Reviewerly/exHarmony huggingface dataset:
Algorithmic and human prediction of success in human collaboration from visual features

As groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in which a group is tasked with escaping a maze by collectively solving a series of puzzles. We investigate (1) the characteristics of successful groups, and (2) how accurately humans and machines can spot them from a group photo. The relationship between these two questions is based on the hypothesis that the characteristics of successful groups are encoded by features that can be spotted in their photo. We analyze >43K group photos (one photo per group) taken after groups have completed the game-from which all explicit performance-signaling information has been removed. First, we find that groups that are larger, older and more gender but less age diverse are significantly more likely to escape. Second, we compare humans and off-the-shelf machine learning algorithms at predicting whether a group escaped or not based on the completion photo. We find that individual guesses by humans achieve 58.3% accuracy, better than random, but worse than machines which display 71.6% accuracy. When humans are trained to guess by observing only four labeled photos, their accuracy increases to 64%. However, training humans on more labeled examples (eight or twelve) leads to a slight, but statistically insignificant improvement in accuracy (67.4%). Humans in the best training condition perform on par with two, but worse than three out of the five machine learning algorithms we evaluated. Our work illustrates the potentials and the limitations of machine learning systems in evaluating group performance and identifying success factors based on sparse visual cues.
BiHand: Recovering Hand Mesh with Multi-stage Bisected Hourglass Networks

3D hand estimation has been a long-standing research topic in computer vision. A recent trend aims not only to estimate the 3D hand joint locations but also to recover the mesh model. However, achieving those goals from a single RGB image remains challenging. In this paper, we introduce an end-to-end learnable model, BiHand, which consists of three cascaded stages, namely 2D seeding stage, 3D lifting stage, and mesh generation stage. At the output of BiHand, the full hand mesh will be recovered using the joint rotations and shape parameters predicted from the network. Inside each stage, BiHand adopts a novel bisecting design which allows the networks to encapsulate two closely related information (e.g. 2D keypoints and silhouette in 2D seeding stage, 3D joints, and depth map in 3D lifting stage, joint rotations and shape parameters in the mesh generation stage) in a single forward pass. As the information represents different geometry or structure details, bisecting the data flow can facilitate optimization and increase robustness. For quantitative evaluation, we conduct experiments on two public benchmarks, namely the Rendered Hand Dataset (RHD) and the Stereo Hand Pose Tracking Benchmark (STB). Extensive experiments show that our model can achieve superior accuracy in comparison with state-of-the-art methods, and can produce appealing 3D hand meshes in several severe conditions.
Students’ attitude to e-learning

In the context of the Covid-19 pandemic, the relevance of the transition to remote forms of training has increased not only for part-time students, but also for full-time ones. Online learning, based on the use of various electronic educational platforms, differs significantly from the traditional system and completely changes the entire educational process, including the forms of knowledge acquisition, control and assessment. ELearning has many advantages, but it can only be considered as an addition to traditional forms of training. However, under the conditions of the pandemic, remote learning for a certain period of time became the only form of communication between students and teachers and showed alongside with its advantages its shortcomings as well. It turned out that higher education is not only technically, but also organizationally unprepared for the transition to online education. This work examines the main aspects of the remote learning system as a necessary and effective technology in the current conditions. At the same time, our task is to analyze the negative assessments that students give to the current state of remote education.
Level Operators over Intuitionistic Fuzzy Index Matrices

The index matrix (IM) is an extension of the ordinary matrix with indexed rows and columns. Over IMs’ standard matrix operations are defined and a lot of other ones that do not exist in the standard case. Intuitionistic fuzzy IMs (IFIMs) are modification of the IMs, when their elements are intuitionistic fuzzy pairs (IFPs). Extended IFIMs are IFIMs whose indices of the rows and columns are evaluated by IFPs. Different operations, relations and operators over IFIMs, and some specific ones, are defined for EIFIMs. In the paper, twelve new level operators are defined for EIFIMs and in the partial case, over IFIMs. The proposed level operators fall into two groups: operators that change the values of the EIFIM elements and operators that change the IFPs associated to the indices of the rows and columns. The basic properties of the operators are studied.
How Do Art Skills Influence Visual Search? – Eye Movements Analyzed With Hidden Markov Models

