Reviewerly/exHarmony huggingface dataset:
Research on Distributed Parallel Eclat Optimization Algorithm

Frequent Itemset Mining (FIM) is the core of tasks such as association rules and sequential pattern mining. With the increasing amount of data, traditional FIM algorithms become inefficient due to excessive resource requirements or high communication costs. In this paper, the Eclat algorithm in the frequent itemset mining algorithm is taken as the research point, and the parallel Eclat optimization algorithm BPEclat (Balanced Parallel Eclat) based on Spark is proposed to solve the performance shortcoming of Eclat algorithm in serial processing large-scale data. The algorithm is improved and optimized from many aspects: combining the pre-pruning and post-pruning depth pruning strategies to reduce the calculation of irrelevant itemset, compressing the candidate set size; using the prefix term to divide the data set, and using the range partitioning idea to balance the calculations node load, improve the parallel computing power of the algorithm. The experimental results show that the proposed BPEclat algorithm reduces the candidate set size by 25.3% and the time consumption by 32.5%. Therefore, it is possible to process massive amounts of data more efficiently and reliably, and has good scalability and universality.
Research on Line-Loss Correlation Analysis Technology of Distribution Network Based on Apriori Algorithm

The association analysis technique and its principle represented by Apriori algorithm are expounded. In this paper, the concept of "line-loss relaxation factor" is introduced to improve the traditional support and confidence, which is helpful to reduce the redundancy and improve the efficiency of the traditional algorithm. Through the analysis of the case verification, the correlation analysis technology can effectively mine the relevant factors affecting the line-loss of the distribution network, and can visually indicate the degree of influence, which is very important for proposing the corresponding impairment strategy.
Applying Holo360 Video and Image Super-Resolution Generative Adversarial Networks to Virtual Reality Immersion

Super-resolution deep learning methods focus on image processing solutions and discussions in two-dimensional super-resolution image processing NOT for 360 equirectangular images. Therefore, the motivation of this research is to establish the deep learning network model Holo360 SRGAN and data set of 360 equirectangular images, and observe whether the sharpness and noise of Holo360 SRGAN compared with the original image reach the optical verification standard. The results of this study point out two significant points: 1) For a convolution training core neuron with the best model architecture of Holo360 SRGAN with 360 images 8 K (8192 × 4096 px), FOV: 360°, the expanded the convolution core neuron size as 5 × 5 to contains more learning features. And 2) Holo360 SRGAN image experiment results, 6 ROI optical analysis clarity increased by 27%, and sharpness increased by 42%. The experimental original image noise SNR is 28.2 dB, and the Holo360 SRGAN (×2) noise SNR is 36.8 dB, so it is increased by +8.6 dB, and the amount of image detail is also increased. Contributions enhance the super-resolution visual experience of equirectangular video or image.
Natural Language Processing based Stochastic Model for the Correctness of Assamese Sentences

Increasing the number of social media users and the way these users write the regional languages have pushed the researchers to think about the originality of these languages. The native speakers are in fear that with time the regional languages may lose their originality. For such reason, the correctness checking at the time of writing is an important task in the field of natural language processing. The conventional methods for checking the correctness of a sentence are normally carried out by applying various syntactic and semantic rules and the rules are endless because of the free word order nature of the Assamese language. On the other hand, in the computational filed, the data-driven models are more appropriate than the rule-based system and the data-driven model is free from the rules. In this research work, a data-driven model is designed by using the different types of n-gram models like unigram, bigram, and trigram to check the correctness of the Assamese sentences along with the linear interpolation method. Assamese is one of the twenty-two official languages of India, predominantly spoken in Assam and its neighboring states. A corpus of the size of around 1 million words is used to train the system. During testing, four different levels of experiments are carried out one for the correct sentences and incorrect sentences. The F1-score of the system in correcting the users' sentences is above 60%.
An Approach for the Evaluation and Correction of Manually Designed Video Game Levels Using Deep Neural Networks

