectory-tracing). Participants reported less physical exertion, felt more in control, and were faster and more accurate when using VR controllers compared to free-hand interaction in the raycast setting. Regarding personal preference, participants chose VR controllers for raycast but free-hand interaction for mid-air touch. Our correlation analysis revealed that participants' physical exertion increased with selection speed, quantity of arm motion, variation in motion speed, and bad postures, following ergonomics metrics such as consumed endurance and rapid upper limb assessment. We also found a negative correlation between physical exertion and the participant's sense of agency, and between physical exertion and task accuracy.
A Decoupling Paradigm With Prompt Learning for Remote Sensing Image Change Captioning

Remote sensing image change captioning (RSICC) is a novel task that aims to describe the differences between bi-temporal images by natural language. Previous methods ignore a significant specificity of the task: the difficulty of RSICC is different for unchanged and changed image pairs. They process the unchanged and changed image pairs in a coupled way, which usually causes confusion for change captioning. In this paper, we decouple the task into two issues to ease it: whether and what changes have occurred. An image-level classifier performs binary classification to address the first issue. A feature-level encoder contributes to extracting discriminative features to help the caption generation module address the second issue. Besides, for caption generation, we utilize prompt learning to introduce pre-trained large language models (LLMs) into the RSICC task. A multi-prompt learning strategy is proposed to generate a set of unified prompts and a class-specific prompt conditioned on the image-level classifier’s results. The strategy can prompt a pre-trained LLM to know whether changes exist and generate captions. Finally, the multiple prompts and the visual features of the feature-level encoder are fed into a frozen LLM for language generation. Compared with previous methods, our method can leverage the powerful abilities of the pre-trained LLM in language to generate plausible captions, which is free of training. Extensive experiments show that our method is effective and achieves state-of-the-art performance. Besides, an additional experiment demonstrates that our decoupling paradigm is more promising than the previous coupled paradigm for the RSICC task. We will make our codebase publicly available to facilitate future research at https://github.com/Chen-Yang-Liu/PromptCC.
South-western Nigerian University Students’ Gender Use of Mobile Technologies for Learning

Gender’s potential impact on the uptake of mobile learning technologies was investigated. The study sought to examine the influence of gender on attitudes and intentions toward adopting mobile technology for learning, as well as perceived utility, usability, and usefulness. The survey method was used for the investigation. A total of 1,214 respondents were chosen on purpose from 18 universities in the south-west of Nigeria, including six Federal, six State, and six Private Universities. The data were gathered using researchers’ designed questionnaire. The instrument’s dependability was determined using Cronbach Alpha to be 0.86. The first through fourth hypotheses were put to the t-test. The study’s findings showed that there was no difference between undergraduates of both sexes in terms of their attitudes (t (1, 1212) = 0.76, p = 0.45), usefulness (t (1, 1212) = 0.32, p = 0.75), perceived ease of use (t (1, 1212) = 0.86, p = 0.39) and their intention (t (1, 1212) = 0.06, p= 0.96) toward using mobile technologies for learning. The study’s conclusions show that none of the criteria (perceived usefulness, perceived ease of use, attitude, and intention) differed by gender. It follows that mobile technologies may have a significant impact on how students learn in Nigerian Universities. The study consequently advised against taking gender imbalance into account when implementing mobile-based learning in Nigeria.
Decision making in open agent systems

Abstract In many real‐world applications of AI, the set of actors and tasks are not constant, but instead change over time. Robots tasked with suppressing wildfires eventually run out of limited suppressant resources and need to temporarily disengage from the collaborative work in order to recharge, or they might become damaged and leave the environment permanently. In a large business organization, objectives and goals change with the market, requiring workers to adapt to perform different sets of tasks across time. We call these multiagent systems (MAS) open agent systems (OASYS), and the openness of the sets of agents and tasks necessitates new capabilities and modeling for decision making compared to planning and learning in closed environments. In this article, we discuss three notions of openness: agent openness, task openness, and type openness. We also review the past and current research on addressing the novel challenges brought about by openness in OASYS. We share lessons learned from these efforts and suggest directions for promising future work in this area. We also encourage the community to engage and participate in this area of MAS research to address critical real‐world problems in the application of AI to enhance our daily lives.
Behavioral analysis of cybercrime: Paving the way for effective policing strategies

