Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Smartphone-Based Interactive Multimedia Integrated Cognitive Conflict Models to Improve 21st-Century Skills

This research aims to develop cognitive conflict-based interactive multimedia that is valid, practical, and effective in improving students’ 21st-century skills, known as 4C skills. The development used the Plomp model, with 3 stages: preliminary research, prototyping/development phase, and assessment phase. In preliminary research, teacher questionnaires were used to discover school problems. In the development phase, interactive multimedia prototypes were designed as solutions. At this stage, a validity test was also carried out on the prototype through an expert review by 5 validators. Practicality tests were also carried out through one-to-one and small-group evaluations of 12 students. The instrument of validity and practicality sheets was used at this stage. Experimental research carried out the effectiveness test in the assessment phase 2 sample classes comprising 56 high school students. The 4C skill observation sheet instrument (critical thinking, creativity and innovation, collaboration, and communication) was used at this stage. The data were analyzed by percentage techniques, V-Aiken, N-Gain, and Mann-Whitney tests. The preliminary research results obtained some problems, including low 4C skills, teacher-centered dominant learning, and minimal use of electronic learning media. Interactive multimedia prototypes were designed as solutions. The prototype validity test results were obtained with valid categories. The results of the practicality test at the one-to-one stage and the small group stage obtained scores of 99.2 and 99.4, respectively which are in the very practical category. The results of the effectiveness test based on the N-Gain value inform the improvement of critical thinking skills, and creative thinking is in the high category, while communication and collaboration skills are in the moderate category. Based on the Mann-Whitney U test on all four skills, Sig. (2-tailed) &lt; 0.05 was obtained, which showed a significant effect. In conclusion, cognitive conflict-based interactive multimedia can effectively improve students' 4C skills.
S3: Social-network Simulation System with Large Language Model-Empowered Agents

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Unsupervised Prototype-Wise Contrastive Learning for Domain Adaptive Semantic Segmentation in Remote Sensing Image

Labeling data in the field of remote sensing is time-consuming and labor-intensive, making domain adaptation between different domains an urgently needed solution. To address the domain gap between diverse datasets in the remote sensing domain, numerous methods tailored for domain adaptation in high-resolution remote sensing imagery have emerged. Some of the existing methods focus on reducing the domain gap at either the feature level or the pixel level, often overlooking their underlying connection. To tackle this issue, we introduce a prototype-wise contrastive feature alignment paradigm (PCFA) aimed at bridging the representations between the feature and pixel levels. By dynamically updating, we acquire prototype information encompassed by different mini-batches and employ an optimal transport mechanism to reasonably apply the prototype feature distribution in guiding the learning of target domain features. We conduct extensive domain adaptation semantic segmentation (DASS) experiments on the ISPRS Vaihingen and Potsdam datasets, achieving an improvement about 4%~5% in mIoU (mean Intersection over Union) compared to previous methods using the DeepLabV2 framework.
Demystifying Just-in-Time (JIT) Liquidity Attacks on Uniswap V3

Uniswap is currently the most liquid Decentralized Exchange (DEX) on Ethereum. In May 2021, it upgraded to the third protocol version named Uniswap V3. The key feature update is “concentrated liquidity”, which supports liquidity provision within custom price ranges. However, this design introduces a new type of Miner Extractable Value (MEV) source called Just-in-Time (JIT) liquidity attack, where the adversary mints and burns a liquidity position right before and after a sizable swap. We begin by formally defining the JIT liquidity attack and subsequently conduct empirical measurements on Ethereum. Over a span of 20 months, we identify 36,671 such attacks, which have collectively generated profits of 7,498 ETH. Our analysis suggests that the JIT liquidity attack essentially represents a whales' game, predominantly controlled by a select few bots. The most active bot, identified as 0xa57…6CF, has managed to amass 92% of the total profit. Furthermore, we find that this attack strategy poses significant entry barriers, as it necessitates adversaries to provide liquidity that is, on average, 269 times greater than the swap volume. In addition, our findings reveal that the JIT liquidity attack exhibits relatively poor profitability, with an average Return On Investment (ROI) of merely 0.007%. We also find this type of attack to be detrimental to existing Liquidity Providers (LPs) within the pool, as their shares of liquidity undergo an average dilution of 85%. On the contrary, this attack proves advantageous for liquidity takers, who secure execution prices that are, on average, 0.139% better than before. We further dissect the behaviors of the top MEV bots and evaluate their strategies through local simulation. Our observations reveal that the most active bot, 0xa57…6CF, conducted 27% of non-optimal attacks, thereby failing to capture at least 7,766 ETH (equivalent to 16.1M USD) of the potential attack profit.
Metaverse and the fashion industry: A systematic literature review

