Reviewerly/exHarmony huggingface dataset:
Innovative Application of Computer Technology in Engineering Management

Abstract With the rapid development of China’s economy, computers have been well developed at a certain level. In most of our project management process, if we can effectively use computer technology, to a certain extent, we can improve the quality and efficiency of the project. Based on this, this paper studies the innovative application of computer technology in engineering management. In this paper, an engineering team is selected as the research object. Based on the theory of computer technology and RDF graph data flow division algorithm, the engineering resource utilization rate, project management efficiency and engineering difficulty of the engineering team before and after the application of computer technology for project management are compared. The research results show that before the application of computer technology for project management, the resource utilization rate of engineering team is 72.5%, the project management efficiency is 61.6%, and the engineering construction difficulty is 76.3%. After the application of computer technology to project management, the data have changed, the resource utilization rate has increased to 88.3%, the pipeline efficiency has increased to 79.3%, and the engineering difficulty has been reduced to It can be seen from the data changes that the same engineering team has changed significantly before and after the application of computer technology in project management, which not only saves engineering resources, but also effectively improves the engineering efficiency.
Using Generative Adversarial Networks Based on Dual Attention Mechanism to Generate Face Images

Traditional Generative Adversarial Networks (GAN) simulates the correlation between different regions through multiple convolution, but correlation between long-distance is relatively small, resulting in unclear details of generated images. To solve this problem, we propose a model based on dual attention mechanism which called DA-GAN. The self-attention mechanism can extract the dependency effectively between the local and global features of images, allowing the network to learn the connections purposefully between the features, thereby generating higher quality images. The channel-attention mechanism obtains the importance degree of each feature's channel automatically, and improves useful features and suppresses the features that are not useful for the current task according to the importance degree, so as to calculate resources more efficiently. Experiments demonstrate that our method achieves better performance than other method on the CelebA dataset and can produce higher-quality images.
Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets

Manifold learning is used for dimensionality reduction, with the goal of finding a projection subspace to increase and decrease the inter- and intraclass variances, respectively. However, a bottleneck for subspace learning methods often arises from the high dimensionality of datasets. In this paper, a hierarchical approach is proposed to scale subspace learning methods, with the goal of improving classification in large datasets by a range of 3% to 10%. Different combinations of methods are studied. We assess the proposed method on five publicly available large datasets, for different eigen-value based subspace learning methods such as linear discriminant analysis, principal component analysis, generalized discriminant analysis, and reconstruction independent component analysis. To further examine the effect of the proposed method on various classification methods, we fed the generated result to linear discriminant analysis, quadratic linear analysis, k-nearest neighbor, and random forest classifiers. The resulting classification accuracies are compared to show the effectiveness of the hierarchical approach, reporting results of an average of 5% increase in classification accuracy.
Writing Futures: Investigations

This chapter begins with imagining the future through Musk’s (2020) argument for telepathy, noting the need to be aware of dramatic shifts in technological futures. We include detail on the Fabric collection—Writing Futures: Collaborative, Algorithmic, Autonomous—noting the emergence of artifacts involving humanoid robots. Our hope is that readers not fall prey to reductive binaries, but rather begin to investigate and prepare for the social, literacy, and civic engagement implications of collaborative, algorithmic, and autonomous writing futures. We emphasize that doing so requires trust and leadership, providing resources to examine the critical link between trust and AI. We summarize the resources, themes, theories, and applications mentioned in earlier chapters; suggest directions for investigating and planning for writing futures; and interpret these through sets of past and proposed investigations across academic, industry, and civic realms. We emphasize the importance of drawing on models from multiple disciplines as we work to refine and reinvigorate our fields, professions, and endeavors. We call upon readers to revitalize writing practices through new forms of collective intelligence amid a changed and changing world.
A distribution independence based method for 3D face shape decomposition

Decomposing a 3D face shape into different attribute components is usually beneficial to many applications, such as 3D face generation and attribute transfer. In this paper, we propose a novel method to learn independent latent representations of 3D face shapes to decompose a given 3D face shape into identity and expression components. We assume that the identity describes the intrinsic geometry of a face while the expression captures the extrinsic one, and thus they are independent of each other. Based on this assumption, we encode a 3D face shape into its identity and expression representations by a variational inference framework, that is equipped with Graph Convolutional Networks (GCN). Furthermore, we introduce a binary discriminator to enforce the latent representations of identity and expression to be distribution independent by adversarial learning. Both qualitative and quantitative experimental results show that the proposed approach can achieve state-of-the-art results in 3D face shape decomposition. Extensive applications on 3D facial expression transfer, 3D face recognition, and 3D face generation further demonstrate that the proposed method can achieve visually better transferred expressions, purer identity representations, and more diverse 3D face shapes, compared with existing state-of-the-art methods.
Design Thinking Application in K-12 Education Utilizing Service Design Methodology

