Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN E-LKPD BERBASIS SALINGTEMAS (SAINS, LINGKUNGAN, TEKNOLOGI, MASYARAKAT) MATERI PERUBAHAN LINGKUNGAN KELAS X SMA

This R &amp; D research aimed to know the category of validity, practicality, effectiveness, and specifications of e-LKPD based on Salingtemas. The product developed with the ADDIE development model consists of five stages: analysis, design, development, implementation, and evaluation. The research was conducted at SMAN 1 Tinambung with 30 students from class X MIA 2 and 12 biology teachers. The validation sheets of e-LKPD, teacher response questionnaires, student response questionnaires, implementation observation sheets, and multiple-choice tests were used as instruments. Data on the level of validity and practicality were collected using a questionnaire, while the data on the level of effectiveness was collected through a learning outcome test. Based on the results of the data analysis of the validity of the e-LKPD based on Salingtemas, it obtained an average value of 4.7 with a valid category. The total average score percentage for the teacher's response questionnaire was 92 with a positive response category, while the total average score percentage for student responses was 87 with a positive response category. The observations showed an average percentage value of 95.4 as well-implemented. The students' test results were 93 percent effective in this category. It appears that the developed Salingtemas-based e-LKPD has met the valid, practical, and effective categories.
On the Convergence Rate of Federated Learning over Unreliable Networks

Considering unreliable wireless networks, the efficiency of training a statistical model via Federated Learning (FL) over wireless networks is studied in this work. The system consists of a server and a swarm of end-user devices that are termed as clients. The server allots the statistical model directly onto the clients for local training, based on the dataset that resides on their devices, periodically collects the trained parameters to produce an improved model, and sends it back to the clients for a new round of local computing. This process repeats for multiple rounds until the global model converges. Due to unreliable wireless channels and limited communication resources, the server can only select a handful of clients for parameter updates at each iteration. Analytical expressions are derived to characterize the FL convergence rate by accounting for the transmission reliability and momentum method. The analyses unveil that in networks with reliable connections, model training can be accelerated by expanding the bandwidth to include more clients in each communication round, but it becomes ineffectual when the connections are unreliable. Moreover, it is also confirmed that adopting momentum in the global aggregation stage of FL improves the convergence rate, and such an approach is more insulated from the effects of communication failures. These theoretical findings are validated by extensive empirical simulations.
Federated Transfer Learning-based Data Security Assurance in Edge Optical Networks for IoT Applications

We propose a federated transfer learning framework to realize data security guarantee in edge optical networks for IoT applications. Results indicate our proposal enables efficient model training and transferring by exploiting local data.
Operator’s Gaze Direction Recognition

This work is devoted to the algorithm for recognizing the direction of a operator’s gaze. The paper considers the method of classifying the direction of gaze. The algorithm proposed in this paper allows to determine the driver’s gaze direction, which helps to protect the driving process. In the proposed work, we reviewed the algorithms and methods used to recognize the human gaze in various conditions. For each given method, a list of their positive and negative qualities is indicated. Based on the results of the review, an algorithm for classifying the car driver’s gaze direction is proposed. The proposed algorithm consists of two components. The first part is responsible for the regression of the gaze direction. The second part is responsible for classifying the results of the first part. Experimental evaluation of the developed algorithm has shown that it is effective for the task of classifying the gaze direction. An important advantage of this algorithm is that it does not require repeated training of the algorithm to adapt to other scenarios of gaze direction classification.
Time Efficient Round Robin Job Scheduling (NARR) in Cloud Computing

Cloud computing simply means the advancement of distributed computing which takes data processing computational aspects over networks to centralized high-power data centers. It refers to the use of a centralized pool of resources that are distributed on a pay-per-view model to a large number of customers. This requires scheduling algorithms that allow us to define which task processes, among which resources are first allocated for performance. The Round Robin (RR) scheduling algorithm is the common scheduling algorithm used in multitasking and real-time environments. Its performance is largely determined by the amount of time it takes to carry out a specific task assigned by the CPU. If the less time is chosen the context switch is high and the higher time is chosen, the first-come first-server (FCFS) is selected. System's performance thus totally depends on the optimal quantum time to choose from. In this paper, I'm examining a different way of improving the performance of the round robin scheduling algorithm by means of a dynamic time quantum and comparison of different performance.
Integration of 2D Textural and 3D Geometric Features for Robust Facial Expression Recognition

