Reviewerly/exHarmony huggingface dataset:
Building Next Generation IoT Infrastructure for Enabling M2M Crypto Economy

As Bitcoin and other cryptocurrencies are becoming part of our lives, there is a growing interest to enable using them in our daily lives even for micropayments. This interest stems from many factors including privacy, convenience and overhead/fraud that comes with credit cards. In this regard, Internet of Things (IoT) devices can also benefit from this feature for enabling touchless payments for users. However, there is even a bigger opportunity there considering the nature and diversity of very large-scale unattended IoT devices. The integration of any IoT device with blockchain including cryptocurrencies and smart contracts can trigger a machine-to-machine (M2M) economy revolution by streamlining business among IoT devices. Under such a future business model, IoT devices can autonomously request a service and make a payment in return. Such a large-scale ecosystem should rely on various components thus requiring a paradigm shift on the current design and understanding of the IoT systems. In particular, decentralized architecture of blockchain with cryptocurrency and smart contract capability can be a key enabler. In this vision paper, we advocate the need and necessary elements of a M2M crypto economy infrastructure and investigate the role of blockchain in realizing this vision. We specifically focus on the advantages and challenges of blockchain-based systems along with the existing proposed solutions. We then offer several future directions in creating such a M2M economy.
Smooth Transition of University Members to Multifactor Authentication

In recent years, multifactor authentication becomes the mainstream to prevent illegitimate access especially caused by compromised passwords. In this study, we design and build an application system for Shimane University members to make transition to multifactor authentication. For smooth transition, we set requirements including that the transition should be triggered by each of the members at any time. After four months of operation, it is shown that the system works successfully.
A New Sustainable Hybrid Software Development Methodology: FIRAT-UG

Software development methods are one of the topics that many researchers have worked on. Methods such as Waterfall, Agile and Scrum are used to develop important software. All of these proposed methods are for software developers to produce manageable software during software development. However, it is important to ensure the continuity of human resources lossless and sustainable as well as managing software development phases. A new software development method is proposed in this study. With this method, the management of the software development phase will be a model in which human resources are managed integrated by participating in the work. With this model, it is aimed to minimize the potential risks of software development in this sector where is a lack of mobility and scarcity of human resource. The aim is to introduce a new sustainable software development methodology. This model was implemented under the coordination of F.U. Digital Transformation and Software Office and achieved successful results within 2 months. The model has reduced the human resources problems by %66.
An Efficient Application of Neuroevolution for Competitive Multiagent Learning

Multiagent systems provide an ideal environment for the evaluation and analysis of real-world problems using reinforcement learning algorithms. Most traditional approaches to multiagent learning are affected by long training periods as well as high computational complexity. NEAT (NeuroEvolution of Augmenting Topologies) is a popular evolutionary strategy used to obtain the best performing neural network architecture often used to tackle optimization problems in the field of artificial intelligence. This paper utilizes the NEAT algorithm to achieve competitive multiagent learning on a modified pong game environment in an efficient manner. The competing agents abide by different rules while having similar observation space parameters. The proposed algorithm utilizes this property of the environment to define a singular neuroevolutionary procedure that obtains the optimal policy for all the agents. The compiled results indicate that the proposed implementation achieves ideal behaviour in a very short training period when compared to existing multiagent reinforcement learning models.
Envisioning Educational Product User eXperience Through Participatory Design Practice

Due to COVID-19 (Coronavirus Disease - 19), most schools have adopted remote or mixed methods to teach; therefore, students need new types of educational products to achieve a high-quality learning experience. There are many difficulties and obstacles to taking classes online. More interactions and formats need to be explored for students to absorb as much classroom knowledge as possible. The research team conducted a design workshop. Thirty-eight psychology and applied psychology students participated in this workshop using different user research methods, such as user journey maps and how-to methods. They explored educational products in five scenarios: taking physical education classes, giving online learning feedback, conducting teamwork, taking an online examination, and studying in the dormitory. Each group proposed design solutions to solve design problems in a particular context. For the 56 design problems, the participants identified 548 design solutions. The facilitator helped the group select six or seven design challenges with higher priorities. All the research data were collected, and the jobs-to-be-done (JTBD) theory was used to analyze users’ needs for future educational products. The users’ needs were summarized into user need clusters. This study has identified seven primary user needs and 20 sub-needs in five scenarios. The insights gained from this study may be of assistance to UX designers and UX researchers to obtain a new perspective on scenarios.
LABORATORY TRAINING IN PHYSICS IN SECONDARY SCHOOLS A TOOL TO INCREASE STUDENT EDUCATIONAL ACTIVITY

