Reviewerly/exHarmony huggingface dataset:
Smart Contracts, Real-Virtual World Convergence and Economic Implications

Smart Contracts (SCs) are usually defined as contracts that are instantiated in computer-executable code that automatically executes all or parts of an agreement with the assistance of block-chain’s distributed trust technology. This is principally a technical description and results in an overly narrow focus. The goal of this paper is to provide an overview of the rapidly evolving multidisciplinary literature on Smart Contracts to provide a synthesis perspective on the economic implications of smart contracts. This necessitates casting a wider-net that ties SCs to the literature on the economics of AI and the earlier Industrial Organization literature to support speculation about the role of SCs in the evolution of AI and the organization of economic activity. Accomplishing this goal builds on a repurposing of the Internet hourglass model that puts SCs at the narrow waist between the real (non-digital) and virtual (digital) realms, serving as the connecting glue or portal by which AIs may play a larger role in controlling the organization of economic activity.
Challenge of Millennials in Project Management

The need for understanding Generation Y employees' attitudes and expectations that considerably differed from previous generations had been a focus of researchers in the last decades. IT industry and specifically software industry had been among the most popular employment areas of Generation Y professionals and hence Generation Y Software Developers constitutes the majority of the work force in the software industry. As known, software development is a project-based business where the project management methodologies and principles are utilized. Similar to other project based works, the effectiveness of human resources management and team development is the determinant of project success in software development. Therefore, to effectively manage and to adapt appropriate approaches for leading the project teams which include Generation Y software professionals, managers and leaders prior in need to understand their perspective. In this context, research aims to understand the expectations and attitudes of Generation Y Software Developer professionals in projects. The research is structured in two parts reflecting these dual aims. The first part of the research addresses the expectations of Y generation employees from project management and project environment in software development projects. The second part of the research explores the attitudes of Y generation Employees in software development project teams. Survey is conducted with the participation of 113 Generation Y Software Professionals who are employed in major software companies in Turkey. Basic concepts that are explored in the study are expectations from working environment, expectations from project type and content, leadership, motivation factors, challenges faced, perceptions on generation conflicts and the attitudes of Generation Y professionals towards project-based work and work environments. Software industry is dominated by project based management methods, perceptions and attitudes of Generation Y in projects are expected to provide practical guidelines to policy makers and HR professionals in developing solutions and tools for improving motivations and effectiveness of Generation Y employees.
Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.
Measuring Meaning

This study will present a tool designed for meaning extraction with monoclausal sentences in Italian. Its main features will be illustrated with instances of the Italian causative clause type featuring the verb fare ‘make’ (e.g. Egli fa piangere il bambino ‘He makes the child cry’), a construction which invariably embeds a clause (e.g. Il bambino piange ‘The child cries’) with which it establishes an entailment relationship. The tool automatically accomplishes the following tasks: it answers relevant wh- questions (e.g. Who cries? Who makes someone cry?) and detects the entailment. Concurrent with this presentation, this study will also encourage a reflection on the research currently being conducted in Computational Linguistics and Natural Language Processing, in which data-driven approaches prevail over rule-based systems.
PolarText: Single-stage Scene Text Detection with Polar Representation

Although deep learning has achieved great success in object detection recently, scene text detection is still a challenging task, due to inherent difficulties of locating texts in complex scenes. Many approaches adopt inspirations from segmentation to detect arbitrary shaped scene text. However, most segmentation based methods have high computation cost and generally needs a lot of refinements to get accurate results. To ease this problem, we propose a novel single-stage method, i.e., PolarText network, which detects text regions by generating contour points in polar coordinates. PolarText not only relieves the burden of high computation cost by directly regressing contour points instead of pixels, but also fits with intrinsic characteristics of text instances by centers and contours, thus suppressing mislabeling boundary pixels caused by pixel-level labeling. To cope with polar representation, PolarText utilizes Polar IoU loss and polar centerness to generalize effective paradigms from box representation for polar representation. In addition, we add a dedicated bounding box branch to work with text detection since most text instances are approximately rectangular in shape. Compared with the existing methods, the proposed method achieves superior results in both accuracy and efficiency by testing on CTW 1500 and ICDAR 2015 datasets.
An Aggregate Anonymous Credential Scheme in C-ITS for Multi-Service with Revocation

