Reviewerly/exHarmony huggingface dataset:
Teaching neuroscience in a political theory course

Political theorists may investigate the ocean flows of politics with the help of the natural sciences. In this chapter, I consider how political theorists may draw upon neuroscience to address the problem of in-your-face politics. In the Constitution of Poland, Rousseau intimates that somatic habits are the soil from which an ethico-political sensibility grows. In this chapter, I engage recent neuroscience research to identify habits that may facilitate civic friendship, including walking outdoors, dancing, eating foods with nutrients that reduce inflammation, smelling calming odors like lavender, and sleeping soundly.
Artificial Intelligence and Phenomenological Ethnography

Abstract While phenomenological ethnography is primarily concerned with the structure of an individual subject’s experience of the sociocultural world, the contents of that experience itself remain a vital concern and problem for phenomenological ethnography given the several problems any ethnographer faces in trying to access what their subjects experience. This chapter describes an experimental methodology in which the ethnographer uses an artificially intelligent, improvising, musical social interactant to address this problem by staging encounters between performers active in free improvisation and these nonhuman virtual systems. Results of this method illustrate its efficacy as a novel means of understanding human social interaction as a distinct form of culture as experience.
Topical issues of secondary vocational education

The issues of development of secondary vocational education in railway transport are considered. Options for solving problem situations in the forced organization of distance learning in the context of the COVID-19 pandemic are given. Examples are shown of the distance learning of the duty oﬃcer at the railway station on the simulator in the traﬃc control laboratory.The relevance of the publication is associated with an active discussion of the prospects for vocational education and the successive stages of the process: from initial vocational education to the training of highly qualiﬁed personnel. The results of observations and primary examination of learning outcomes based on distance technologies in new forms of interaction with students both horizontally and vertically (ﬂipped class, joint implementation of course projects and ﬁnal qualifying works, etc.) are shown. The importance of the prospect of developing targeted training for the training of workers, taking into account the requirements of a particular transport company, is explained. Cognitive interest in the development of professional competencies is proposed to be developed through mentoring and volunteer initiatives of associations of young professionals at enterprises. The professional orientation of the educational process is traditionally enhanced by solving speciﬁc production problems, analyzing speciﬁc situations, conducting business games at the enterprise, etc. The issues of organizing championships according to WorldSkills Russia standards and conducting demonstration exams in the process of motivating professional activity in secondary vocational education.
Comprehensive Survey and a Proposal for Blockchain-Enabled Supply Chain Management

Supply chain management (SCM) involves the whole process starting from supplying the raw material to the manufacturer, to the delivery of the final product. But, tracking each step of the process is becoming difficult taking into account its authenticity, quality, traceability and accountability. There is no proper record for the stakeholders to verify these parameters. Blockchain being one of the most popular and innovative technologies, can solve the issue by storing all this information in a decentralized, immutable and traceable manner. This allows all the stakeholders of the system to be up-to-date ensuring privacy. This paper proposes a blockchain-enabled SCM for the secure trading of militant weapons and goods for governments across the globe. The work also provides a comprehensive literature survey of papers in the same domain and a brief introduction and working of blockchain technology to point future research towards the same direction.
Payment and settlement systems

Since bitcoin's peer-to-peer payment was the first popular blockchain application, blockchain is often regarded as a technology for financial services. A number of blockchain applications have been developed for the payment and settlement systems. Indeed, payment and settlement have become a popular blockchain use case since this technology enables new ways of managing money and facilitating payments with ease and in more efficient way. This chapter offers an analysis of how blockchain can transform payment and settlement systems by facilitating the implementation of clearing, transfer of funds and execution of the final settlement in order to provide a payment to a beneficiary. It provides a perspective on how blockchain solutions can address the existing challenges such as slow processing times, high fees and the lack of transparency in cross-border transfers. This chapter provides a perspective on how substantial efficiency gains can be achieved by using blockchain for digital assets' settlement process by decreasing transaction costs and reducing settlement risk as well as counterparty risk (the risk that a party can default on its obligation). It presents a variety of examples of payment and settlement systems being transformed by blockchain such as remittances, letter of credit and export transactions, trade finance, and supply chain finance. Moreover, several blockchain-based solutions with promising potential applications to increase the bankability of unbanked people are currently in proof-of-concept prototype and experiment phases. This chapter also reviews these applications.
Combining with other technologies to amplify blockchains value creation