This article describes the current state of laboratory classes in physics in secondary schools by grades and years, the shortcomings and problems observed in this process, and analytical comments on their solutions.
On Asymptotic Efficiency of the M2M4 Signal-to-Noise Estimator for Deterministic Complex Sinusoids

The moment-based M2M4 signal-to-noise (SNR) estimator was proposed for a complex sinusoidal signal with a deterministic but unknown phase corrupted by additive Gaussian noise by Sekhar and Sreenivas. The authors studied its performances only through numerical examples and concluded that the proposed estimator is asymptotically efficient and exhibits finite sample super-efficiency for some combinations of signal and noise power. In this paper, we derive the analytical asymptotic performances of the proposed M2M4 SNR estimator, and we show that, contrary to what it has been concluded by Sekhar and Sreenivas, the proposed estimator is neither (asymptotically) efficient nor super-efficient. We also show that when dealing with deterministic signals, the covariance matrix needed to derive asymptotic performances must be explicitly derived as its known general form for random signals cannot be extended to deterministic signals. Numerical examples are provided whose results confirm the analytical findings.
User Experience Best Practices for Human-Robot Interaction

User experience (UX) design of human-robot interaction (HRI) is an emerging practice [1]. Best practices for this discipline are actively evolving as robotics expands into commercial markets. As is typical of emerging technologies, the technology itself takes center stage, continuing to present challenges to proper functioning as it is tested in real world applications. Also, deployment comes at a high price until the market is competitive enough to drive hardware and development prices down. All these aspects preclude an emphasis on UX design, until the technology and associated market reaches a tipping point and good user experience is in demand. If robots continue to be deployed at rates the industry currently predicts, the need for user experience design knowledge and best practices for HRI is eminent. Best practices are a collection of methods, specifically principles, heuristic evaluators and taxonomies [2]. Principles embody high level guidance for design direction and design processes [3]. Heuristics ensure measurable “must have” base-functionality [4–6]. Taxonomies provide a conceptual understanding of possible categories of interactivity [7–9]. This paper focuses on two aspects of best practices, 1.) proposing a robustly user-centric set of emerging technology principles for HRI, which is the area of best practices least explored by the literature, and 2.) proposing a design matrix as a beginning step in addressing the complexity of HRI.
Scalable Object Detection for Edge Cloud Environments

Object detection is an important problem in a wide variety of computer vision applications for sustainable smart cities. Deep neural networks (DNNs) have attracted increasing interest in object detection due to their potential to provide high accuracy detection performance in challenging scenarios. However, DNNs involve high computational complexity and are therefore challenging to deploy under the tighter resource constraints of edge cloud environments compared to more resource-abundant platforms, such as conventional cloud computing platforms. Moreover, the monolithic structure of conventional DNN implementations limits their utility under the dynamically changing operational conditions that are typical in edge cloud computing. In this paper, we address these challenges and limitations of conventional DNN implementation techniques by introducing a new resource-adaptive scheme for DNN-based object detection. This scheme applies the recently-introduced concept of elastic neural networks, which involves the incorporation of multiple outputs within intermediate stages of the neural network backbone. We demonstrate a novel elastic DNN design for object detection, and we show how other methods for streamlining resource requirements, in particular network pruning, can be applied in conjunction with the proposed elastic network approach. Through extensive experiments, we demonstrate the ability of our methods to efficiently trade-off computational complexity and object detection accuracy for scalable deployment.
The Evolutionary Equilibrium of Block Withholding Attack

Abstract Bitcoin is the most famous and the most used cryptocurrency in the world, such that it has received extreme popularity in recent years. However the Bitcoin system is accompanied by different attacks, including the block withholding (BWH) attack. When a miner plays the BWH attack, it will withhold all the blocks newly discovered in the attack pool, damaging the honest miners’ right to obtain the fair reward. In this paper, we consider a setting in which two miners may honestly mine or perform the BWH attack in a mining pool. Different strategy profiles will bring different payoffs, in addition influence the selection of the strategies. Therefore, we establish an evolutionary game model to study the behavior tendency of the miners and the evolutionary stable strategies under different conditions, by formulating the replicator dynamic equations. Through numerical simulations, we further verify the theoretical results on evolutionary stable solutions and discuss the impact of the factors on miners’ strategic choice. Based on these simulation results, we also make some recommendations for the manager and the miners to mitigate the BWH attack and to promote the cooperation between miners in a mining pool.
Research on Visual Cognitive of Museum Guide System Based on Augmented Reality Technology