This study aims to establish a system of employing design thinking in the K-12 education based on service design methodology. (1) Find a way to introduce design thinking in K-12; (2) Construct an education system inspired from service design for future practices. (3) Proposes as curriculum framework that puts design thinking as the key strategy with a series of real-life assessments. Service design’s methods and tools is used to identify the stakeholder’s characteristic and demands, figure out the target audience along with service objectives, sort out the relationships between them and establish a service process. Through teaching practice, this research studies and proposes a new alternative to employ design thinking in K12 education. Moreover, proposes a curriculum model centered on design thinking by combining the K12 preliminary courses with design workshops, research activities, etc. The new model is exemplified through practices in the real world’s teaching. The construction of the new educational framework based on service design methods will effectively promote the development of design thinking in the future K12 education.
Implementing Q-learning on Simple Vehicle: Learning Obstacle Avoidance in Different Environments

Abstract Q-learning has been applied to many research projects in the past few decades. Its simple logic of mapping a corresponding action to each possible state has made it one of the most popular methods in reinforcement learning. In this implementation of Q-learning, a simple controlling scheme was used. The percept of the robot is taken as the state, and the actions give instruction to the actuating device of the robot. The goal is for this robot to pick up the obstacle avoidance skill after series of training. A sanity check for such an idea was provided in this essay. The basic disc robot from Robotics Playground was used, and the basic form of Q-learning was utilized. Our vehicle’s obstacle avoidance ability was assessed in environments with different levels of complexity using the Collision-Time graphs produced. The results showed very limited improvements in terms of reducing the number of collisions in all of the environments, and some fluctuations in the number of collisions were shown in the diagram. Combining the fluctuations with observations during the experiment process, we came to a conclusion that using Q-learning to navigate an arbitrary environment, the state space, and action space could be too large for any efficient training to be done.
Block Chain for Efficient Indian Health Care System Implementation

Abstract Health care is undergoing a major transformation worldwide due to technological advancements and increase in number of diseases. In the wake of current pandemic Covid ‘19 impact across the Globe, stresses the need to overhaul the Health Management system in a large country like India. A country with two-thirds of population living in rural setting, their access to quality Health System is dubious. In Urban setting also the availability of affordable and accessible health care also left a lot to be of concern. Block Chain Technology integration in all the facets of Health care management can cut costs and dramatically improve accessibility and affordability for all segments of people in India. Suggested Block Chain Modules integration and implementation measures emanating from this study in delivering different medicines and paramedics support aims at rendering efficiency to this strained system. Hence this study acclaims on the key performance metrics on health care system to be integrated with the efficient block chain implementation in India.
Conclusions and Future Research Lines

This last chapter provides a summary of the main contributions of the work presented in the thesis and discusses some of the possible future research directions.
Argentina, Trans Persons and the Development of a Human Rights Discourse

This chapter analyzes the juridical and political context in which the 2012 Gender Identity Act was enacted in Argentina.
Information counteraction to falsification of history and rehabilitation of nazism

In the article the characteristic of the problem of information countermeasures to falsification of history and rehabilitation of Nazism is given. The main manipulative technologies of falsification of history and justification of Nazism are revealed, the key modern forms of the spread of the ideology of Nazism are shown. Authors share positive experiences of counterpropaganda work in countering the falsification of history and preventing the rehabilitation of Nazism
Vivian: A Technical Framework for Deploying and Operating Virtual Prototypes in XR

In this paper, we describe Vivian, a technical framework for deploying and operating Virtual Prototypes (VPs) in Virtual and Augmented Reality (XR). Vivian is based on a static model of a VP, e.g., a microwave oven. We extend this model with a functionality specification describing the intended functionality of the VP. Based on this, the Vivian Framework is capable of deploying the VP in XR, i.e., on a desktop PC, in mobile AR, and in VR, as well as providing an interaction with it. Through this, it lays the basis for fast and efficient usability evaluations of VPs, which may also take place in a remote fashion. We evaluated the Vivian Framework by modeling and interacting with four VPs. Our study shows that the principles of Vivian cover many aspects to model VPs and their functionality, but that it also has some limitations, e.g., regarding the modeling complexity.
Identification of The Number of Wireless Channel Taps Using Deep Neural Networks