The metaverse presents increasing potential for fashion companies. However, given its rapid evolution, a clear understanding of the role and scope of the metaverse and its implications for brands is lacking. Hence, this study presents a systematic literature review on the recent body of knowledge in the management and marketing fields on the role of the metaverse in the fashion industry. Accordingly, 34 articles published between 2009 and 2022 were analyzed. The results revealed that the literature is still in its infancy. The extant research is articulated into four clusters: (1) metaverse and consumer experience, (2) the marketing mix in the metaverse, (3) opportunities and challenges in the metaverse, and (4) the future of retail in the metaverse. Subsequently, we propose a research agenda based on the future strategies to be adopted by fashion companies, to advance the scholarly debate on these issues leveraging neglected research topics and emerging themes.
Darkweb research: Past, present, and future trends and mapping to sustainable development goals

The Darkweb, part of the deep web, can be accessed only through specialized computer software and used for illegal activities such as cybercrime, drug trafficking, and exploitation. Technological advancements like Tor, bitcoin, and cryptocurrencies allow criminals to carry out these activities anonymously, leading to increased use of the Darkweb. At the same time, computers have become an integral part of our daily lives, shaping our behavior, and influencing how we interact with each other and the world. This work carries out the bibliometric study on the research conducted on Darkweb over the last decade. The findings illustrate that most research on Darkweb can be clustered into four areas based on keyword co-occurrence analysis: (i) network security, malware, and cyber-attacks, (ii) cybercrime, data privacy, and cryptography, (iii) machine learning, social media, and artificial intelligence, and (iv) drug trafficking, cryptomarket. National Science Foundation from the United States is the top funder. Darkweb activities interfere with the Sustainable Development Goals (SDG) laid forth by the United Nations to promote peace and sustainability for current and future generations. SDG 16 (Peace, Justice, and Strong Institutions) has the highest number of publications and citations but has an inverse relationship with Darkweb, as the latter undermines the former. This study highlights the need for further research in bitcoin, blockchain, IoT, NLP, cryptocurrencies, phishing and cybercrime, botnets and malware, digital forensics, and electronic crime countermeasures about the Darkweb. The study further elucidates the multi-dimensional nature of the Darkweb, emphasizing the intricate relationship between technology, psychology, and geopolitics. This comprehensive understanding serves as a cornerstone for evolving effective countermeasures and calls for an interdisciplinary research approach. The study also delves into the psychological motivations driving individuals towards illegal activities on the Darkweb, highlighting the urgency for targeted interventions to promote pro-social online behavior.
Experimental solutions to the high-dimensional mean king’s problem

Vaidman, Aharanov, and Albert [ Phys. Rev. Lett. 58 ( 14 ), 1385 ( 1987 ) 10.1103/PhysRevLett.58.1385 ] put forward a puzzle called the mean king’s problem (MKP) that can be solved only by harnessing quantum entanglement. Prime-powered solutions to the problem have been shown to exist, but they have not yet been experimentally realized for any dimension beyond two. We propose a general first-of-its-kind experimental scheme for solving the MKP in prime dimensions ( D ). Our search is guided by the digital discovery framework Pytheus , which finds highly interpretable graph-based representations of quantum optical experimental setups; using it, we find specific solutions and generalize to higher dimensions through human insight. As proof of principle, we present a detailed investigation of our solution for the three-, five-, and seven-dimensional cases. We obtain maximum success probabilities of 82.3%, 56.2%, and 35.5%, respectively. We therefore posit that our computer-inspired scheme yields solutions that implement Alice’s strategy with quantum advantage, demonstrating its promise for experimental implementation in quantum communication tasks.
Exploring the Use of Metaverse for Collaborative Learning in Higher Education: A Scoping Review

Student engagement in higher education is strongly associated with positive learning outcomes; however, not all learning methods are equally engaging or stimulating. Recent technological developments hold the potential to make learning more exciting and attractive, especially through the use of Virtual Reality (VR) and Augmented Reality (AR) technologies. These two technologies, blending the real and virtual worlds, create a digital space called the 'Metaverse'. In the Metaverse, people can interact in a virtual environment with lifelike avatars, providing an immersive learning experience. Several studies have explored the potential of Metaverse technology as a learning tool, highlighting its ability to make learning more engaging, interactive, and interesting. Other advantages of Metaverse include the visualisation of learning materials and allowing teachers to innovate learning processes. This research extensively reviews the literature from designated databases such as Scopus, ProQuest, and Google Scholar to explore the possibilities, effectiveness, and advantages of using Metaverse and AR/VR technologies to facilitate collaborative learning in higher education sectors. Finally, the paper concludes by acknowledging the limitations of using Metaverse and AR/VR technologies, such as the costs associated with their implementation and the skills required in the industry.
Representing Nonmonotonic Inference Based on c-Representations as an SMT Problem

