Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Applications for Smart Societies

Artificial intelligence (AI) is a widespread branch of computer science focused on the development of smart machines, which can perform processes that generally require human information. AI is a multidisciplinary field that finds its applicability in several domains ranging from smart homes, smart buildings, intelligent transportation, healthcare, military to commercial products. The design of AI technique in a smart society, in which the analysis of human habits is obligatory, requires automated data scheduling and analysis using smart applications, a smart infrastructure, smart systems, and a smart network. To identify the possible application areas of AI in smart society, this chapter discusses different AI techniques employed for diverse applications in smart societies. The study mainly focused on the brief discussion of AI applications in urban computing, sustainability, healthcare, security, and justice. In addition, a general explanation of the issues that exist in AI-based applications for smart societies has been discussed. At the end o f the chapter, a brief overview of AI concepts and its applicability in diverse possible domains is given.
A Virtual Exploration of al-Masjid al-Nabawi Using Leap Motion Controller

Religious tourism is getting popularity and more and more people wants to visit the religious places. Al-Masjid Al-Nabawi holds a historical priority for 1.5 billion visitors with different languages, cultures, and ethnicity. As part of the Saudi leadership to govern the affairs of Al-Masjid Al-Nabawi, many expansions have been planned\performed to provide better services for Saudi Arabia’s guests and visitors around the years. Large and ongoing expansions of the holy sites in Saudi Arabia may impact the ease of mobility for own guests especially in the time of high seasons (i.e., Haij and Omrah times). Virtual Reality technology started to play a critical role in the tourism industry by virtually exposing users to a certain place. The primary purpose of this paper is to visualize the Al-Masjid Al-Nabawi site in an interactive style and ease of use for the guests. The interaction with the 3D environment is ensured via the Leap motion Controller in a very simple way without need to special learning. We designed and implemented a virtual reality-based touring guide prototype, and an initial user validation was conducted. The results are promising for future investigation.
The influence of innovative digital technologies in education on the life quality of students during the pandemic

This article substantiates the relevance of studies on estimation the life quality of students during forced implementation of online digital educational technologies. The article presents the main trends of program target approach to life quality of population subdivided by the subjects of socio-economic relations. The main positive changes are revealed in the students’ life manifested during the pandemic upon obtaining online educational services, including elements related with digital competences: labor market demand, conditions of formation and implementation, amounts and desired forms, emerging opportunities, relations with professional environment and social institutions of communications, methods and technologies of digital content. Peculiar features of the most important educational elements of online technologies, which change students’ lifestyle and quality of life, are considered in the frames of blended learning: event systems, storytelling systems, gamification systems. Positive and negative phenomena have been revealed on the basis of analysis of changes in students’ life during total application of online technologies.
Sophisticated Matlab Coding in Regression Inquiry and resolving

MATLAB is an elite language for specialized registering. It coordinates calculation, perception, and programming condition. In addition, it has powerful built-in routines that enable a very wide variety of computations. Regression is that the method of fitting models to information. Regression analysis is employed to model the link between a response variable and one or many predictor variables. In this paper, we exercised refined MATLAB code for linear regression analysis and solved problems are demonstrated through graphs.
Real-Time Compression Artifact Reduction for Low Bandwidth Video Conferencing

Video conferencing has been proved to be beneficial in terms of productivity, saving time and travel expenses as well. People have relied on video conferencing for business and academic purposes and the current pandemic situation has increased its usage. However, many people face issues related to low internet bandwidth. In order to transmit images or videos over the available low bandwidth, they are compressed so that take less space and can be sent over even on low bandwidth. Low bandwidth adds compression artifacts to transmitted media. Many systems have proposed various deep learning approaches to reduce these artifacts. However, they are not suited for real-time usage. We are proposing a solution to reduce compression artifacts by utilizing a deep learning model which will reduce compression artifacts in real time to improve video conferencing experience.
Integrating aspect-aware interactive attention and emotional position-aware for multi-aspect sentiment analysis