Recognition of facial expressions is critical for successful social interactions and relationships. Facial expressions transmit emotional information, which is critical for human-machine interaction; therefore, significant research in computer vision has been conducted, with promising findings in using facial expression detection in both academia and industry. 3D pictures acquired enormous popularity owing to their ability to overcome some of the constraints inherent in 2D imagery, such as lighting and variation. We present a method for recognizing facial expressions in this article by combining features extracted from 2D textured pictures and 3D geometric data using the Local Binary Pattern (LBP) and the 3D Voxel Histogram of Oriented Gradients (3DVHOG), respectively. We performed various pre-processing operations using the MDPA-FACE3D and Bosphorus datasets, then we carried out classification process to classify images into seven universal emotions, namely anger, disgust, fear, happiness, sadness, neutral, and surprise. Using Support Vector Machine classifier, we achieved the accuracy of 88.5 % and 92.9 % on the MDPA-FACE3D and the Bosphorus datasets, respectively.
Geophysics Bright Spots

Welcome to the latest installment of Geophysics Bright Spots. For most of us, the pandemic has upended our daily routines. Personally, a minor silver lining in all of this chaos has been the gain of a few extra hours every week. I have used this time to catch up on the fascinating work being done in the field of artificial intelligence and its applications to numerous disciplines. An emerging trend is physics-informed machine learning, which will help us bridge the gap between traditional theoretical approaches and more recent data-driven methodologies, leading to physically plausible and meaningful results. To follow is a list of research that the editors found interesting in the latest issue of Geophysics. I sincerely hope these articles enlighten you and take your mind off some of the chaos surrounding us.
Competence-based Approach in Vocational Education and Training: Systematic Review of the Russian Literature

The article is devoted to analysis of the Russian literature related to the implementation of the competence-based approach in vocational education and training (VET). The objective of the study is to systematize existing publications on this issue and to formulate an agenda for future research. The paper presents a systematic literature review: a uniform description of a sample of publications from the most influential Russian journals for the period 2005-2019 within a pre-defined category system. Moreover, the research includes an in-depth study based on content analysis that reveals how different aspects of the competency-based approach are currently elaborated in the literature. The results include some basic conceptual features of the construct that underlies the competencebased approach in Russia. It summarizes the data on the conceptual and terminological apparatus used in the discussion on competency-based vocational education. The paper also presents the data on the dominant research methods and the target audience of the articles in the sample. The most important result of the study is an evidence of deficiencies in research that support the introduction and adaptation of competence-based approach in the Russian VET. In particular, there are practically no judgments, which indicate that researchers understand or delve into changes in assessment of educational outcomes that are reformulated in terms of competences. The practical use of the results will fill the gaps in scientific support for the implementation of the competency-based approach in the Russian VET. The article will be useful for representatives of the research community, pedagogical and managerial personnel of vocational schools as well as for decision-makers in the field of VET policy.
Deep Learning Based Low Complexity Symbol Detection and Modulation Classification Detector

This paper presents a deep neural network (DNN) based symbol detection and modulation classification detector (SDMCD) for mixed blind signals detection. Unlike conventional methods that employ symbol detection after modulation classification, the proposed SDMCD can perform symbol recovery and modulation identification simultaneously. A cumulant and moment feature vector is presented in conjunction with a low complexity sparse autoencoder architecture to complete mixed signals detection. Numerical results show that SDMCD scheme has remarkable symbol error rate performance and modulation classification accuracy for various modulation formats in AWGN and Rayleigh fading channels. Furthermore, the proposed detector has robust performance under the impact of frequency and phase offsets.
Generalized Galois and Fibonacci Matrices in Cryptographic Applications

The terms of the Galois matrices , as well as those bijectively associated with them the Fibonacci matrices connect by the operator of the right-hand transposition (that is, transposition to the auxiliary diagonal), are borrowed from the theory of cryptography, in which generators of pseudorandom number (PRN) widely use according to Galois and Fibonacci schemes (in configuration). A distinctive feature of both the and matrices is that the identical binary sequences can programmatically calculate the sequences formed by the PRN generators. The latter's constructions are based on linear feedback shift registers, implemented by software or hardware methods in Galois and Fibonacci architecture. The proposed generalized Galois matrices, discussed in the Chapter, significantly expand the variety of PRN generators. That is achieved both by increasing the number of generating elements (in the classical version used a single element ) and since generalized generators can construct not only using PRN but also polynomials, not necessarily (as in classical generators), which are primitive. The listed features of generalized Galois matrices provide PRN generators with significantly higher cryptographic security than generators based on conventional matrices.
Digital Bookstore

