Reviewerly/exHarmony huggingface dataset:
Meta-learning Advisor Networks for Long-tail and Noisy Labels in Social Image Classification

Deep neural networks (DNNs) for social image classification are prone to performance reduction and overfitting when trained on datasets plagued by noisy or imbalanced labels. Weight loss methods tend to ignore the influence of noisy or frequent category examples during the training, resulting in a reduction of final accuracy and, in the presence of extreme noise, even a failure of the learning process. A new advisor network is introduced to address both imbalance and noise problems, and is able to pilot learning of a main network by adjusting the visual features and the gradient with a meta-learning strategy. In a curriculum learning fashion, the impact of redundant data is reduced while recognizable noisy label images are downplayed or redirected. Meta Feature Re-Weighting (MFRW) and Meta Equalization Softmax (MES) methods are introduced to let the main network focus only on the information in an image deemed relevant by the advisor network and to adjust the training gradient to reduce the adverse effects of frequent or noisy categories. The proposed method is first tested on synthetic versions of CIFAR10 and CIFAR100, and then on the more realistic ImageNet-LT, Places-LT, and Clothing1M datasets, reporting state-of-the-art results.
Algorithm Analysis of Face Recognition Robot Based on Deep Learning

With the continuous improvement of computer software and hardware performance, a large amount of image and video data can be easily collected and quickly transmitted, and new recognition methods that introduce deep learning are emerging, making the application and research of face recognition technology. The value is also increasingly prominent. The purpose of this paper is to study the face recognition robot implementation algorithm based on deep learning. The research background and significance of face recognition and expression recognition, which are the core of facial biological information extraction, are introduced. The face feature extraction network structure of Inception-ResNet-V1 has been improved, and high recognition features of faces can be obtained. At the same time, the training of the feature extraction model of the self-built training set and the adjustment of hyperparameters are completed. Finally, the effectiveness of the improved network in this paper is fully verified in the LFW test set and the actual robot environment. It is verified by experiments that the proposed optimization method can improve the performance of the network. It also verified the significant research significance of the current deep learning direction through practice.
A study of the barriers to communication and learning of university students with hearing impairment during the COVID-19 pandemic

ABSTRACTAfter the declaration of the State of Emergency in Spain in 2020 due to the Coronavirus pandemic, essential services were paralysed, including education. This change brought about consequences in the quality of education, especially in the support of students with hearing impairment. The general objective is to analyse the barriers to learning for students with hearing impairments at a Spanish university due to the COVID-19 pandemic. A qualitative descriptive study is presented in which 6 semi-structured interviews were conducted -four with hearing impaired university students, one with a Spanish Sign Language Interpreter and another with a technician from the university's diversity service. The results show students with hearing impaired became vulnerable students, suffering processes of educational exclusion. The Coronavirus and the consequences arising from its appearance have had a great impact on the academic and social lives of the participants. This research identifies which specific barriers were experienced by the group with hearing impaired, different from those found by the rest of the groups. The conclusions show that barriers to communication increased significantly due to the use of masks, the delivery of classes in a digital format and the lack of preparation of the faculty members regarding online teaching.KEYWORDS: Hearing impairmentcommunication barriersinclusionvirtual teachingCOVID-19 Disclosure statementNo potential conflict of interest was reported by the authors.
Generalized Boomerang Connectivity Table and Improved Cryptanalysis of GIFT

Boomerang connectivity table (BCT), an essential tool in boomerang attack, gives a unified description of the probability in the middle round of a boomerang distinguisher. However, it suffers the drawback that the asymmetric relationship between the upper and lower differentials in the middle round is ignored. To make up for this deficiency, we propose the generalized boomerang connectivity table (GBCT), which characterizes all combinations of upper and lower differentials to provide a more precise probability in the middle round. We first study the cryptographic properties of GBCT and introduce its variants applied in multiple rounds and Feistel structure. Then, we provide an automatic search algorithm to increase the probability of the boomerang distinguisher by adding thorough considerations that more trails can be included, which is applicable to all S-box based ciphers. Finally, we increase the probabilities of the 20-round GIFT-64 distinguisher from $$2^{-58.557}$$ to $$2^{-57.43}$$ and the 19-round GIFT-128 distinguisher from $$2^{-109.626}$$ to $$2^{-108.349}$$ , both of which are the highest so far. Applying the key recovery attack proposed by Dong et al. at Eurocrypt 2022 on the new distinguisher, we achieve the lowest complexities of the attack on GIFT-64 and the best rectangle attack on GIFT-128.
Deep Structural Causal Shape Models

