Reviewerly/exHarmony huggingface dataset:
Feature Selection Based on Evolutionary Algorithms for Affective Computing and Stress Recognition

In the area of affective computing, machine learning is used to recognize patterns in datasets based on extracted features. Feature selection is used to select the most relevant features from the large number of extracted features. Conventional feature selection methods are associated with a high computational cost depending on the classifier used. This paper presents a feature selection approach based on evolutionary algorithms using techniques inspired by natural evolution to optimize the computational process. Our method is implemented using an Optimize Selection operator from RapidMiner and is integrated within our previously developed workflow for affective computing and stress recognition from biosignals. The performance is evaluated based on the random forests classifier and a cross validation using our uulmMAC database for machine learning applications. Our proposed approach is faster than the forward selection method at similar recognition rates and does not stop at a local optimum, allowing a promising feature selection alternative in the field of affective computing.
Trace Match &amp; Merge: Long-Term Field-Of-View Prediction for AR Applications

Photorealistic Augmented Reality (AR) experiences require high bandwidth. Streaming approaches have already been proposed to guarantee a low-latency and yet high quality experience for the end user despite this bandwidth requirement. To provide the best streaming performance, these approaches usually require accurate prediction of the Field-Of- View(FOV) of the AR device to prioritize the download of digital objects that are most likely to be viewed by the user. While current prediction approaches can be successfully applied for short prediction horizons (<2 seconds), it remains challenging to predict the user behavior for longer horizons. In this paper, we therefore present the Trace Match & Merge (TMM) algorithm for the FOV prediction in AR applications. TMM employs an improved nearest-neighbor-based approach that examines the traces of previous users of a particular AR scene, and merges the most similar segments to predict the future position and rotation of the AR device. Particularly, user traces are selected based on the overlap of the users' FOV, modeled as a pyramid. Extensive experimental results on an open-source AR exploration dataset, composed of 4 AR scenes explored by 50 users, confirm the benefits of the proposed solution. Particularly, TMM consistently outperforms a set of popular baselines in terms of predicted position, rotation and objects-in-view - especially for challenging, lona-term prediction horizons (>2 seconds).
A Shared Representation for Photorealistic Driving Simulators

A powerful simulator highly decreases the need for real-world tests when training and evaluating autonomous vehicles. Data-driven simulators flourished with the recent advancement of conditional Generative Adversarial Networks (cGANs), providing high-fidelity images. The main challenge is synthesizing photorealistic images while following given constraints. In this work, we propose to improve the quality of generated images by rethinking the discriminator architecture. The focus is on the class of problems where images are generated given semantic inputs, such as scene segmentation maps or human body poses. We build on successful cGAN models to propose a new semantically-aware discriminator that better guides the generator. We aim to learn a shared latent representation that encodes enough information to jointly do semantic segmentation, content reconstruction, along with a coarse-to-fine grained adversarial reasoning. The achieved improvements are generic and simple enough to be applied to any architecture of conditional image synthesis. We demonstrate the strength of our method on the scene, building, and human synthesis tasks across three different datasets. The code is available <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/vita-epfl/SemDisc</uri> .
Determination of optimum tilt angle of photovoltaic panels with monthly variations for Coatzacoalcos, Veracruz

The tilt angle is one of the most important parameters for solar radiation collection in a photovoltaic panel, its optimization helps to increase the total solar radiation received. Numerous methods have been used to calculate the tilt angle around the world; unfortunately, many of these models are for fixed angles, which fail to adapt to the position of the sun throughout the year, resulting in the loss of significant amounts of irradiation that could be exploited. In this work we propose a series of optimal monthly tilt angles for the city of Coatzacoalcos, Veracruz, obtained by means of a neural network that considers day of the year, declination, and the clearness index as input variables. Its values range from 39.10° in January, in June it has its lowest value with −4.88 and ends with values of 41.18 in December. Theoretical calculations of incident irradiation on a tilted plane are also made, using the isotropic Liu-Jordan model and the anisotropic Klucher model, to make comparisons of energy reception between a surface with a fixed tilt angle equal to the latitude (18.1378°) and the proposed angles. The results show that using an isotropic method the irradiation obtained by panels with monthly angles increases 4.11% more and under the anisotropic method it increases 5.06% more. Thus, it is corroborated that it is possible to use neural networks to predict monthly tilt angles.
Threats, Vulnerabilities and Security Functions in Critical Information Infrastructure

