Reviewerly/exHarmony huggingface dataset:
An Analysis on Daily and Sports Activities’ Recognition Using Data Analytics and Artificial Intelligence Model

The understanding of collective tactical behavior has developed into an essential component of sports data analysis. The utilization of ever-increasing quantities of complex information requires the usage of data analysis methods that are both interactive and automatic. The collection and analysis of sportsperson monitoring data are a standard practice in professional team sports. Since being sedentary was found to be one of the primary risk factors of death, classifying activities that people do on a daily basis as well as those that they do for sports has become an important activity that may improve the quality of human life. These actions entail many different types of fundamental occurrences. It is necessary to be able to identify the action being carried out by the human in order to construct a human–computer interaction system. A module for recognizing daily and sports activities would do feature extraction from the available sensor data in order to find relevant information. The suggested system centers on the automatic recognition of the actions carried out by humans through the utilization of the raw data collected from Internet of Things wearable sensors. This paper's goal is to conduct an analysis of the existing system that is related for daily and sports activity recognition system.
Recreation of a Sub-pod for a Killed Pod with Optimized Containers in Kubernetes

Recently, cloud computing has been adopting containerization, created by virtualizing the operating system, for its several service offerings. These software bundles contain all the necessary functions to run for a specific environment. Kubernetes, an open-source system, is used to automate containerized applications, deployment, scaling, and management. Kubernetes assumes a pod as the smallest execution unit and may contain several related containers. One of the significant drawbacks associated with Kubernetes is when a pod is killed due to the lack of hardware resources, decommissioning of a node, or adopting any load-balancing strategy, it cannot be recreated automatically. Since a pod contains several associated containers, one requires some mechanism to recreate the killed pod. When a pod is killed, several possibilities exist, (i) Execution of all pod containers is completed (ii) None completed, and (iii) Out of n containers, only m finished their execution (n> m). In this work, an efficient algorithm is proposed which opens the related .yaml file, finds the logs of all container actions, removes the executed container information, and recreates the .yaml file, in turn, and sub-pod. This process helps to move the pod to another execution unit during load balancing, resource pooling, and rapid elasticity, which are desirable characteristics of any cloud.
Decentralised Blockchain-Based Framework for Securing eVoting System

Building a safe voting system that delivers the fairness and privacy of the present voting systems is quite difficult. We assess a Blockchain as a service application to construct distributed electronic voting systems in this implementation study. Our goal is to offer a decentralised infrastructure that can support and administer an open, equitable, and independently verifiable voting system. The experimental finding demonstrates the value of our proposed solution for both the current and upcoming voting systems. Our proposed solution implements the protocol that achieves fundamental e-voting properties, as well as offers a degree of decentralisation and allows for the voter to change/update their vote.
Q-learning Approach to Mitigate Bacterial Contamination in Food Supply Chain

Due to globalization of markets, products are moving throughout the country and exported. Quality monitoring and traceability in a supply chain is hence essential. Blockchain technology ensures the privacy or resistance to data immutability of a decentralized database in case an attacker tries to alter or delete data inside the blockchain. The purpose of our research is to develop a holistic approach to make a perishable food supply chain resilient by ensuring a secure way of storing and monitoring supply chain data and identifying and optimizing the relevant factors that lead to bacterial contamination in food supply chain under uncertainty. We developed a blockchain interface to add, validate, and send data related to detailed wheat supply chain from farmers growing crops to harvesting, storing in the silos or elevators, then processors for milling and sifting to develop end products (bread, biscuit, buns, tortillas, etc.) and distribute to retailers who finally sell them to consumers. After the data is securely stored in the decentralized ledger, in our next step, we came up with a data-driven technology such as machine learning (Q-learning model) to filter relevant parameters impacting food quality at all stages of the supply chain and optimize them to ensure good quality.
Underwater Image Enhancement Using Color Correction and Fusion

Visibility of images is adversely affected by scattering and absorption in underwater environment. This paper proposes an enhancement technique for the underwater images that are captured at large depth in the ocean. The dataset is chosen with low-lighting conditions and the images have large meaningful natural or artificial objects, whose valuable information is hidden due to poor illumination. The enhancement model comprises channel compensation and, white balance using adaptive gray world algorithm. The output is applied in parallel to gamma correction and sharpening steps to balance the dark and bright regions. The two results are fused to get the enhanced image. The quality metrics are calculated in terms of UIQM and UCIQE for the enhanced image and compared with the available techniques. Good improvement in UIQM quality measure is achieved (upto 92%) with a slight degradation in computation time.
Feature Based Techniques for a Face Recognition using Supervised Learning Algorithms based on Fixed Monocular Video Camera

