Reviewerly/exHarmony huggingface dataset:
A synthesis framework for stitching surface code with superconducting quantum devices

Quantum error correction (QEC) is the central building block of fault-tolerant quantum computation but the design of QEC codes may not always match the underlying hardware. To tackle the discrepancy between the quantum hardware and QEC codes, we propose a synthesis framework that can implement and optimize the surface code onto superconducting quantum architectures. In particular, we divide the surface code synthesis into three key subroutines. The first two optimize the mapping of data qubits and ancillary qubits including syndrome qubits on the connectivity-constrained superconducting architecture, while the last subroutine optimizes the surface code execution by rescheduling syndrome measurements. Our experiments on mainstream superconducting architectures demonstrate the effectiveness of the proposed synthesis framework. Especially, the surface codes synthesized by the proposed automatic synthesis framework can achieve comparable or even better error correction capability than manually designed QEC codes.
Composite effects of human, natural and social capitals on sustainable food-crop farming in Sub-Saharan Africa

This study analyzes the spontaneous impact of human, social and natural capital on food crop technical efficiency (TE) in Sub-Saharan Africa (SSA). Our study contributes to the literature by adopting the meta-analysis method to investigate the relationship between TE and the three groups of capitals to better shed light on the TE in SSA regions. Our results highlight that social capital is the most critical factor among the three groups of capitals in promoting farming productivity. In particular, agriculture efficiency benefits from increasing people’s trust in institutions and the frequency of extension visits. Natural capital like temperature and elevation is essential in determining the farming TE in SSA regions. Outstandingly, our results also indicate that calorie intake, a proxy of labor quality, should be improved to achieve better productivity.
A YOLO Based Approach for Traffic Light Recognition for ADAS Systems

Regulating traffic in urban cities is highly dependant on traffic lights, particularly at intersections, where crossing a red light could jeopardize many lives. For this specific reason it is crucial for self-driving cars to recognize traffic lights and abide by the rules that traffic lights help enforce. In this paper a deep neural network was trained to detect and classify traffic lights, moreover, the output of this model was given to an algorithm that would alert the driver in case the computer detects that the driver might cross the red light, this feature could be incorporated in current Advanced Driver Assistance Systems (ADAS) systems in a car. Additionally, a camera-based method was proposed to filter out the irrelevant traffic lights in a scene. To train the model, transfer learning based on the You Only Look Once version 4 (YOLOv4) algorithm was employed, and the LISA dataset - which includes images from streets in California, USA - was used for the training process, as for the testing process different images were used including ones from Cairo, Egypt. The model trained in this paper reached a mAP score of 92.16%.
DeepASPeer

Peer review is the widely accepted mechanism to determine the quality of scientific work. Even though peer-reviewing has been an integral part of academia since the 1600s, it frequently receives criticism for the lack of transparency and consistency. Even for humans, predicting the peer review outcome is a challenging task as there are many dimensions and human factors involved. However, Artificial Intelligence (AI) techniques can assist the editor/chair anticipate the final decision based on the reviews from the human reviewers. Peer review texts reflect the reviewers' opinions/ sentiments on various aspects (e.g., novelty, substance, soundness, etc.) of the paper concerning the research in the paper, which may be valuable to predict a manuscript's acceptance or rejection. The exact types and number of aspects could vary from one to the other venue (i.e., the conferences or journals). Peer review texts, however, which often contain rich sentiment information about the reviewers and, therefore, their overall opinion of the paper's research, can be useful in predicting a manuscript's acceptance or rejection. Here in this work, we study how we could take advantage of aspects and their corresponding sentiment to build a generic controllable system to assist the editor/chair in determining the outcome based on the reviews of a paper to make better editorial decisions. Our proposed deep neural architecture considers three information channels, including reviews, review aspect category, and its sentiment, to predict the final decision. Experimental results show that our model can achieve up to 76.67% accuracy on the ASAP-Review dataset (Aspect-enhanced Peer Review) consisting of ICLR and NIPS reviews considering the sentiment of the reviews. Empirical results also show an improvement of around 3.3 points while aspect information is added to the sentiment information1.
An Introduction to Federated Computation