This comprehensive survey paper offers a panoramic exploration of the dynamic realm of cybercrime policing, encompassing a wide spectrum of intricacies from cybercrime definitions to establishing a robust investigative framework. Navigating through the intricate tapestry of cybercrime, the study meticulously classifies and contextualizes cybercrime taxonomies, typologies, and defining attributes. Emphasizing the importance of a holistic understanding, the research dissects the stages of cybercriminal activities, shedding light on the multifaceted strategies employed in executing cybercrimes. By deconstructing the entire cybercrime lifecycle, this paper enhances the granularity of comprehending cyber incidents, consequently facilitating more effective investigative preparedness. An essential focal point of this investigation is the indispensable role of digital artifacts in the cybercrime landscape. The study underscores their pivotal significance in catalyzing cybercrime investigations and shaping the trajectory of law enforcement efforts. Additionally, the study probes the repercussions of cybercrime on victims, dissecting the complex interplay between public perception of policing and law enforcement’s perspective on victims. Amid the intricate challenges faced by cybercrime policing, the research uncovers diverse investigation approaches and underscores the criticality of well-defined digital investigation procedures. Moreover, the study delves into the symbiotic collaboration between public and private sectors, highlighting the joint efforts of private entities and law enforcement agencies in combatting cybercrimes. A cornerstone of this survey is the evaluative scrutiny of existing investigative models, culminating in the proposition of a robust and comprehensive cybercrime investigation framework. Rooted in meticulous research methodologies, this endeavor contributes a valuable resource poised to inform and guide future research in the ever-evolving field of cybercrime policing. As the digital landscape continues to evolve, this survey paper equips law enforcement with essential insights to navigate the complexities of cybercrime and safeguard digital realms with utmost efficacy.
Graph IRs for Impure Higher-Order Languages: Making Aggressive Optimizations Affordable with Precise Effect Dependencies

Graph-based intermediate representations (IRs) are widely used for powerful compiler optimizations, either interprocedurally in pure functional languages, or intraprocedurally in imperative languages. Yet so far, no suitable graph IR exists for aggressive global optimizations in languages with both effects and higher-order functions: aliasing and indirect control transfers make it difficult to maintain sufficiently granular dependency information for optimizations to be effective. To close this long-standing gap, we propose a novel typed graph IR combining a notion of reachability types with an expressive effect system to compute precise and granular effect dependencies at an affordable cost while supporting local reasoning and separate compilation. Our high-level graph IR imposes lexical structure to represent structured control flow and nesting, enabling aggressive and yet inexpensive code motion and other optimizations for impure higher-order programs. We formalize the new graph IR based on a λ-calculus with a reachability type-and-effect system along with a specification of various optimizations. We present performance case studies for tensor loop fusion, CUDA kernel fusion, symbolic execution of LLVM IR, and SQL query compilation in the Scala LMS compiler framework using the new graph IR. We observe significant speedups of up to 21 x .
Investigating the use of virtual reality to improve speaking skills: insights from students and teachers

Abstract There is ongoing scientific discussion on the role of innovative technologies in enhancing teaching and learning. Technologies like augmented reality, virtual reality, mixed reality, artificial intelligence, and generative artificial intelligence have sparked debates in the broader literature. To contribute to ongoing discussions on these topics and to bridge gaps existing in works of literature on the potentials and challenges of innovative technologies like virtual reality, this paper provides insights from students and teachers on the use of virtual reality for teaching speaking skills so far lacking in academic prose in this domain. Given that this study only focused on obtaining student and teacher insights, a mixed-method research design that used questionnaires and interviews was implemented to investigate this study. After obtaining and analyzing data from 85 participants, the study found that although virtual reality could have improved students' speaking skills more efficiently, it was a fun and exciting learning experience for the students and teachers. Other novel findings of the study were instrumental in making pedagogic conclusions on the study's objective.
Fragmented imaginary-time evolution for early-stage quantum signal processors

