Reviewerly/exHarmony huggingface dataset:
Adaptive adversarial prototyping network for few-shot prototypical translation

Translating multiple real-world source images to a single prototypical image is a challenging problem. Notably, these source images belong to unseen categories that did not exist during model training. We address this problem by proposing an adaptive adversarial prototype network (AAPN) and enhancing existing one-shot classification techniques. To overcome the limitations that traditional works cannot extract samples from novel categories, our method tends to solve the image translation task of unseen categories through a meta-learner. We train the model in an adversarial learning manner and introduce a style encoder to guide the model with an initial target style. The encoded style latent code enhances the performance of the network with conditional target style images. The AAPN outperforms the state-of-the-art methods in one-shot classification of brand logo dataset and achieves the competitive accuracy in the traffic sign dataset. Additionally, our model improves the visual quality of the reconstructed prototypes in unseen categories. Based on the qualitative and quantitative analysis, the effectiveness of our model for few-shot classification and generation is demonstrated.
Augmented telework with avatar technology: impact on workplace and required actions

Under the prolonged influence of COVID-19, telework has become more common globally, though this workstyle is mainly for office workers. Human augmentation technology is expected to broaden the application targets of telework, including hospitality services with frequent human-to-human interactions. Especially avatar technologies attract more attention to reduce the infection risk and sustain business continuity. These technologies can realise close interactions between customers and employees remotely. They could bring a fundamental change in the workplace concept and new opportunities and challenges in human-technology cooperation at work. In this chapter, I present two cases of avatar technologies applied by Japanese companies. Based on the case study, I discuss the impact of avatar technologies on workstyle, skill development and well-being at workplaces, and required actions for workplace innovation. The research result implies that avatar technologies could enable employees to create new customer experiences without infection risks. In contrast, a new style of hospitality and the associated skill sets would be required. Adequate job design and a co-creative environment among employees and customers will effectively utilise avatar technologies in the workplace.
Linear-time 2-party secure merge from additively homomorphic encryption

We present a linear-time, space and communication data-oblivious algorithm for securely merging two private, sorted lists into a single sorted, secret-shared list in the two party setting. Although merging two sorted lists can be done insecurely in linear time, previous secure merge algorithms all require super-linear time and communication. A key feature of our construction is a novel method to obliviously traverse permuted lists in sorted order. Our algorithm only requires black-box use of the underlying additively homomorphic cryptosystem and generic secure computation protocols for comparison and equality testing.
Group-wise interactive region learning for zero-shot recognition

Zero-shot learning (ZSL) methods mainly associate global or region features to semantic vectors within a single image, for transferring semantic knowledge from the seen classes to unseen ones. However, the interactive region learning among a group of images from different categories, which can enhance the discrimination of region features and thus lead to a desirable knowledge transfer between seen and unseen classes, is seldom considered. To remedy the above challenge, we propose a group-wise interactive region learning (GIRL) model to guarantee a comprehensive and explicit region interaction. Specifically, GIRL consists of an attentive region interaction (ARI) module and a holistic semantic embedding (HSE) module. ARI utilizes the semantic commonalities and differences of group regions to produce refined region features. HSE holistically maps these region features to the semantic space for a more stable semantic transfer. We also present a semantic consistency loss and a relation alignment loss that can distill the refined/original region features and introduce unseen class semantic vectors for training, respectively. Extensive experiments demonstrate the effectiveness of GIRL over other methods, achieving 68.9%, 42.9%, 75.5%, and 47.8% the Generalized ZSL (GZSL) H scores on CUB, SUN, AWA2, and APY. The code is publicly available at https://github.com/TingML/GIRL.
A scoping review of research exploring teachers’ experiences with Digital Technologies curricula

For over a decade, we have seen a global shift toward preparing students for the predicted labor market and society through the introduction of digital technology curricula. This scoping review uncovered 26 studies published between 2012 and 2022 that examined empirical research on K–12 or pre-service teachers’ experiences with Digital Technologies curricula. Most of the research involved primary school teachers (n = 20), focused on teachers’ experiences with Professional Learning and Development (n = 13), and adopted a mixed methods methodological approach (62%) utilizing a questionnaire data collection instrument (n = 18). Methodological trends and areas for improvement were uncovered and analyzed to aid future research that can be used to support teachers’ implementation of Digital Technologies curricula.
This issue of G<scp>eophysics</scp>

In this article, the Editor of Geophysics provides an overview of all technical articles in this issue of the journal.
‘Transition into relatable reality’: experience analysis in a 3D desktop virtual-reality-based new student online orientation

