Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition Based on CSI fragment with Action-value Method

The application of Human Activity Recognition (HAR) technology makes human life more convenient. As an emerging HAR technology, WiFi based wireless sensor can sense the state of the target, such as body movement, gesture, position and so on. Aiming at the problem that the current WIFi based HAR methods need more training samples and have low real-time performance, this paper proposes a novel HAR method based on action-value method. In this method, each complete Channel State Information (CSI) sample signal of each activity is sliced into piece samples, and these piece samples are trained and tested to improve the real-time performance and reduce the number of training samples. The piece sample and the sequence of piece samples are respectively regarded as the state information and environment, and the classification of each piece sample is regarded as the execution of the classification-action. A deep neural network is used to simulate the reward of classification-actions in each state, and the recognition model is established by the action-value method. We tested our approach on SignFi data set. The highest recognition accuracy rate of active is 99&#x0025; and 91&#x0025; respectively.
Comparison of Computerized and Manual Assessment of Dyslexia Children Test using Text Processing

Dyslexia is a specific learning disorder that can have a negative influence, especially on children’s learning success. Early detection and continuous therapy can help children achieve better success in school. Children with Dyslexia need several evaluations and therapy during the rehabilitation programs from therapists and psychologists. The Assessment was carried out manually and recorded using paper documents until recently. Besides, the children must meet the therapist physically. A computerized method for assessing Dyslexia children by using text processing is a new approach to better record the rehabilitation progress and provide a home evaluation application so that some social distancing can also be done, especially in the era of the Pandemic. In this study, the data was taken from 24 Dyslexia children during their three-month therapy program. The assessment program is done after the children follow the rehabilitation program from the therapist. During those three months, there were two times scheduled for rehabilitation and two times for Assessment. The Assessment uses three criteria, namely Writing Letter Series (WLS), Arranging Sentences (AS), and Copying Sentences (CS), that each Dyslexia child must do. Computer application for testing is developed with a scoring system implemented using text processing technique. Hamming distance and Text similarity system were applied to score the test result and then be compared with the manual scoring method performed by the therapists. In this study, The Hamming distance was done for the criteria of the WLS, reaching an accuracy of S3% compared to manual Assessment. For the criteria of AS, the Text Similarity technique achieves 96% accuracy compared to the manual Assessment, and in CS reaches 88Xs%. The results showed the high potential of applying text processing techniques in assessing Dyslexia children.
Comparative analysis of energy yield of photovoltaic module mounting systems without solar tracker with tilt update for urban applications

This paper presents a comparative analysis of energy yield of different mounting systems of PV modules without solar tracker with tilt update for urban applications. Three tilt update frequencies have been considered: daily, monthly, and yearly, in ten cities around the world, in the Northern Hemisphere, with a Mathematica <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">©</sup> code. The optimum tilts angles for each day, month and year have been calculated. The energy gain with respect to the annual optimum tilt angle have been calculated. Daily tilt updates is the most energy productive, but it more expensive to implement. Updating the tilt angle daily is slightly better than doing so monthly. The use of a manual system for monthly tilt updates is beneficial, due to the reduced number of PV module position changes and the energy gain.
Energy Efficiency in Magnetic Clamping Applications

Magnetic clamping systems are a good substitute for mechanical ones in the industrial field to clamp workpieces on machine tools or to lift and handle ferrous material. Concerning big dimensions lifting applications, electromagnetic and electro-permanent are the most suitable magnetic clamping systems. A considerable energy efficiency difference is present between these two systems, but often it is not simple to evaluate. So a prototype has been developed to put in evidence the main differences: a hybrid control system for electromagnetic or electro-permanent systems has been created. A single control unit exploited by both systems only by changing the type of lifting magnet has been the idea behind the realization of such a hybrid control system, here described to evaluate the energy differences between these two magnetic clamping systems.
CNN-based Classification of Contaminated High Voltage Insulator Surface

High voltage (HV) insulators operate under different environmental conditions such as wind, snow, icing, unexpected incidents and tolerate overvoltage or flashovers. An undesirable performance of HV insulators might occur due to the pollution layers formed over the insulator surface that function as conducting coatings in a humid environment or even in normal dry operational conditions. This may lead to the insulator breakdown. Thus, detection of the insulator's defect in the early stage is essential. From a supervised learning point of view, accurate classification of contaminated HV insulator surface requires: i) collecting insulator surface images under different conditions; and ii) developing classifiers of contamination given a surface image. To this end, in this study, we used Unmanned Aerial Vehicle (UAV) to collect several insulator surface images under different contaminations including water drop (water spray), cement, snow, soil, and a mixture of snow and water. We then employed convolutional neural networks (CNNs) to construct accurate classifiers of contamination. In developing our CNN-based classifier, we use a grid search model selection and compare the performance and efficiency of the constructed model with pre-trained CNN models that are fine-tuned on our dataset.
Mapping Source Code to Modular Architectures Using Keywords