Automatic face recognition has ample significance in biometric research. Recent decades have witnessed enormous growth in this research area. Face-based identification is always considered more expedient as compared to other biometric authentications owing to its uniqueness and wide acceptance. The major contribution of this work is twofold; firstly, it comprises an extension of manual thresholding feature-based face recognition approach to an automatic feature-based supervised learning face recognition. Secondly, various new feature sets are proposed and tested on several classifiers for 2, 3, 4, and 5 persons. In addition, the use of slope features of facial components, such as the nose, right eye, left eye, and lips along with other conventional features for face recognition is also a unique contribution of this research. Multiple experiments were performed on the UMT face database. The results demonstrated a comparison of 5 different sets of feature-based approaches on 7 classifiers using the metrics of time efficiency and accuracy. They also depicted that the proposed approaches achieve a percentage accuracy of up to 95.5%.
Pengelompokan Provinsi di Indonesia Berdasarkan Indikator Pendidikan Menggunakan Metode K-Means Clustering

The education level of the Indonesian people has improved, but has not yet reached the entire population. The educational disparity that occurs between economic groups is still a problem and widens as the level of education increases. The education gap is also still high when compared between regions. Quality learning also has not run optimally and evenly between regions. Accurate and complete information is needed as a reference in planning and determining the right strategy in facing development challenges in the education sector. This information is expected to explain the current condition and situation of education development in Indonesia. This study aims to group provinces in Indonesia based on educationalindicators using the K-Means method. The data and parameters used are based on a portrait of education statistics in Indonesia in 2020. This study shows that clustering produces the best cluster quality at K=3 based on the Silhouette Coefficient
Smart Vehicle Tracking in Harsh Condition

In this project, we have tried to develop a system that detects and tracks moving cars on road using cameras. Detected vehicles are given a specific id and information is stored by the system which is further used for the re-identification of the vehicles. The quality of the camera is also important because bad weather and vibrations can affect its capacity of catching images and performing operations on it. So, for this purpose, high-quality cameras with the vibrating lens can be used. Different datasets are fed to this model, so that rain, snow, wind, and night will not affect its performance of detecting. If this project is done on a high scale, then RGB sensors for collecting information and LIDAR can be used in addition to further improve efficiency. R CNN with YOLO makes a good combination for this system and improves accuracy. Our system works in two parts. CNN is used in this model so that it can develop its own neural network and with more and more practice with different types of inputs, it can become better and its accuracy will also increase with time. Such systems are helpful in today’s generation and are still developing and spreading at an enormous rate.
Image Processing-Based Presentation Control System Using Binary Logic Technique

Providing a perfect presentation becomes a challenging job because of various factors like changing the slides and the correct keys to be used to change the slides while maintaining composure in front of the audience. There are a lot of interruptions during a presentation when using the keyboard for controlling the slides. Therefore, this paper proposes a smart presentation system using hand gestures which provides an easy way to change or operate the slides. The proposed system uses various algorithms to detect different hand gestures to perform several operations.
Hybrid Machine Learning Algorithm for Prediction of Malaria

As malaria is a fatal disease that can occur anywhere, prompt diagnosis is crucial to stopping the disease in its tracks and reducing its overall impact. To better foresee future malaria epidemics, a hybrid machine learning model was created in this research. The model performance can be improved using various strategies, such as ensemble methods or fine-tuning the hyperparameters. Choosing a proper ensemble technique impacts the model accuracy. The performance of the ensemble model was measured using several well-known machines learning algorithms, such as Decision Tree, Support Vector Machine, Naïve Bayes, K-Nearest Neighbors, and Random Forest. This methodology’s stacking strategy of ensemble technique allowed the integration of five separate algorithms. Compared to other machine learning classifiers, the results obtained using this ensemble method is superior with accuracy 92.8%, AUC 91%, recall 100%, precision 85%, F1-score 91%, specificity 100%, macro-average 94%, weighted average 98%, and error rate 5.4%. The relative importance of each variable and the degree of connection between them in explaining malaria prevalence are calculated. The results indicate that the hybrid approach is valuable for anticipating malaria outbreaks.
A Conceptual Model for the Selection of Methods for Software Engineering Process Improvement