This chapter provides an analysis of how many of the trust-related challenges in in inter-organizational relations can be overcome by combining blockchain with other advanced technologies, mainly so called the Fourth Revolution technologies, also known as the Fourth Industrial Revolution (4IR) technologies. Specifically, it gives special consideration to the potential of significant added value as well as complementary and synergistic effects by combining blockchain with technologies such as artificial intelligence, Internet of things, analytical fingerprinting, satellite imagery, machine vision and digital twins. This chapter also explains how blockchain has been a missing link in current technological developments.
<scp>Virtuoso</scp> : Energy- and Latency-aware Streamlining of Streaming Videos on Systems-on-Chips

Efficient and adaptive computer vision systems have been proposed to make computer vision tasks, such as image classification and object detection, optimized for embedded or mobile devices. These solutions, quite recent in their origin, focus on optimizing the model (a deep neural network) or the system by designing an adaptive system with approximation knobs. Despite several recent efforts, we show that existing solutions suffer from two major drawbacks. First , while mobile devices or systems-on-chips usually come with limited resources including battery power, most systems do not consider the energy consumption of the models during inference. Second , they do not consider the interplay between the three metrics of interest in their configurations, namely, latency, accuracy, and energy. In this work, we propose an efficient and adaptive video object detection system— Virtuoso , which is jointly optimized for accuracy, energy efficiency, and latency. Underlying Virtuoso is a multi-branch execution kernel that is capable of running at different operating points in the accuracy-energy-latency axes, and a lightweight runtime scheduler to select the best fit execution branch to satisfy the user requirement. We position this work as a first step in understanding the suitability of various object detection kernels on embedded boards in the accuracy-latency-energy axes, opening the door for further development in solutions customized to embedded systems and for benchmarking such solutions. Virtuoso is able to achieve up to 286 FPS on the NVIDIA Jetson AGX Xavier board, which is up to 45× faster than the baseline EfficientDet D3 and 15× faster than the baseline EfficientDet D0. In addition, we also observe up to 97.2% energy reduction using Virtuoso compared to the baseline YOLO (v3)—a widely used object detector designed for mobiles. To fairly compare with Virtuoso , we benchmark 15 state-of-the-art or widely used protocols, including Faster R-CNN (FRCNN) [NeurIPS’15], YOLO v3 [CVPR’16], SSD [ECCV’16], EfficientDet [CVPR’20], SELSA [ICCV’19], MEGA [CVPR’20], REPP [IROS’20], FastAdapt [EMDL’21], and our in-house adaptive variants of FRCNN+, YOLO+, SSD+, and EfficientDet+ (our variants have enhanced efficiency for mobiles). With this comprehensive benchmark, Virtuoso has shown superiority to all the above protocols, leading the accuracy frontier at every efficiency level on NVIDIA Jetson mobile GPUs. Specifically, Virtuoso has achieved an accuracy of 63.9%, which is more than 10% higher than some of the popular object detection models, FRCNN at 51.1% and YOLO at 49.5%.
Water-obstacle segmentation based on Swin transformer and sea-sky-line detection

Unmanned surface vehicles (USV) has been widely used in the fields of surface quality monitoring and dangerous water exploration, among which the detection and segmentation of obstacles is an important condition for USV to realize autonomous obstacle avoidance. Recently, most of the segmentation methods of water surface obstacles are based on CNN, but due to the limited receptive field of CNN, most of the methods do not achieve satisfactory results in the segmentation of obstacle edge details, sea-sky segmentation and surface reflection. To solve the above problems, we propose a water surface obstacle segmentation network WSSS with sea-sky-line detection as the prior knowledge for feature fusion, Swin Transformer as the dominant backbone and feature pyramid network (FPN) as the feature fusion network. In this network, sea-sky-line detection is used as a preprocessing module to increase the receptive field through the self-attention mechanism and sliding window, and feature extraction is enhanced by FPN, which makes the network segmentation of obstacle edge and water Sky more detailed and reduces the influence caused by reflection and weather. Extensive experiments on the USV datasets MaSTr1325 and Modd2 show that the proposed method WSSS outperforms the current state-of-the-art methods for water surface obstacle segmentation.
The Effect of Early Childhood Teachers’ Play Beliefs and Play Teaching Efficacy on the Implementation Level of the 2019 Revised Nuri Curriculum

