Reviewerly/exHarmony huggingface dataset:
CNN for Image Identification of Hiragana Based on Pattern Recognition using CNN

Hiragana is one of the letters in Japanese. In this study, CNN (Convolutional Neural Network) method used as identication method, while he preprocessing used thresholding. Then carry out the normalization stage and the filtering stage to remove noise in the image. At the training stage use maxpooling and danse methods as a liaison in the training process, wherea in testing stage using the Adam Optimizer method. Here, we use 1000 images from 50 hiragana characters with a ratio of 950: 50, 950 as training data and 50 data as testing data. Our experiment yield accuracy in 95%.
Artificial Excellence

This article introduces a new field called "Artificial Excellence”. "Artificial Excellence” is a new field that falls under the area of Artificial Human Optimization. Evolutionary Computing has a subfield called Artificial Human Optimization. Computational Intelligence has a subfield called evolutionary computing. Artificial Intelligence includes the field of Computational Intelligence. As a result, "Artificial Excellence (AE)" will gain popularity as a new area of Artificial Intelligence following the publication of this paper (AI). This paper proposes a novel algorithm called "Artificial Satish Gajawada and Durga Toshniwal Algorithm (ASGDTA)."This article includes a definition of AE as well as numerous opportunities in the new AE field. Following the definition of Artificial Intelligence, the Literature Review of the Artificial Excellence field is displayed. The novel ASGDTA Algorithm is discussed, then Results and Conclusions are shown.
Personal Factors Associated with Digital Competencies in University Students in the Context of Pandemic

The present research aimed to establish the association between personal factors (age, sex, hours connected and school of origin) and the digital competencies of university students, which have been originated and developed with a greater degree of intensity during the pandemic, generating as a consequence the need for distance education. The study was basic, explanatory, and quantitative, with a non-experimental and cross-sectional design. The sample consisted of 1242 students of the Faculty of Medicine from the tenth to the fourth cycle: 658 males (53.0%) and 584 females (47.0%). A probabilistic sampling by strata was performed and the questionnaire of Gutiérrez et al. (2017), an instrument for the evaluation of the digital competence of the university student with a reliability of 0.997, was used. It was concluded that personal factors are associated with digital competencies in students of a private university because the personal factors of sex, age, school of origin and hours connected to social networks presented p &lt; 0.05. Regarding descriptive results, 52.3% of male students presented ineffective level. Of students aged 22 years or younger, 58.2% presented an ineffective level. It was observed that 57.7% of the students who come from private schools presented an ineffective level, and 57.1% of the students who connect for 3 hours or less presented an ineffective level.
Credit evaluation for power infrastructure contractors based on blockchain technologies and fuzzy analytic hierarchy process

With the continuous expansion of power infrastructure scale and the ceaseless growth of project funding, problems such as the opaque flow of capital, the arrears of workers’ wages and project funds, and the lack of a sound management mechanism become more and more serious. Therefore, a credit management method for infrastructure contractors of power systems based on blockchain is proposed. First, a blockchain-based framework of contractor credit management is designed for solving the infrastructure problems above. On this basis, a credit evaluation system is constructed for infrastructure contractors of power systems. Then, a credit behavior analysis method based on cross-chain technology and the credit result query mechanism based on proxy signature is proposed to realize the data interaction and the credit result query. Finally, the effectiveness and accuracy of the proposed method are illustrated by the case study. The simulation results show that the method proposed in this work can effectively reflect the credit status of power infrastructure contractors and the wages payment has the highest weight of 0.2240 among all the third level indicators with its obvious important role in power infrastructure management.
Multimedia Computer-Aided Industrial System Design Based on the Study of Big Data Mining Algorithm

Aiming at the problems of the multimedia computer-aided industrial system, this paper puts forward the application of big data mining algorithm to multimedia computer-aided industrial system design and analyzes in detail the impact of multimedia technology on industrial quality. This paper introduces the advantages of using big data mining algorithm in multimedia computer technology course, shows the operating environment to be met by using the multimedia computer-aided industrial system, follows the guiding principles of the overall design learning theory and artistic conception cognition theory, supplements specific industrial examples, and discusses multimedia industrial design.
Review in Authentication for Mobile Ad Hoc Network

As far back as the 90s, Mobile Ad hoc Networks have successfully gained the scientific community interest because of the ease of their deployment and administration in many domains such as military, collaborative work, crisis conditions and environmental applications. This network technology provides opportunities for companies to reduce software operating costs at anytime and anywhere. However, authentication of nodes is a challenging task due to its open environment and resource constraint infrastructure. For this purpose, we will present in this paper a survey about some different techniques of authentication of Mobile Ad hoc Networks existing in the literature.
Comparative Analysis of Native and Cross-Platform iOS Application Development

