Reviewerly/exHarmony huggingface dataset:
Five Senses’ Experience Model for Mirroring Online Shopping in IoT

The scene of experience shopping from mirror image of IoT was introduced in order to solve the distortion of consumer’s experience in traditional online shopping. And the technical process as well as the mathematical model for that scene were constructed, by which the consumer’s experience dimensions were raised to "sight-hearing-touch-smell & taste" five senses in mirroring online shopping of IoT from "audiovisual" two senses in traditional online shopping. At the same time, the online experience gray level was significantly reduced so that hoisting the consumers’ remote experience near to the scene of physical stores.
Text Detection by Jointly Learning Character and Word Regions

Text detection in natural scenes has developed significantly in recent years. Segmentation-based methods are widely used for text detection because they are robust to detect text of any shape. However, most state-of-the-art works are limited to word/line level detection as character-level data annotation is too expensive. Considering the close connection between characters and words, we propose a detector containing four different headers: Gaussian map, offset map, mask map, and centerline map, to obtain word-level and character-level prediction results simultaneously. Besides, we design a weakly supervised method to fully use the word-level labels of the real dataset to generate character-level pseudo-labels for training. We perform rigorous experiments on multiple benchmark datasets. Results demonstrate that our method achieves state-of-the-art results. Specifically, we achieve an F-measure of 85.2 on the dataset CTW1500, which is 1.3% higher than the state-of-the-art methods.
Scalable sub-game solving for imperfect-information games

Counterfactual regret minimization (CFR) is a popular and effective method for solving a game with imperfect information. The effect of CFR is limited by the size of the game state space. With the increase in the number of game participants, the game state space will increase rapidly. Although the vanilla CFR is suitable for two-player imperfect-information games, it does not work well in imperfect-information games with three or more players. In this paper, we design a framework for imperfect-information games, which can not only deal with two-player imperfect-information games but also can efficiently solve three-player imperfect-information games. Compared with traditional solving methods, in this framework we propose real-time hand abstraction (RTHA), which can reduce the error caused by the abstraction. We also propose a warm-start online solution of sub-game (WSOS-SG) method, which can improve the accuracy of the action estimation and solve the sub-game in real time. Experimental results show that the agent based on our method achieve better performances than traditional methods. The agent based on our method took part in the 2018 AAAI-ACPC poker competition and won third place in heads-up no-limit Texas hold’em.
Analysis and Prediction of Crime Using Machine Learning Techniques

Crime is an exceedingly alarming factor in India and it should be regulated at all costs. We are now able to use various strategies to reduce the crime rate in our nation, India, with the development of technology. This paper focuses on the detection of crime using deep learning and machine learning techniques. Pre-processing technique has been used to convert the data into a suitable format for analysis and for detection. Primarily focus on providing a forecast of the type of crime that may happen built on the place where it has now shown location. Machine learning has been used to build a model with the use of a training dataset that has gone through the data maintenance and renovation process. The proposed methodology is to analyze detection by making the use of machine learning processes such as decision tree and support vector machine for detection and also explored the k-nearest neighbors and artificial neural networks techniques to predict the crime, and the relevant authorities should then take precautionary steps to remain alert and monitor the situation. The characteristics of data are analyzed and realized by using data visualization tools. The algorithms are implemented in python language, and after training, the model performed testing using test data, and results were pretty good. The precision rate is achieved at 92% using neural network as compared with other algorithms, and also the processing time for confirmation is lowest with the neural network algorithm. Overall, of the three selected algorithms, the linear regression algorithm performed the best. The purpose of the work is to determine the precise and effective machine learning algorithms used in data analysis and in the prediction of models of violent crime.
Augmented Reality Based Data Improvement for the Planning of Aircraft Cabin Conversions

The introduction of digitalization technologies and methods into existing industrial processes can be seen in all industry sectors. The goal is to increase efficiency and productivity. In the aircraft industry, processes are still marked by paper-based documentation and a lack of digitalized data. This is especially the case in the planning of aircraft cabin maintenance, repair and overhaul (MRO). This causes regularly unexpected problems during MRO tasks and an extension of costly aircraft ground times. This paper describes a concept of increasing the planning reliability by implementing Augmented Reality (AR) into existing processes as a digitalization technology for the purpose of data acquisition. For this, the particular challenges of the addressed application need to be taken into account. The paper also describes a developed software tool. This tool is designed to make the acquired data accessible to planning engineers.
The influence of distance learning on the level of preparation of participants of the profile-level Mathematics unified exam of the Russian state