Causal reasoning provides a language to ask important interventional and counterfactual questions beyond purely statistical association. In medical imaging, for example, we may want to study the causal effect of genetic, environmental, or lifestyle factors on the normal and pathological variation of anatomical phenotypes. However, while anatomical shape models of 3D surface meshes, extracted from automated image segmentation, can be reliably constructed, there is a lack of computational tooling to enable causal reasoning about morphological variations. To tackle this problem, we propose deep structural causal shape models (CSMs), which utilise high-quality mesh generation techniques, from geometric deep learning, within the expressive framework of deep structural causal models. CSMs enable subject-specific prognoses through counterfactual mesh generation (“How would this patient’s brain structure change if they were ten years older?”), which is in contrast to most current works on purely population-level statistical shape modelling. We demonstrate the capabilities of CSMs at all levels of Pearl’s causal hierarchy through a number of qualitative and quantitative experiments leveraging a large dataset of 3D brain structures.
A Report on the Fifth Workshop on Emerging Software Engineering Education (WESEE 2023)

In this report, we present a pre-organization summary of the fifth workshop on emerging software engineering education (WESEE) to be held on February 23, 2023, co-located with the 16th Innovations in Software Engineering Conference (ISEC 2023), February 23–25, 2023. The 5th edition of WESEE, a half-day workshop, focuses on open discussions and brainstorming sessions on software engineering education. The workshop will discuss "Artificial Intelligence (AI), Human Flourishing, Learning Goals, Collaboration in Software Engineering (SE)". The workshop consists of two invited talks, one hands-on activity, and an open-ended concluding discussion session. The academic faculty members, students (UG & PG), researchers working in SE education, and industry practitioners can attend the workshop.
Rotational and Positional Jitter in Virtual Reality Interaction in Everyday VR

One element that affects 3D tracking performance in virtual reality (VR) systems is fluctuations in the signal, i.e., jitter, which occurs regardless of the sensor technology used. In real-life VR systems, positional and rotational jitter can be found in all tracked objects, including the headset, controllers, or other trackers. Previous work had identified that $${\pm }0.5^{\circ }$$ rotational jitter negatively affects user performance for distal pointing. Yet, they also found that even using a second controller to reduce the “Heisenberg effect” introduced by the button press does not address the problem completely. Moreover, with jitter on the position of a virtual object, user performance significantly decreases with jitter above one fourth of the size of that virtual object. Still, users preferred to have positional jitter on a virtual target rather than rotational jitter on a VR controller. In this chapter, we extended the previous literature by conducting a user study on angular jitter with controllers held with two different grip styles and targets at two different depth distances. The results revealed that user performance decreases (already) with $${\pm }0.25^{\circ }$$ additional jitter. Thus, we suggest that practitioners/developers who design 3D user interfaces, controllers, and interaction techniques for daily 3D VR usage should focus on reducing jitter. Decreasing jitter not only improves user performance but also decreases frustration, which improves the user experience.
SR-MuSig2: A Scalable and Reconfigurable Multi-signature Scheme and Its Applications

Multi-signature is a kind of digital signature with a wide range of uses, such as certificate authorities signing certificates, which can enable a group of signers to sign the same message in a very short period, thereby aggregating a compact signature. In this work, we propose SR-MuSig2, a multi-signature scheme with scalability and reconfigurability. First of all, we use a tree structure to significantly improve the efficiency of computation and communication of signers, so that the scheme can support a large number of participants signing simultaneously and has better scalability. For the reconfigurability, SR-MuSig2 supports the signers to revoke passively or actively from the signer group, while can effectively generate the signature and complete the verification. Then we implement a prototype system in Python, and evaluate our scheme in the simulation network environment. The experimental results show that SR-MuSig2 is able to generate aggregated signature in an acceptable time with up to thousands of signers, and it can complete the signing process in only 12 s when there are $$ 2^{11} $$ signers. In addition, when 5% of the nodes in the signer group (up to $$ 2^{11} $$ signers) go offline, SR-MuSig2 only needs to update the values of 2.6% of the remaining nodes (nearly 66 s) instead of updating the values of all the remaining nodes to recover signing process.
Who cares about corruption in Africa? China or the <scp>USA</scp>?