The results of two experiments are analyzed to find out how artistic expertise influences visual search. Experiment I comprised survey data of 1,065 students on self-reported visual memory skills and their ability to find three targets in four images of artwork. Experiment II comprised eye movement data of 50 Visual Literacy (VL) experts and non-experts whose eye movements during visual search were analyzed for nine images of artwork as an external validation of the assessment tasks performed in Sample I. No time constraint was set for completion of the visual search task. A latent profile analysis revealed four typical solution patterns for the students in Sample I, including a mainstream group, a group that completes easy images fast and difficult images slowly, a fast and erroneous group, and a slow working student group, depending on task completion time and on the probability of finding all three targets. Eidetic memory, performance in art education and visual imagination as self-reported visual skills have significant impact on latent class membership probability. We present a hidden Markov model (HMM) approach to uncover underlying regions of attraction that result from visual search eye-movement behavior in Experiment II. VL experts and non-experts did not significantly differ in task time and number of targets found but they did differ in their visual search process: compared to non-experts, experts showed greater precision in fixating specific prime and target regions, assessed through hidden state fixation overlap. Exploratory analysis of HMMs revealed differences between experts and non-experts in image locations of attraction (HMM states). Experts seem to focus their attention on smaller image parts whereas non-experts used wider parts of the image during their search. Differences between experts and non-experts depend on the relative saliency of targets embedded in images. HMMs can determine the effect of expertise on exploratory eye movements executed during visual search tasks. Further research on HMMs and art expertise is required to confirm exploratory results.
Detecting Nuisance Calls over Internet Telephony Using Caller Reputation

Internet telephony permit callers to manage self-asserted profiles without any subscription contract nor identification proof. These cost-free services have attracted many telemarketers and spammers who generate unsolicited nuisance calls. Upon detection, they simply rejoin the network with a new identity to continue their malicious activities. Nuisance calls are highly disruptive when compared to email and social spam. They not only include annoying telemarketing calls but also contain scam and voice phishing which involves security risk for subscribers. Therefore, it remains a major challenge for Internet telephony providers to detect and avoid nuisance calls efficiently. In this paper, we present a new approach that uses caller reputation to detect different kinds of nuisance calls generated in the network. The reputation is computed in a hybrid manner by extracting information from call data records and using recommendations from reliable communicating participants. The behavior of the caller is assessed by extracting call features such as call-rate, call duration, and call density. Long term and short term reputations are computed to quickly detect the changing behavior of callers. Furthermore, our approach involves an efficient mechanism to combat whitewashing attacks performed by malicious callers to continue generating nuisance calls in the network. We conduct simulations to compute the performance of our proposed model. The experiments conclude that the proposed reputation model is an effective method to detect different types of nuisance calls while avoiding false detection of legitimate calls.
Distributed Trust, a Blockchain Election Scheme

Voting systems are as useful as people are willing to use them.Although many electronic election schemes have been proposed through the years, and some real case scenarios have been tested, people still do not trust electronic voting.Voting is not only about technological challenges but also about credibility, therefore, we propose a voting system focused on trust.We introduce political parties as active partners in the elections as a mechanism to encourage more traditional electors to participate.The system we propose here preserves elector's privacy, it operates publicly through a blockchain and it is auditable by third parties.
Real-time all-optical random numbers based on optical Boolean chaos

In this work, a method of generating all-optical random numbers based on optical Boolean chaotic entropy source is proposed. This all-optical random number generation system consists of a Boolean chaotic entropy source and an optical D flip-flop. The Boolean chaotic entropy source is composed of an optical XOR gate and two self-delayed feedback; meanwhile, the optical D flip-flop is composed of two optical AND gates and one SR latch. The optical Boolean chaotic signal possesses the dynamic characteristics of complexity and binarization, so random numbers would be generated only by extracted from chaotic signals with the optical D flip-flop. This all-optical random number generation system achieves the result of 5 Gb/s random numbers that is testable. The whole process of random number generation could be completed in the optical domain without photoelectric conversion, more importantly, the device could be integrated.
New binary self-dual codes of lengths 56, 58, 64, 80 and 92 from a modification of the four circulant construction