Virtual reality (VR) applications have displayed remarkable development in the past two decades and have now been used vastly across the world for various educational purposes. The aim of this study was to explore the experience of a three dimensional (3D) desktop VR-based orientation for new online university students. The research study adopted a qualitative approach that employed a series of semi-structured interviews for the data collection. The data obtained from 76 students were carefully examined, and the thematic content analysis revealed that the 3D desktop virtual reality could serve as a good tool to host an online orientation for the incoming students and distance learners into the academic program. The study findings also provided the corresponding stakeholder with references for the development of activities within this kind of VR-based orientation.
Do Object Affordances Modulate the Sense of Embodiment in Virtual Human--Tool Interaction? Reflection for the Interactive Artificial Environment

Abstract Object manipulation is essential to build the surrounding reality, and affordances—the action possibilities offered by the environment—have a crucial role in human--tool interaction. Due to the exponential growth of the metaverse, a research question arises: Does the theoretical model behind the human--tool interaction also work in artificial reality? The present study aimed to investigate the difference in the sense of embodiment in human--tool interaction between usual and unusual objects in an immersive 360-degree video. The environment is a recording of a human arm that interacts with various tools on a table. Forty-four participants took part in the study, and they were randomized into two groups, usual or unusual objects, and in two within-participants conditions, reach to move or reach to use. Results showed no significant difference in the embodiment between usual and unusual objects, demonstrating that the ventral and dorsal streams may perfectly integrate information in the artificial environment as in the real world. Participants felt present in the virtual environment, as demonstrated by the factor location of embodiment, so they believed they could interact with any tools, independently of their affordances. The study contributes to understanding the mechanisms behind human--tool interaction in the artificial environment.
Proposed Extensions to the Third Edition of JPEG XS (ISO/IEC 21122) Standard

At its 94th meeting (17–21 January 2022), International Organization for Standardization (ISO)/IEC SC29WG1 (also known as JPEG Committee) decided to create a third edition of the JPEG XS standard (ISO/IEC 21122) for lightweight, low-latency image coding. While existing coding tools in the standard already support the compression of natural content and color filter array (CFA) RAW Bayer pattern data well, this third edition will extend the previous editions with coding tools that improve its performance on the compression of screen content and image sequences with static background. The first experiments demonstrate that the proposed coding tools can improve the average peak signal-to-noise ratio (PSNR) performance of JPEG XS on such sequences of more than 10 dB, and approximately 3 dB on computer-generated content. In this work, the authors will present the proposed coding tools and will evaluate their performance objectively by PSNR.
Real-World Use of AI for Better Video Compression

Video compression is a complex task, with an almost unlimited number of potential encoding decision permutations. While software-based encoders can, in theory, exhaustively explore all the possible encoding options in a bid to maximize compression efficiency, such an approach means there is the potential to consume as much central processing unit (CPU) power as is available. A direct consequence is a poor tradeoff between the processing required and the reduction in bitrate achieved, especially since technical and commercial limitations often place constraints on the amount of processing for any given application. A long-standing aim for encoders is, therefore, to optimize the use of the available computing resources, either by driving down the bitrates (and therefore delivery costs) or reducing the CPU cost to achieve the required quality. A common approach to reduce CPU costs is to take shortcuts and make decisions upfront based on less information, in an attempt to guess or shortlist decisions without exhaustively evaluating all options. However, the diverse and extensive number of permutations of options available in modern codecs make these decisions very complex, meaning that this approach normally leads to a less bitrate-efficient compression. If means can be found to utilize the CPU more effectively, then the objective of improved encoder performance, measured in either bitrate reduction, infrastructure cost, or a combination of both, can be achieved. This is the application space considered in this article: how to use artificial intelligence (AI) and machine learning (ML) to efficiently perform encoding decisions in realtime based on the characteristics of the incoming content, allowing the use of the CPU power available more effectively (or equivalently, reducing the need for CPU power). Considering the AI processing itself also requires CPU power, such an application requires carefully tuned approaches to guarantee that the combination of the AI processing plus its dynamically adapted encode algorithm achieves a better bitrate versus CPU for the same quality than can be achieved using standard algorithms.
The Impact of Capstone Design Experience on University Students’ Study and Career Readiness