Abstract Considering the growing criticism by the USA of the bribery and corruption surrounding China's trade with Africa, this study uses panel data on 48 African countries over 17 years to examine whether the relationship between corruption and China's trade in Africa differs from that of USA–Africa trade relations. Contrary to expectations, we find that the relationship between corruption and trade with Africa is the same for both countries. Africa's trade with China and the USA increases as corruption increases. Our results imply that both countries have a similar trend of trading with African countries, and they might not care about corruption in Africa. Ultimately, both countries engage with Africa for their parochial economic and political interest. The results are robust to an alternative measure of corruption and endogeneity test using a two‐stage least‐square estimation technique.
Development and validation of a robot social presence measurement dimension scale

Robots that can exhibit human-like behaviour, build social relationships with humans, and carry out social interaction that can be considered to have a social presence. Measuring the social presence of robots can provide an important basis for optimizing the design of robots' human-like behaviour, but until now, there has been no research and discussion on the presence of robots from the perspective of social interaction. Based on related studies, such as the theory of the presence of interpersonal interaction society, the mechanism of network social presence measurement and human-computer interaction, this study constructs a theoretical model of robot social presence, and develops corresponding measurement scales with five dimensions, namely, the presence, attention distribution, interactive expression and information understanding, perceived emotional interdependence, and interaction behaviour perception. The 5-dimensional robot social presence model was finalized, and a 17-question questionnaire scale was developed. The validation of the model and the development of the scale includes one expert assessment (involving three experts), one user interview (involving five interviewees), and two questionnaire surveys (involving 172 valid questionnaires and 494 valid questionnaires respectively). The final model shows good reliability of the measurement questionnaire, high inter-factor correlation, high model fit, high internal consistency of the dimensions, high reliability of the scale, and high convergent validity of all dimensions. This study provides a theoretical basis for the study of robot social presence and human-robot collaboration and provides a reference measurement tool for social robot-related development and research.
Invertible network for unpaired low-light image enhancement

Existing unpaired image enhancement approaches prefer to employ the traditional two-way generative adversarial network (GAN) framework, in which two convolutional neural network (CNN) generators are deployed for enhancement and degradation separately. However, such data-driven models ignore the inherent characteristics of transformation between the low-light and normal-light images, leading to unstable training and artifacts. Here, we propose to leverage the invertible neural network to enhance the low-light images in the forward process and degrade the unpaired normal-light photograph inversely. The generated and real images are then fed into discriminators for adversarial learning. In addition to the adversarial loss, we design transformation-consistent loss to ensure the stability of training, detail-preserving loss to preserve more image details, and reversibility loss to alleviate the over-exposure problem. Moreover, we present a progressive self-guided enhancement process in inference and achieve favorable performance against the state-of-the-art methods.
Technology and Creativity in Teaching: Audiovisual Translation as a Profession and Method in Multilingual World

Teaching foreign languages at the university in multilingual world has become a many-sided action. As an experience shows today, this is an interdisciplinary learning process, which integrates different disciplines, educational profiles, methods, and approaches. The teaching a foreign language is considered not as a lexical and grammatical system learning, but the formation of a new professional skills in definite field with the help of new methods and means. The training an audiovisual interpreter is a multi-stage process that requires special means to achieve its goals. The most suitable means for developing the necessary skills and abilities of a future AVT specialist are available electronic learning platforms, playlists of video and audio materials. Within the framework of this study, some methods and techniques for teaching students audiovisual translation will be analyzed and demonstrated from the standpoint of the formation of creative and pragmatic competencies. Moreover, the authors of the article stated that methodic of play list and silent films is efficient to form important professional competences of AVT specialist, speaking different native languages. The authors also stated that the method of audiovisual translation had showed its efficiency in teaching students of humanitarian (non-linguistic) universities a foreign language, in the context of developing creative and professional approaches in multilingual environment.
Technologies for Higher Education Digitalization

The article discusses the questions of higher education digitalization technologies nowadays. The important definitions of this research like digitalization and digitalization in education are considered in the article. The main idea of the educational digital transformation is the movement towards the educational process personalization based on the digital technologies’ usage. The properties and the main elements of digitalization are given in the article. The modern E-technologies, online boards, multifunctional online generators, timeline in education, gamification, their pros and cons, and their possibilities for the transition to digital education are considered. Massive online education is developing at a rapid pace nowadays. Massive Open Online Courses (MOOCs) are online courses with free or partially free access that an unlimited number of people can watch. The advantages and disadvantages of MOOCs are given. A diagram of the various people groups’ attitude to the idea of obtaining online education, respondent attitude to the quality of MOOC, to the quality of the educational process in MOOC, to the variety of educational interaction forms in MOOC is presented in the article.
Working Knowledge: What Do I Know About How I Do Something?