This study investigates the effect of play beliefs and play teaching efficacy on the execution of the 2019 revised Nuri_curriculum execution. A survey was conducted on 223 teachers of kindergartens/nurseries in Seoul and Gyeonggi-do, and data were analyzed using one-way-ANOVA, Scheffe-post-hoc analysis, Pearson correlation, multiple regression, and hierarchical regression. First, at the age of 30s, graduate school or higher education, and early childhood teachers with a minimum of two Nuri_curriculum training sessions showed high play teaching efficacy and revised Nuri_curriculum execution levels. Second, 'play-centered' beliefs had a significant relationship with the execution of the curriculum, but 'learning-centered' beliefs did not. With all play teaching efficacy and its sub-variables significantly related to the curriculum execution, higher play teaching efficacy led to higher execution of organization/operation, teaching/learning and assessment of the revised Nuri_curriculum. Third, the factor best explaining the execution of the revised Nuri_curriculum execution was 'beliefs in play teaching efficacy' (47%), while 'play-centered' beliefs also had significant effect (36%). This study can be a basis to raise the level of the execution of the 2019 revised Nuri_curriculum execution as it found 'play-centered' beliefs and play teaching efficacy as the predictor variables for such.
Distribution grid fault diagnosis based on SR-GRU and fault indicator

Efficient utilization of massive recorded data from fault indicators in distribution grid is beneficial to improve fault diagnosis. In this paper, the state detection and fault diagnosis of distribution grid based on fault indicator data were realized by combining high-dimension statistical analysis and artificial intelligence methods. Spectral residual algorithm was used to obtain saliency map of fault recorded data in rolling time windows, which can filter out useless background information. By training the distribution grid fault diagnosis model based on saliency map, a GRU network with high real-time and accuracy was obtained, and the overall accuracy of fault diagnosis reached 98%. The distribution grid fault diagnosis based on SR-GRU can not only make efficient use of massive fault indicator data, but also improve the universality and intelligence of the distribution grid fault diagnosis.
Genetic Programming + Multi-Agent Reinforcement Learning: Hybrid Approaches for Decision Processes

This paper details progress within the Multi-Agent Reinforcement Learning (MARL) research area with application to agent decision processing in complex battle-space scenarios, including air, surface, sub-surface, and space domains. We implement a Double Deep Q-Network (DDQN) with Minimax Q-Learning in order to model simultaneous, zero-sum, two team engagements involving multiple Blue agents &#x0026; Red opponents. This is a game theoretic approach that models both ally and opponent policies while viewing a battle as a Multi-Stage Markov Stochastic Game (MSMSG). We contrast our agent with a DDQN &#x002B; Traditional Q-Learning algorithm in a single stage 2v1 battle scenario with mixed optimal strategies. In order to help mitigate learning sensitivities and local optima convergence, we implement a Genetic Programming (GP) algorithm, which outperforms both the Minimax Q-Learning and Traditional Q-Learning DDQN agents trained using traditional stochastic gradient descent in a dynamic 1v1 battle. Lastly, we create a hybrid approach that combines stochastic gradient descent learning (Minimax Q-Learning) and gradient-free learning (GP) and apply our hybrid approach within the StarCraft II (SC2) 3m map, which simulates a 3v3 battle. We contrast this hybrid MARL approach with another state-of-the-art MARL method (QMIX) for the SC2 3m combat scenario.
An Architectural Approach for Enabling and Developing Cooperative Behaviour in Diverse Autonomous Robots

The paper introduces an architecture for robot-to-robot cooperation which takes into consideration how situational context augmented with peer modeling fosters cooperation opportunity identification and cooperation planning. The presented architecture allows developing, training, testing, and deploying dynamic cooperation solutions for diverse autonomous robots using ontology-based reasoning. The architecture operates in three different worlds: in the Real World with real robots, in a 3D Virtual World by emulating the real environments and robots, and in an abstract Block World that enables developing and studying large-scale cooperation scenarios. We describe an assessment practice for our architecture and cooperation procedures, which is based on scenarios implemented in all three worlds, and provide initial results of stress testing the cooperation procedures in the Block World. Moreover, as the core part of our architecture can operate in all the three worlds, development of the robot cooperation with the architecture can regularly accommodate insights gained from experimenting and testing in one world as improvements in another. We report our insights from developing the architecture and cooperation procedures as additional research outcomes.
How to Identify Programming Skills from Source Code?