In this work, we give a new technique for constructing self-dual codes over commutative Frobenius rings using $\lambda$-circulant matrices. The new construction was derived as a modification of the well-known four circulant construction of self-dual codes. Applying this technique together with the building-up construction, we construct singly-even binary self-dual codes of lengths 56, 58, 64, 80 and 92 that were not known in the literature before. Singly-even self-dual codes of length 80 with $\beta\in\{2,4,5,6,8\}$ in their weight enumerators are constructed for the first time in the literature.
ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks

Recently, learning algorithms motivated from sharpness of loss surface as an effective measure of generalization gap have shown state-of-the-art performances. Nevertheless, sharpness defined in a rigid region with a fixed radius, has a drawback in sensitivity to parameter re-scaling which leaves the loss unaffected, leading to weakening of the connection between sharpness and generalization gap. In this paper, we introduce the concept of adaptive sharpness which is scale-invariant and propose the corresponding generalization bound. We suggest a novel learning method, adaptive sharpness-aware minimization (ASAM), utilizing the proposed generalization bound. Experimental results in various benchmark datasets show that ASAM contributes to significant improvement of model generalization performance.
An Improved Double Channel Long Short-Term Memory Model for Medical Text Classification

There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model.
Academic Writing: A Discipline or Disciplines?

In the last five years, the discipline “Academic Writing” in one form or another has become part of the programs of many Russian universities. The goal of the discipline is the achievement of academic literacy. Within the framework of state policy in education and the need to increase publication activity, the discipline should teach students, graduate students and researchers to write a scientific article in accordance with the requirements of highly rated journals. In Russian education, the model of teaching academic writing was adopted from Western educational discourse. Since the 2010s, university teachers introduce courses, focusing on the experience of Western colleagues and sharing the results achieved. Researchers of academic writing point out a number of problems in teaching students. It is noted that students experience difficulties in mastering the competencies of the course, and teachers are not satisfied with the results. A number of articles appear in the media discourse, the authors of which express doubts about the appropriateness of practice of academic writing borrowed from the Western educational space. Indeed, in Western educational institutions, the development of academic writing skills begins at school, and then an extensive standard program is implemented at universities, covering various subject and cross-subject areas, within which the discipline is taught. In Russia, university students are confronted with a new field of knowledge and find themselves in a whirlpool of new rules, abilities, skills, competencies that they have to master in a short period of study a far as at the undergraduate level. The Western academic writing program is hardly applicable to the realities of Russian education. This article attempts to find the reason for the difficulties in teaching the discipline of academic writing to Russian students. The results of the study on three different groups of students studying the discipline of academic writing are presented. As a way out of a problem situation, the author proposes to divide the discipline into three levels, each of which covers a number of educational competencies necessary to create a specific product within the framework of the academic text genre.
CSNAS: Contrastive Self-Supervised Learning Neural Architecture Search Via Sequential Model-Based Optimization

This article proposes a novel contrastive self-supervised neural architecture search (NAS) algorithm, which completely alleviates the expensive costs of data labeling inherited from supervised learning. Our algorithm capitalizes on the effectiveness of self-supervised learning for image representations, which is an increasingly crucial topic of computer vision. First, using only a small amount of unlabeled train data under contrastive self-supervised learning allows us to search on a more extensive search space, discovering better neural architectures without surging the computational resources. Second, we entirely relieve the cost for labeled data (by contrastive loss) in the search stage without compromising architectures’ final performance in the evaluation phase. Finally, we tackle the inherent discrete search space of the NAS problem by sequential model-based optimization via the tree-parzen estimator, enabling us to significantly reduce the computational expense response surface. An extensive number of experiments empirically show that our search algorithm can achieve state-of-the-art results with better efficiency in data labeling cost, searching time, and accuracy in final validation.
An Augmented Reality-Based Remote Collaboration Platform for Worker Assistance