We implement an automatic mapper that can find the corresponding architectural module for a source code file. The mapper is based on multinomial naive Bayes, and it is trained using custom keywords for each architectural module. The mapper uses the path and file name of source code elements for prediction. We find that the needed keywords often match the module names; however, ambiguities and discrepancies exist. We evaluate the mapper using ten open-source systems with a mapping to an intended architecture and find that the mapper can successfully create a mapping with perfect precision. Still, it cannot cover all source code elements in most cases. However, other techniques can use the mapping as a foothold and automatically create further mappings. We also apply the approach to two cases where the architecture has been recovered from the implementation and find that the approach currently has limitations of applicability in such architectures.
A systematic overview on methods to protect sensitive data provided for various analyses

Abstract In view of the various methodological developments regarding the protection of sensitive data, especially with respect to privacy-preserving computation and federated learning, a conceptual categorization and comparison between various methods stemming from different fields is often desired. More concretely, it is important to provide guidance for the practice, which lacks an overview over suitable approaches for certain scenarios, whether it is differential privacy for interactive queries, k -anonymity methods and synthetic data generation for data publishing, or secure federated analysis for multiparty computation without sharing the data itself. Here, we provide an overview based on central criteria describing a context for privacy-preserving data handling, which allows informed decisions in view of the many alternatives. Besides guiding the practice, this categorization of concepts and methods is destined as a step towards a comprehensive ontology for anonymization. We emphasize throughout the paper that there is no panacea and that context matters.
A Novel Approach to Design Distribution Preserving Framework for Big Data

In several fields like financial dealing, industry, business, medicine, et cetera, Big Data (BD) has been utilized extensively, which is nothing but a collection of a huge amount of data.However, it is highly complicated along with time-consuming to process a massive amount of data.Thus, to design the Distribution Preserving Framework for BD, a novel methodology has been proposed utilizing Manhattan Distance (MD)-centered Partition Around Medoid (MD-PAM) along with Conjugate Gradient Artificial Neural Network (CG-ANN), which undergoes various steps to reduce the complications of BD.Firstly, the data are processed in the pre-processing phase by mitigating the data repetition utilizing the map-reduce function; subsequently, the missing data are handled by substituting or by ignoring the missed values.After that, the data are transmuted into a normalized form.Next, to enhance the classification performance, the data's dimensionalities are minimized by employing Gaussian Kernel (GK)-Fisher Discriminant Analysis (GK-FDA).Afterwards, the processed data is submitted to the partitioning phase after transmuting it into a structured format.In the partition phase, by utilizing the MD-PAM, the data are partitioned along with grouped into a cluster.Lastly, by employing CG-ANN, the data are classified in the classification phase so that the needed data can be effortlessly retrieved by the user.To analogize the outcomes of the CG-ANN with the prevailing methodologies, the NSL-KDD openly accessible datasets are utilized.The experiential outcomes displayed that an efficient result along with a reduced computation cost was shown by the proposed CG-ANN.The proposed work outperforms well in terms of accuracy, sensitivity and specificity than the existing systems.
VLSI Implementation of Optimized 2D SIMM Chaotic Map for Image Encryption

The current research work proposed a novel optimization-based 2D-SIMM (Two-Dimensional Sine Iterative chaotic map with infinite collapse Modulation Map) model for image encryption. The proposed 2D-SIMM model is derived out of sine map and Iterative Chaotic Map with Infinite Collapse (ICMIC). In this technique, scrambling effect is achieved with the help of Chaotic Shift Transform (CST). Chaotic Shift Transform is used to change the value of pixels in the input image while the substituted value is cyclically shifted according to the chaotic sequence generated by 2D-SIMM model. These chaotic sequences, generated using 2D-SIMM model, are sensitive to initial conditions. In the proposed algorithm, these initial conditions are optimized using JAYA optimization algorithm. Correlation coefficient and entropy are considered as fitness functions in this study to evaluate the best solution for initial conditions. The simulation results clearly shows that the proposed algorithm achieved a better performance over existing algorithms. In addition, the VLSI implementation of the proposed algorithm was also carried out using Xilinx system generator. With optimization, the correlation coefficient was −0.014096 and without optimization, it was 0.002585.
Language modelling for speaker diarization in telephonic interviews