Research on critical information infrastructure protection is currently increasing. This research complements existing security standards and becomes an alternative to the latest technical controls. Based on previous research, research on CII security did not meet four of seven security functions. Therefore, it is also necessary to know whether the security parameters already refer to the security function for each threat and vulnerability. This research is a systematic literature review of scientific articles in CII protection based on the security functions needed to control threats and vulnerabilities. The Kitchenham SLR method was used to search the literature, and 28 publications were obtained. As a result, the research found six threats and four vulnerabilities in critical information infrastructure and all security functions available for All threat and vulnerability groups. The study results also found the threat of Destruction of Information which in theory should exist, but the security controls have never been identified.
Text Preprocessing Impact for Sentiment Classification in Product Review

In the Covid-19 pandemic situation, the e-commerce platform has significant data of product reviews in real-time. Businesses need rating and review systems to immediately expose their consumers' feelings about their products and services and use every volume of data to strengthen their competitive strategies. Amazon is one platform that can provide a vast quantity of product review data. Unfortunately, data from product reviews are typically unstructured and unmanageable. Therefore, this experimental study observed text preprocessing impact to process unstructured product review data using sentiment classifier Decision Tree, Naïve Bayes, and Support Vector Machine (SVM) with better accuracy. The SVM performed higher evaluation model performance, with an accuracy of 88,13%, but the Naïve Bayes classifier has minimum execution time. Furthermore, the experimental result using our approach TF-IDF for feature extraction may significantly improve classification accuracy. As a result, our approach reveals that a good text preprocessing sequence is critical to the classifier's prediction performance for unstructured product review data.
Performance Analysis of Secure Hash Algorithm-2 (SHA-) and Implementing on FPGA

In this modern and clumpsy world, security is more important than everything. Today, security of system is one of the fastest growing field in the world. With the goal of reduction in the accumulating area, throughput and power of the hardware performance of a crypto-analyzed system, secured hash functions of hardware working are extremely efficient and great importance. Now for most of the security services, hash function algorithm has become the default choice. SHA-2 (Secure Hashing Algorithm-2) hash family is a new security algorithm which is widely used in modern computer systems. Sometimes SHA-1 Hashing algorithm produce same hash for different files which is also known as a collision, as the number of hash functions increases, the smaller the number of chances that the standards will create the same hash function, and hence, SHA-2 algorithm is always being used in numerous cryptographic algorithms. Confidentiality, integrity, and authenticity are the major application areas of cryptography. The use of FPGA has vital advantages for implementation of cryptographic algorithms. The proposed work implements SHA-2 algorithm on cryptographic processor for efficient performance of cryptosystem using Verilog hardware description language (HDL).
A Deep Learning based Approach for Real Time Face Recognition System

Face recognition technology provides us with a wide range of possibilities for creating a safe and secure environment. In this paper, two separate approaches are applied mutually while designing a hybrid solution for face identification. The MTCNN method is applied to the real-time video frame produced by the security camera. Moreover, a deep learning-based approach was used to generate the facial landmarks from the obtain face images, and the model was trained using the Res-Net architecture. Furthermore, MongoDB is used as a database to store the facial landmarks and corresponding identity, and if the system cannot identify the person, an alarm is generated, and an auto-generated message immediately sends to the owner's email address. This way it creates a strong security system.
Self-powered Wireless UV Sensor with Intervals of Wireless Transmission

A self-powered wireless ultraviolet (UV) sensor system that changes the wireless transmission intervals according to the amount of UV radiation was developed. The system consists of a solar cell, which is the power source, a charge pump to step up the solar cell voltage, a UV sensing circuit that converts the current difference of a differential Si-photo diode into wireless transmission intervals, and an input regulator that has a function to charge a capacitor for wireless transmission while supplying power to the UV sensing circuit. We evaluated the system by comparing it with the measurement results of a UV power meter and found that the average error was 8%.
Professional development of teachers in the context of digitalization of education: from conceptual ideas to practice