In connection with the harsh situation in the world in the spring of 2020, there was a problem with the organisation of school education throughout Russia. This article is devoted to the influence of distance learning on the results of the unified state exam in Mathematics (profile level). First of all, the dynamics of the total number of participants in the Maths exam over the past three years was considered. The article goes on to analyse the changes that occurred in 2020 in the content of control and measurement materials for Kostroma Region. The results of the exam participants were analysed in several groups – those who had not reach the minimum score, those who had scored 40 points or less, from 41 to 60 points, from 61 to 80 points and from 81 to 100 test points. Methods of qualitative and quantitative assessment (Pearson's criterion is applied) of percentage distributions of results of exam participants in various groups for 2019-20 years are described. Conclusions are drawn about the impact of the distance learning period on the results of the Maths exam.
Towards a Multidimensional Self-assessment for Software Process Improvement: A Pilot Tool

The SPI Manifesto provides a framework for guiding software development organisations in their improvement efforts. Based on the Values of People, Business and Change, which are supported and informed by one or more of the ten principles, the designer and developer can avoid pitfalls, minimise risks and make their business successful. It has been established, in theory and practice, that improving the process results in improvement of the products and services emanating from that process. Following a number of earlier multidimensional analyses of the SPI Manifesto, carried out by the authors, a series of tabular representations identifying the nature, importance and strength of relationships between the Manifesto’s Values and Principles in terms of eight dimensions encapsulated in the acronym STEEPLED (Sociocultural, Technical, Economic, Environmental, Political, Legal, Ethical and Demographic) were developed. In this paper, we present a conceptualisation of a pilot automated tool (based on the STEEPLED Analysis), which could, potentially, be implemented/realised and used for self-assessment by software developing organisations. Starting with a self-assessment, current issues and requirements could be identified and revealed. The self-assessment using the pilot automated tool would, additionally, reveal areas requiring improvement, and would serve as a guide for the participating organisation to put focus on prioritising candidate process areas that require improvement. Also, the field testing of the pilot tool could enable the design and improvement of the tool itself, which, in turn, will be used in future for expert external/independent process assessment.
The Direction Analysis on Trajectory of Fast Neural Network Learning Robot

This study is to efficiently apply artificial neural network (ANN) to the robotics, so as to provide experimental basis for mobile robots to learn the optimal trajectory planning strategy. An algorithm model is innovatively proposed based on back propagation neural network (BPNN) and reinforcement learning (Q-Learning) by combining the motion space, selective strategy, and reward function design. The simulation experiment environment is set and the ROS mobile robot is adopted for simulation experiments. The algorithm proposed in this study is compared with other neural network algorithms from the perspectives of accuracy, precision, recall, and F1. It can be found that the accuracy of algorithm proposed was at least 5.47% higher than that of the model algorithm proposed by other scholars, and the values of precision, recall, and F1 were at least 5.5% higher. The results show that the mobile robot could find the shortest trajectory and the best trajectory in a discrete obstacle environment, no matter the more or less the discrete obstacles or the large or small the space. Therefore, compared to the advanced model algorithms proposed by other scholars in related fields, the robot trajectory planning based on the improved BPNN combined with Q-Learning constructed in this study could realize better results, and can be used in practical applications with robot trajectory planning, providing practical value for the field of machine vision.
Meta-analysis of distortions effects on quality of digital images using standard IQA datasets

The research on image quality assessment has been an area of concern for the researchers from last many years. Images, during their life cycle, passes through several phases including acquisition, compression and restoration, for ease of transmission and storage, encryption and decryption for better security. During their processing, the quality of images is affected by many distortions, including compression artifacts, noise, bandwidth loss, etc., that are not present in the original images. The main emphasis of this research work is to analyze the effect of such artifacts on the quality of images. Further, a brief description of available image quality datasets, along with the popular quality evaluation metrics, is also presented. From experimental analysis in the present work, it is observed that when the images are degraded by impurities during their processing phases, then their quality degenerates by a relatively high degree, varying from minimum 1% to maximum of 55% under different sets of distortions, when measured on standard IQA metrics such as MSE, PSNR, PFE, SSIM, etc. This study will motivate the future researchers to have an elaborate quality assessment before employing these images for practical applications like medical sciences, remote sensing, etc.
Spatio–Spectral Limiting on Redundant Cubes: A Case Study