Aspect-level Sentiment Analysis is a fine-grained sentiment analysis task, which aims to infer the corresponding sentiment polarity with different aspects in an opinion sentence. Attention-based neural networks have proven to be effective in extracting aspect terms, but the prior models are based on context-dependent. Moreover, the prior works only attend aspect terms to detect the sentiment word and cannot consider the sentiment words that might be influenced by domain-specific knowledge. In this work, we proposed a novel integrating Aspect-aware Interactive Attention and Emotional Position-aware module for multi-aspect sentiment analysis (abbreviated to AIAEP) where the aspect-aware interactive attention is utilized to extract aspect terms, and it fuses the domain-specific information of an aspect and context and learns their relationship representations by global context and local context attention mechanisms. Specifically, in the sentiment lexicon, the syntactic parse is used to increase the prior domain knowledge. Then we propose a novel position-aware fusion scheme to compose aspect-sentiment pairs. It combines absolute distance and relative distance from aspect terms and sentiment words, which can improve the accuracy of polarity classification. Extensive experimental results on SemEval2014 task4 restaurant and AIChallenge2018 datasets demonstrate that AIAEP can outperform state-of-the-art approaches, and it is very effective for aspect-level sentiment analysis.
Native gold in the weathering crust of the Nizhneselemdzhinsky gold-bearing node (Amur region)

Research subject. The weathering crust of Proterozoic slates in the Neklya River basin and Paleozoic granites in the Nizh- neselemdzhinsky gold-bearing node (NGBN) in the Tatarka River basin. Methods. The research was carried out using the methods of atomic absorption, X-ray fluorescent and mineralogical analysis of rocks and minerals. The method of raster electron microscopy was used to study the element structure, morphological and microstructural features of minerals. Results. Specific features of native gold from the weathering crust of NGBK were defined. It was established that the NGBN weathering crust contains both hypogene gold, partially changed in the course of hypergenesis, and neogenic gold. A considerable share of gold is of high purity (1000%o). Occasionally, gold in the form of complex accretions from grains of different morphology and structure is present. A specific feature of weathering crust gold is its interpenetrations within the rock matrix of a varying mineral structure. Gold-bearing carbonaceous structures in the form of films and outgrowths on gold grains were revealed; the presence of carbon in rock components associating with the noble metal was defined. In the crust, the participation of carbon in physicochemical processes was established, as a result of which the release of Au, encapsulated in minerals-concentrators, and its redeposition on geochemical barriers occur. Gold nanoparticles can be long-acting growth centres in the host rocks, first coalescing with each other to yield nanoformations, then microforms, etc. In the weathering crust of gold deposits, both the transformation of the hypogenic noble metal and the formation of its new forms occur. Conclusion. This work contributes to the expansion of the mineral resource base of gold in the Amur Region, including through such unconventional sources as the NGBN weathering crust.
Design and Realization of Modern Smart Home System Based On Multimedia Network Technology

Abstract With the development of science and technology and the progress of multimedia network technology, various intelligent electronic products are more and more widely used in people’s lives. High-tech and intelligent technology have moved from intelligent buildings to residential home users, and home intelligent technology has become a cutting-edge technology topic. In this regard, the purpose of this article is to study the design and implementation of modern smart home systems based on multimedia network technology. This article first studies the development status of domestic and foreign multimedia network technology and smart home technology, and conducts a questionnaire survey on some residents to understand the current status of smart home. Finally, based on this, this article uses high-level programming language to code the system. According to the method and technology of test theory, the function and performance of the software system are tested completely, and compared with the traditional smart home system. The experimental results show that the modern smart home system using multimedia network technology is more recognized and loved by residents, and the residents’ preference for smart home has increased by about 20%, which has played a significant role in increasing the utilization rate of smart home systems and is a multimedia network the future development of smart home systems provides an important reference.
Information Assurance and IT Management: &lt;i&gt;The Key Issues, Solutions in Indian Scenario based on International Trends&lt;/i&gt;

Information Technology as a field of study is growing due to its importance and applications in diverse fields, areas, and sectors. Initially, IT treated as a small domain but gradually different areas have been added into it and this includes web technology, network technology, database technology, etc. Hence all kind of domains and fields of IT is employing everywhere. The wider applications of IT subfields for different purposes lead the concern of security and that includes in its all the sub fields. Gradually security fields became started with various nomenclature with different names and objective viz. Computer Security, IT Security, Information Security, Cyber Security and most recent Information Assurance. Importantly, Information Assurance is most wider incorporating all the areas, most interdisciplinary, skill based, management and socially touched. Though it is important to note that, human resources and skilled manpower is still limited in developing countries in this field. This paper talks about the traditional areas of Information Assurance including educational opportunities, challenges, issues, etc. Moreover, the field is concerned with the technologies related to security. Hence this paper showcases the emergence of security as a technical and HR context.
Edge-guided CNN for Denoising Images from Portable Ultrasound Devices