Abstract In the current state of the video game productions, most of the video game levels are created by the human operators working as level designers. This manual process is not only time-consuming and resource-intensive but also hard to guarantee uniform quality in the contents created by the level designers. One way to address this issue is to use computer-assisted level design techniques. In this paper, we have proposed a novel framework for computer-assisted video game level design that leverages neural networks, particularly generative adversarial networks (GANs) and autoencoders. The general idea is to learn over a dataset of high-quality levels and subsequently improve the ones created by the level designers. The proposed method is independent of the graphical dimensionality of the game and will work for 2D and 3D games in general. The autoencoder is used to create an intermediate representation of the level that is itself changed using the backpropagation technique according to the feedback obtained by feeding the output of the autoencoder to the discriminator component of the GAN. After performing a series of evaluations on the proposed framework and by automatically improving a series of purposefully corrupted game levels, the results demonstrate a noticeable improvement compared with the usage of simple autoencoders used to improve the video game levels in the previous researches.
Wizard of Oz and the Design of a Multi-player Mixed Reality Game

This paper describes the use of the WOz method in the development of a prototype for a multi-player mixed reality game for children. It is an adventure game with hidden treasures, clues to hiding places, and information that should not be revealed. The game design, however, includes deceptive elements aimed at luring players to give up information. The game’s underlying intent is to raise children’s online risk awareness. The WOz was used in the early developmental stage to evaluate and explore the game concept, and to find a way to synchronise and integrate different in-game processes. We describe four central game mechanics for which the wizarding proved to be highly useful. We also discuss some ethical aspects related to the method a such as well as to the game design. In sum, we found the WOz method as such to be very useful for game design and development.
Study on Automated Approach to Recognize Characters for Handwritten and Historical Document

Script recognition is the mechanism of automatic script analysis and recognition whereby intensive study has been carried out and a significant amount of papers on this problem have been released over the past. But there are still a few issues to be solved, particularly in Indian historical manuscripts. This literature examines the Script recognition with reference to multi-script document and different historical scripts such as Kurdish-Latin, Devanagari, Grantha, Arabic handwritten characters, Bangladesh, Devanagari and Gurumukhi, ancient Chinese, Arabic, Nam Character, Greek, Nastalique Urdu, Georgian handwritten, Nandinagari, and Hebrew, which provide the course of study that focuses on the framework for script recognition. This review concentrates on scope of prediction, dataset type, the methods used for data preprocessing, and measures of performance used for analysis. On the basis of this survey, Current research constraints have been recognized and future study specifications are emphasized in the area of modeling historical manuscripts. CCS Concepts:
Automating Bulk Commodity Trading Using Smart Contracts

Blockchain technology as an approach for decentralized and distributed ledgers provides a natural fit solution for trading applications.A number of existing approaches have been developed in the recent years aiming to support trading in various markets by means of this technology.However, the role of blockchain in these approaches is in many cases confined to recording the consensus on the transferred assets prescribed by trade agreements that are made outside of the chain itself.The premise of this work is that blockchain smart contracts can actually be leveraged to automate the creation of those trading agreements, adding a new level of autonomy in the operation of the market itself.For these purposes we design and realize as a proof of concept an Ethereum-based network of peers trading electricity automatically using Solidity smart contracts.Beyond introducing automated matchmaking capabilities for bids in the market, the proposed approach also offers balancing of computational effort across peers.An additional benefit is that it minimizes the influence of single nodes to the operation of the automated market by moving into a proof of stake-like scheme of rotating leadership for block mining.Experimental evaluation of the work shows promise, but further work is required for a full blown solution.Lessons learned for Ethereum-based solution development are also offered.
All You Need is Web: Visual Interaction with No Graphic Background

Effective communications between designers and developers remain a crucial aspect of the software development process. In the current study, we propose the approach of sharing the visual interaction design. The proposed approach is based on the idea that the designer shares animations directly from the designer’s application environment with a single click. Developers access design data via the web browser in a convenient form where the visual interaction design represents an interactive video. According to the conducted user study, most designers (69%) and developers (81%) were satisfied with the approach concept in general. Both groups of specialists highly evaluated the user interface of the implementation. Performance measurements revealed that interactive video is about 92% faster than pure graphics on average design complexity. Most survey participants (82%) agreed with the idea that interactive video is good enough to express animation. The results show that application development using the proposed approach improved human-computer interaction. It allows designers to save time on sharing design information and allows developers to effectively adopt comprehensive design for new applications.
Introducing Mobile Device-Based Interactions to Users: An Investigation of Onboarding Tutorials