In order to meet the cognitive needs of visitors to the large and complex space environment in museums and improve the visiting efficiency, a museum guide system based on augmented reality technology is designed. And user’s wayfinding time obtained through experiments in three kinds of guide systems, including traditional graphic, static AR and interactive AR guide systems, are compared and analyzed. Finally, through the method of user questionnaire, the clarity, readability and aesthetics of different guide systems are evaluated. The results show that the interactive AR guide system can effectively improve the efficiency of user wayfinding, and provide users with a good visiting experience.
Robotic Hand Experience

Augmenting rubber hand illusion paradigms extends experimental possibilities and can disentangle human action-perception loops. This chapter presents technical and experimental merits with a focus on tactile feedback and the influence of delays. A pilot study proofs the applicability of the RobHI setup with tactile feedback. Additionally, two extensive experiments analyze the interplay of visuotactile and visuomotor feedback to unravel their contributions to spatial body representation. Both vary motor and tactile feedback availability and delays while measuring the spatial calibration of body coordinates and subjective embodiment experience. The results indicate that both feedback types contribute similarly to embodiment and, interestingly, show that synchronous feedback in one factor can even compensate for asynchronous information from the other. These fundamental insights on multisensory enhancing effects confirm human-in-the-loop experiment potentials.
Модель социона и эволюция общества

The socion is considered as a 16-component model. Roles of informational metabolism types in the social progress loops are described.
Efficient Spatial-Attention Module for Human Pose Estimation

Not only for human pose estimation but also other machine vision tasks (e.g. object recognition, semantic segmentation, image classification), convolution neural networks (CNNs) have obtained the highest performance today. Besides, their performance over other traditional networks is shown by the Attention Module (AM). Hence, this paper focuses on a valuable feed-forward AM for CNNs. First, feed the feature map into the attention module after a stage in the backbone network, divided into two different dimensions, channel and spatial. After that, by multiplication, the AM combines these two feature maps and gives them to the next stage in the backbone. In long-range dependencies (channel) and spatial data, the network can capture more information, which can gain better precision efficiency. Our experimental findings would also demonstrate the disparity between the use of the attention module and current methods. As a result, with the change to make the spatial better, the expected joint heatmap retains the accuracy while decreasing the number of parameters. In comparison, the proposed architecture benefits more than the baseline by 1.3 points in AP. In addition, the proposed network was trained on the benchmarks of COCO 2017, which is now an open dataset.
Proportional Optimization of Traditional and Online Forms of Technical Specialties Studying in Higher Educational

The article describes the experience of using the electronic educational portal for distance traditional education. The scope of application of LMS (LMS - learning management system) for training and retraining of specialists of engineering specialties is presented, advantages and advantages of distance learning are highlighted. The authors discuss the most effective combination of traditional and remote forms of training, give the results of an educational experiment on the use of intermediate tests for in-depth mastering of content. The labour intensity of distance learning in comparison with face-to-face training is assessed. The conclusion is made about the most optimal correspondence of forms of training for technical specialties at the current stage. A pedagogical experiment conducted in two groups of students approximately equal in intellectual opportunities is described.
Quality Analysis Using Motif Discovery in Small Data Sets

Manufacturers are increasingly interested in applying machine and deep learning to automate quality control monitoring to save time and costs. A typical approach is the use of Convolutional Neural Network (CNN) to assess and classify quality issues in images of the production outputs. However, many companies report project challenges and prohibitive computational costs. The challenges arise because of lack of big data required for machine and deep learning model training and lack of data scientists with domain knowledge to build and run complex models. Today manufacturers operate at six sigma levels of quality, and thus have extremely few defect samples. In many cases production changes frequently, and thus the data collection and model training have to be re-done for the new outputs. In this paper we propose two approaches - motif discovery from small sample data and visual motif discovery being simple and intuitive for any engineer to use. We compare them for ease of use and computational efficiency with a Convolutional Neural Network (CNN) and conclude that our proposed approach significantly improves the existing performance, provides fast and reliable training with small samples, and empowers engineers to do it themselves (DIY).
Improving Recommender Systems by Using Time-Weighted Sentiment Analysis