The aim of this paper is to investigate the benefit of combining both language and acoustic modelling for speaker diarization. Although conventional systems only use acoustic features, in some scenarios linguistic data contain high discriminative speaker information, even more reliable than the acoustic ones. In this study we analyze how an appropriate fusion of both kind of features is able to obtain good results in these cases. The proposed system is based on an iterative algorithm where a LSTM network is used as a speaker classifier. The network is fed with character-level word embeddings and a GMM based acoustic score created with the output labels from previous iterations. The presented algorithm has been evaluated in a Call-Center database, which is composed of telephone interview audios. The combination of acoustic features and linguistic content shows a 84.29% improvement in terms of a word-level DER as compared to a HMM/VB baseline system. The results of this study confirms that linguistic content can be efficiently used for some speaker recognition tasks.
A Light-Weight Deep Learning-Based Architecture for Sign Language Classification

With advancements in computing powers and the overall quality of images captured on everyday cameras, a much wider range of possibilities has opened in various scenarios.This fact has several implications for deaf and dumb people as they have a chance to communicate with a greater number of people much easier.More than ever before, there is a plethora of info about sign language usage in the real world.Sign languages, and by extension the datasets available, are of two forms, isolated sign language and continuous sign language.The main difference between the two types is that in isolated sign language, the hand signs cover individual letters of the alphabet.In continuous sign language, entire words' hand signs are used.This paper will explore a novel deep learning architecture that will use recently published large pre-trained image models to quickly and accurately recognize the alphabets in the American Sign Language (ASL).The study will focus on isolated sign language to demonstrate that it is possible to achieve a high level of classification accuracy on the data, thereby showing that interpreters can be implemented in the real world.The newly proposed Mobile-NetV2 architecture serves as the backbone of this study.It is designed to run on end devices like mobile phones and infer signals (what does it infer) from images in a relatively short amount of time.With the proposed architecture in this paper, the classification accuracy of 98.77% in the Indian Sign Language (ISL) and American Sign Language (ASL) is achieved, outperforming the existing state-of-the-art systems.
Calico

We explore Calico, a miniature relocatable wearable system with fast and precise locomotion for on-body interaction, actuation and sensing. Calico consists of a two-wheel robot and an on-cloth track mechanism or "railway," on which the robot travels. The robot is self-contained, small in size, and has additional sensor expansion options. The track system allows the robot to move along the user's body and reach any predetermined location. It also includes rotational switches to enable complex routing options when diverging tracks are presented. We report the design and implementation of Calico with a series of technical evaluations for system performance. We then present a few application scenarios, and user studies to understand the potential of Calico as a dance trainer and also explore the qualitative perception of our scenarios to inform future research in this space.
Teaching Simulation Based on Artificial Intelligence and Big Data Algorithm in Sports Dance Group Dance

Due to the rapid development of information technology, it is necessary to cultivate a large number of sports dance group dance talents, so the teaching simulation research has become more and more important. Although there are many research results of teaching simulation, the results are somewhat general. In order to explore the teaching simulation technology based on artificial intelligence and big data algorithm in sports dance group dance and the benefits brought to people by learning sports dance and group dance, this paper studies and discusses the simulation teaching for the training of sports dance group dance talents. Experimental research shows that teaching simulation is more time-saving and labor-saving than teachers’ one-to-one explanation for group dance education; it can greatly improve the subjective initiative of students' learning; it can be very convenient to simulate various situations in the real world according to the required requirements; the application of teaching simulation in sports dance group dance can also develop students’ imagination and reduce teachers’ burden. Because simulation teaching is an emerging industry, it is not yet common in life and learning, and experts have not done too much research on simulation teaching, so the current results are somewhat general.
Improving fractal compression technique for CT scan images

A computerized tomography (CT) scan in hospitals and medical clinics produces a colossal amount of medical images every day, which are used for different purposes such as surgical, disease diagnostics, and research. Therefore, high-quality compression of CT scan images is necessary for storing, managing, and transferring. However, one of the disadvantages of the image compression process is that it requires time, so this paper proposed using the method to accelerate fractal image compression. It is based on using some features of the moment that process the domain blocks. The results of tests conducted on CT scan images (256 pixels, resolution 24 bits/pixel) image showed a minimum encoding time of 0.55 sec with appropriate PSNR 28.33. The reduction in ET is about 19% for CT scan images.
Utilizing remote sensing and GIS techniques to locate optimal sites for thermal solar plants in Iraq