The article presents the results of a study of the practice of teachers professional development in the context of digitalization of education carried out in order to identify the features of teachers professional development and the main conceptual ideas implemented in different countries. The article examines the practice of teachers professional development in other countries as well as in a number of Russian regions. The analysis of pedagogical literature and scientific articles was carried out using the method of apperception and the descriptive method. The use of apperception method allowed us to expand information about the existing professional development practices implemented in different countries and regions. The use of descriptive method in working with scientific literature made it possible to select articles based on keywords, descriptors. In this study, the following phrases were used as descriptors: professional development of teachers, concepts of professional development of teachers, advanced training of teachers, practice of professional development of teachers. As a result, practices of teachers professional development have been identified that have both similar conceptual ideas and some national or regional features. Two of the conceptual ideas reflected in almost all countries are particularly noteworthy: that of continuity and that of personalization of teachers professional development in the context of education digitalization. Implementation of these ideas makes it possible to ensure continuous professional development of a teacher taking into account his or her professional experience, expertise, professional competencies and interests, as well as the interests and demands of the educational organization in which he or she works. The implementation of these ideas is carried out through integration of formal and non-formal education, as well as through self-education and self-development of teachers in the context of education digitalization.
Research on information fusion mechanism algorithm of blockchain nodes based on 5G

The actual route of blockchain virtual link may be different every time. The larger the network scale, the more nodes need to reach a consensus. The slower the progress of consensus, the lower the throughput. To solve this problem, this paper uses PSO execution algorithm to fuse SGslice and blockchain, and designs a block chain node information fusion mechanism algorithm based on 5g. The experimental results show that the fusion algorithm greatly improves the communication efficiency of each node of the blockchain, so that the blockchain can not affect the communication speed between nodes while increasing the number of nodes, so as to greatly improve the security of the blockchain and make it better applied in various scenarios.
Progressive Point Cloud Compression with the Fusion of Symmetry Based Convolutional Neural Pyramid and Vector Quantization

In this paper, we present a novel 3D structure-awareness image-based point cloud compression scheme, which applies the proposed Symmetry based Convolutional Neural Pyramid (SCNP) to compress colored point clouds view-by-view for 3D model transmission. Input a 3D model to the system, a preprocessing step is first applied to represent the input point cloud as a sequence of view-specific six-dimensional (6D) images, where each pixel is characterized by an RGB color vector and a XYZ 3D point. The transformed 6D images preserve the regular grid structure and thus the redundant information is easy to be removed by conventional image/video compression techniques. Our SCNP first represents each 6D image as a multiple-level pyramid structure for progressively compressing and transmission. The lowest resolution image at the highest level of the pyramid is then decomposed into multiple patches with each of them being coded as the index of a small dictionary through vector quantization. The residual images at other levels are also represented by the vector quantization codes with different patch sizes for progressively reconstructing the input colored point cloud. This process results in a multiple description coding scheme for 3D point cloud compression. With the pre-learned set of dictionaries, the projected view-specific 6D images of the input 3D model are encoded one-by-one to obtain the compressed results for 3D model transmission. In the receiver end, the 3D model is reconstructed by merging all the reconstructed point clouds where each of them is decoded from the corresponding view-specific image. Finally, the conventional 3D reconstruction approach has been applied to remove redundant 3D points for reconstructing the 3D model. Experiments demonstrate the effectiveness of our approach which attains better performance than the current state-of-the-art point cloud compression methods.
Research on autonomous exploration motion planning method of mobile robots for unstructured scenarios

To realise the safe and efficient autonomous exploration of mobile robots in unstructured scenarios, this paper proposes an autonomous exploration motion planning method. This method divides the motion planning process into two parts, namely, obstacle avoidance planning for selecting the optimal desired point, and motion state planning for tracking the desired point and generating the motion speed. The optimal and safest target point is obtained by establishing the detection and free planning areas of the mobile robot. Meanwhile, the environment-speed conversion mechanism is established to output the movement speed of the mobile robot. An emergency collision avoidance planning method is proposed to avoid the mobile robot from falling into the local minimum area. Finally, the mobile robot can move safely and steadily in an unstructured scenario. The experiment verifies the safety of the method and the efficiency of exploration. Results show that the proposed autonomous exploration motion planning method for unstructured scenes can realise collision-free exploration and obtain complete scene plane information.
Syllable-level Korean Fingerspelling Recognition from a Video

Fingerspelling is a communication system that expresses each character with a series of hand gestures. The system is important for deaf people because it can indicate new nouns which cannot be expressed in sign language. Since the expression style of the system is based on the existing writing system, understanding unique characteristics of each writing system is important to recognize fingerspelling properly. In this paper, we propose a syllable-level fingerspelling recognition framework that utilizes characteristics of Hangul. Unlike the conventional frameworks that recognize fin-gerspelling in the phoneme-level, the proposed framework adopts a Hangul-specific attention mechanism that pays more attention to important video frames in terms of recognizing each syllable. In our experiments on the KETI Korean finger-spelling dataset, the proposed framework exhibits more accurate results than a phoneme-level recognition framework.
Scrum Team Competencies in Information Technology Professionals in the Global Software Development Environment