본 연구는 캡스톤 디자인 수업 경험이 4년제 대학생의 학업(평점 및 전공만족도) 및 진로준비도(진로결정여부 및 취업준비행동)에 미치는 영향을 분석하여 정책적 시사점을 도출하는데 목적이 있다. 이를 위해 ‘한국교육고용패널조사Ⅱ’의 3∼5차년도 자료를 활용하여 통상최소자승회귀분석과 로지스틱 회귀분석을 실시했고, 표본선택편의 문제를 해소하고자 경향점수매칭 방식을 활용하였다. 분석 결과, 첫째, 캡스톤 디자인 수업을 경험한 학생들의 평점(GPA)과 전공만족도가 캡스톤디자인 수업을 경험하지 않은 학생들에 비해 높은 수준인 것으로 나타났다. 둘째, 캡스톤디자인 수업을 경험한 학생들이 캡스톤디자인 수업을 경험하지 않은 학생들에 비해 진로를 결정했을 확률이 높고, 취업준비행동을 보다 적극적으로 취하는 것으로 나타났다. 연구결과를 바탕으로 본 연구는 캡스톤 디자인 수업의 성과가 공학 외의 다양한 전공에서도 확산될 수 있도록 정부와 대학 차원의 지원이 필요함을 제안하였다. 또한, 캡스톤디자인 수업이 사회와 산업의 변화에 대응하여 효과적으로 운영될 수 있도록 지속적인 관심과 지원이 필요함을 제안하였다. The purpose of this study is to analyze the impact of capstone design class experience on four-year university students’ study (GPA and major satisfaction) and career readiness (career decision-making and employment preparation behavior) and to draw policy implications. For this purpose, ordinary least squares regression and logistic regression analyses were conducted using data from the third to fifth years of the Korean Education and Employment Panel Survey II, and the propensity score matching method was used to resolve the problem of sample selection bias. The results of the analysis showed that, first, students who experienced the capstone design class had higher grade point average and major satisfaction than students who did not experience the capstone design class. Second, students who experienced the capstone design class were more likely to have decided on a career path and take more active job search behaviors than students who did not experience the capstone design class. Based on the results, this study suggests that the government and universities need to support capstone design classes so that the results of capstone design classes can be spread to various majors other than engineering. It also suggests that that the capstone design class needs ongoing attention and support to ensure that it can operate effectively in response to changes in society and industry.
Nextcloud Storage Design as E-Learning Data Storage Media in SMA Negeri 2 Manokwari

Conventional learning systems in general have difficulty in distributing teaching materials which certainly require costs, giving assignments to students that burden the teacher himself in the collection of assignments and the process of assessing assignments, subject matter whose storage security in memory/flashdisk is not guaranteed so that with the nextcloud storage media which is cloud storage and integrated with e-learning applications, has features that are in accordance with the learning environment and easy to use both teachers and students. The system will be built using Moodle on hosting with Nextcloud as a storage medium, then using the SDLC method as a system design so that this system is well integrated. With this application, the needs of students and teachers in online teaching and learning activities are answered by using every feature in e-learning with Nextcloud helping the performance of the e-learning system application.
Dynamic interactions between memory and viewing behaviors: Insights from dyadic modeling of eye movements.

Humans use eye movements to build visual memories. We investigated how the contributions of specific viewing behaviors to memory formation evolve over individual study epochs. We used dyadic modeling to explain performance on a spatial reconstruction task based on interactions among two gaze measures: (a) the entropy of the scanpath and (b) the frequency of item-to-item gaze transitions. To measure these interactions, our hypothesized model included causal pathways by which early-trial viewing behaviors impacted subsequent memory via downstream effects on later viewing. We found that lower scanpath entropy throughout the trial predicted better memory performance. By contrast, the effect of item-to-item transition frequency changed from negative to positive as the trial progressed. The model also revealed multiple pathways by which early-trial viewing dynamically altered late-trial viewing, thereby impacting memory indirectly. Finally, individual differences in scores on an independent measure of memory ability were found to predict viewing effectiveness, and viewing behaviors partially mediated the relation between memory ability and reconstruction accuracy. In a second experiment, the model showed a good fit for an independent dataset. These results highlight the dynamic nature of memory formation and suggest that the order in which eye movements occur can critically determine their effectiveness. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Evaluating the Feasibility of Emotion Expressions in Avatars Created From Real Person Photos: Pilot Web-Based Survey of Virtual Reality Software

The availability and potential of virtual reality (VR) has led to an increase of its application. VR is suggested to be helpful in training elements of social competence but with an emphasis on interventions being tailored. Recognizing facial expressions is an important social skill and thus a target for training. Using VR in training these skills could have advantages over desktop alternatives. Children with autism, for instance, appear to prefer avatars over real images when assessing facial expressions. Available software provides the opportunity to transform profile pictures into avatars, thereby giving the possibility of tailoring according to an individual's own environment. However, the emotions provided by such software should be validated before application.Our aim was to investigate whether available software is a quick, easy, and viable way of providing emotion expressions in avatars transformed from real images.A total of 401 participants from a general population completed a survey on the web containing 27 different images of avatars transformed, using a software, from real images. We calculated the reliability of each image and their level of difficulty using a structural equation modeling approach. We used Bayesian confirmatory factor analysis testing under a multidimensional first-order correlated factor structure where faces showing the same emotions represented a latent variable.Few emotions were correctly perceived and rated as higher than other emotions. The factor loadings indicating the discrimination of the image were around 0.7, which means 49% shared variance with the latent factor that the face is linked with. The standardized thresholds indicating the difficulty level of the images are mostly around average, and the highest correlation is between faces showing happiness and anger.Only using a software to transform profile pictures to avatars is not sufficient to provide valid emotion expressions. Adjustments are needed to increase faces' discrimination (eg, increasing reliabilities). The faces showed average levels of difficulty, meaning that they are neither very difficult nor very easy to perceive, which fits a general population. Adjustments should be made for specific populations and when applying this technology in clinical practice.
A Continuous-Time Ising Machine using Coupled Inverter Chains Featuring Fully-Parallel One-Shot Spin Updates