Abstract Simulating quantum imaginary-time evolution (QITE) is a significant promise of quantum computation. However, the known algorithms are either probabilistic (repeat until success) with unpractically small success probabilities or coherent (quantum amplitude amplification) with circuit depths and ancillary-qubit numbers unrealistically large in the mid-term. Our main contribution is a new generation of deterministic, high-precision QITE algorithms that are significantly more amenable experimentally. A surprisingly simple idea is behind them: partitioning the evolution into a sequence of fragments that are run probabilistically. It causes a considerable reduction in wasted circuit depth every time a run fails. Remarkably, the resulting overall runtime is asymptotically better than in coherent approaches, and the hardware requirements are even milder than in probabilistic ones. Our findings are especially relevant for the early fault-tolerance stages of quantum hardware.
LLMs4OL: Large Language Models for Ontology Learning

We propose the LLMs4OL approach, which utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Our LLMs4OL paradigm investigates the following hypothesis: Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text? To test this hypothesis, we conduct a comprehensive evaluation using the zero-shot prompting method. We evaluate nine different LLM model families for three main OL tasks: term typing, taxonomy discovery, and extraction of non-taxonomic relations. Additionally, the evaluations encompass diverse genres of ontological knowledge, including lexicosemantic knowledge in WordNet, geographical knowledge in GeoNames, and medical knowledge in UMLS. The obtained empirical results show that foundational LLMs are not sufficiently suitable for ontology construction that entails a high degree of reasoning skills and domain expertise. Nevertheless, when effectively fine-tuned they just might work as suitable assistants, alleviating the knowledge acquisition bottleneck, for ontology construction.
Economic Feasibility of PV Mounting Structures on Industrial Roofs

This study determines the viability and profitability of photovoltaic (PV) mounting structures on industrial roofs. For this purpose, more than 656,000 different cases have been analyzed, combining different consumption patterns, energy prices, locations, inclinations, azimuths, capacity installed, and excess income. The results show that the industry’s consumption pattern is a key factor, leading to significant reductions in the available assembly budget for inclined structures compared to the coplanar option when the pattern is seasonal and/or irregular. The increase in energy prices experienced in the last 2 years represents a substantial change in the viability of the structures. The budget for inclined structures increases by hundreds of euros compared to the coplanar option. Depending on the azimuth and inclination of the roof, the maximum available budget can vary by more than a thousand euros per kWp, being highly profitable in orientations close to the east and west and on roofs partially inclined to the north. Differences between low-irradiation and high-irradiation locations can mean variations in the average budget of more than 1 k€/kWp, especially with high electricity prices.
OPINION OF STUDENTS ABOUT THE ORGANIZATION OF DISTANCE LEARNING IN UNIVERSITIES: PROBLEMS AND WAYS OF SOLUTION

In 2020, many areas of society were forced to be transferred to a remote work format. Education is no exception. Universities across the country made decisions on the organization of the educational process online. Based on a secondary analysis of sociological research and a survey of university students, the article analyzes the main problems of distance learning, offers recommendations for solving the identified problems and a possible combination of full-time and distance learning. The students quickly adapted to the distance. Students quickly and successfully adapted to distance learning, but they are not ready to consider its alternative to the traditional learning format. In addition, students admit to a decrease in the level of motivation for learning during a distance student. The main advantages of the distance form are: the ability to study in a comfortable and familiar environment; the ability to combine work with study; flexibility of the educational process. The key problems of distance learning, according to students, are: an increase in independent educational work; lack of „working“ forms of interaction with the teacher; inconvenience of using the platform for distance learning. The authors of the article believe that it is necessary to combine distance and traditional training formats by conducting lectures at the distance, and continue to conduct practical classes in the usual format. In addition, it is advisable to involve interesting speakers with the help of remote technologies to participate in seminars who can share their practical experience on the topic with students.
Enriching the E2E dataset

This study introduces an enriched version of the E2E dataset, one of the most popular language resources for data-to-text NLG. We extract intermediate representations for popular pipeline tasks such as discourse ordering, text structuring, lexicalization and referring expression generation, enabling researchers to rapidly develop and evaluate their data-to-text pipeline systems. The intermediate representations are extracted by aligning non-linguistic and text representations through a process called delexicalization, which consists in replacing input referring expressions to entities/attributes with placeholders. The enriched dataset is publicly available.
Metasurface‐Based Optical Logic Operators Driven by Diffractive Neural Networks