The Product Owner and Scrum Master are members of the Scrum Team, which play an essential role in Global Software Development. They must have some competencies that can overcome challenges in the global environment. This study aims to develop the competencies of these two roles and explore issues of global competencies. Researchers conducted a literature study to produce a list of competencies and in-depth interviews to explore the issues. Data processing uses content analysis and descriptive statistics. The author compiled questionnaires and generated three issues, then held a Focus Group Discussion for validation. Without reducing the nature of scrum, the consensus of experts had approved 31 competencies for the product owner while 34 for the scrum master. This research enriches scrum.org and has a global impact on improving Scrum and Global Software Development human capital and IT professionals.
Nonlinear registration as an effective preprocessing technique for Deep learning based classification of disease

A number of machine learning (ML), and particularly in recent years, deep learning (DL) approaches have been proposed for automatic classification of Alzheimer's disease (AD) using brain structural magnetic resonance imaging (MRI) data. However, the data available are limited in the case of this specific disease. Training a DL model with a large number of feature parameters on a small dataset of MRI scans will likely lead to overfitting. Overfitting reduces the generality and efficiency of the model. In this study, we show that a traditional nonlinear transformation from native space to template space, as a preprocessing stage, is effective in reducing overfitting through the reduction of spatial variations in the input data. To evaluate this effectiveness, we compare two different pre-processing approaches for DL-based AD classification task: (1) affine registration and (2) nonlinear diffeomorphic anatomical registration using exponentiated Lie algebra (DARTEL). The results show that the accuracy of the nonlinear registration based approach is much higher than the affine registration based approach. Furthermore, from the classification results obtained with noisy images, DARTEL is less susceptible to noise than affine registration. In summary, our experimental results suggest that nonlinear transformation is a preferable preprocessing step for training DL-based AD classification models on limited size datasets.
Effectiveness Research of the Apriori Algorithm Implementations as Part of the Recommendation System

The article presents a comparison of the author&#x0027;s implementation of the Apriori algorithm with implementation presented in the Accord&#x002E;NET library. A comparison of various parallel implementations of this algorithm is also shown. The main objective of the article is evaluating the efficiency of the Apriori affinity analysis algorithm implementation by the speed of creating an association rules list. The results of testing on the data provided by the company &#x201C;FB Consult&#x201D; are given. The test results show an increase in performance of the optimized parallelized implementation by about 5.28 times compared to the implementation from Accord&#x002E;NET. This article clearly shows that optimization of the algorithm in order to reduce the running time by using binary vectors and operations on them gives more significant speedup than parallelization. Whereas when parallelizing, attention should be paid to the computation of support for association rules, because based on the results of this function, most of the association rules will be eliminated.
Power Generation Prediction of Residential Photovoltaic Equipment Based on Online Transfer Learning Model- A Case Study of a Residential Solar Power System

Abstract. Power generation prediction of residential photovoltaic systems has always been a more and more crucial topic when such new types of energy have been widely applied in people's daily life. In this paper, the four seasons are identified more scientifically by studying the variation of solar altitude angles in a year, and hence the meteorological factors hidden in the data collected from a PV system are extracted by clustering and used in the model. Combined with the advantages of the online learning and transfer learning approach, the online transfer learning model is developed to predict power generation. Finally, our experimental results show that the proposed online transfer learning model outperforms the other learning methods.
Artificial neural network for solving multi-parameter optimization problems

Abstract An artificial neural network (ANN) is a mathematical or computational model that simulates the structure and function of biological neural networks used to evaluate or approximate functions at given points. After developing the training algorithm, the resulting model will be used to solve image recognition problems, control problems, optimization, etc. In the process of ANN training, the algorithm of backpropagation is used in the case of convex optimization functions. The article is analyzed test functions for experiments and also study the effect of the number of ANN layers on the quality of approximation in cases one-, two- and three-dimensional. The backpropagation method is improved during the experiments with the help of adaptive gradient, as a result of which more accurate approximations of the functions are obtained. This article also presents the numerical results of test functions.