Reviewerly/exHarmony huggingface dataset:
Metaverse applications in education: a systematic review and a cost-benefit analysis

Purpose Many educators are increasingly acquainting themselves and becoming adept with interactive technologies like augmented reality and virtual reality. Some of them are also looking forward to using Metaverse applications, as they want to benefit from its immersive three-dimensional capabilities. Therefore, the purpose of this study is to critically review the extant literature to investigate how, why, where and when the Metaverse can be used for educational purposes. This study also discusses opportunities, challenges and risks related to this disruptive technology. Design/methodology/approach A Preferred Reporting Items for Systematic Reviews and Meta-Analyses rigorous protocol is used to search, extract, scrutinize and synthesize content from high-impact articles focused on the use of the Metaverse technology in the realms of education. Afterwards, this study theorizes on the costs and benefits of using this interactive technology with students. Findings A number of researchers are already experimenting with virtual technologies that are very similar to the Metaverse, in different contexts. This research indicates that most students are lured by immersive multi-sensory three-dimensional environments as well as by virtual reality applications that could simulate real-life situations and provide engaging experiences with virtual representations of people, places and objects. On the other hand, this study reveals that educators ought to consider the potential pitfalls of the Metaverse, including privacy breaches and security risks, as well as possible addictions and the development of mental health issues, among others. Practical implications Students and educators can use the Metaverse to catapult themselves in a simulated digital universe that could reconfigure their sensory inputs, definitions of space, time and points of access to information. This research calls for the development of regulatory instruments, including sound principles, guidelines and procedures that are intended to safeguard and protect Metaverse users. Originality/value This contribution implies that there is scope for educators to continue developing the Metaverse’s virtual spaces to improve their students’ motivations, aptitudes and learning outcomes. This study clarifies that the use of the Metaverse in education can create infinite possibilities to enhance their knowledge, competences and abilities through its immersive applications. Yet this paper also raises awareness about possible challenges in the short term as well on other risks associated to the prolonged use of this captivating technology.
XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation

Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance. The underlying principle of CL-based recommendation models is to ensure the consistency between representations derived from different graph augmentations of the user-item bipartite graph. This self-supervised approach allows for the extraction of general features from raw data, thereby mitigating the issue of data sparsity. Despite the effectiveness of this paradigm, the factors contributing to its performance gains have yet to be fully understood. This paper provides novel insights into the impact of CL on recommendation. Our findings indicate that CL enables the model to learn more evenly distributed user and item representations, which alleviates the prevalent popularity bias and promoting long-tail items. Our analysis also suggests that the graph augmentations, previously considered essential, are relatively unreliable and of limited significance in CL-based recommendation. Based on these findings, we put forward an e <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</b> tremely <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sim</b> ple <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ontrastive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> earning method ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XSimGCL</b> ) for recommendation, which discards the ineffective graph augmentations and instead employs a simple yet effective noise-based embedding augmentation to generate views for CL. A comprehensive experimental study on four large and highly sparse benchmark datasets demonstrates that, though the proposed method is extremely simple, it can smoothly adjust the uniformity of learned representations and outperforms its graph augmentation-based counterparts by a large margin in both recommendation accuracy and training efficiency. The code and used datasets are released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Coder-Yu/SELFRec</uri> .
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR

This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by ≈11 %. This is not via complicated design though, but by addressing two critical issues facing the community (i) the gold standard triplet loss does not enforce holistic latent space geometry, and (ii) there are never enough sketches to train a high accuracy model. For the former, we propose a simple modification to the standard triplet loss, that explicitly enforces separation amongst photos/sketch instances. For the latter, we put forward a novel knowledge distillation module can leverage photo data for model training. Both modules are then plugged into a novel plug-n-playable training paradigm that allows for more stable training. More specifically, for (i) we employ an intra-modal triplet loss amongst sketches to bring sketches of the same instance closer from others, and one more amongst photos to push away different photo instances while bringing closer a structurally augmented version of the same photo (offering a gain of ≈4-6%). To tackle (ii), we first pre-train a teacher on the large set of unlabelled photos over the aforementioned intra-modal photo triplet loss. Then we distill the contextual similarity present amongst the instances in the teacher's embedding space to that in the student's embedding space, by matching the distribution over inter-feature distances of respective samples in both embedding spaces (delivering a further gain of ≈ 4-5%). Apart from outperforming prior arts significantly, our model also yields satisfactory results on generalising to new classes. Project page: https://aneeshan95.github.io/Sketch_PVT/
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) sample target blocks with sufficiently large scale (semantic), and to (b) use a sufficiently informative (spatially distributed) context block. Empirically, when combined with Vision Transformers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong downstream performance across a wide range of tasks, from linear classification to object counting and depth prediction.
Can GPT-3 Perform Statutory Reasoning?