Cooperative intelligent transport systems (C-ITS) are advanced applications providing various services to enable a more secure, efficient, and accessible transportation system. When requesting services in C-ITS, privacy-preserving is crucial to avoid attempts in tracking vehicles who send continuous messages containing their authentication information. In this paper, we propose a revocable privacy-preserving authentication scheme through attribute-based credentials with complete subtree method. Differing from the existing anonymous credential works in C-ITS, our scheme supports the aggregation of attributes which facilitates the construction of a constant-size credential for multi-service. Not only maintaining the privacy-preserving of identity, the aggregate credential is also able to improve the efficiency of credential presentation. For illegal vehicles, our scheme revokes and disallows them to generate valid signatures via using complete subtree method in the non-revoked epoch. Moreover, we present analyses on aspects of security and performance, respectively. The former focuses on the security goals including privacy-preserving of identity, unlinkability, unforgeability, revocability and anti-replay attacks; while the latter shows that our scheme makes more practical sense for C-ITS.
Application of evolutionary algorithms to analysis the possibilities of solar energy use

This publication presents the application of evolutionary algorithms to the analysis of the possibilities of solar energy use. The evolutionary method was discussed, the influence of different values of evolutionary algorithm coefficients on the final result of the calculations was examined. An evolutionary method was used to calculate the dependence of solar radiation no a presence of impurities in the air. The values of solar radiation, PM2.5, PM10, and cloud cover were measured over a period of several months each time at noon in the selected location. The influence of the number of crossover and mutation rate on the result of the algorithm was investigated. The Matlab / Simulink software was used for calculations.
Privacy-Preserving Neural Architecture Search Across Federated IoT Devices

While deploying on edge devices, deep learning mod-els often encounter various strict resource constraints. Automated machine learning becomes popular in finding various neural architectures that fit diverse Internet of Things (IoT) scenarios to handle these problems with less human efforts. Recently, there is an emerging trend to integrate federated learning and Neural Architecture Search (NAS) to prevent private data leakage while enabling automated machine learning. The algorithm development is quite challenging because of the coupling of difficulties from both tenets, although promising as it may seem. Especially, it is a hard nut to efficiently search the optimal neural architecture directly from massive non-Independent and Identically Distributed (non-IID) data among IoT devices in a federated manner. In this paper, by leveraging the advances in ProxylessNAS, we propose a Federated Direct Neural Architecture Search (FDNAS) framework that allows hardware-friendly NAS from non-IID data across devices to tackle the challenge. Extensive experiments on non-IID datasets demonstrate the state-of-the-art accuracy-efficiency trade-offs achieved by proposed methods.
Online Optimization in Cloud Resource Provisioning

Several different control methods are used in practice or have been proposed to cost-effectively provision IT resources. Due to the dependency of many control methods on having accurate predictions of the future to make good provisioning decisions, there has been a great deal of literature on prediction workload demand. However, even with all of this literature on workload predictions and their utilization in control algorithms, the understanding of prediction error and how to handle it remains an important open issue and research challenge [1].
Illicit Firearms and Weapons on Darknet Markets

This study provides a snapshot of the availability of weapons across eight omnibus or ‘High Street” and 12 specialist dark-net or illicit crypto-markets between July and December 2019. Overall 2,124 weapons were identified of which 11.1% were found on niche markets. These comprised 1,497 handguns, 218 rifles, 41 sub-machine guns and 34 shotguns for sale. Also available were ammunition (n=79), explosives (n=37), and accessories such as silencers (n=24). Omnibus markets also sold other weapons (n=70) such as tasers, pepper spray and knives, and digital products (n=112) mostly DIY weapon manuals as well as chemical, biological, nuclear, and radio-logical weapons (n=12). The data allowed for estimates of the cost of weapons and some description of the 215 vendors identified, 18 (8.4%) of whom were active across more than one market.
Research on Path Planning of Airport VIP Service Robot Based on A* Algorithm and Artificial Potential Field Method