Green energy (clean energy) origin is one of the nature elements like sun, wind, rain, tides, plants, algae and geothermal heat. Those elements are renewable, meaning they're free regenerated in earth environment. In contrast, fossil fuels are a finite resource that need very long time interval to develop and will diminish by utilize. Also, the impact of (Renewable Energy elements) is very small on the environment comparing with fossil one that bring pollutants into it such as greenhouse gases as a by-product which are the main cause of climate change. The fundamental resource or fuel required for radiation-based clean energy applications, such as solar thermal or photovoltaic conversion systems, is net solar radiation, so the goal of this search is integrating the usage of Remote Sensing and Geographic Information System Technologies to Determine Optimized Solar-Based Electric Station Location in Iraq. (i.e. using spatial analysis schemes with remote sensing and meteorological data to choose best site in Iraq for building green electric power station that benefits from sun renewable thermal energy. In this work the annual average value of net solar radiation for all of Iraq in (1987, 1997, 2007 and 2017) respectively was estimated and used as the first most critical condition for construction a solar thermal power plant in an optimal location within Iraq. After that filters (conditions) were introduced in a logically manner to restrict thermal solar energy plant location in Iraq. Those conditions are (Working condition, Efficiency condition, Safety condition, Land cover convenient condition, and the transport cost condition). The filtration process restrict interest regions to only two sites in southern Baghdad province (barren soil large locations) as an optimal for thermal solar plant construction; those are AL-Madain and AL-Mahmoodiya.
The influence of digital twins on the methods of film and television creation

All walks of life in the world have entered the "big data era", and China's film and television industry is also developing rapidly. Digital twins can also have a huge impact on how film and television are created. This paper aims to study the theoretical knowledge of digital twins and film and television creation methods, and propose an image and video super-resolution reconstruction algorithm based on video computing and reconstruction. Both algorithms make perfect sense in digital twins. This paper proposes how to study the impact of digital twins on the way of film and television creation. It can be seen from the data in Table 1 of the experiment in this paper that the director's attention to the way of film and television creation has increased from 54% in 2013 to 70% in 2019. It can be seen that the decoding quality of the two reconstruction algorithms is higher than that of the traditional reconstruction algorithm. It can be seen that it is very meaningful to explore the impact of digital twins on the way of film and television creation.Digital twin is a multi-disciplinary, multi-physical, multi-scale, multi-probability simulation process that makes full use of physical models, sensor updates, and operating history to complete the mapping in virtual space, thereby reflecting the full life cycle of the corresponding physical equipment. process. A digital twin is a concept beyond reality that can be viewed as a digital mapping system of one or more important, interdependent equipment systems.
Construction of Alumni Information Analysis Model Based on Big Data

In order to integrate and utilize alumni resources in a better way, big data is utilized to construct alumni information analysis model based on improved hierarchical clustering algorithm, so as to realize mining and retrieval of alumni information. First,the basic principle of hierarchical clustering algorithm is analyzed concretely. Moreover, the improvement is performed on this basis, and a method of calculating the distance between class clusters based on the ant colony optimization is proposed, which uses the shortest distance of the ant colony algorithm to optimally solve the distance between hierarchical class clusters, so as to improve the clustering accuracy. Then, the alumni information analysis model based on improved hierarchical clustering algorithm is constructed, and the model is divided into text preprocessing, keyword extraction, text feature vector generation, name disambiguation, and alumni recognition modules. Finally, the improved hierarchical clustering algorithm and construction of model are verified by experiments. The results show that the accuracy of the improved agglomerative hierarchical clustering algorithm is as high as 86.4% on average and 3.8% and 4.8% more than the two traditional algorithms. Thus, the clustering effect of the algorithm is better, and the proposed alumni analysis model can effectively process text disambiguation of web pages and identification of alumni information, which has certain effectiveness.
Low-complexity deep unfolded neural network receiver for MIMO systems based on the probability data association detector

Abstract The interest on applications where machine learning algorithms and communications are combined has been on a rising in recent years. Machine learning and neural networks are being advocated as a way of improving the performance of several functions across all layers of future communication systems. Furthermore, in applications where complexity reduction is essential for the system feasibility at the cost of an affordable performance loss, more efficient systems might be achieved with the aid of machine learning algorithms. Signal detection for multiple-input multiple-output (MIMO) systems has become a hot topic in recent years given its prominent role in fourth and fifth generations of mobile networks. However, the computational complexity in MIMO systems can become prohibitive when the number of antennas increases. Therefore, by leveraging neural networks architectures we propose a deep unfolded detector, whereby the algorithm of the probability data association (PDA) detector is adapted and enhanced by means of neural network learning capabilities. We unveil that the proposed detector is orders-of-magnitude less complex than the PDA detector, yet presenting no severe penalties in performance in terms of bit error rate (BER).