Various built-in sensors enable interacting with mobile devices beyond the screen. So-called mobile device-based interaction techniques are characterized by movements and positions in real space, e.g. twisting the device to switch between front and rear camera or pouring photos from one device into another for sharing. Although interactions should be as intuitive as possible, it is often necessary to introduce them, especially if they are complex or new to the user. Applications have to present interactions appropriately so that users can understand and use them easily. We conducted a user study to investigate the suitability of onboarding tutorials for mobile device-based interaction techniques. Results show that these types of tutorials are insufficient for communicating mobile device-based interactions, mainly because of their spatial and tangible characteristics but also their collaborative and representative interdependencies. Based on this, we propose suggestions for improving the design of tutorials for device-based interactions with mobile phones.
Regulations and Standards in Public Cloud

Security is a critical issue particularly in public cloud as it rests with the cloud providers. During security implementation, prevailing security threats and regulatory standards are borne in mind. Regulatory compliance varies from one cloud provider to another according to their maturity and location of the data center. Thus, subscribers need to verify the security requirement meeting their objective and the one implemented by the public cloud provider. To this end, subscribers need to visit each cloud provider's site to view the compliance. This is a time-consuming activity at the same time difficult to locate on a website. This work presents the prominent security standards suggested by the leading security institutions including NIST, CSA, ENISA, ISO, etc., that are applicable to the public cloud. A centrally-driven scheme is proposed in order to empower the subscriber to know the regulation and standards applicable according to their services need. The availability of an exhaustive list at one place will lower the users hassle at subscription time.
Opportunities for Personalization for Crowdsourcing in Handwritten Text Recognition

Transcribing historical handwritten documents is a difficult task. One facet is that it is a very tedious task normally performed by experts. Some newer techniques rely on crowdsourcing of manual transcription. Crowdsourcing helps speeding up the transcription process, but it is still limited and brings with it new challenges.
Algorithms of Experimental Medical Data Analysis

The paper is devoted to the development of a computational technique for assessing the performance of tissue regeneration in an experiment using mesh nickel-titanium implants with shape memory. Observational data obtained from electron microscopy and classical histological examination are processed and analyzed by the use of proprietary algorithms and their modifications. This can significantly facilitate the procedure of data analysis and increase the accuracy of the estimates by 5-10%. As a computational method for examining the dynamics of the studied process and determining the internal geometrical characteristics of empirical images of objects concerned, the suggested technique includes algorithms of shearlet transform, wavelet transform and construction of elastic maps for efficient visualization of spatial data. The significant part of the recommended method is the computing means of visual data preprocessing for increasing the brightness and contrast of the examined images based on the Retinex technology. This part has a significant impact on the quality of applying the tools of the computer-based evaluation presented in this work.
Construct a biased SVM classifier based on Chebyshev distance for PU learning

In some real applications, only limited labeled positive examples and many unlabeled examples are available, but there are no negative examples. Such learning is termed as positive and unlabeled (PU) learning. PU learning algorithm has been studied extensively in recent years. However, the classical ones based on the Support Vector Machines (SVMs) are assumed that labeled positive data is independent and identically distributed (i.i.d) and the sample size is large enough. It leads to two obvious shortcomings. On the one hand, the performance is not satisfactory, especially when the number of the labeled positive examples is small. On the other hand, classification results are not optimistic when datasets are Non-i.i.d. For this reason, this paper proposes a novel SVM classifier using Chebyshev distance to measure the empirical risk and designs an efficient iterative algorithm, named L∞ - BSVM in short. L∞ - BSVM includes the following merits: (1) it allows all sample points to participate in learning to prompt classification performance, especially in the case where the size of labeled data is small; (2) it minimizes the distance of the sample points that are (outliers in Non-i.i.d) farthest from the hyper-plane, where outliers are sufficiently taken into consideration (3) our iterative algorithm can solve large scale optimization problem with low time complexity and ensure the convergence of the optimum solution. Finally, extensive experiments on three types of datasets: artificial Non-i.i.d datasets, fault diagnosis of railway turnout with few labeled data (abnormal turnout) and six benchmark real-world datasets verify above opinions again and demonstrate that our classifier is much better than state-of-the-art competitors, such as B-SVM, LUHC, Pulce, B-LSSVM, NB and so on.
Anti-LGBT Free Speech and Group Subordination