Aiming at the path planning problem of service robot movement in complex environment, this paper proposes a path planning method combining global planning with local planning algorithm. And then, a new algorithm is carried out by optimizing A*algorithm and artificial potential field algorithm. The results show that the new algorithm has reduced inflection points and break points, made the route smoother and decreased the collision probability of the service robot in the different environments of path planning. Compared with the traditional algorithms, it is found that the new algorithm has better results of path planning through shortening the running time by 59.13% and reducing the length of the optimal path by 52.3%. The algorithm has certain advantages for the service robot to adapt to the path planning in various environments.
Fast Bulk String Matching

XQuery developers deserve to have access to libraries of implementations of useful algorithms, just as developers in other languages do. This paper describes an implementation of the Aho-Corasick algorithm and some applications of it, as part of an effort to build up one such a library. The code described in this paper is available at https://mathling.com/code/ with CC BY 4.0 licensing.
Choosing and Adapting a Mobile Learning Model for Teacher Education

Teachers and instructors end up choosing any instructional design and tools for luck of guiding frameworks especially when integrating teaching and learning with mobile technology. The decision by the instructor becomes even more complex when presented with multiple traditional frameworks to choose from. The Technological Pedagogical Content Knowledge (TPACK) model has lately become popular in teacher training, however, research continues to explore on its effectiveness especially in a fast growing technological environment. New models are being created and this chapter has attempted to present such model that can aid teachers and instructors in making a good choice. Content Relevance and Serving, Content Format and Packaging, Learner Attention, Learner Feedback and Context Awareness where factors identified as critical in making a choice for an instructional design model to adopt.
Outlier Detection of Mixed Data Based on Neighborhood Combinatorial Entropy

Outlier detection is a key research area in data mining technologies, as outlier detection can identify data inconsistent within a data set.Outlier detection aims to find an abnormal data size from a large data size and has been applied in many fields including fraud detection, network intrusion detection, disaster prediction, medical diagnosis, public security, and image processing.While outlier detection has been widely applied in real systems, its effectiveness is challenged by higher dimensions and redundant data attributes, leading to detection errors and complicated calculations.The prevalence of mixed data is a current issue for outlier detection algorithms.An outlier detection method of mixed data based on neighborhood combinatorial entropy is studied to improve outlier detection performance by reducing data dimension using an attribute reduction algorithm.The significance of attributes is determined, and fewer influencing attributes are removed based on neighborhood combinatorial entropy.Outlier detection is conducted using the algorithm of local outlier factor.The proposed outlier detection method can be applied effectively in numerical and mixed multidimensional data using neighborhood combinatorial entropy.In the experimental part of this paper, we give a comparison on outlier detection before and after attribute reduction.In a comparative analysis, we give results of the enhanced outlier detection accuracy by removing the fewer influencing attributes in numerical and mixed multidimensional data.
GA-Based Optimized Image Watermarking Method With Histogram and Butterworth Filtering

The present paper proposes a new and significant method of optimization for digital image watermarking by using a combination of Genetic Algorithms (GA), Histogram and Butterworth filtering. In this proposed method, the histogram range selection of low frequency components is taken as a significant parameter which assists in bettering the imperceptibility and robustness against attacks. The tradeoff between the perceptual transparency and robustness is considered as an optimization puzzle which is solved with the help of Genetic Algorithm. As a result, the experimental outcomes of the present approach are obtained. These results are secure and robust to various attacks such as rotation, cropping, scaling, additive noise and filtering attacks. The peak signal to noise ratio (PSNR) and Normalized cross correlation (NC) are carefully analyzed and assessed for a set of images and MATLAB2016B software is employed as a means of accomplishing or achieving these experimental results.
Research on the Application of Network Video Teaching in PE Teaching in Colleges