Federated Computation is an emerging area that seeks to provide stronger privacy for user data, by performing large scale, distributed computations where the data remains in the hands of users. Only the necessary summary information is shared, and additional security and privacy tools can be employed to provide strong guarantees of secrecy. The most prominent application of federated computation is in training machine learning models (federated learning), but many additional applications are emerging, more broadly relevant to data management and querying data. This tutorial gives an overview of federated computation models and algorithms. It includes an introduction to security and privacy techniques and guarantees, and shows how they can be applied to solve a variety of distributed computations providing statistics and insights to distributed data. It also discusses the issues that arise when implementing systems to support federated computation, and open problems for future research.
Data Sensemaking in Self-Tracking: Towards a New Generation of Self-Tracking Tools

Human-Computer Interaction (HCI) researchers have been increasingly interested in investigating self-trackers’ experience with self-tracking tools (STT) to get meaningful insights from their data. However, the literature lacks a coherent, integrated and dedicated source on designing tools that support self-trackers’ sensemaking practices. To address this, we carried out a systematic literature review by synthesizing the findings of 91 articles published before 2021 in HCI literature. We identified four data sensemaking modes that self-trackers go through (i.e., self-calibration, data augmentation, data handling, and realization). We also identified four design implications for designing self-tracking tools that support self-trackers’ data sensemaking practices (i.e., customized tracking experience, guided sensemaking, collaborative sensemaking, and learning sensemaking through self-experimentation). We provide a research agenda with nine directions for advancing HCI studies on data sensemaking practices. With these contributions, we created an analytical information source that could guide designers and researchers in understanding, studying, and designing for self-trackers’ data sensemaking practices.
Understanding employees’ information security–related stress and policy compliance intention: the roles of information security fatigue and psychological capital

Purpose This study aims to explore the emotion-based mediator of information security fatigue in the relationship between employees’ information security–related stress (SRS) and information security policy (ISP) compliance intention and the effects of psychological capital (PsyCap) on relieving SRS and promoting compliance. Design/methodology/approach The authors tested a series of hypotheses by applying partial least squares–based structural equation modeling to survey data from 488 employees in Chinese enterprises. Findings The results suggest that the relationship between SRS and ISP compliance intention is fully mediated by information security fatigue. Employees’ SRS promotes their information security fatigue, which reduces their intention to follow ISPs. In addition, employees with high PsyCap may experience low levels of SRS and information security fatigue, which promotes their willingness to comply with ISPs. Originality/value This study extends knowledge by introducing information security fatigue and PsyCap to the field of information security management, and it calls attention to the effects on information security behaviors of employee emotions and positive psychological resources in an organization. The authors reveal the emotion-based mediating effect of information security fatigue and the positive influence of PsyCap in information security management.
Exploration and Research on Smart Sports Classrooms in Colleges in the Information Age

Smart classrooms, made possible by the growing use of Internet information technology in the sphere of education, as one of the important foundations for the realization of smart education, have become the current hot direction of the development of educational information innovation and intend to propose some ideas and directions for smart sports teaching research in IA colleges and universities. The smart classroom is an intelligent and efficient classroom created by the "Internet +" way of thinking and the new generation of information technologies such as big data and cloud computing. This article puts forward the exploratory research methods of smart sports classrooms in colleges and universities in the IA, methods, such as document retrieval, expert interviews, questionnaire surveys, and practical research, and field investigation method, which are used in the exploration and research of college smart sports classrooms in the IA experiment. According to the findings of this study, 96.34 percent of students have a positive attitude toward the smart sports classroom teaching model, which is favorable to the growth of smart sports classroom teaching.
Music Art Teaching Quality Evaluation System Based on Convolutional Neural Network

