Reviewerly/exHarmony huggingface dataset:
A Fuzzy based naturalness preserving image color compensation method for colorblindness

Eyesight is one of the key senses possessed by human beings to perceive the world of colors. Color blindness is a form of Color Vision Deficiency (CVD) that affects about 0.5% of female and 8% of the male population [1]-[4]. The colorblind have difficulty perceiving specific colors called confusion colors. Hence, they face difficulty in handling routine activities [5]. With the increased use of the web, it is imperative to provide digital solutions to help the colorblind perceive colors. The authors propose a novel method using fuzzy based clustering to provide color compensation in images for red green color blindness. The method aims at optimizing the color enhancement by clustering the confusing colors and color compensating them. To achieve this, the authors have used the fuzzy C means algorithm and the delta E method to calculate the Euclidean distance between the original color and the compensated color. The delta E method ensures that the difference computed is within the perception of colors by humans. The efficiency of color compensation is measured by a) subjective evaluation by normal vision users b) evaluation by colorblind users and c) comparison with state-of-the-art method. The evaluation results affirm the efficiency. With the increased use of visualization and digitization of daily activities, the proposed method would aid colorblind users and help them to distinguish confusion colors in the images effectively.
ARTIFICIAL INTELLIGENCE AND MUSIC: A DIALOGUE OF OPPOSITES?

The study of rational algorithms of musical creative activity is an important element on the way to creating working models of artificial intelligence in the future. Although nonverbal information is very difficult to discursively analyze and interpret, non-discursive methods of obtaining information, storing it, and transmitting it are increasingly used along with traditional rationality. To day, quite successful experiments have been conducted on verbalization and algorithmization of musical creative activity. Of course, there are more questions than answers, but, nevertheless, it is already necessary tostate a fairly successful application of computer technologies not only for describing musical creative processes, but alsofor simulating and reproducing the creative potentials of composers by artificially created computer programs. It is at this point that the possibilities of the human mind and computer programming converge, and this is an important step towards creating artificial intelligence.
Graphical Models

The beginning of this book illustrates that linear regression models can describe the relationships between the genes’ copy numbers and a biomarker. However, those models do not provide information about the relationships among the copy numbers themselves. To describe such relationships, we use a different type of models, called graphical models. We neglect the biomarker and summarize the measured copy numbers in vector-valued observations, where each vector corresponds to a specific subject and each coordinate of these vectors to a specific gene (in the linear regression model, these vectors are the rows of the design matrix). Graphical models then formulate the relationships among the copy numbers as conditional dependence networks among the coordinates of the vector-valued observations. If the observations follow a multivariate Gauss distribution, we speak of Gaussian graphical models. This is the most common class of graphical models and, therefore, the focus of this chapter.
Ultra-short-term Distributed Photovoltaic Power Forecasting Based on Cloud Image Feature Extraction

Distributed renewable resources have the characteristics of scattered geographical location, different scale and violent power fluctuation. In order to make full use of the potential of distributed renewable resources, high requirements are put forward for prediction methods. This paper proposes a forecasting method for distributed photovoltaic power prediction. Firstly, this paper establishes a fixed square window with the sun as the center, processes the cloud information and creates an image with only the relationship between the gray and the position of cloud. Secondly, according to the historical data, the position and grayscale picture of the cloud cluster at the next moment are generated. In the third step, proposes the influence factor of cloud cluster to evaluate the influence of the cloud distribution and thickness on the surface illumination, then input it into the improved CNN network as an auxiliary parameter, combined with the current outer atmospheric tangent plane solar radiation to get the predicted illumination at the next time. Finally, outputs the predicted photovoltaic power value, which combine the predicted illumination, current real-time temperature with the efficiency of the inverter. The case study demonstrates that this method has strong adaptability and it is capable of forecasting ultra-short-term changes in photovoltaic power. The method can greatly reduce the amount of data to the prediction network and also accelerate the convergence speed while ensuring the predict accuracy.
Timing Side-Channel Attack Resistant Key Derivation Functions for Cryptosystems

In today&#x2019;s digital world, security is required at each level of the implementation, even for Cryptographic algorithms, as we depend digitally on various needs of our daily life. Also, hackers are trying many ways to hack confidential information, and side-channel attacks are one of them. Key Derivation Functions (KDFs) are the basic and essential components of cryptographic systems; therefore, their security becomes more crucial for such systems. This paper presents the implementation of KDFs based on Block Ciphers and Hash functions (AES-128 and SHA-256, SHA- 512). The key feature of this implementation is that it generates a unique and secure key. Moreover, the generated key is less prone to the Timing side-channel attacks. These KDFs are implemented using high-level language C in Xilinx Vivado HLS. The results show that the proposed design offers a highly secure generated key while mitigating the Timing side-channel attacks possibility. Security analysis is done in terms of Hamming Distance and Avalanche Effects. The reported KDF based on AES-128 operates at 152.3 MHz with a max throughput of 9.728 Gbps, whereas KDF based on Hash Function, i.e., SHA-256 and SHA-512, operate at 108.6 MHz and 118.1 MHz with the max throughput of 2.28 Mbps and 2.28 Mbps, respectively.
Design of library smart cloud service system based on blockchain technology