The operator that first truncates to a neighborhood of the origin in the spatial domain then truncates to a neighborhood of zero in the spectral domain is investigated in the case of redundant cubes—Boolean cubes with added generators. This operator is self-adjoint on a space of spectrum-limited signals. Certain invariant subspaces of this iterated projection operator, in which eigenspaces lie, are studied for a specific example. These observations suggest a general structure of eigenspaces of spatio–spectral limiting on redundant cubes.
Trends in the Transformation of Modern Law under the Influence of a New Stage of Scientific and Technological Progress

Th e a rt i cl e exami n e s t he tr e n d s of tr a ns fo r ma t io n of mode r n l a w u nde r t he i nflu ence of a technological breakthrough as a new stage of scientific and technological progress. The general trends of globalization and differentiation of law are highlighted, and the consequences of these trends, expressed in the growth of the number of complex branches of law that combine public and private interests, are considered. For the purpose of systematization, three main forms of globalization in law are identified: universalization, internationalization and integration. The analysis of the evolution of the legal system in the historical dimension allows to distinguish three stages of differentiation of modern law. At the first stage of the differentiation of law in the XX century, it was mainly expressed in the fragmentation of traditional branches of law. The second stage of the differentiation of law at the turn of the XX and XXI centuries is characterized by the intensive formation of complex mixed branches of law that join the institutions of public and private law for certain purposes of legal regulation. At the third, modern stage at the junction of the first and second decades of the XXI century. under the influence of scientific and technological progress, there is not only a further diversification and integration of traditional and new complex branches of law, but also a group of functional and instrumental branches of law. Th se branches of auxiliary law (due to its functional significance) determine the socio-legal technologies of regulation, implementation and development of law (for example, collision law, mediation law, digital law). Th e a ut ho r p uts fo rw a r d t he c o nc ep t a n d r e v ea ls t he syst em of b r a nc he s of l a w of t he ne w g e n e r a t io n a s t he hi g he st fo r m of e v o lut io n of c omp l ex b r a nc he s of l a w, just i fi e s t hei r s pe ci a l n a tur e a n d s i gn i fic a nc e i n t he syst em of l a w. Th e a ut ho r a ls o syst ema tiz e s t he b r a nc he s of l a w of t he ne w g e n e r a t io n, t aki ng i nt o a cc o unt t he de gr ee of t hei r de v e l opme nt in legal doctrine and law practice. S pe ci a l a tt e nt io n is paid to digital law and bio-law, which most clearly indicate the impact of scientific and technological progress on law, as well as exposing the problem of the need for closer interaction of the principles and norms of law and ethics.
Predicting Epidemic Outbreaks Using IOT, Artificial Intelligence and Cloud

All COVID-19 affected countries putting their efforts to deal with the outspread of this death-dealing disease in terms of infrastructure, economics, medical treatments and many other resources. Nowadays, there are number of coronavirus analysis and prediction models are available to make decisions and to informed, aware people. But, absence of necessary data, these models are not able to show precise values. Based on the datasets, reports and on account of the uniform nature of the coronavirus and variations in its behaviour from place-to-place, this study recommend ML as well as deep learning as worthwhile tool to model the outbreak. To come up with for the well-being of living society, we prefer to utilize the ML and deep learning models with the focus for understanding its everyday exponential behaviour in addition to the prediction graphs of further growth of the COVID-2019 over the world by utilizing the available facts and dataset.
Improving Billboard Advertising Revenue Using Transactional Modeling and Pattern Mining

Billboard advertisement is among the dominant modes of outdoor advertisements. The billboard operator has an opportunity to improve its revenue by satisfying the advertising demands of an increased number of clients by means of exploiting the user trajectory data. Hence, we introduce the problem of billboard advertisement allocation for improving the billboard operator revenue, and propose an efficient user trajectory-based transactional framework using coverage pattern mining. Our experiments validate the effectiveness of our framework.
Teaching Lexical Chunks in the Online English Language Classroom through Learning Management Systems

Current educational trends, globalization and the global Covid-19 pandemic have forced institutions of higher education (HEI) worldwide to accelerate the changes already in progress in teaching and learning approaches incorporating more technological enhanced learning. Among the wide range of information-communication-technology tools, the focus here is on Learning Management Systems (LMS), which play a major role in teaching and learning pedagogy, and can help HEIs to achieve more effective learning outcomes. This exploratory research examines the usage of Learning Management Systems to support the teaching of lexical chunks in the online English language classroom at HEIs. The investigation will focus on Moodle and will identify the factors that support students’ interaction with language content and input in this digital learning environment, by looking at the use of Moodle features and activities to teach and practice lexical chunks. Findings suggest that Moodle proves of great value for online English language teaching and the practice of lexical chunks, whereby quiz activities can be used to great effect, producing motivation to engage with lexical chunks among students.
Coded Matrix Chain Multiplication