Ultrasound is a non-invasive tool that is useful for medical diagnosis and treatment. To reduce long wait times and add convenience to patients, portable ultrasound scanning devices are becoming increasingly popular. These devices can be held in one hand, and are compatible with modern cell phones. However, the quality of ultrasound images captured from the portable scanners is relatively poor compared to standard ultrasound scanning systems in hospitals. To improve the quality of the ultrasound images obtained from portable ultrasound devices, we propose a new neural network architecture called Edge-guided Denoising Convolutional Neural Network (EDCNN), which can preserve significant edge information in ultrasound images when removing noise. We also study and compare the effectiveness of existing deep learning methods and classical filtering approaches in removing speckle noise in these images. Experimental results show that after applying the proposed EDCNN, various organs can be better recognized from ultrasound images. This approach is expected to lead to better accuracy in diagnostics in the future.
Minimizing Energy of DNN Training with Adaptive Bit-Width and Voltage Scaling

Training DNN mostly relies on GPUs with FP32 format. While FP16 is acknowledged for its advantage of high computation and memory efficiencies for training DNN, the training must be accompanied with techniques dedicated for a particular dataset. Therefore, a hardware engine with a configurable bit-width feature is desirable for covering any datasets and applications. This work proposes an adaptive bit-width and voltage scaling (ABVS) scheme for DNN training. The key idea is to increase fraction bit-width (FB) gradually from a small value according to current training quality (e.g., accuracy, mAP). Since less FB achieves shorter hardware latency, this training scheme concurrently adapts bit-width and voltage scaling and intensify energy reduction. Experimental results show that the ABVS scheme achieves the comparable quality to FP32 with at most 0.5% accuracy drop, but up to 63% energy reduction.
Lightweight Proofs of Storage with Public Verifiability from Lattices

Proof of storage (POS) is a useful cryptographic primitive that enables users to efficiently verify the integrity of their data outsourced in the cloud. Usually, users prefer to utilize POS schemes with public verifiability for integrity checking tasks, since such schemes allow a third-party on behalf of a user to implement these tasks without disclosure of any secret information of the user. However, most of the existing publicly verifiable POS schemes are somehow cumbersome because they involve a great deal of heavy operations especially on the user side so that they may not be competent for some applications.To compensate for this defect, we explore a simple and lightweight POS scheme with public verifiability from hardness problems relative to lattices. Our scheme only requires simple cryptographic computations and, in particular, significantly decreases the computation overhead of users. We prove our scheme is secure assuming the ring small integer solution problem is intractable. Moreover, we also conduct extensive simulations to evaluate the performance of our scheme, and the experimental results do speak volumes about its effectiveness.
Zero-correlation entanglement vs. Schmidt rank

In this paper, we find the minimal number of correlation tests required to certify independence of two classical random variables of arbitrary finite support. Moreover, we completely characterize the constraints implied by zero correlations upon an arbitrary heterogeneous quantum state, and we solve the conjecture proposed by Ohira in 2020. Finally, we find the first ever algorithm for computing (or checking) the Schmidt rank of any unknown pure quantum state using only zero-correlation tests, finding a sufficient amount of tests certifying separability or full entanglement.
Application research of Sports Place System based on big data classification technology

With the rapid development of China's economic construction and the promotion of advocating comprehensive fitness, sports venues and supporting facilities will be built in succession, the construction of high-level sports venues is imperative. At present, intelligent construction has begun to take shape, and the construction of intelligent system has entered the implementation stage. Under such social conditions, the intelligent system construction of sports venues has also entered a new stage. Based on big data classification technology, sports venues are developing towards system integration and comprehensive management. This paper introduces the definition of sports venues of intelligent system, development present situation, the system characteristics and system composition, introduced the intelligent system design process of the sports venues, sports venues are summarized based on the practice of intelligent system of the problems in such aspects as planning, design, find out the solution, summarizes the relevant experience. It is hoped that it can play a certain reference and guiding role in the construction of intelligent system of sports venues in the future and provide reference for the government to formulate norms and standards.
Superimposing Periodic Subgraph Mining in Dynamic Social Network