Abstract Blockchain is equivalent to a transparent billing, and it eventually becomes a perfect storage or storage technology through many ways. Smart cloud uses internet cloud computing and other computing methods to create a scientific and humanized intelligent service system based on office. This article aims to use blockchain technology to design and study a smart cloud service system suitable for optimizing services and managing libraries. This article proposes that a special algorithm using blockchain technology can effectively improve the performance of all aspects of the smart cloud service system and improve the rating scores of users on the service system. In the experiment, the calculation of different iteration times in different algorithms of the blockchain obtains the experimental comparison chart, and it is obtained that the FUJK3 algorithm is better than the other two algorithms. The algorithm can increase the overall performance of the smart cloud service system by about 10 points, and the user's rating score for the service system by about 15 points.
Aspect-words Sentiment analysis of commodity comments based on deep memory network

The social model is a huge virtual platform where to freely express themselves and give their views and feelings, influencing any aspect of life, with implications for marketing and communication alike. Aspect words sentiment analysis can more accurately understand user needs and improve enterprise marketing strategies. In current researches on aspect words sentiment analysis, researchers use the integration of attention mechanism and LSTM to obtain key information. However, there are few studies on the fusion of aspect words, context, and multi-layer deep memory networks. Therefore, we proposed a multi-layer deep memory network model based on the splicing of aspect terms and context vectors. The model can further strengthen the fusion between aspect words and context vectors, and make up for the shortcomings of LSTM in transmitting information loss. Experimental results on Restaurant and Laptop datasets show that the proposed method has a better performance.
Research on danger diagnosis system using image recognition AI

現代の日本は高齢化社会と言われ介護は誰しもが他人事ではなく,親の面倒を見ないといけない可能性がある.そのため,誰しもが職業上の訓練や準備など無しに介護に従事する必要が生じる可能性がある.そこで本研究はAIによる見守りの目を増やし,事故を未然に防止するため,危険察知システムを開発する.介護者の負担を軽減させることを目的に深層学習を用い介護の中でも最も危険な事案である転倒に焦点を当て,どんな危険があるかを明らかにし,そのシステムの設計をした.本論文では,介護における危険を調査した結果と,危険察知システムの設計内容について報告する.
Gesture Recognition Algorithm Based on Lightweight 3DCNN Network

Aiming at the slow recognition speed of the video- based dynamic gesture recognition method and the large number of parameters in the deep 3D CNN network, we propose a fast gesture recognition method, which is an improved lightweight network Lite-HRNet. Firstly, we convert the 2D lightweight network Lite-HRNet to a 3D CNN network to recognize video-based dynamic gestures while generating fewer parameters. Secondly, this work only converts the first half of the Lite-HRNet network to a 3D CNN network, which can significantly improve the recognition speed of the method. We extensively evaluate our method on the EgoGesture gesture dataset. Experiments show that our proposed method dramatically improves the speed of dynamic gesture recognition and demonstrates superior recognition results.
Sequential multi‐headed attention for entity‐based relational neural networks

Abstract The capacity of relational interaction between high‐level information and reason is the defining characteristic of human intelligence. Regardless of the remarkable progress in artificial intelligence, recent machine reading comprehension models still heavily rely on high‐dimensional word‐based distributed representations. Since these models employ statistical means to answer questions of complex textual corpus and employ an accuracy‐based metric system, their learning capacity of the required skills is not guaranteed. To ensure the capacity of MRC models to learn the desired skills, explainability has become an emerging requirement. In this paper, we propose an end‐to‐end natural language reasoning model that is based on sets of high‐level aggregated representations which promote operational explainability. To this end, sequential multi‐head attention, and a loss regularization function is proposed. We show analysis of the proposed approach on two natural language reasoning oriented question and answering datasets (bAbI and NewsQA).
Secure Internet Voting using Blockchain Technology

Many countries have been using online voting systems instead of ballot papers. In such voting systems, voters can cast votes online using their cell phones, laptops, and other internet-connected devices. Online voting systems have centralized databases where a malicious attacker can hack, change or manipulate the database. Therefore, current online voting systems present a security concern for any government. Blockchain is a peer-to-peer technology in which data is stored in a tamper-proof ledger and maintained by participating peers. In this paper, we propose a secure online voting system using Ethereum Blockchain and Ethereum Smart Contracts. A voter can cast her vote securely after scanning her fingerprint and iris recognition. Our proposed methodology uses Paillier homomor-phic encryption and the private key shifting technique to ensure voter privacy and identity security.
Introduction