With the rapid growth of music and art education in colleges and universities today, the development of associated teaching quality assessment (TQE) is still in its infancy. In truth, most modern music and art education has yet to build a rigorous and appropriate evaluation system based on actual classroom teaching quality. Simply adopting classroom TQE indicators and approaches from other disciplines would unavoidably lead to formalization of music TQE findings in some schools and institutions. It has no bearing on evaluation, feedback, or advancement. Therefore, this paper uses the superior performance of neural network to solve nonlinear problems and constructs a music art TQE method based on convolutional neural network (CNN). The completed work is as follows: (1) The basic situation of domestic and foreign research on music art TQE is introduced. Several commonly used TQE methods at home and abroad are analyzed, and the CNN evaluation method is comprehensively introduced. (2) The principle and network structure of CNN are expounded, and a TQE system conforming to music art is constructed. (3) The final experimental results reveal that the CNN model has higher accuracy and better performance than the BP neural network when using the trained CNN, TQE model to conduct tests.
Analysis of Requirements for the Digital Competence of a Future Teacher

&lt;p style="text-align: justify;"&gt;The article discusses the topical issue of a model of digital competencies for a future teacher. The analysis of the composition and structure of the most relevant models of digital competencies of a citizen and a modern teacher is carried out. In addition, the article reveals approaches to the formation of the content of digital competence, and provides the results of an empirical study, which consists in analyzing the results of a survey of practicing teachers and teacher-training students in order to identify the most demanded digital competencies of a future teacher. The article substantiates the authors’ content of the competence of a future teacher, a university student. The purpose of this article is to develop a theoretical model of digital competence of a future teacher, taking into account the dynamic technologisation of the modern world and the peculiarities of Russian education, based on an analysis of approaches to determining the content of its digital competencies. According to the analysis of studies, the issue of teachers’ digital competence is not sufficiently disclosed. Numerous studies on digital competencies of a person, teacher, etc. do not fully solve the problem of assessing the digital competence of future teachers.&lt;/p&gt;
Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation

In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized.
OPPORTUNITIES TO USE MOBILE GIS APPLICATIONS IN THE FORMATION OF TOURIST AND LOCAL LORE COMPETENCIES IN STUDENTS: CASE STUDY IN ALMATY, KAZAKHSTAN

Since the use of mobile GIS-applications in the formation of tourist and local lore competencies of students affects the knowledge and professional competence of future teachers, it is very important to first determine the attitude of teachers and professors to the use of mobile GIS-applications and assess the level of use of mobile applications by students. Therefore, this article provides for the possibility of using mobile GIS applications in the organization of tourist and local lore activities of students of the educational program "Geography". The effectiveness of organizing tourist and local lore events using mobile GIS applications was determined by conducting interviews and questionnaires. The survey consisted of two parts, and a total of 72 students took part in it voluntarily. In the course of the study, we studied the formation of tourist and local lore competencies from mobile GIS applications as a result of the study: 1) "Road navigation" from mobile GIS applications - 2GIS (79.1%); 2) "For viewing and studying" from mobile applications - ArcGIS QuickCapture (56.9%); 3) Google planet Earth "Virtual globe" (52.8%); 4) based on the mobile applications "Cartography and Navigation", we determined the efficiency of using the GIS4MOBILE-x (41.7%) and 5) the City bus for "GPS monitoring" (100%). In this regard, we are confident that the use of these mobile applications will be effective in organizing tourist and local lore events. The use of these technologies in teaching makes it possible to update educational approaches, introduce new pedagogical technologies and form competencies.
Reivew of Light Field Image Super-Resolution

Currently, light fields play important roles in industry, including in 3D mapping, virtual reality and other fields. However, as a kind of high-latitude data, light field images are difficult to acquire and store. Thus, the study of light field super-resolution is of great importance. Compared with traditional 2D planar images, 4D light field images contain information from different angles in the scene, and thus the super-resolution of light field images needs to be performed not only in the spatial domain but also in the angular domain. In the early days of light field super-resolution research, many solutions for 2D image super-resolution, such as Gaussian models and sparse representations, were also used in light field super-resolution. With the development of deep learning, light field image super-resolution solutions based on deep-learning techniques are becoming increasingly common and are gradually replacing traditional methods. In this paper, the current research on super-resolution light field images, including traditional methods and deep-learning-based methods, are outlined and discussed separately. This paper also lists publicly available datasets and compares the performance of various methods on these datasets as well as analyses the importance of light field super-resolution research and its future development.
Multi-scale Feature Extraction and Fusion for Online Knowledge Distillation