As a semantics for conditional knowledge bases, ranking functions order possible worlds by mapping them to a degree of plausibility. c-Representations are special ranking functions that are obtained by assigning individual integer impacts to the conditionals in a knowledge base $$\mathcal R$$ and by defining the rank of each possible world as the sum of these impacts of falsified conditionals. c-Inference is the nonmonotonic inference relation taking all c-representations of a given knowledge base $$\mathcal R$$ into account. In this paper, we show how c-inference can be realized as a satisfiability modulo theories problem (SMT), which allows an implementation by an appropriate SMT solver. We develop a transformation of the constraint satisfaction problem characterizing c-inference into a solvable-equivalent SMT problem, prove its correctness, and illustrate it by a running example. Furthermore, we provide a corresponding implementation using the SMT solver Z3, demonstrating the feasibility of the approach as well as the superiority in comparison to former implementations.
Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series

Abstract The field of time series anomaly detection is constantly advancing, with several methods available, making it a challenge to determine the most appropriate method for a specific domain. The evaluation of these methods is facilitated by the use of metrics, which vary widely in their properties. Despite the existence of new evaluation metrics, there is limited agreement on which metrics are best suited for specific scenarios and domains, and the most commonly used metrics have faced criticism in the literature. This paper provides a comprehensive overview of the metrics used for the evaluation of time series anomaly detection methods, and also defines a taxonomy of these based on how they are calculated. By defining a set of properties for evaluation metrics and a set of specific case studies and experiments, twenty metrics are analyzed and discussed in detail, highlighting the unique suitability of each for specific tasks. Through extensive experimentation and analysis, this paper argues that the choice of evaluation metric must be made with care, taking into account the specific requirements of the task at hand.
Mixed compensation for the testing of large convex aspheres

Since the testing of convex mirrors often requires a compensator with a larger aperture than the mirror under test, this is undoubtedly a trade-off not worth pursuing. Moreover, for surfaces with higher levels of asphericity, it further escalates the challenges in testing. In this study, high-accuracy testing of large convex aspheric surfaces with rotary symmetry was achieved via a method that combines sub-aperture stitching and Computer-Generated Holograms (CGH) compensation testing. Applying the methodology outlined in this study, practical testing was conducted on a large-aperture convex aspheric mirror with a diameter of 538 mm. The outcomes demonstrated that the center testing data and the outer ring testing data were stitched by the stitching algorithm to obtain the surface error of the full-aperture. The results were compared to those of Luphoscan and compensation testing, with a residual Root Mean Square (RMS) less than λ/20 (λ = 632.8 nm). Finally, the error analysis of the entire testing process showed that the accuracy was better than λ/50. The results demonstrate that the method can achieve highly accurate test of large convex aspheric surfaces.
Reliable knowledge graph fact prediction via reinforcement learning

Knowledge graph (KG) fact prediction aims to complete a KG by determining the truthfulness of predicted triples. Reinforcement learning (RL)-based approaches have been widely used for fact prediction. However, the existing approaches largely suffer from unreliable calculations on rule confidences owing to a limited number of obtained reasoning paths, thereby resulting in unreliable decisions on prediction triples. Hence, we propose a new RL-based approach named EvoPath in this study. EvoPath features a new reward mechanism based on entity heterogeneity, facilitating an agent to obtain effective reasoning paths during random walks. EvoPath also incorporates a new postwalking mechanism to leverage easily overlooked but valuable reasoning paths during RL. Both mechanisms provide sufficient reasoning paths to facilitate the reliable calculations of rule confidences, enabling EvoPath to make precise judgments about the truthfulness of prediction triples. Experiments demonstrate that EvoPath can achieve more accurate fact predictions than existing approaches.
EffSegmentNet: Efficient Design for Real-time Semantic Segmentation

This paper represents the EffSegmentNet, which is a powerful real-time semantic segmentation model. It consists of two segments: (1) A novel MetaFormer-based encoder, termed the EffVisionFormer, is introduced. It captures multiscale image features efficiently. (2) A lightweight decoder which utilizes multiscale image features from the encoder is applied to conduct rapid yet accurate segmented result. The proposed EffSegmentNet achieves remarkable performance which takes the inference speed, accuracy, and model parameters into account. On the Cityscapes test set, we attain 71.9% mIoU with 195.1 frames per second (FPS) on a NVIDIA RTX 2080Ti card. Furthermore, the proposed EffSegmentNet utilizes only 4.4 million parameters, which demonstrates its advantage on real-time segmentation.
The Influence of Augmented Reality (AR) on the Motivation of High School Students