Abstract Since the emergence of Internet technology, its development momentum has shown an unstoppable trend. The emergence of the Internet has changed people’s work and life. Especially the impact on the education field is even more significant. The micro-video teaching method under the background of Internet technology is a major innovation in the field of education, which has greatly promoted the development of education and teaching. As a new teaching mode, it makes good use of the integration of information technology and teaching methods to make classroom teaching more intuitive, vivid, and vivid, greatly improving students’ initiative and enthusiasm for learning, and expanding students’ horizons. Expand the classroom capacity, thereby improving classroom efficiency. With the gradual maturity of network technology, more and more micro-video teaching is applied to teaching practice, especially in college physical education work. It has played an incalculable role. This article will discuss the concept, characteristics and types of micro-video teaching, Production, questions and suggestions to discuss the application of micro-video teaching in college sports centers.
Mental Health Clinical Decision Support Exploiting Big Data

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.
Design and Implementation of Infrared Image Preprocessing System based on FPGA

Infrared image processing is a conventional topic, but the hardware design and implementation for infrared image processing algorithms are full of challanges, especially for special purpose infrared detectors which have customized interfaces and protocols. Moreover, some customized infrared detectors only generate the infrared image with very poor quality. Therefore, in this paper, we design and implement an infrared image preprocessing system based on FPGA for customized infrared detectors. We propose a hardware architecture that includes the whole process from the input of detector, image processing, to final infrared video stream output and display. Our hardware architecture is able to adapt to different customized infrared detectors, which has the characteristics of real-time, low-power, dynamic parameter configuration, high processing efficiency and stable imaging effect. We have implemented it on Xilinx FPGA. The infrared imaging effect is significantly improved with limited FPGA resources (about only 1.6M LUT, 420 BRAM for all algorithm models on Xilinx zc706 evaluation board), the image processing speed can reach to 225Hz with the resolution of 320*256. Our preprocessing system can generate real-time video for display with 1920*1080@60Hz.
Detecting Voter Understanding of Ideological Labels Using a Conjoint Experiment

Understanding voters’ conception of ideological labels is critical for political behavioral research. Conventional research designs have several limitations, such as endogeneity, insufficient responses to open-ended questions, and inseparability of composite treatment effects. To address these challenges, we propose a conjoint experiment to study the meanings ascribed to ideological labels in terms of policy positions. We also suggest using a mixture model approach to explore heterogeneity in voters’ understandings of ideological labels, as well as the average interpretation of labels. We applied these approaches to conceptions of left–right labels in Japan, where the primary issue of elite-level conflicts has been distinctive compared with other developed countries. We found that, on average, while Japanese voters understand policy-related meanings of “left” and “right,” they primarily associate these labels with security and nationalism, and, secondarily, with social issues; they do not associate these labels with economic issues. Voters’ understandings partly depend on their birth cohort, but observed patterns do not necessarily coincide with what many researchers would predict regarding generational differences in Japanese politics. Mixture model results suggest that some individuals tend to associate left–right labels with security and nationalism policies, while others link them to social policies. Over one-third of respondents seemed to barely understand the usage of left–right labels in policy positions. Our study improves upon existing methods for measuring voter understanding of ideological labels, and reconfirm the global diversity of meanings associated with left–right labels.
Introduction

This chapter describes how machine learning is applied, the dawn of machine learning, and an example use case of Industry4.0 and transaction processing. In addition, this chapter introduces the types of machine learning being studied and what problems or issues they are used for. Further, previously deployed services and applications are also discussed. First, remarkable examples of machine learning are introduced. IBM's Watson AI machine, which indicated the advent of machine learning in the research domain, and Google's Go Playing machine, are introduced. Next, definitions of inference and learning are introduced, and a full perspective is provided through examples. The inference obtained from learning is then described. Before the learning of a neural network model, input data are cleaned and modified to effectively improve the learning task; the techniques used for this are also provided. In addition, a common learning method, taxonomy of learning, and its performance metrics and verifications are introduced. Then, Industry4.0 is introduced as a use case of machine learning. Two typical example models of factory automation are applied in the explanation. Finally, the use of transaction processing is introduced. Machine learning is applied to process huge datasets, and a transaction is a procedure applied between each data processing. Although there is no direct relationship between machine learning and the transaction process, a block chain used in the transaction process can be applied anywhere and combined with machine learning. Therefore, a block chain is introduced in this chapter.