Online knowledge distillation conducts knowledge transfer among all student models to alleviate the reliance on pre-trained models. However, existing online methods rely heavily on the prediction distributions and neglect the further exploration of the representational knowledge. In this paper, we propose a novel Multi-scale Feature Extraction and Fusion method (MFEF) for online knowledge distillation, which comprises three key components: Multi-scale Feature Extraction, Dual-attention and Feature Fusion, towards generating more informative feature maps for distillation. The multi-scale feature extraction exploiting divide-and-concatenate in channel dimension is proposed to improve the multi-scale representation ability of feature maps. To obtain more accurate information, we design a dual-attention to strengthen the important channel and spatial regions adaptively. Moreover, we aggregate and fuse the former processed feature maps via feature fusion to assist the training of student models. Extensive experiments on CIFAR-10, CIFAR-100, and CINIC-10 show that MFEF transfers more beneficial representational knowledge for distillation and outperforms alternative methods among various network architectures.
PCNsim: A Flexible and Modular Simulator for Payment Channel Networks

Payment channel networks (PCN) enable the use of cryptocurrencies in everyday life by solving the performance issues of blockchains. Nevertheless, the main implementations of payment channel networks lack the flexibility to test new proposals that can address fundamental challenges, such as efficient payment routing and maximization of the payment success rate. In this demo paper, we propose PCNsim, an open-source simulator based on OMNeT++, which fully reproduces the default behavior of a payment channel network. We build the simulator in a modular architecture that allows easy topology/workload customization and automates result visualization. The core mechanism of PCNsim implements the specifications of the Lightning Network. We evaluate our proposal with a dataset of credit card transactions in a scale-free topology and show that it successfully demonstrates the difference between two routing methods in different setups.
Does openness increase vulnerability to digital frauds? Observing social media digital footprints to analyse risk and legal factors for banks

Purpose The purpose of this study is to find out if there is a sequence or pattern to digital frauds and whether the openness quotient of any individual has a meaningful role to play in them becoming victims of digital frauds. The legal and regulatory angle of digital frauds and relief measures for social media openness and frauds are discussed to examine the connection between openness, identity data, visibility, vulnerability, digital frauds and social media sharing of information. Design/methodology/approach This study is empirical and investigative research. Primary data was collected via questionnaires circulated among participants from various age groups to understand the implications of different demographic factors such as age, websites used, profiles created and data shared on the internet, social media and e-commerce websites. Findings The results exhibit that the openness factor does affect vulnerability and has an influence on the risk and legal component of inclination towards digital frauds. Practical implications This research study results in bridging the gap by increasing the level of understanding and awareness in users toward achieving proactive and regulated behaviour and comprehending the violation of various acts of digital frauds and its implications, especially in the Indian banking sector. Originality/value This study will be beneficial to all individuals alike in understanding the implications of excessive openness toward digital platforms and evaluating generic and legal solutions to avoid becoming victims of digital fraud.
Quasi-Cryptographic Methods in the Management of Educational Tasks

A method of organizing the educational process and knowledge control in large groups of students by issuing assignments from a large (almost unlimited) sample of options is proposed. The method of generating variants based on the last name, first name, patronymic of the student is shown. It is shown that a suitable sample of variants should be at least the square of the size of the stream/group. The possibility of accelerated verification of tasks with the help of functional connections in the space of answers, i.e., peculiar keys known to the teacher, is demonstrated. Separate examples of tasks important both in the methodological aspect and in terms of application in the spectrum of disciplines of the departments of applied mathematics are given.
Parrondo's paradox in quantum walks with three coins

Parrondo's paradox refers to the apparently paradoxical effect whereby a certain combination of biased random walks displays a counterintuitive reversal of the bias direction. We show that Parrondo's paradox can occur not only in the case of one-dimensional discrete-time quantum walks with a deterministic sequence of two quantum coins but also in the case of one-dimensional discrete-time quantum walks with a deterministic sequence of three quantum coins. Moreover, we show how Parrondo's paradox affects the time evolution of quantum entanglement for such quantum walks.