Remote working and collaboration is important towards helping workplaces to become flexible and productive. The significance of remote working has also been highlighted in the COVID-19 pandemic period in which mobility restrictions were enforced. This paper presents the development of an augmented reality platform, aiming to assist workers in remote collaboration and training. The platform consists of two communicating apps intended to be used by a remote supervisor (located e.g., at home) and an on-site worker, and uses intuitive digital annotations that enrich the physical environment of the workplace, thereby facilitating the execution of on-site tasks. The proposed platform was used and evaluated in user trials, demonstrating its usefulness and virtue by assessing its performance, worker satisfaction and task completion time.
Aerial filming with synchronized drones using reinforcement learning

Usage of multiple drones is necessary for aerial filming applications to ensure redundancy. However, this could inevitably contribute to higher risks of collisions, especially when the number of drones increases. Hence, this motivates us to explore various autonomous flight formation control methods that have the potential to enable multiple drones to effectively track a specific target at the same time. In this paper, we designed a model-free deep reinforcement learning algorithm, which is mainly based on the Deep Recurrent Q-Network concept, for the aforementioned purposes. The proposed algorithm was expanded into single and multi-agent types that enable multiple drones tracking while maintaining formation and preventing collision. The involved rewards in these approaches are two-dimensional in nature and are dependent on the communication system. Using Microsoft AirSim simulator, a virtual environment that includes four virtual drones was developed for experimental purposes. A comparison was made among various methods during the simulations, and the results concluded that the recurrent, single-agent model is the most effective method, being 33% more effective than its recurrent, multi-agent counterparts. The poor performance of the non-recurrent, single-agent baseline model also suggests that the recurrent elements in the network are essential to enable desirable multiple-drones flight.
Method for Subjective Assessment of Immersion in Audiovisual Experiences

Studying immersion in audiovisual experiences can help technologists deliver engaging and enhanced experiences.As a first step toward this goal this paper details an investigation conducted to establish an experimental paradigm for quantifying immersion and determining the influence of immersive tendency (susceptibility to become immersed) on immersion.A balanced incomplete block design was employed where 21 assessors rated 15 commercially available stimuli (representative of the highest quality encountered in domestic AV applications) without repetitions and simultaneous comparisons.The assessors were instructed to rate immersion on a graphic line scale and document their familiarity with the content.A questionnaire was administered to measure the immersive tendency after the rating experiment.The results show that the assessors can comprehend the description of immersion and follow the experimental protocol.It is found that immersion is a graded experience and the correlation between immersive tendencies and immersion ratings is predominantly statistically insignificant.The experimental paradigm presented in this paper can form the framework for assessing immersion and developing novel methods to thoroughly explore the concept of immersion in audiovisual experiences.
Current Situation and Suggested Measures of Japanese Teaching in Colleges and Universities Based on Computer Aid

Abstract Colleges and universities are the cradle of talent training in China, which undertakes the responsibility of exporting professional talents to the society. With the increase of economic and trade between China and Japan, colleges and universities should strengthen the Japanese teaching, which will better provide applied professional Japanese talents. Japanese is becoming popular, popular and folk, which has become a tool for international communication. However, there are still many problems in Japanese Teaching in Colleges and universities in China, which requires colleges and universities to constantly improve their teaching ability. By optimizing teachers and teaching content with the computer aid, colleges and universities can improve their Japanese teaching. First of all, this paper analyzes the current situation of Japanese teaching from three aspects: teaching materials, teachers and students [1] . Then, this paper puts forward some questions. Finally, some suggestions are put forward [2] .
Mobile health approaches to disease surveillance in Africa; Wellvis COVID triage tool

COVID-19 has rapidly spread across the globe and was declared a pandemic by the World Health Organization (WHO). The COVID-19 infection continues to spread across Africa. In Africa, mobile phone applications have been used for the surveillance and reporting of infectious diseases such as malaria, measles, polio, and other notifiable diseases as mandated by the WHO. A good example is the early warning alert and response system. We developed an eight-question triage tool using the Nigerian Centre for Disease Control surveillance case definitions for new coronavirus disease. Based on the assessed risk level we offered advice and guidance on the next steps. A user-administered tool such as this is vital to COVID19 control. It is also significant in relieving the burden on health systems, providing information on national health guidelines for prevention and control, fostering the participation of citizens, and giving them the next steps, pandemic control efforts become more effective.