Statutory reasoning is the task of reasoning with facts and statutes, which are rules written in natural language by a legislature. It is a basic legal skill. In this paper we explore the capabilities of the most capable GPT-3 model, text-davinci-003, on an established statutory-reasoning dataset called SARA. We consider a variety of approaches, including dynamic few-shot prompting, chain-of-thought prompting, and zero-shot prompting. While we achieve results with GPT-3 that are better than the previous best published results, we also identify several types of clear errors it makes. We investigate why these errors happen. We discover that GPT-3 has imperfect prior knowledge of the actual U.S. statutes on which SARA is based. More importantly, we create simple synthetic statutes, which GPT-3 is guaranteed not to have seen during training. We find GPT-3 performs poorly at answering straightforward questions about these simple synthetic statutes.
Comparing global and regional downscaled NWP models for irradiance and photovoltaic power forecasting: ECMWF versus AROME

Inspecting the literature, much effort has been placed on the verification of irradiance forecasts from numerical weather prediction (NWP) models, as such forecasts are thought to have profound implications on the photovoltaic (PV) power forecasts, which in turn affects grid operators' confidence in integrating such power into the electricity grid. However, perhaps due to the proprietary nature of PV plants and lack of access to state-of-the-art NWP model output, only few have had the chance to conduct head-to-head comparisons of global mesoscale and regional downscaled NWP models, in terms of how their irradiance forecast inaccuracies propagate to PV power forecasts. In this regard, this work presents such a study, in which irradiance and PV power forecasts from the European Centre for Medium-Range Weather Forecasts' High-Resolution (HRES) and Météo-France's Application of Research to Operations at Mesoscale (AROME) models are thoroughly verified against the ground-based measurements from 32 research-grade radiometry stations and 94 actual PV plants in Hungary. A wide range of techniques and case studies concerning verification is herein considered, including variance ratio analysis, Murphy–Winkler decomposition, point-versus-areal verification, and seasonal verification. Despite that the results are too numerous to be summarized in a few sentences, the overarching observation from the verification exercise is that the performance of irradiance forecasts can only be used to infer that of PV power forecasts to a certain extent, which contrasts the conventional wisdom.
PARSAT: Fuzzy logic for adaptive spatial ability training in an augmented reality system

Personalized training systems and augmented reality are two of the most promising educational technologies since they could enhance engineering students? spatial ability. Prior research has examined the benefits of the integration of augmented reality in increasing students? motivation and enhancing their spatial skills. However, based on the review of the literature, current training systems do not provide adaptivity to students? individual needs. In view of the above, this paper presents a novel adaptive augmented reality training system, which teaches the knowledge domain of technical drawing. The novelty of the proposed system is that it proposes using fuzzy sets to represent the students? knowledge levels more accurately in the adaptive augmented reality training system. The system determines the amount and the level of difficulty of the learning activities delivered to the students, based on their progress. The main contribution of the system is that it is student-centered, providing the students with an adaptive training experience. The evaluation of the system took place during the 2021-22 and 2022-23 winter semesters, and the results are very promising.
Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts

Sifting through vast textual data and summarizing key information from electronic health records (EHR) imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy on a diverse range of clinical summarization tasks has not yet been rigorously demonstrated. In this work, we apply domain adaptation methods to eight LLMs, spanning six datasets and four distinct clinical summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not improve results. Further, in a clinical reader study with ten physicians, we show that summaries from our best-adapted LLMs are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis highlights challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and the inherently human aspects of medicine.
ACOUSTIC 'BLACK HOLES' FOR FLEXURAL WAVES AND THEIR POTENTIAL APPLICATIONS