This Article is about the tension between liberty and equality. It examines this tension in the context of disputes over free speech and LGBT rights. In the modern Civil Rights Era, the social and legal climate has become increasingly intolerant of bullying, embraced liberal sexual and gender norms, and sought to institute formal equality for formerly disfavored groups. The conservative movement has responded in part by seeking refuge from progressive change in constitutional jurisprudence, articulating theories of both the First and Fourteenth Amendments to effectively protect the status quo. Because of a receptive Supreme Court, dominant conceptions of both equal protection and free speech are informed today by libertarian ideology, reflecting a commitment to limited government oversight and regulation. A libertarian view of the Constitution, however, ensures that meaningful liberty and equality exist not for everyone but only for some. To create a more equitable society, constitutional interpretation must more adequately balance libertarian interests in the exercise of individual rights—like free speech—with the need for governmental action to promote the public good and address group-based harms.This Article draws from the field of critical race theory to advocate for an antisubordination approach in mediating competing claims of equality and liberty. Unlike a libertarian free speech jurisprudence that treats all speakers and viewpoints as equally worthy of constitutional respect, an antisubordination approach to free speech is attentive to historical and contemporary modes of group- based oppression. Simply put, if the triumph of a free speech claim would enforce a status hierarchy that positions historically marginalized groups as inferior, that free speech claim should fail.The need to reconceptualize liberty and equality came into sharp relief in the Supreme Court’s 2020 decision in Bostock v. Clayton County. The Bostock Court held that Title VII prohibits discrimination against gay and transgender employees,but the majority concluded by expressing concern for religious and conservative dissenters. The Court suggested that constitutional and statutory principles of religious freedom and free speech might override antidiscrimination commands in appropriate cases. The jurisprudence that emerges from these developing cases will help determine the scope of equality, the breadth of First Amendment liberties, and the Constitution’s role in addressing inequity for all marginalized groups.
From Artificial Neural Networks to Deep Learning: A Research Survey

Abstract Deep learning (DL) is one of the hot topics in the field of artificial intelligence. As an extension of artificial neural network (ANN), deep learning models have more powerful learning and adaptation capabilities and they develop more and more rapidly. At present, because of the great advantages compared with the traditional methods, the development of convolutional neural network (CNN) is the hottest and it is widely used. This article first introduces the development of DL in chronological order and then analyses and summarizes artificial neural networks, and then uses the example of the LeNet-5 to explain the basic network structure of CNN and describe its training methods comprehensively. Based on the CNN model, many new convolutional neural networks with improved structure are analysed, such as AlexNet, ZFNet, VGGNet, GoogleNet and so on. The principle of each method is introduced in detail and the accuracy is compared with each other to conclude the correlation between the depth and the accuracy of DL. Then, for each method, it discusses some current problems existing in the corresponding method of DL, and points out future research and application directions worth exploring of each method. Finally, it summarizes the history of DL and the application of specific methods and the existing problems and solutions.
Indoor Pedestrian Detection Based on Deep Learning

The aim of this paper is pedestrian detection for campus surveillance video to achieve pedestrian flow statistics. The core of this paper mainly includes two aspects: one is that corresponding to the problem of insufficient training data set, a sample fusion strategy is proposed to build a rich and highly matched training data set; the other is that according to the characteristics of experimental data based on the YOLO v3 algorithm, the model and loss function is optimized based on YOLO v3 algorithm. Experimental results on indoor pedestrian detection show that the detection accuracy is improved, which demonstrates the effectiveness of the proposed method.
Enriched and Discriminative Human Features for Person Re-Identification Based on Explainable Behaviors of Convolutional Neural Networks

Understanding pedestrian behaviors such as their movement patterns in urban areas could contribute to the design of pedestrian-friendly facilities. With the commonly deployed surveillance cameras, pedestrian movement in a wide region could be identified by the person re-identification (ReID) technique across multiple cameras. Convolutional neural networks (CNNs) have been widely studied to automate the ReID task. CNN models equipped with deep learning techniques could extract discriminative human features from images and show promising ReID performance. However, some common challenges such as occlusion and appearance variation are still unsolved. Specifically, our study infers that over-relying on discriminative features only may compromise ReID performance. Therefore, this paper proposes a new model that extracts enriched features, which is more reliable against those ReID challenges. By adding a feature dropping strategy during model training, our model learns to focus on rich human features from different body parts. Moreover, this paper presents an explainable approach of model design, by visualizing which human parts a deep learning model focuses on. Based on an intuitive interpretation of model behaviors that lead to inaccurate results, specific improvement of model architecture is inspired. Our improved results suggest that making existing models explainable could effectively shed light on designing more robust models.