The matrix multiplication is a fundamental building block in many machine learning models. As the input matrices may be too large to be multiplied on a single server, it is common to split input matrices into multiple submatrices and execute the multiplications on different servers. However, in a distributed infrastructure it is common to observe stragglers whose performance is lower than other servers at some time. In order to mitigate the adversarial effects of potential stragglers, various coding schemes for the distributed matrix multiplication have been recently proposed. While most existing works have only considered the simplest case where only two matrices are multiplied, we investigate a more general case in this paper where multiple matrices are multiplied, and propose a coding scheme that the result can be directly decoded in one round, instead of in multiple rounds of computation. Compared to completing the matrix chain multiplication in multiple rounds, our coding scheme can achieve significant savings of completion time by up to 90.3%.
Multiplex network embedding for implicit sentiment analysis

Abstract As one of the hot research directions in natural language processing, sentiment analysis has received continuous and extensive attention. Different from explicit sentiment words indicating sentiment polarity, implicit sentiment analysis is a more challenging problem due to the lack of sentiment words, which makes it inadequate to use traditional sentiment analysis method to judge the polarity of implicit sentiment. This paper takes sentiment analysis as a special sign link prediction problem, which is different from traditional text-based method. In particular, by performing the word graph-based text level information embedding and heterogeneous social network information embedding (i.e. user social relationship network embedding, and user-entity sentiment network embedding), the proposed scheme learns the highly nonlinear representations of network nodes, explores early fusion method to combine the strength of these two types of embedding modeling, optimizes all parameters simultaneously and creates enhanced context representations, leading to better capture of implicit sentiment polarity. The proposed method has been examined on real-world dataset, for implicit sentiment link prediction task. The experimental results demonstrate that the proposed method outperforms state-of-the-art schemes, including LINE, node2vec, and SDNE, by 20.2%, 19.8%, and 14.0%, respectively, on accuracy, and achieves at least 14% gains on AUROC. For sentiment analysis accuracy, the proposed method achieves AUROC of 80.6% and accuracy of 78.3%, which is at least 31% better than other models. This work can provide useful guidance on the implicit sentiment analysis.
Arbitrarily large <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="script">O</mml:mi></mml:math>-Morita Frobenius numbers

We construct blocks of finite groups with arbitrarily large O-Morita Frobenius numbers. There are no known examples of two blocks defined over O, with isomorphic defect groups, that are not Morita equivalent but the corresponding blocks defined over k are. Therefore, the above strongly suggests that Morita Frobenius numbers are also unbounded, which would answer a question of Benson and Kessar.
Fast Reachability Queries Answering based on RCN Reduction

Answering reachability queries is a fundamental graph operation. Considering that the size of the input graph has a great impact on query performance, there are studies focusing on reducing the graph size, such that queries can be answered over a smaller graph. Although the input graph can be compressed significantly by existing approaches, a good compression ratio does not always mean a positive effect on query performance. In this paper, we study graph reduction to accelerate reachability queries answering. We propose a novel graph reduction approach, namely RCN reduction, to compress the input graph into a smaller one. Let a be the compression ratio of the number of nodes in the reduced graph over that of the input graph, we show that based on our approach, the lower bound probability that a query q can be answered in constant time is 1-a^2. We show the difficulties of RCN reduction and propose efficient algorithms to improve the compression ratio. Based on the result of RCN reduction, we further propose a novel labeling scheme to accelerate queries answering. We confirm the efficiency of our approach by extensive experimental results for graph reduction and reachability queries processing using 20 real datasets.
Smartphones Verification and Identification by the Use of Fingerprint

The use of fingerprints as a form of identification is authentic, accurate and reliable in smart phones. Due to privacy, and people feel safe that their information is secured by using locks on their android phones and can be accessed through fingerprints and other methods. An application was developed in this paper named ‘closet’ that will collect finger prints from different individuals that you want to give access to the device but will make one of the fingerprints the main which will give full access to all the applications, documents, pictures, videos, phone call logs and messages but when the other fingerprint is identified on the android phone it automatically hides the information that want to be kept private making others not access what you want to be kept hidden. This application will only be accessed and utilized by android users. Due to the wide use of fingerprint to unlock mobile devices and the authenticity, this application will be proven to secure all the users information.