This paper was presented at the Institute of Acoustics Spring Conference 2002, Salford, UK, 25-27 March 2002.
Evaluation Complexity Bounds for Smooth Constrained Nonlinear Optimization Using Scaled KKT Conditions and High-Order Models

Evaluation complexity for convexly constrained optimization is considered and it is shown first that the complexity bound of O(𝜖 −3∕2) proved by Cartis et al. (IMA J Numer Anal 32:1662–1695, 2012) for computing an 𝜖-approximate first-order critical point can be obtained under significantly weaker assumptions. Moreover, the result is generalized to the case where high-order derivatives are used, resulting in a bound of O(𝜖 −(p+1)∕p) evaluations whenever derivatives of order p are available. It is also shown that the bound of $$O(\epsilon _{\mbox{ P}}^{-1/2}\epsilon _{\mbox{ D}}^{-3/2})$$ evaluations (𝜖 P and 𝜖 D being primal and dual accuracy thresholds) suggested by Cartis et al. (SIAM J. Numer. Anal. 53:836–851, 2015) for the general nonconvex case involving both equality and inequality constraints can be generalized to yield a bound of $$O(\epsilon _{\mbox{ P}}^{-1/p}\epsilon _{\mbox{ D}}^{-(p+1)/p})$$ evaluations under similarly weakened assumptions.
Local appearance-based face recognition using adaptive directional wavelet transform

The latest research has shown that adaptive directional wavelet transform can constitute edges and textures in images efficiently due to the adaptive directional selectivity. This paper is primarily focused on the application of adaptive directional wavelet transform in conjunction with linear discriminant analysis (LDA) for capturing the discriminant directional multiresolution facial features. The intention of this paper is to explore the efficacy of adaptive directional wavelet transform in facial feature extraction and to offer a stepping stone for further research in this direction. The proposed approach is compared with existing subspace and local descriptor feature extraction methods. A performance comparison is also demonstrated with existing non-adaptive multiresolution analysis methods such as discrete wavelet transform (DWT), Gabor wavelet transform (GWT), curvelets, ridgelets, contourlets, and local Gabor binary pattern. Evaluation of the proposed approach on famous databases such as ORL, Essex Grimace, Yale, and Sterling face convinces the effectiveness of the adaptive directional wavelet transform based subspace features.
Wearable Devices: Sociotechnical Imaginaries and Agential Capacities

In this chapter, I address the sociocultural dimensions of wearable devices: small, lightweight technologies that can be readily placed on human bodies as they move around in time and space. I adopt a feminist materialism perspective to examine how the promises and affordances of wearable devices are articulated in sociotechnical imaginaries, and are animated or closed off via agential capacities. I discuss the ways in which wearables come to matter in people’s lives; or alternatively, fail to engage users and realise their agential capacities. I begin with outlining the sociotechnical imaginaries that are frequently employed in public forums to give meaning to wearable devices. I go on to review research that has investigated the lived experiences of using wearables, identifying the agential capacities and affects that are central to these experiences. The discussion then moves to the lively data generated by these devices, and how people make sense of their personal information. The concluding section summarises the major arguments developed in the chapter and outlines directions for further research.
What determines the ultimate precision of a quantum computer

A quantum error correction (QEC) code uses $N_{\rm c}$ quantum bits to construct one "logical" quantum bits of better quality than the original "physical" ones. QEC theory predicts that the failure probability $p_L$ of logical qubits decreases exponentially with $N_{\rm c}$ provided the failure probability $p$ of the physical qubit is below a certain threshold $p<p_{\rm th}$. In particular QEC theorems imply that the logical qubits can be made arbitrarily precise by simply increasing $N_{\rm c}$. In this letter, we search for physical mechanisms that lie outside of the hypothesis of QEC theorems and set a limit $\eta_{\rm L}$ to the precision of the logical qubits (irrespectively of $N_{\rm c}$). $\eta_{\rm L}$ directly controls the maximum number of operations $\propto 1/\eta_{\rm L}^2$ that can be performed before the logical quantum state gets randomized, hence the depth of the quantum circuits that can be considered. We identify a type of error - silent stabilizer failure - as a mechanism responsible for finite $\eta_{\rm L}$ and discuss its possible causes. Using the example of the topological surface code, we show that a single local event can provoke the failure of the logical qubit, irrespectively of $N_c$.
Scalable Key Rank Estimation (and Key Enumeration) Algorithm for Large Keys