In this paper, a novel optical logic operator based on the multifunctional metasurface driven by all-optical diffractive neural network is reported, which can perform four principal quantum logic operations (Pauli-X, Pauli-Y, Pauli-Z, and Hadamard gates). The two ground states |0⟩$|0 \rangle $ and |1⟩$|1 \rangle $ are characterized by two orthogonal linear polarization states. The proposed spatial- and polarization-multiplexed all-optical diffractive neural network only contains a hidden layer physically mapped as a metasurface with simple and compact unit cells, which dramatically reduces the volume and computing resources required for the system. The designed optical quantum operator is proven to achieve high fidelities for all four quantum logical gates, up to 99.96% numerically and 99.88% experimentally. The solution will facilitate the construction of large-scale optical quantum computing systems and scalable optical quantum devices.
Tuning the Plexcitonic Optical Chirality Using Discrete Structurally Chiral Plasmonic Nanoparticles

Constructing chiral plexcitonic systems with tunable plasmon–exciton coupling may advance the scientific exploitation of strong light–matter interactions. Because of their intriguing chiroptical properties, chiral plasmonic materials have shown promising applications in photonics, sensing, and biomedicine. However, the strong coupling of chiral plasmonic nanoparticles with excitons remains largely unexplored. Here we demonstrate the construction of a chiral plasmon–exciton system using chiral AuAg nanorods and J aggregates for tuning the plexcitonic optical chirality. Circular dichroism spectroscopy was employed to characterize chiral plasmon–exciton coupling, in which Rabi splitting and anticrossing behaviors were observed, whereas the extinction spectra exhibited less prominent phenomena. By controlling the number of molecular excitons and the energy detuning between plasmons and excitons, we have been able to fine-tune the plexcitonic optical chirality. The ability to fine-tune the plexcitonic optical chirality opens up unique opportunities for exploring chiral light–matter interactions and boosting the development of emerging chiroptical devices.
Role of HRM Practices in Organization Performance

In recent years, HRM has received a lot of traction. Human resource management is essential for all types of enterprises. The bulk of research indicates that HRM and organizational performance have a positive link. Each organization's main goal is to attain high levels of performance in its goals and objectives. In this paper, HRM and practices were investigated. What part do human resources play in achieving organizational objectives? This survey will also evaluate HRM practices by analyzing the 40 papers that have been submitted. In addition, this article offers a thorough examination of the chronological assessment based on each publication. In addition, the results of each research paper's analysis are displayed. A review of the literature was utilized to focus on and review the issue knowledge in this study. Finally, it expands on many research concerns that may be useful to researchers in doing more studies on HRM practices.
Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark

Large language models (LLMs) have been shown to perform well at a variety of syntactic, discourse, and reasoning tasks. While LLMs are increasingly deployed in many forms including conversational agents that interact with humans, we lack a grounded benchmark to measure how well LLMs understand social language. Here, we introduce a new theory-driven benchmark, SocKET, that contains 58 NLP tasks testing social knowledge which we group into five categories: humor & sarcasm, offensiveness, sentiment & emotion, and trustworthiness. In tests on the benchmark, we demonstrate that current models attain only moderate performance but reveal significant potential for task transfer among different types and categories of tasks, which were predicted from theory. Through zero-shot evaluations, we show that pretrained models already possess some innate but limited capabilities of social language understanding and training on one category of tasks can improve zero-shot testing on others. Our benchmark provides a systematic way to analyze model performance on an important dimension of language and points to clear room for improvement to build more socially-aware LLMs. The resources are released at https://github.com/minjechoi/SOCKET.
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness

The emergence of generative large language models (LLMs) raises the question: what will be its impact on crowdsourcing? Traditionally, crowdsourcing has been used for acquiring solutions to a wide variety of human-intelligence tasks, including ones involving text generation, modification or evaluation. For some of these tasks, models like ChatGPT can potentially substitute human workers. In this study, we investigate whether this is the case for the task of paraphrase generation for intent classification. We apply data collection methodology of an existing crowdsourcing study (similar scale, prompts and seed data) using ChatGPT and Falcon-40B. We show that ChatGPT-created paraphrases are more diverse and lead to at least as robust models.