Why do texts and readers need authors? Why is “authorship talk” so prevalent in literary conversations – whether at book fairs, book clubs, or readers’ groups, in literary magazines, newspapers, university seminars, or social media? These questions may seem absurd, at least to those who are blissfully unaware of, or have happily moved on from, twentieth-century debates about “the intentional fallacy,” “the death of the author,” or indeed his or her “return.”1 But, as we hope to show in this handbook, these questions have a relevance for literary studies that transcends the theory wars of the past or the narrow confines of the discipline itself. They are – or should be – central to the field if only because questions of authorship are of great popular interest, given the media attention devoted to, for instance, celebrity authors and the size of their advances, accusations of plagiarism, the gender of an anonymous author, or the Shakespeare authorship cottage industry.2
Energy and Cost Considerations for GPU Accelerated AI Inference Workloads

Recent advances in AI have motivated hardware manufacturers to design deep learning friendly accelerators to keep with the ever-growing increases in model sizes and compu-tational requirements. While early accelerators were utilized for model training, newer accelerators are capable of running deep neural network (DNN) model inferences and are increasingly used in robotics, vision, and edge applications. In this paper, we compare several popular embedded and desktop GPUs with respect to their performance and energy efficiency. Our results show that although larger devices always provide higher throughput, they are not always the most energy-efficient. GPUs vary in terms of their energy efficiency. To aid the process of hardware selection for a system designer, we use our experimental results to design a recommendation algorithm that chooses the ideal hardware accelerator under cost, power, and performance constraints.
Application of Metaheuristic Approaches for the Variable Selection Problem

Variable selection is an old topic from regression models. Besides many conventional approaches, some metaheuristic approaches from the realm of optimization such as GA (Genetic Algorithm) or simulated annealing have been suggested to date. These methods have a considerable advantage to deal with many problems over the classical methods, but they must control relevant fine-tuning parameters associated with cross-over or mutation, which can be difficult and time-consuming. In this paper, Jaya, one of several parameter-free approaches will be suggested and explored. Several metaheuristic methods will be compared using results from a real-world dataset and a simulated dataset. The impact of using local search will be analyzed.
A Novel Rough Set Based Image Denoising Algorithm

The primary focus of the paper is towards image enhancement via removal of salt and pepper noise from images. In this paper, a novel statistical approach based on the properties of rough set theory is proposed, where noisy pixel identification and removal are controlled by decision rough parameters. Each enhancement decision is directly governed by four parameters &#x2013; the pixel is noisy or not, the pixel has any non-noisy neighbor compatible enough to replace it, the deviation of the neighboring pixel from the central pixel value, and matching of the threshold criterion. The four phase decision making algorithm fetches highly accurate results and with consecutive iterations and upgradation, the algorithm is able to remove all noisy pixels while maintaining fine details of the image for even 95% corruption levels.
Computer intelligent recognition and studied of the pronunciation of Xiahe dialect database system through speech aerodynamics

This paper takes Xiahe dialect of Tibetan Amdo as the research object, and there is a special linguistic phenomenon in Xiahe dialect-compound consonants. The paper sorted out the compound consonant system of Xiahe dialect and showed the air flow waveform of different compound consonants. According to the analysis of the air flow parameters of different pre consonant pronunciation, it is concluded that the clarity of pre consonant has a certain impact on the pronunciation duration and average air flow speed.
Clustering Text Documents Using High Utility Itemset Mining

The focus of the study in this paper is on clustering text documents using high utility itemset mining. The proposed algorithm groups two documents based on the number of high utility words which are common to both the documents. The utility of a word in a document reflects the frequency of occurrence of the word in the document. High utility words are the once having high frequency of occurrence. Utility of a word can be influenced by the section of the document to which it belongs. Each section is assigned weight depending on the importance of the section. Depending on the section to which words belong, although not frequent, they reflect the content of document better than some frequent word. Such words are given more weight so that they belong to the set of high utility words. Therefore, in this paper, the utility of a word is expressed as a function of its frequency of occurrence and the weight of the section it belongs to.
Unraveling the Rubik’s Cube with Autodidactic Iteration Algorithm