Abstract Nowadays, mobile applications are developed for more and more areas, providing great help for our everyday lives. When designing a mobile application, the first important decision to make is to choose the targeted platform. Is it only phone or tablet as well? Should the app run on Android or iOS, or should it be available on both mobile operating systems? In the latter case, besides the native development environments, it is worth considering a cross-platform development environment to write the software. This study investigates both the development and performance aspects of some possibilities for iOS application development, namely, native iOS development in Xcode, Xamarin.iOS, and Xamarin.Forms frameworks.
Human recognition in a cluttered indoor environment by sensor fusion

The detection of human in a cluttered space is an incumbent requirement for a mobile robot functioning in an indoor environment. This paper addresses this issue in a threefold approach method to solve the human detection in an indoor environment by a mobile robot. Firstly, the distance information is obtained from the RGB-D camera and the LiDAR sensor and then it is evaluated with an extrinsic calibration method for an accurate tracking. Then, an object detector YOLO v3 is used for the human classification and detection from the image generated by the RGB-D camera. YOLO then returns the region of interest (ROI) which will be selected for the depth data for generating the human position. Lastly, the selected depth information of human is added to the position information of LiDAR sensor for more accurate localization of the human. The proposed approach shows effective human tracking result for small mobile robot interacting with human.
Designing an Arabic Google Play Store User Review Dataset for Detecting App Requirement Issues

User reviews in app stores are considered very rich maintenance information texts that developers need to know. A huge number of app reviews contain requirement details, such as bugs or problems, evaluation of user experience with some features, suggestions for improvements, and ideas for new features. Previous literature has illustrated different techniques and approaches to reduce the work needed to analyze and extract valuable content from mobile app reviews. However, little attention has been paid to analyzing and studying Arabic app reviews. This paper presents a dataset of some reviews under governmental service applications, which can provide an overview of the types of requirement issues that users report on the apps. The dataset contains around 51,000 user reviews from six applications retrieved from the Google Play Store, which were extracted with the scraping technique. In this dataset, different forms of analysis were conducted. The potential usefulness of the dataset was discussed for future research in the field of software development and improvement tasks. The dataset was made accessible to the public to support the community in further studies on the detection of app deficiency or requirement issues from Arabic reviews.
Application of Augmented Reality (AR) Technology in Power Grid Emergency Training

Abstract Strong reality has been applied to training operations, and the use of virtual and augmented reality in aerospace, manufacturing and shipbuilding industries has yielded significant results. This paper mainly studies the application of Augmented Reality (AR) technology in power grid emergency training. This paper designs and implements an intelligent operation and maintenance system based on mobile augmented reality under the Android operating system. Augmented reality technology is applied to substation equipment operation and maintenance. Through the design and development of modules such as data management, equipment identification, holographic display of equipment information, integrated management and remote assistance, the application of Augmented Reality technology in substation equipment operation and maintenance is realized. Based on augmented reality and identification technology, the auxiliary information is transmitted to the intelligent terminal display of field operators in real time to assist the power grid emergency training and improve efficiency.
Research on Human Motion Analysis in Moving Scene Based on Timing Detection and Video Description Algorithm

Technical movement analysis requires specialized domain knowledge and processing a large amount of data, and the advantages of AI in processing data can improve the efficiency of data analysis. In this paper, we propose a feature pyramid network-based temporal action detection (FPN-TAD) algorithm, which is used to solve the problem that the action proposal module has a low recall rate for small-scale temporal target action regions in the current video temporal action detection algorithm research. This paper is divided into three parts. The first part is an overview of the algorithm; the second part elaborates the network structure and the working principle of the FPN-TAD algorithm; and the third part gives the experimental results and analysis of the algorithm.
A systematic approach to identify the information captured by Knowledge Graph Embeddings