Unconventional computers based on superconducting qubits [1] and analog/digital hardware accelerators [2]–[5] have been developed and solved hard combinatorial optimization problems (COPs) with higher efficiency than classical computers by integrating the Ising models in hardware and observing their natural convergence behavior towards the lower Ising Hamiltonian (or energy). Ouantum annealer based on superconducting qubits [1] has demonstrated its capability of finding solutions to COPs using the quantum tunneling effect. However, the quantum computer requires operating at ultra-low temperatures (15-20mK), consumes excessive power (>10kW), and is not scalable yet to solve real-world problems. Recently, ASIC hardware accelerators [2]–[5] using low-cost CMOS technologies have implemented the Ising model by integrating many physical spins (i.e., computing units of the Ising model) to accelerate the finding of solutions to hard COPs. The hardware accelerators, so-called Ising machines, solved hard COPs much faster than classical computers and consumed several orders of magnitude lower energy. Nevertheless, they still have to overcome the fundamental scalability issues for solving large-scale COPs in the real world. The performance of existing discrete-time Ising machines has been limited by the exponentially increasing number of operation cycles when the problem size increases for the large-scale COPs. in addition, prior works have not been able to integrate massive random number generators (RNG), the essential building blocks of the Ising machines for finding better solutions, that could occupy a significant hardware footprint. Prior works have implemented them off-chip, or a few have been integrated on-chip and shared by many spins. This paper proposes a novel continuous-time analog Ising machine based on coupled inveiter chain circuits to address the scalability issues of the existing discrete-time Ising machines. Besides the benefits of the continuous-time operation, which significantly lowers the computing energy and latency, we introduce an equalization method for finding better solutions without RNGs.
A Reconfigurable lsing Machine for Boolean Satisfiability Problems Featuring Many-Body Spin Interactions

Ising machines [1–6] have recently shown their unique capabilities in solving hard combinatorial optimization problems (COPs) intractable using conventional computers. We map the COPs to the Ising model implemented in a network of hardware spins (i.e., computing units of the Ising machine) and find optimal solutions by observing the natural convergence of binary states of spins based on interactions between them. The existing Ising machines [3–5] have implemented hardware spins that only interact with their neighbors, lacking the generality to solve more complicated COPs [1] with arbitrary interactions between spins. A prior work [6] proposed a flexible circuit to map an arbitrary interaction between remote spins. While it resolves the overhead of mapping complicated Ising models to the hardware, the mapping has been limited to the Ising model with two-body spin interactions, while there are many COPs, such as Boolean satisfiability (SAT), requiring N-body interactions (where N > 2). Approximate algorithms have been used to conveit the problem with many-body spin interactions to the Ising model with two-body spin interactions. However, they result in a significant hardware overhead with many ancillary spins and lower accuracy due to the approximation. Recent works [1–2] implemented many-body interactions in quantum and optical machines. However, they suffer from limited scalability and require high development and operation cost. in this paper, we present a CMOS Ising machine with reconfigurable processing elements to solve complex combinatorial optimization problems mapped to the model with more than two-body spin interactions. The popular 3SAT problems and their variants are demonstrated using 128 reconfigurable processing elements (PEs) integrated into a 65nm prototype chip. Each PE consists of eight spins with up to 56 temporally reconfigurable spin interactions based on a King’s graph topology. The proposed Ising machine computes massively parallel spin operations in a complex spin network based on multi-cycle spin operations.
Low power cryogenic RF ASICs for quantum computing

Quantum computing is a promising new approach to computing that may allow the solution of certain problems that are very difficult to solve on classical computers. The field received its biggest impetus in 1994 when Shor [1] devised a quantum algorithm for finding the prime factors of large numbers and showed that this algorithm could greatly surpass the performance of known classical algorithms. Since that time, many additional applications have been proposed, and much effort has been devoted to developing hardware capable of running quantum algorithms.