One way of improving the efficiency of system development is through the adoption of new methods. These, upon adoption, can provide the development team with the capabilities to address the current challenges. Once a team decides to follow this path, selecting which methods to adopt is a task ahead. The state-of-the-art provides a plethora of options. The criteria should be choosing methods yielding the highest net benefit towards the adoption goal. Many criteria influence the assessment of methods’ value. Knowing which ones are relevant and how they are related is essential to complete this task with excellence. In this paper, we propose a conceptual model describing elements of the decision-making process when selecting software engineering methods to be adopted by development teams. We aim to make explicit much of the knowledge involved in this process, i.e., mechanisms and influencing factors, to foster proper value assessment of methods. For researchers, our work can serve as guidelines to describe methods, for the industry, the model allows the comparison of assessment methods and better-motivated business plans.
On Tools for Practical and Effective Security Policy Management and Vulnerability Scanning

An organization’s ability to protect itself against risks originating from system vulnerabilities is determined by its ability to apply and maintain an operating system security policy and detect, prioritize, and timely apply patches to known system vulnerabilities. This paper discusses the usability of openly available Security Content Automation Protocol (SCAP) tools and source data for system security policy management and vulnerability scanning. Usability evaluation was conducted with use a case where installation of the tools in the target environment was coupled with election, setup, and execution of the security policy and vulnerability scanning. The use case revealed variation in tool usability depending on implemented features and availability of published source data. Implementing security compliance automation with SCAP tools was found to be feasible in operating systems that provide adequate source feed and supported by OpenSCAP.
The Effectiveness of Bima Folktales as Learning Materials to Teach Reading Comprehension

Bima is one of the region located at West Nusa Tenggara, Indonesia. This tribe has many well-known folk stories between their people that can be considered as learning materials to teach reading comprehension in EFL class. The previous studies showed that folktales is effective to teach reading comprehension, it enhance reading comprehension achievement of students. However, how effective Bima folktales as learning materials to teach reading comprehension especially at the level senior high school’s students should be investigated because there is no study about it before. Being designed in true experimental with post-test only control group, the objective of this study is to find out whether using of Bima folktales as learning materials is effective or not to teach reading comprehension on high school‘s students. Conducted on tenth grade students of IPA at SMA Negeri 2 which consists of 62 students who were divided into experimental group and control group. These two groups were proved on the same level of reading comprehension ability by the scores of mid-term test. Based on independent t-test in SPSS 20.0 version for windows calculation, it was proved that the significant values of variances assumed was .097. It is indicated that the students’ reading comprehension achievement of experimental group and control group were homogenous. Meanwhile, sig. (2-tailed) value was .005 which less than .050 and it means that the H0 is rejected and H1 is accepted. The results of study showed that students of experimental group were performed better than students of the control group, although they have the same level of reading comprehension before the treatments were given. Suggestions purposed for further researchers to conduct the study with the related topic by providing more learning materials about Bima folktales. Because the time limited during Covid-19, the next researcher also could add more times for treatments, choosing more samples and preparing the test items more than the available items in order to have back up items if there are many difficulty items than the moderate items or if there are items deleted.
Nadezhda Abramovna Kanover — “Above all for Her Were Books”

The article tells about the Far Eastern librarian Nadezhda Abramovna Kanover, a follower of the bibliographer Z.N. Matveev, who played a great role in the development of librarianship in Vladivostok. The publication is based on the documents from the Current Archive of the Presidium of the Far East Branch of Russian Academy of Science (FEB RAS), Central Scientific Library (CSL) of FEB RAS and Society for Study of Amur Region (SSAR) that for the first time reveal unknown facts from N.A. Kanover life and characterize the situation in library science in the Russian Far East in 1930s — 1950s. Heading several libraries including the future CSL FEB RAS and library of Primorskiy branch of State Geographical Society (SFGS), N.A. Kanover put much efforts in collecting collections and making conditions for book storage, as well as promoted book culture by publishing articles and organizing book exhibitions. Thanks to her, Vladivostok received a unique old-believers’ collection that belonged to bibliophile S.Ya. Ponosov. Printed editions of the collection are now kept in the SSAR library (former SFGS library), icons and religious artefacts are in the Primorsky Picture Gallery and the V.K. Arsenyev Museum of Far East History. These organisations presented a part of Ponosov’s collection at the joint exhibition “Legend of Belovodie” (Vladivostok, February 18 — July 30, 2023). The pages of Kanover’s biography connected with the last months of life of V.K. Arsenyev, the famous traveller and explorer of the Far East, are remarkable. While working in the library of SFGS, N.A. Kanover became its last secretary, dealing with the correspondent network of the branch. Unfortunately, almost nothing is known about the last years of her life, but N.A. Kanover’s work in the field of Far Eastern library science is not forgotten.
In what ways does user experience design improve printed educational materials?