In the last decade Knowledge Graphs have undergone an impressive expansion, mainly due to their extensive use in AI-related applications, such as query answering or recommender systems. This growth has been powered by the expanding landscape of Graph Embedding techniques, which facilitate the manipulation of the vast and sparse information described by Knowledge Graphs. Graph Embedding algorithms create a low-dimensional vector representation of the elements in the graph, i.e. nodes and edges, suitable as input for Machine Learning tasks. Although their effectiveness has been proved on many occasions and for many contexts, the interpretability of such vector representations remains an open issue. In this work, we aim to tackle this issue by providing a systematic approach to decode and make sense of the knowledge captured by Graph Embeddings. We propose a technique for verifying whether Graph Embeddings are able to encode certain properties of the graph elements and we present a categorization for such properties. We test our approach by evaluating the embeddings computed from the same Knowledge Graph through several embedding techniques. We analyze the results on the level of encoding of each property by all the benchmarked algorithms with the final goal of providing insights into the choice of the most suitable technique for each context and encouraging a more conscious use of such approaches.
Introduction to Interpretability and Explainability

In recent years, we have seen gains in adoption of machine learning and artificial intelligence applications. However, continued adoption is being constrained by several limitations. The field of Explainable AI addresses one of the largest shortcomings of machine learning and deep learning algorithms today: the interpretability and explainability of models. As algorithms become more powerful and are better able to predict with better accuracy, it becomes increasingly important to understand how and why a prediction is made. Without interpretability and explainability, it would be difficult for us to trust the predictions of real-life applications of AI. Human-understandable explanations will encourage trust and continued adoption of machine learning systems as well as increasing system safety. As an emerging field, explainable AI will be vital for researchers and practitioners in the coming years.
The impact of psychological transformation game “My Dao” on value orientations of participants

Background and Aim of Study: Psychological transformation games that have great potential for solving a wide range of problems related to clients’ mental health are becoming increasingly popular among psychologists and psychotherapists. These techniques should be not only interesting and attractive to clients but also effective as a form of psychological practice. The aim of the study: to investigate the impact of the psychological transformation game “My Dao” on value orientations of participants using the diagnostic method of Schwartz. Material and Methods: The study used the toolkit of the psychological transformation game “My Dao”. The effectiveness of the game impact on the participants was determined by means of the diagnostics of value orientations suggested by Schwartz. The treatment group consisted of 134 people, 75 men (55.97%) and 59 women (44.03%) aged 17-35 years. The control group included 126 people, 69 men (54.76%) and 57 women (45.24%) aged 18-30 years. The results of the treatment and control groups were measured using the SPSS Statistics Module. The obtained results prove statistical significance (p&lt;0.05). Results: The greatest impact of the developed game on the participants is observed at the level of normative ideals: tradition, conformity, security, hedonism and universalism. This indicates their certain change towards increasing harmony and stability, as well as reducing humility and acceptance of their destiny. At the level of individual priorities, there is an increase in hedonism, power and stimulation, which indicates an increase in the role of satisfaction, success, ambition, the desire for novelty and strong emotions. The obtained high results of universalism, hedonism, self-direction and conformity indicate an increase in understanding, tolerance, enjoyment of life, self-control, self-government, self-discipline and politeness of game participants. Conclusions: The use of the transformation game “My Dao” in psychological practice is an effective psychotherapeutic method that affects the value orientations of participants, changes in self-esteem and motivation, disclosure of their personal resources to solve problems and develop further. This technique is universal due to the age category of participants and the ability to solve psychological problems.
Efficient Recognition of Facial Expression with Lightweight Octave Convolutional Neural Network

In various social activities, people usually focus on the other person&#x2019;s facial expression, which is an important element in interpersonal communication. The facial expression typically reflects a person&#x2019;s current mood and conveys emotional information. Perceiving the facial expressions of people in images through cameras has always been a popular research topic. Previous studies have classified facial expressions into different categories, such as happy, sad, fear, angry, calm, disgust and surprise, and have identified them using image processing methods. However, traditional image processing methods have a low detection efficiency and low recognition accuracy due to variation of perspectives. As a result, most of them can only be applied to the front face and short distance situations. In this paper, we propose a lightweight deep learning framework for facial expression recognition using Octave convolutional neural network (FerOctNet). FerOctNet including multi-scale convolutional processing and residual learning is able to obtain multi-scale features with enriched representation ability by integrating the deep level features with rich semantic information with shallow details of the features. Compared with other deep learning networks, not only does the proposed method have a good recognition rate, but also contains fewer parameters in the network.
A Power-Efficient Specific Emitter Identification Hardware Accelerator With SNR-Aware Adaptive Precision Reconfiguration