The article discusses the works by Alfred Nordmann, Natalia Yastreb published in 2022 at “Semiotic Studies” and devoted to the problem of “working” or “technical” knowledge. Traditionally determined knowledge (as a justified true belief, as subjectively and objectively sufficient positing that something is true) describes the result of internalizing activity of cognition and is aimed at detection of an actual relation between the state of affairs and their language expressions. “Working knowledge” as a concept is introduced by Nordmann and represents a form of knowledge not on the state of affairs as such but on the method of achieving the target result in case of changing the state of affairs. To describe the method of implementation of working knowledge, a concept of epistemic practice is introduced by Yastreb. The article analyses the working knowledge as a technical knowledge in the context of philosophy of technology by Peter Engelmeyer and Friedrich Dessauer. As a result, it is demonstrated that the question about a “thing” as a reference object of the working knowledge is solved within the “three-act” concept. “Thing” as a reference is either an intellectual idea or a rational formation, or a sensuous artefact, i.e. a result of technical activity achieved by a subject at each of three stages, some sum of stages. A semiotic expression of each stage of the three-act is formed, the thing in each case is demonstrated to be the implementation of a semantic rule. As a general conclusion, the formulated by Nordmann task of generating the theory of working knowledge types is defined more precisely as the task of generating the types of epistemic practices in their general semiotic expression.
International Students’ Creative Thinking Development via the TRIZ Method in a Multilingual Audience

The article validates the possibility and efficiency of improving creative thinking ability while teaching English via the TRIZ (theory of inventive problem solving) method to international students. The objectives of the current research are (a) to investigate the lingoudidactic potential of TRIZ problem-solving method in the process of teaching English to international students, (b) to describe the algorithm of international students` creative thinking development via the TRIZ method, (c) to check the effectiveness of the devised algorithm. In our research TRIZ is considered as a set of methods for solving logical tasks, consisting of the following steps: general preparation, situation analysis, questions preparation, hypotheses generation, hypotheses selection, hypotheses testing. The ambitions behind the devised algorithm are to teach in-depth analysis of the situation, to intensify critical and logical thinking in a non-standard situation, to overcome stereotypical thinking, to stimulate teamwork and communication abilities. The methodology has been tested on the second-year international students from Peter the Great St. Petersburg Polytechnic University majoring in Public Relations in the year 2021. The methods of this research include both theoretical (assessment of the key pedagogical principles of the TRIZ Method integration and generalization of facts and concepts, data analysis) and empirical ones (methodological algorithm development, a set of experiments: educational and control, evaluation of educational outcomes). We have obtained satisfactory results demonstrating that the problem-based learning algorithm devised leads to an improvement in students’ creative thinking ability.
The Use of Augmented Reality for Temporal Coordination in Everyday Work Context

Augmented reality (AR) has been used in a large variety of scenarios from assembly support over gaming to collaborative work. Teamwork is a specific type of collaborative work and emerges in numerous everyday contexts. It is characterized (in addition to other aspects) by specific needs in terms of time-related synchronization of team members to enable them to coordinate the team task. This is specifically relevant in situations where team members are spatially dispersed, such that direct communication (orally and visually) is not possible. These restrictions reduce task-related communication and coordination, which are key for successful teamwork. AR has the potential to enable coordination in spatially dispersed teamwork by enhancing each team members’ view with information of the current status of the team task and additional feedback if needed, all adapted to the specific needs of the team members. As a key element of AR, this digital information is registered to the team member’s individual perspective on the work object in question. This chapter will first give an introduction to collaborative work, teams, teamwork, and taskwork followed by a broad overview of existing AR-based team-supportive methods. We will first present a taxonomy for the development and implementation of AR-based support systems and give a short example on its usage in the context of a user-centered design followed by a review of the current research conducted by the authors of the chapter and a discussion of future work.
Deep Multi-modal Representation Schemes for Federated 3D Human Action Recognition

The present work investigates the problem of multi-modal 3D human action recognition in a holistic way, following the recent and highly promising trend within the context of Deep Learning (DL), the so-called ‘Federated Learning’ (FL) paradigm. In particular, novel contributions of this work include: a) a methodology for enabling the incorporation of depth and 3D flow information in DL action recognition schemes, b) multiple modality fusion schemes that operate at different levels of granularity (early, slow, late), and c) federated aggregation mechanisms for adaptively guiding the action recognition learning process, by realizing cross-domain knowledge transfer in a distributed manner. A new large-scale multi-modal multi-view 3D action recognition dataset is also introduced, which involves a total of 132 human subjects. Extensive experiments provide a detailed analysis of the problem at hand and demonstrate the particular characteristics of the involved uni/multi-modal representation schemes in both centralized and distributed scenarios. It is observed that the proposed FL multi-modal schemes achieve acceptable recognition performance in the proposed dataset in two challenging data distribution scenarios.