Both open-source and proprietary software systems have become increasingly complex. Despite their growing complexity and increasing size, software systems must satisfy strict release requirements that impose quality, putting significant pressure on developers. Therefore, the success of software projects is dependent on the identification and hiring of qualified developers to build a solid and cohesive team with different programming skills. Our main goal is to develop and evaluate a method able to compute programming skills from source code analysis. Our method uses software metrics such as Changed Files and Changed Lines of Code, to compute the skills. Our results showed that our method is able of identifying programming skills of the developers about mainly libraries used, programming languages, and profile concerning back-end &amp; front-end and unit test.
Reinforcement Learning Agent for Path Planning with Expert Demonstration

The problem of path planning is a challenging task for mobile robots. A practical example can be seen in the robots commonly employed in warehouses: they must navigate to pick up goods and move them to certain locations. Therefore, the robot needs a method of moving from an initial location in the warehouse to a final location repeatedly. In this paper, we propose a technique that allows a robot to path plan in generalized environments, from different starting and goal locations. The method is based on a graph representation of the environment, and is capable of finding the shortest path between two points in the environment. In order to generalize appropriately, we show that a neural network is able to effectively choose the correct actions to take at each time step in the path planning problem.
Students’ Opinions on the Functions and Usefulness of Communication on Whatsapp in the Efl Higher Education Context

In a preparatory English as a foreign language (EFL) context at a Saudi University, the WhatsApp application is observed as being one the fastest growing social media platforms, and one with a massive potential of being used in communication for academic as well as social purposes. This study seeks to explore students’ opinions and perceptions on the usage and functions of WhatsApp communication within the EFL context. Findings from a descriptive survey analysis have shown that WhatsApp is a popular communication platform amongst the students, and therefore possibly has the potential of facilitating language learning in this EFL context. However, WhatsApp is currently mostly utilized as a platform for inquiries and announcements with minimum preset language learning goals. The present study aims to inform feasible recommendations regarding the presence and functionality of the WhatsApp platform, owing to the need to utilize such popular and readily used program in making EFL learning processes efficient and effective.
A Proactive Defense Strategy Against SGX Side-channel Attacks via self-checking DRL in the Cloud

Intel software guard extensions (SGX) technology allows cloud vendors to provide customers with an independent and trusted execution environment (TEE). It protects critical data confidentiality and integrity from malicious software. However, more and more SGX side-channel attacks have appeared, which seriously undermine the confidence of tenants in cloud security. The related research focuses on system hardware and SGX compiler solutions for specific attacks, which also has difficulties in deployment. Differently, we propose an intelligent-driven proactive defense strategy, which is based on live migration. To the best of our knowledge, this is the first proactive defense against SGX side-channel attacks. We adopt the Markov decision process to solve the migration programming problem. The innovative deep reinforcement learning (DRL) solves problems of the unknown state transition probability and large machine load states, which is called self-checking proximal policy optimization (SPPO). It changes the reward pattern, improving the convergence speed and stability of DRL. In prototype experiments, we deploy the strategy in the OpenStack platform agilely to prove the defense performance and low virtual machine costs.
ReNego: A Requirements Negotiation Tool based on Voting of Social Choice Theory

Requirements elicitation and analysis is an im-portant activity in the requirements engineering (RE) process. In this activity, requirements engineers work with clients and end-users to find more about the application domain, what functionalities the system should provide, constraints, and the required performance and efficiency of the system. Requirements Negotiation (RN) is an important process activity in the elicitation and analysis process. RN is concerned with finding and resolving requirements conflicts through negotiation. In this paper, we describe ReNego, a requirements negotiation tool based on voting of social choice (SC) theory. We discuss the key features and functionalities of the tool and its potential improvements for future work.
Efid: Edge-Focused Image Denoising Using a Convolutional Neural Network

In this paper, we propose an edge-focused image denoising convolutional neural network for the restoration of noisy images corrupted with additive white Gaussian noise (AWGN). First, the edge information for an input image is obtained for each RGB channel using the simple Laplacian edge operator on a smoothened image using the smoothen mask. Second, residual convolutional blocks, where each block comprises two parallel depth layers (one for image channel and the other for respective edge channels), perform image and edge processing. Finally, the processed image and edge features are mixed and mapped using a single convolutional layer into the restored image. Our proposed parallel image and edge processing blocks can recover edges and fine structures while smoothing out noise. In addition, our proposed network is efficiently designed such that the total number of weight parameters can be considerably reduced compared with conventional methods. Experimental results show that the proposed model is more effective in preserving textures and edges while removing noise and achieves up to 0.8 dB higher PSNR and 0.05 higher SSIM on a real image dataset than conventional methods.
Ciber educación

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.