For users, keeping track of the film industry's ever-growing range of movies and TV series is increasingly difficult and exacerbates the paradox of choice: that too many choices hinder decision-making. As a countermeasure, recommender systems can personalize offers and limit the variety of media to what users find relevant. In this paper, we present an approach to improving the accuracy of a collaborative filtering system that applies auction theory by differentiating data into private versus common value information. Incorporating objective, time-weighted information with reference to discounted utility theory, we tested the approach in a prototype using the MovieLens dataset expanded with movie reviews published in The New York Times. By optimizing the algorithm used, we improved recommendation quality by 0.78% compared with the unmodified collaborative filtering approach. With that novel combination of dataset expansion, auction theory, and discounted utility theory, we have narrowed a particular gap in research on recommender systems.
Over-Complete Sparse Coding for Action Similarity Recognition

For the action similarity recognition of videos, a classifying method based on over-completely sparse coding is proposed. Firstly, the Gaussian mixture model is adopted to model the submanifold of features. Then, for each GMM component, the features are sparsely encoded by the corresponding smaller codebook, which not only reduces the requirements of computing, but also raises the ability to describe action. Classifying rate on the ASLAN database is 64.4%, which is 4% percent higher than the benchmark method.
Building Conversational Agents for Military Training: Towards a Virtual Wingman

In military training simulations there is an increasing need for synthetic agents as team members that are capable of replacing human role players. Human-agent interaction using spoken natural language is an important capability for such agents, especially when communication skills are part of training. Interaction technologies such as speech-to-text, text-to-speech, natural language understanding and dialogue management are maturing, though their use in human-in-the-loop simulations is scarce. In this paper we address design challenges for building conversational agents in the domain of military training. In this domain, agents often have to adhere to specific communication standards, protocols and use of jargon. We propose a data-driven design method to tailor conversational agents to a particular application domain, while minimizing human authoring. We demonstrate this method within an existing military training simulation capability and show that agents are able to effectively build shared situational awareness and coordinated their activities in a shared task environment.
Towards a Practice of Palimpsestic Listening

This article invites reflection on the ambiguity of sonic temporalities as the lines between physicality and immediacy become increasingly blurred. Through the notion that digital technologies are haunted by analogic process, I foreground the concept of Palimpsestic Listening to explore the musical qualities and critical resonances of sonic acts and objects in hybrid physical/digital systems that evoke layered temporalities that are ‘historically distinct nonetheless linked’. I also seek to illustrate the significance of engaging practically with this concept by discussing the methods behind my composition D/ta Ro} – A Dialectical Trash Heap , a sound installation that interrogates the relationship between sonic materiality and digital audio processing and how acts of erasure and time-stretching might influence the layering of disparate sound materials.
A Study on Blockchain Technology

The Blockchain is the newest and perspective technology in modern economy. A blockchain is essentially a distributed database of records or public ledger of all transactions or digital events that have been executed and shared among participating parties. It provides provenance, immutability and finality for the transfer of value within a business network. - It enables value exchange in real time, reducing costs and errors. Based on a network consensus approach, whereby trust between the parties involved in a transaction is provided by cryptography. This paper aims to show brief overview of Blockchain Technology, applications and have discussed limitations in the perspective of future research on security in fanatical transaction.
Utilizing Center-Based Sampling Theory to Enhance Particle Swarm Classification of Textual Data

The curse of dimensionality is a well-known problem in data classification. In this paper, the Center-Based Sampling (CBS) theory is utilized to develop a new variant of Particle Swarm Optimization (PSO), dubbed CBS-PSO, capable of dealing with the curse of dimensionality problem in text classification. More specifically, the CBS is exploited to equip PSO with two specialized mechanisms to attract the search toward the center region of the search space. The first mechanism estimates the coordinates of the center point of the search space using Rocchio Algorithm (RA), whereas the second mechanism uses the RA-based estimation to generate informed particles, located at the center region, and incorporate them in the swarm to gradually attract the search for the optimal classifiers toward this promising region. The performance of the CBS-PSO is evaluated against three Machine Learning (ML) approaches on three classification tasks of textual datasets from UC Irvine ML repository. The results indicate that the CBS-PSO can be regarded as a very competitive and promising text classifier with much space for improvement.
Perceptual Evaluation of Low-light Image Enhancement Algorithms

Images taken in low-light environment often face with low dynamic range or color shift caused by lack of illumination. Although many algorithms have been proposed to enhance these images, not much effort has been made on quality assessment of these enhancement results. In our work, we built a database which contains 30 low-light images (both outdoor and indoor scene included) and enhanced images processed by 11 enhancement algorithms. We conducted a subjective experiment based on this database. We found that no algorithm can behave best with images in all situations. Generally, learning-based methods behave better. Further, we also did objective assessment on the database.