In wireless communication systems, identifying the number of channel taps offers an enhanced estimation of the channel impulse response (CIR). In this work, efficient identification of the number of wireless channel taps has been achieved via deep neural networks (DNNs), where we modified an existing DNN and analyzed its convergence performance using only the transmitted and received signals of a wireless system. The displayed results demonstrate that the adopted DNN accomplishes superior performance in identifying the number of channel taps, as compared to an existing algorithm called Spectrum Weighted Identification of Signal Sources (SWISS).
When Distributed Ledger Technology Meets Traditional Payment Systems—Benefits and Challenges

Blockchain technology is a distributed ledger system that focuses on certain features such as scalability, decentralization, high throughput, and a user-friendly environment. In the upcoming era, it can be seen that with the help of these characteristics, and this technology can act as the most crucial need of the organizations or industries. Most of the industries involve financial database functionalities and have to ensure security in the online payments or transactions between the companies. This work is aimed at defining a solution based on understanding the payment channel being trustworthy and secure on the basis of transactions with the help of blockchain technology. The comparison is made to traditional payment systems. Understanding the consensus algorithms is concerning the solutions provided by blockchain for the conventional payment system. Hence, this paper on blockchain technology payment medium comparison would improve the methods of safe and secure payments and will create an understanding for secure payments using consensus algorithms.
Security Mechanisms in Human Centered Smart Systems-Overview

Mobile crowd sensing is the process of sensing, carried out on behalf of the task provider by the user capable of completing the task. The user with their smart gadgets involves in the sensing process. This sensing process includes various applications geo-publicly supporting, monitoring, smart systems, etc. Since large number of users involved in crowd sensing process there is a threat for security both for the users and the data. In concern with the users both the task provider and the participant, issue a task or carry out the task issued based on the trust between each other. To pick up the task and carry out the task the participant must share their private details which may cause privacy issue to the participant. Whereas with the data the participants report at the completion of the task may be a false data or incomplete data. This cause a situation where the truthiness of the data is affected. This makes the complete process useless. These security issues like privacy, truth and trust must be addressed. In order to provide a clear idea of Crowd sensing network a taxonomical approach is followed to classify the network based on the data involved and various participants. Based on the various participants, the malicious users in the crowd sensing network is classified and the security issues caused by them are addressed.
COLLECTIVE FORMS OF INTRODUCTION TO MUSIC AS A MEANS OF SELF-REALIZATION OF STUDENTS

The article highlights the problem, the contradiction of necessity and freedom, the struggle of opposites, their complementarity. Therefore, it is so important that the requirements of social discipline and internal free conviction coincide. Collective forms of musical activity help to find their place in life, to know themselves, to develop self-discipline. Authoritarianism is characterized by the need to subordinate partners to their influence as much as possible. One alternative to authoritarianism is humanism. Humanistic ideas play a huge role in balancing the organization of our world.
Research on the Application of Building Information Model in Interior Design Under the Background of Big Data

Designers in the era of big data not only use computers as modern design media, but more importantly, they must have the ability to grasp, control and use various information quickly and accurately. With the rapid development of digital technology and the construction market, the concept of BIM came into being. At present, BIM is gradually accepted by the construction industry. As a new technical concept, BIM is promoting the development of architectural design and other industries. At the same time, big data has become a hot topic and a starting point for solving problems. This article introduces the specific concept and development trend of the new computer information technology-building information model. This article focuses on analyzing the revolutionary changes brought about by the application of BIM in interior design, and further elaborates the advantages of building information modeling technology in interior design through two examples.
Managing Credit Card Fraud Risks by Autoencoders

This chapter introduces a risk control framework on credit card fraud instead of providing a solely binary classifier model. The anomaly detection approach is adopted to identify fraud events as the outliers of the reconstruction error of a trained autoencoder (AE). The trained AE shows fitness and robustness on the normal transactions and heterogeneous behavior on fraud activities. The cost of false-positive normal transactions is controlled, and the loss of false-negative frauds can be evaluated by the thresholds from the percentiles of reconstruction error of trained AE on normal transactions. To align the risk assessment of the economic and financial situation, the risk manager can adjust the threshold to meet the risk control requirements. Using the 95th percentile as the threshold, the rate of wrongly detecting normal transactions is controlled at 5% and the true positive rate is 86%. For the 99th percentile threshold, the well-controlled false positive rate is around 1% and 83% for the truly detecting fraud activities. The performance of a false positive rate and the true positive rate is competitive with other supervised learning algorithms.
Towards Explainable Image Classifier: An Analogy to Multiple Choice Question Using Patch-level Similarity Measure