The project's main goal is to build an online book store where users can search for and buy books based on title, author, and subject. The chosen books are shown in a tabular style and the customer may buy them online using a credit card. Using this Website, the user may buy a book online rather than going to a bookshop and spending time. Many online bookstores, such as Powell's and Amazon, were created using HTML. We suggest creating a comparable website with .NET and SQL Server. An online book store is a web application that allows customers to purchase ebooks. Through a web browser the customers can search for a book by its title or author, later can add it to the shopping cart and finally purchase using a credit card transaction. The client may sign in using his login credentials, or new clients can simply open an account. Customers must submit their full name, contact details, and shipping address. The user may also provide a review of a book by rating it on a scale of one to five. The books are classified into different types depending on their subject matter, such as software, databases, English, and architecture. Customers can shop online at the Online Book Store Website using a web browser. A client may create an account, sign in, add things to his shopping basket, and buy the product using his credit card information. As opposed to a frequent user, the Administrator has more abilities. He has the ability to add, delete, and edit book details, book categories, and member information, as well as confirm a placed order. This application was created with PHP and web programming languages. The Online Book Store is built using the Master page, data sets, data grids, and user controls.
MQTT Based Event Detection System for Structural Health Monitoring of Buildings

Structural Health Monitoring (SHM) consists in a fundamental research field which aim to evaluate the current status of an infrastructure with the main purpose to identify damages and prevent catastrophic events. This paper presents an SHM solution that implements an automatic system based on the MQTT protocol and IoT devices for detecting seismic events. In particular, the architecture consists of a set of accelerometer sensors which communicate by means of a decentralized network topology (i.e., an Ad hoc Network configuration). Moreover, the system has the capacity to transmit the information about the events detected in real-time using cloud services. In order to verify the proper operation, the system was deployed on an actual building and the information acquired by the sensors was registered along four months. In this context, a relevant event detected was selected for analyzing the dynamic response of the building during a seism. Results show that the acceleration values increase as a function of the building height. Regarding the seismic event analyzed, the RMS values of acceleration identified on the basement were 0.26, 0.22, and 0.22 cm/s2 and in the case of the eighth floor were 1.18, 1.33, and 0.59 cm/s2 for the longitudinal, transverse, and vertical axes, respectively. Additionally, a first assessment regarding the structural health status of the building was performed through the OMA methodology (Operational Modal Analysis). Specifically, the FDD (Frequency Domain Decomposition) mechanism was used to determine the first four frequencies and its respective vibration modes.
Blood Content Prediction using Deep Learning Techniques

Cells in the human circulatory system and identifying the types and its functionalities cannot be done through naked eye. This asks for greater accurate methods of visualizing it and hence is vital in understanding blood disease causes, symptoms and the solution for them. But this field lacked clearance for the imaging system. Image Recognition was innovated using Deep Learning Technique.Human body cells assume an astounding job in the human resistant framework. To know more about blood-related infections and its effects, pathologists need to think about the attributes of cells. To diagnose a blood related disease, we need to identify and characterize blood samples of patients. In the medical field, automation for detecting and classifying blood cells and its subtypes have gained more importance nowadays. Recognition of an object is a basic piece for the vision of a computer that distinguishes an article in the given picture regardless of foundations, impediment, lighting or the edge of the view. Problems that are too difficult to solve can be handled using architectures that run deep using algorithms that dive deeper into the features extracted from the input and this can be possible using Deep Learning.
Deep Belief Neural Networks for Eye Localization Based Speeded up Robust Features and Local Binary Pattern

Eye localization and detection are essential in security applications and human recognition and verification. The multi-pose variations of the pupil are still the major challenge in eye detection algorithms. Furthermore, facial expression recognition related to eye detection is still dropped in the recent security applications. This paper used a speeded-up roust feature (SURF) algorithm to localize facial parts, especially the eye and pupil, quickly and easily. Moreover, we detect the boundary box of face components by initializing the eye position based on Hough circle transform (HCT) and local binary pattern (LBP). Afterward, we classify the individuals who successfully detected their eye images using the confusion matrix of two class labels based on deep belief neural networks (DBNN). Fine-tuning the hyper-parameter values of the DBNN is performed as well as a stochastic gradient descent optimizer to handle the overfitting problem of the proposed method. The proposed algorithm’s accuracy based on the combination of SURF, LBP with the DBNN classifier reached 95.54%, 94.07%, and 96.20% for the applied ORL, BioID, and CASIA-V5, respectively. The comparison of the proposed algorithm with the state-of-the-art is performed to indicate that the proposed algorithms are more reliable and superior.
Dynamic Sign Language Recognition Based on Real-Time Videos