The autodidactic iteration algorithm was designed in hopes of finding an approximate solution to combinatorial optimization puzzles such as the prediction of protein tertiary structures. The prediction of protein tertiary structures allows a better understanding of its function in an organism. The Rubik’s cube is selected as a placeholder for the combinatorial optimization puzzle to represent the protein tertiary structures. The autodidactic iteration algorithm is used in environments with large state spaces and sparse rewards such as combinatorial puzzles like the Rubik’s cube. This research to solve a classic Rubik’s cube using the implementation of the autodidactic iteration algorithm is a reinforcement learning algorithm that can teach itself to solve the Rubik’s cube without human assistance. A neural network model is then trained to solve a scrambled Rubik’s cube while implementing Keras as the deep learning library. The Rubik’s cube is generated in a graphical user interface, GUI using Magic Cube. The Rubik’s cube generated in the GUI can be interacted with by turning the faces of the Rubik’s cube and changing the viewing angle of the Rubik’s cube. The interactive Rubik’s cube then uses the neural network model trained based on the model trained to solve a scrambled Rubik’s cube.
Creator2Vec: Creator Feature Embedding for Deep Learning Recommender System

The current embedding method neglects to deeply explore the associations between the items created by the same item creator, and directly puts the long-tail distributed categorical creator feature into the model for end-to-end training. Because the tail data provides too little information, it is difficult to train stable and meaningful embedding vectors. To this end, we propose an improved embedding method Creator2Vec used for recommender systems, which is based on the items produced by the creator to characterize the creator. Specifically, we first use Word2Vec to generate item embedding, and take the average of item embedding created by the creator as creator embedding. Then, according to the quality of item comments, we design a feature extraction method weighted to mine the information of item comments and comments on item comments. Benefit from the proposed method, we not only use item ratings as features, but are also concern that item comments with the same rating and different qualities have different recommendation effects. Finally, we uses the number of likes of item reviews as a standard to measure the quality of item comments. The empirical results on a practical dataset show that Creator2Vec and weighted cumulative item rating features, as the input layer of common deep learning models, have good effects on binary classification recommendation tasks.
Learning Strategies Public University Students at the Time of COVID-19

After more than a year of a pandemic that forced educational institutions of all levels to make changes in teaching practice and continue with the teaching-learning process in a virtual way, the presence of the COVID-19 pandemic has allowed experience the needs or limitations of access to Internet connectivity, technological resources, management of digital tools and appropriate strategies; not only in teaching but also in student learning strategies. The following research objective was proposed: to determine the learning strategies used by University students in the time of COVID-19, in a Public University of Peru. A sample of 302 students from the 2020 academic period was considered, for which the ACRA-Abridged learning strategies instrument (ACRA-A) for university students from De la Fuente Arias et al was applied; 65% were students of Health Sciences and 35% students of Social Sciences and Humanities, comprised in two age groups: the Middle includes students from 16 to 19 years old and the Late group includes students from 20 years old and over. The 20% of the students in the area of Health Sciences and Social Sciences and Humanities used comprehension strategies and study habits in online learning, supported by technological resources, provided by the Integrated University Management System (SIGE) of the University.
Object Detection with Few-Shot Learning and Data Augmentation

Deep learning based object detection techniques require large amount of training data and long training time. However, the availability of large annotated image dataset is usually limited and expensive to generate. Moreover, training with large dataset is slow and thus not suitable for real-time applications such as video surveillance which need to learn to detect new object categories or adapt to environmental changes quickly. In this paper, a framework is proposed for object detection with little data samples using few-shot learning and data augmentation. The proposed framework adopts the meta-learning paradigm where a base detection network first learns to extract meta-features from the base classes with abundant training samples and then an adaptation network is trained to generate class-specific weights to adapt the meta-features to detecting novel object classes using only a few support samples. Moreover, data augmentation is applied on the support set to boost the detection performance further. The proposed framework shows superior few-shot object detection performance over state-of-the-art methods on benchmark dataset.
Deep Learning Based Text Detection and Recognition in Natural Scenes

Natural scene images usually contain important text information, which has a strong correlation with what is displayed in the image. It can help us better understand the image and identify the scene at a higher semantic level. With the development of society, the demand for automatic detection and recognition of text in images is increasing, which is a challenging topic and has strong application value, such as unmanned driving systems, mass image search, intelligent transportation systems, etc.The traditional optical character recognition (OCR) technology is becoming more and more mature, and the scanning text detection technology has reached an advanced level. However, due to the high complexity of the background and font itself in the natural scene, there are still many technical problems in character recognition, such as font bending, deformation, high ambiguity, etc. With the development of artificial intelligence, deep learning has gradually become the mainstream method. In order to further improve the accuracy and robustness of detection and recognition, this paper designs an end-to-end text detection and recognition system based on deep learning algorithm by analyzing and improving the SegLink text detection model and the recognition model combining CTC and attention mechanism. Experiments show that the proposed system framework can effectively accomplish the task of text detection and recognition in natural scenes.