Augmented reality (AR) is a technology whose presence has increased in the field of education in recent years. However, its role in secondary education has not been thoroughly explored. Therefore, this research aims to analyse the influence of AR on the motivation of students at this stage while considering gender and previous information and communication technology (ICT) experience. This research uses a quantitative methodology that follows Keller’s Attention, Relevance, Confidence and Satisfaction (ARCS) motivational model. We implemented this instructional design model for a sample of 321 students from the same educational centre. They were divided into two categories: an experimental group (n = 159) and a control group (n = 162). The control group were studied in a slide-based learning environment, while the experimental group worked with an AR mobile application. For data collection, we used the Instructional Materials Motivation Survey (IMMS). The results showed that the students who used AR displayed greater motivation, highlighting great interest in the integration of this technology into the learning process. However, no significant differences were obtained in the motivation of the students according to gender and previous experience with the use of ICT. In conclusion, this research shows that the use of AR improves motivation in secondary education.
Variational quantum eigensolvers in the era of distributed quantum computers

The authors show that limited intermodule information exchange in distributed quantum computing architectures can enhance quantum problem solving, as demonstrated with a dual-core variational quantum eigensolver. These results indicate that quantum processors that use modular operations may be viable in the near future.
Comparative Study on 2D and 3D User Interface for Eliminating Cognitive Loads in Augmented Reality Repetitive Tasks

The two-dimensional (2D) and three-dimensional (3D) user interfaces have been a prominent topic of augmented reality (AR) research, and their impact on the efficacy and usability of one-time tasks has been extensively examined. As AR is increasingly adopted in industry for repetitive tasks, there is an urgent need for research into the effect of the 2D and 3D user interfaces. In this study, we developed two prototypes of the user interfaces and conducted a comparison study with forty participants to assess their respective influence on cognitive load, perceived usability, and learnability. The results showed that the two user interfaces differed significantly in cognitive load and perceived usability. In particular, the 3D user interfaces exhibited substantially shorter eye blinking durations, shorter eye fixation durations, less dispersed eye gaze areas, lower subjective ratings of cognitive load, and higher overall scores of perceived usability than the 2D user interfaces. However, there was no difference in learnability between the two user interfaces throughout the repetitive tasks.
Parallel Implementation of CNOTN and C2NOT2 Gates via Homonuclear and Heteronuclear Förster Interactions of Rydberg Atoms

We analyze schemes of high-fidelity multi-qubit CNOTN and C2NOT2 gates for alkali metal neutral atoms used as qubits. These schemes are based on the electromagnetically induced transparency and Rydberg blockade. The fidelity of homonuclear multi-qubit CNOTN gate based on Rydberg blockade was limited by the undesirable interaction between the target atoms and by the coupling laser intensity. We propose overcoming these limits by using strong heteronuclear dipole–dipole interactions via Förster resonances for control and target atoms, while the target atoms are coupled by a weaker van der Waals interaction. We optimized the gate performance in order to achieve higher fidelity, while keeping the coupling laser intensity as small as possible in order to improve the experimental feasibility of the gate schemes. We also considered the optimization of the schemes of the C2NOT2 gates, where the fidelity is affected by the relation between the control–control, control–target and target–target interaction energies. Our numeric simulations confirm that the fidelity of the CNOT4 gate (single control and four target atoms) can be up to 99.3% and the fidelity of the C2NOT2 (two control and two target atoms) is up to 99.7% for the conditions which are experimentally feasible.
Enhancing the Super-Resolution of Medical Images: Introducing the Deep Residual Feature Distillation Channel Attention Network for Optimized Performance and Efficiency

In the advancement of medical image super-resolution (SR), the Deep Residual Feature Distillation Channel Attention Network (DRFDCAN) marks a significant step forward. This work presents DRFDCAN, a model that innovates traditional SR approaches by introducing a channel attention block that is tailored for high-frequency features-crucial for the nuanced details in medical diagnostics-while streamlining the network structure for enhanced computational efficiency. DRFDCAN's architecture adopts a residual-within-residual design to facilitate faster inference and reduce memory demands without compromising the integrity of the image reconstruction. This design strategy, combined with an innovative feature extraction method that emphasizes the utility of the initial layer features, allows for improved image clarity and is particularly effective in optimizing the peak signal-to-noise ratio (PSNR). The proposed work redefines efficiency in SR models, outperforming established frameworks like RFDN by improving model compactness and accelerating inference. The meticulous crafting of a feature extractor that effectively captures edge and texture information exemplifies the model's capacity to render detailed images, necessary for accurate medical analysis. The implications of this study are two-fold: it presents a viable solution for deploying SR technology in real-time medical applications, and it sets a precedent for future models that address the delicate balance between computational efficiency and high-fidelity image reconstruction. This balance is paramount in medical applications where the clarity of images can significantly influence diagnostic outcomes. The DRFDCAN model thus stands as a transformative contribution to the field of medical image super-resolution.