We report on a series of four studies that investigated how user experience design (UXD) can improve printed educational materials (PEMs). We examined the perceived usability of an existing PEM for breast cancer screening and observed the usability problems associated with it (Study 1). We then compared a breast cancer screening PEM created by user experience designers with two other breast cancer screening PEMS, finding that the PEM based on UXD had higher perceived usability, and lower mentions of usability problems, than the other two PEMs (Study 2). We next examined the impact of individual differences in design expertise on perceived usability, this time including a PEM on cervical cancer screening as well as one on breast cancer screening (Study 3). Our concluding study (Study 4) then examined the impacts of UXD on learnability of PEM content as defined by answers to a knowledge questionnaire about screening administered before and after reading the PEM, and by intention to screen for cancer after reading the PEM. The first three studies showed that the involvement of UXD improved the perceived usability of PEMs, and Study 3 showed that designers differ in their ability to create useable PEMs. Study 4 failed to find a corresponding improvement in learnability or intention to screen when UXD was used to improve perceived usability. We conclude that a user experience design approach that incorporates graphic design can improve the perceived usability of PEMs in some situations (e.g., when the PEM material is not too lengthy or complex, and when the graphic designer is sufficiently skilled). However, we found no evidence that lack of perceived usability accounted for the failure of PEMS (found in previous research) to improve knowledge or intention to screen.
Hubungan Intensitas Belajar Ekonomi, Gaya Hidup Siswa dan Prestasi Belajar Ekonomi Siswa terhadap Rasionalitas Berkonsumsi Siswa Kelas XI Paket C Setara SMA di Kabupaten Sumbawa

Economic is one of the compulsory subjects of social science in package C equivalence of Senior High School. Student’s consumption attitude can be observed based on their understanding of that subject. This research is important to do that. This is quantitative research designed explanatory causality. The population in this research is students of Class XI package C of all Sumbawa regency and the sampling technique is multistage random sampling. It is since this research aims to know the effect of intensity of economic learning, lifestyle, and student’s achievement of economic lesson towards student’s consumptions rationality of package C equivalence to Senior High School. So that the data analysis used the causal model by Baron and Kenny. The findings of this study showed that (1) the intensity of economic have study significant effect on the student’s consumption rationality, (2) lifestyles have significant negative effect on the student’s consumption rationality, (3) lifestyles of student’s life have a significant effect on students learning outcomes, (4) the intensity of economic learning has a significant effect on learning outcomes, (5) economic learning has a significant effect on student’s consumption rationality, (6) the lifestyles of students have a significant negative effect on consumption rationality through learning outcomes of economics, and (7) the intensity of economics learning significantly influence the consumption rationality through economic learning outcomes.
Case Study of User-Centered Emotional Safety Design Development and Application: centered on the Samcheok Light Dream Headquarters of Korea Southern Power Co.

While the number of industrial fatalities has been declining in recent years, companies' safety and health management systems are still inadequate, and more than half of industrial accidents are caused by failure to follow basic safety rules. It is important to raise safety awareness among workers and create a safety culture in the workplace. Especially in large-scale, complex environments with many inherent risks, such as power plants, it is necessary to develop solutions specific to power plant sites, such as an emotional design approach that improves worker awareness of safety rules and drives user behavior. To this end, we used user-centered service design techniques to diagnose potential risk factors for each process in the power plant, identify problems from the worker's perspective, and conduct idea competitions and co-creation workshops to develop solutions. The final nine solutions were designed and demonstrated, and received positive evaluations through satisfaction and effectiveness analysis. Through this, it was confirmed that in power plants where risk factors are scattered in various places, an approach that enhances the effectiveness of problem discovery through worker participation and co-development of solutions is necessary. In the future, if safety design considering user characteristics is further developed to minimize potential safety factors and risks by considering the user's lifecycle, it will contribute to improving the safety achievement of power plants.