Specific emitter identification (SEI) plays a crucial role in spectrum management applications. Recently many SEI algorithms based on deep convolution neural network (DCNN) have been proposed. However, the high computational complexity of DCNN leads to large power consumption. Also, there is a lack of dedicated SEI hardware design. In this work, we proposed a SNR-aware adaptive scalable SEI algorithm based on DCNN, and implemented a power-efficient SEI hardware accelerator based on it. The SEI hardware accelerator can adaptively reconfigures itself between 16-bit weight computing mode and binary weight computing mode by sensing signal-to-noise ratio (SNR) condition to improve power efficiency while maintaining high accuracy. The experiment results show that our accelerator can achieve a high power efficiency of 6.51Gops/W for the high SNR condition and a high accuracy of 80% in the low SNR condition, compared with conventional non-adaptive designs with 16-bit or binary computing mode.
An Improved Retinex Algorithm for Underwater Image Enhancement Based on HSV Model

In underwater navigation and map construction, it is necessary to process and use the underwater image, but there are many impurities in the water and the water has strong light absorption and scattering effect. As a result, there are often some problems in the underwater imaging of the camera, such as color deviation, low contrast, dark brightness, serious noise and so on. These problems will directly lead to large errors in underwater mapping, so it is very important to enhance and update the image in real time. In this paper, an improved Retinex algorithm based on HSV(Hue-Saturation-Value) space is proposed to improve the above problems. Firstly, the RGB (Red-Green-Blue) model of the underwater image improved by Retinex is transformed into HSV model, and the high pass component in the converted V component is enhanced by NSST (Non-subsampled shearlet transform) to enhance the value component. Finally, the image enhancement is completed by inverse transform. Through experimental simulation, the improved Retinex algorithm proposed in this paper is compared with the original three Retinex algorithms, and it is proved that this algorithm has certain practicability and superiority in underwater image enhancement.
Image Denoising with GAN Based Model

Image denoising is often used as a preprocessing step in computer vision tasks, which can help improve the accuracy of image processing models. Due to the imperfection of imaging systems, transmission media and recording equipment, digital images are often contaminated with various noises during their formation, which troubles the visual effects and even hinders people’s normal recognition. The pollution of noise directly affects the processing of image edge detection, feature extraction, pattern recognition, etc., making it difficult for people to break through the bottleneck by modifying the model. Many traditional filtering methods have shown poor performance since they do not have optimal expression and adaptation for specific images. Meanwhile, deep learning technology opens up new possibilities for image denoising. In this paper, we propose a novel neural network which is based on generative adversarial networks for image denoising. Inspired by U-net, our method employs a novel symmetrical encoder-decoder based generator network. The encoder adopts convolutional neural networks to extract features, while the decoder outputs the noise in the images by deconvolutional neural networks. Specially, shortcuts are added between designated layers, which can preserve image texture details and prevent gradient explosions. Besides, in order to improve the training stability of the model, we add Wasserstein distance in loss function as an optimization. We use the peak signal-to-noise ratio (PSNR) to evaluate our model and we can prove the effectiveness of it with experimental results. When compared to the state-of-the-art approaches, our method presents competitive performance.
Short-Term Forecasting of Solar Irradiance using STL, Wavelet and LSTM

We propose a hybrid framework for short-term forecasting based on decomposition techniques and LSTM (Large Short Term Memory) algorithm.The study aims to analyze and quantify solar irradiance forecast using two types of decomposition techniques: decomposition of time series into the time domain and frequency domain via locally weighted regression based on Loess (STL) and using discrete wavelet transformation (DWT) respectively.LSTM is used to forecast the decomposed detail and approximation components.The final forecast of GHI (Global Horizontal Irradiance) is then obtained by combining these components using inverse wavelet transform.Hourly data from the Indian Meteorological Department (IMD) Jodhpur Rajasthan from January 2017 to June 2019 is used to demonstrate the proposed algorithm.The forecast accuracy of the proposed model is compared with other competitive models.The observed results show that the proposed combination of STL, wavelet transform, and LSTM outperforms (1) LSTM, (2) combination of STL and LSTM, (3) Bi-LSTM and (4) the persistence model.
Cooperative Search Algorithm for UAVs Based on Multi-ant Colony

The cooperative search problem of multiple unmanned aerial vehicles (UAVs) in unknown sea environment is studied. Considering the influence of detection distance on target detection probability, UAV steering and collision avoidance threat, a multi ant colony cooperative search algorithm based on prior information is proposed. Firstly, according to the prior information of target distribution, the probability distribution map of target based on searching region is established. Then, the pheromone concentration is initialized according to the target probability distribution, and the prior information is used to guide various ant colony searches. According to the target probability, the state transition rules are designed to maximize the target discovery probability. Finally, the pheromone concentration is updated according to the advantages and disadvantages of the search path solution, and the effectiveness of the search strategy is verified by simulation.