Evaluation of security margins after a side-channel attack is an important step of side-channel resistance evaluation. The security margin indicates the brute force effort needed to recover the key given the leakages. In the recent years, several solutions for key rank estimation algorithms have been proposed. All these solutions give an interesting trade-off between the tightness of the result and the time complexity for symmetric key. Unfortunately, none of them has a linear complexity in the number of subkeys, hence these solutions are slow for large (asymmetric) keys. In this paper, we present a solution to obtain a key rank estimation algorithm with a reasonable trade-off between the efficiency and the tightness that is suitable for large keys. Moreover, by applying backtracking we obtain a parallel key enumeration algorithm.
A key exchange protocol using matrices over group ring

The first published solution to key distribution problem is due to Diffie–Hellman, which allows two parties that have never communicated earlier, to jointly establish a shared secret key over an insecure channel. In this paper, we propose a new key exchange protocol in a non-commutative semigroup over group ring whose security relies on the hardness of Factorization with Discrete Logarithm Problem (FDLP). We have also provided its security and complexity analysis. We then propose a ElGamal cryptosystem based on FDLP using the group of invertible matrices over group rings.
Temperature fringe method with phase-shift for the 3D shape measurement

Linear laser fast scanning thermography technology has a good application prospect in the nondestructive detection of defects. Meanwhile, due to the modulation of the surface topography, the thermal response fringes excited by the line laser are distorted in the scanning process, whereby the three-dimensional topography of the specimen can be reconstructed. However, the accuracy of this method is currently limited by unfavorable factors, such as background noise of the specimen surface and the extraction error of the deformed temperature fringes. In this paper, by optimizing the initial position difference during line laser scanning and converting the laser fringes with specific position difference into temperature fringes with specific phase difference, the phase shift technique is combined with the temperature fringe method. What's more, some post-processing algorithms were developed to further develop the temperature fringe method and greatly improve the accuracy of the method. By measuring some specimens with different shapes and different colors, the results show that the technology and algorithms developed here can obtain the three-dimensional shape of the measured specimen with high accuracy while having a non-destructive testing function, without interference from the surface color of the specimen.
Autonomous space target tracking through state estimation techniques via ground-based passive optical telescope

The presence of operational satellites or small-body space debris is a challenge for autonomous ground-based space object observation. Although most space objects exceeding 10 cm in diameter have been cataloged, the position of each space object (based on six orbital parameters) remains important and should be updated periodically, as the Earth’s orbital perturbations cause disturbances. Modern ground-based passive optical telescopes equipped with complementary metal-oxide semiconductors have become widely used in astrometry engineering, being combined with image processing techniques for target signal enhancement. However, the detection and tracking performance of this equipment when employed with image processing techniques primarily depends on the size and brightness of the space target, which appears on the monitor screen under variable background interference conditions. A small and dim target has a highly sensitive tracking error compared to a bright target. Moreover, most image processing techniques for target signal enhancement require large computational power and memory; therefore, automatic tracking of a space target is difficult. The present work investigates autonomous space target detection and tracking to achieve high-sensitivity detection and improved tracking ability for non-Gaussian and dynamic backgrounds with a simple system mechanism and computational efficiency. We develop an improved particle filter (PF) using the ensemble Kalman filter (KF) for track-before-detect (TBD) frameworks, by modifying and optimizing the computational formula for our non-linear measurement function. We call this extended version the “ensemble Kalman PF-TBD (EnKPF-TBD).” Three sequential astronomical image datasets taken by the Asia-Pacific Ground-Based Optical Space Objects Observation System (APOSOS) telescope under different conditions are used to evaluate three proposed TBD baseline frameworks. Given an optimal random sample size, the EnKPF-TBD exhibits superior performance to PF-TBD and threshold-based unscented KF with two-dimensional peak search (2dPS). The EnKPF-TBD scheme achieves satisfactory performance for all variable background interference conditions, especially for a small and dim space target, in terms of tracking accuracy and computational efficiency.