Nowadays, complex network has been inspired by the observation of many real-world network patterns such as pattern of mobile phone usage, gene expression, e-mail habits, protein interactions in networks, regional interaction in archaeological contexts and co-authorship in research publications. The detection of interaction patterns that occur at fixed time periods can be mined in a complex network. This article proposes an algorithm to predict an interaction pattern hidden behind these networks, namely periodic subgraphs embedded in a complex network. The primary data structures used in tracking the periodic subgraphs are lists, tables, data frames, matrices and vectors. The algorithm can be implemented using analytical tools that do not require advanced data structures such as pattern trees and indexing techniques such as hash maps. In this paper, our algorithm processes a dynamic network in two stages. The first stage of the algorithm extracts all maximal subgraphs which appear between successive time steps. In second stage, it maintains the periodicities of the maximal subgraphs identified and flushes out those graphs whose periodicities ceased to exist. The algorithm is tested by generating a dynamic network using random graph generating function and by superimposing periodic subgraphs on the network at different time interval periods.
What nodes vote to? Graph classification without readout phase

In recent years, many researchers have started to construct Graph Neural Networks (GNNs) to deal with graph classification task. Those GNNs can fit into a framework named Message Passing Neural Networks (MPNNs), which consists of two phases: a Message Passing phase used for updating node embeddings and a Readout phase. In Readout phase, node embeddings are aggregated to extract graph feature used for classification. However, the above operation may obscure the effect of the node embedding of each node on graph classification. Therefore, a node voting based graph classification model is proposed in this paper, called Node Voting net (NVnet). Similar to the MPNNs, NVnet also contains the Message Passing phase. The main differences between NVnet and MPNNs are: 1, A decoder for graph reconstruction is added to NVnet to make node embeddings contain graph structure information as much as possible; 2, In NVnet, the Readout phase is replaced by a new phase called Node Voting phase. In this new phase, an attention layer based on the gate mechanism is constructed to help each node to observe the node embeddings of other nodes in the graph, and each node predicts the class of the graph from its own perspective. The above process is called node voting. After voting, the results of all nodes are aggregated to get the final graph classification result. In addition, considering that aggregation operation may also obscure the differences between node voting results, a regularization term is added to drive node voting results to reach group consensus. We evaluate the performance of NVnet on 4 benchmark datasets. The experimental results show that NVnet performs well on graph classification task.
A Novel Developer Portrait Model based on Bert-Capsule Network

In order to ensure code quality, it's necessary to construct portraits for developers, which could analyze their behavior to provide personalized programming suggestions. However, most of the existing developer portrait algorithms only use global features and ignore local features extracted from log texts, which leads to the lack of comprehensive personality analysis. To solve this problem, the proposed method proposes a novel developer portrait model, which could describe developers' programming styles more accurately with both global and local information extracted from texts. The proposed model firstly collects the log data produced in the process of continuous integration development. Afterwards, the proposed method proposes the personality portrait model based on BERT-Capsule network, which successfully combines global semantic features and local emotional features. The experimental results show that the proposed BERT-Capsule model can effectively extract the contextual information and the local emotional information of the text, thus improving classification performance of the developer portrait model.
FREE SPEECH, PRIVACY, AND AUTONOMY

Abstract While autonomy arguments provide a compelling foundation for free speech, they also support individual privacy rights. Considering how speech and privacy may be justified, I will argue that the speech necessary for self-government does not need to include details that would violate privacy rights. Additionally, I will argue that if viewed as a kind of intangible property right, informational privacy should limit speech and expression in a range of cases. In a world where we have an overabundance of content to consume, much of which could be called “information pollution,” and where there are numerous platforms to broadcast one’s expressions, it is increasingly difficult to maintain that speech should trump privacy.
A performance predictor for implementation selection of parallelized static and temporal graph algorithms

Abstract Task‐based execution of graph workloads allows various ordered and unordered implementations, with tasks representing dependencies between graph vertices and edges. This work explores graph algorithms in the context of ordered and unordered task‐based implementations, that trade‐off work‐efficiency with parallelism. The monotonicity of convergent graph solutions is the reason behind the trade‐off between work‐efficiency and parallelism. This trade‐off results in variable performance‐based choices within and across different machines (CPUs and GPUs), graph algorithms, implementations (ordered, relaxed, and unordered). Input graphs also augment this choice space, with this work analyzing temporally changing graphs in addition to the static graphs explored by prior works. These algorithmic and architectural choices are first explored in this work, and it is seen that different graph workload‐input combinations perform optimally on diverse architectural configurations. The resulting choice space is analyzed and this work represents it in the form of characteristic variables that correlate with each choice space. Using these characteristic variables, this work proposes analytical and neural network models to correlate these choice spaces to find the best performing implementation. The variables and the prediction models proposed in this work are also integrated with a state‐of‐the‐art performance predictor on a multiaccelerator setup, and shows geometric performance gains of 54% on a CPU, 14% on a GPU, and 31.5% in a multiaccelerator setup over baseline implementations without performance prediction.