With increased interests in Explainable Artificial Intelligence (XAI), many researches find ways to provide explanations for machine learning algorithms and their predictions. We propose Multiple Choice Questioned Convolutional Neural Network (MCQ-CNN) to better understand the prediction of image classifier by considering the problem of multi-class classification as the problem of multiple choice question. MCQ-CNN not only performs classification of the query image, but also explains the classification result by demonstrating the elimination process of multi-class labels in patch-level. The proposed model consists of two modules. Classification module is to classify class label of the query. Elimination module is to perform similarity measure in patch-level to distinguish whether the target object part shares the feature of certain class label or not. Classification module is trained using ResNet with high classification accuracy. Elimination module performs similarity measure by distance metric learning based on Large Margin Nearest Neighbor (LMNN). Our experiments have shown notable performances in both classification and elimination modules.
Dynamic Weight of Adaptive Information Density Network for Image Super-Resolution

A model algorithm based on image information density characteristics is proposed to achieve network structure adjustment. The image dense region classification is fed back to the subsequent network. According to the classification information, the image sampling window is sent to different network to realize pixel-level channel switching, thereby reducing the network deployment process's computational pressure. The dynamic weighting network adjusts the weight coefficients of pixels in the sampling window to approximate the image's shape and generate better texture effects than FSRCNN. When using the public test sets to evaluate the adaptive information density network structure, the computation complexity of SRCNN and FSRCNN was reduced by about 28%, and the PSNR only reduced by about 0.1dB.
The Immersive Guitar Project: Imagining Possibilities for Enriching Audience Experience through Architectural Innovation

Abstract This article introduces The Immersive Guitar (TIG) Project, a proposed sonic performance installation that doubles as an intimate acoustic venue. The TIG Project responds to several needs, highlighting relations between place and performance, music and architecture. The needs concern a rarity of suitable, intimate spaces for acoustic performance and an appeal for more creative solutions in the provision of such spaces, which would afford novel ways of accessing performing arts experiences. This article introduces the proposition and provides project background and rationale.
Distributed and Anonymous E-Voting Using Blockchain and Ring Signatures

Digitization of bureaucratic processes has been a long suited method for the growth of a country.Madhani, Nishay Gajria, Vikrant Kanani, Pratik E-Voting (Electronic Voting) is a method of casting, tallying and verifying votes from citizens using electronic means. With the advent and boom of Blockchain and Distributed Computing, many methods to improve E-voting have come forward that allows us to assure the privacy of voters and security against voter fraud. In this paper, we analyze the different types of Blockchains and which ones would be suitable for these purposes. In the end, we propose a method using Blockchain and Linkable Ring-Signature that provides the privacy of voters in a large-scale election by deploying smart contracts for “rings” of registered voters and analyze its performance on consumer devices.
Compression Function Based Authenticated Encryption Mode for Big Data

With the rise of big data, it is still an open problem to securely store and process large amounts of data on lightweight devices. This paper focuses on the data security protection in the context of big data and proposes a new provably secure authenticated encryption mode - called Hash-then-CounTeR (HtCTR). The encryption part of HtCTR is set up by a compression function based stream cipher mode and the authentication part follows a “Hash-then-Encrypt” approach to build a message authentication code. Then, we prove that HtCTR enjoys adaptive levels of security for adversaries in distinct environments. It ensures close-to-optimal security against nonce-respecting adversaries and offers birthday bound security against nonce-misusing adversaries, if the underlying compression function is a secure pseudo-random function. Finally, we discuss the properties of HtCTR and present an instantiation.
Application experience on radar networking and data fusion principles

Multi-sensor data fusion is a key processing architecture for both defense and civilian application systems. By means of (i) suitable fusion algorithms applied to data from heterogeneous sensors and (ii) an optimal sensor management policy, it is possible to obtain better performance than achievable with either single or homogeneous sensors. While the concept of data fusion is not new, the emergence of new sensors, advanced processing techniques, powerful processing hardware and adequate man-machine interface, make real-time fusion of data increasingly profitable and operationally valuable.