Sign language is the main communication tool for the deaf and hard of hearing. Deaf people cannot interact with others without a sign language interpreter. Accordingly, sign language recognition automation has become an important application in artificial intelligence and deep learning. Specifically, the recognition of Arabic sign language has been studied using many smart and traditional methods. This research provides a system to recognize dynamic Saudi sign language based on real time videos to solve this problem. We constructed a dataset for Saudi sign language in terms of videos in the proposed system. The dataset was then used to train a deep learning model using convolutional long short-term memory (convLSTM) to recognize the dynamic signs. Implementing such a system provides a platform for deaf people to interact with the rest of the world without an interpreter to reduce deaf isolation in society.
Ontology Based Recommender System using Fuzzy Clustering Technique

Recommender systems (RS) are the agents of information filtering processes. With a myriad of data in the world wide web, reaching the exact entity of interest is the need of the hour. Collaborative filtering is a type of RS concerned with studying past data to predict assumptions for the future. In this work, the collaborative filtering recommender is improved by incorporating ontology and fuzzy clustering algorithms to provide ranked recommendations. Ontology models semantic information from raw natural language data to better represent domain knowledge and user preferences. In this way, data sparsity problem could be sorted. The proposed recommender framework runs on top of a historical data that is not only semantic but also represents users preferences perfectly. Another problem of cold start can also be solved by including maximum expectation strategy by constructing the user ontology and domain ontology separately and mapping it to achieve a unified recommender ontology. Using fuzzy clustering introduce a degree of membership for each user with their interests. It also allows for attributes to be distributed in more than one cluster, thus enabling users to be clustered in the most inclusive manner possible. Which provides for ranked recommendations that represent diversified interests of a single user. The dynamic framework is compared with baseline models. Experimental results have shown significant improvement in the recommendation accuracy.
Tree-Based Group Diffie-Hellman for subgroup communication in M2M networks

Machine to Machine (M2M) communication deals with exchanging messages between any large group of machines over a wired or wireless channel. Such systems are usually not actively monitored by people. M2M devices could be communicating sensitive data for applications such as healthcare and remote payment. So proper security protocols to prevent eavesdropping and tampering are of utmost importance. In addition to group communication, forming and communicating securely within subgroups could be necessary for specific M2M applications. This paper demonstrates the use of Tree-based Group Diffie-Hellman (TGDH) for secure encrypted communication in M2M networks and extends the functionality of TGDH using a swap operation. This operation facilitates the formation of subgroups and allows for peer-to-peer multicast communication within them. We show how the tree structure of TGDH allows for efficient addition, removal, and swapping of nodes in the network by comparing its performance with standard Group Diffie-Hellman (GDH) based encryption.
Performance analysis of photovoltaic inverter system for predicting energy yield

The paper aimed to explore the model which can be considered to investigate the environmental factors affect energy yield based on photovoltaic inverter system. The multiple regression analysis was employed for predicting of energy yield. The data were collected through the Net ECO system database at the Prince of Songkla University, Surat Thani Campus. The results showed that the maximum level of energy yield was in July followed by May and, June. The results suggested that the amounts of energy yields were positively correlated with the irradiance factor. The irradiance factor could explain variation in the existence of energy yield which can predict the effect for 96.49%. The model can be used to create a system automatically tracking and notifying the energy yield based on the measured environmental factors.
Sosialisasi Tangguh Bencana Banjir Di Desa Sungai Tabuk, Kecamatan Simpang Empat, Kabupaten Banjar, Provinsi Kalimantan Selatan

At the beginning of the year, high rainfall triggered overflows of river water which, in flooding, one of which was in Sungai Tabuk Village, Simpang Empat District, Banjar Regency. The purpose of this service is to provide knowledge about potential disasters, characteristics, impacts, and cycles of flood disaster management for the village government and the people of Sungai Tabuk Village. The methodology used to provide understanding and increase awareness and improve communication between communities is through the Socialization of Flood Disaster Resilient. From this activity, it will increase public understanding and awareness to know the threat of disasters in their area so that they are able to organize and reduce disaster risk to reduce vulnerability to disasters with preparedness and make efforts to prevent and recover post-floods.