Reviewerly/exHarmony huggingface dataset:
Clustering and Multidimensional Scaling for Individual Difference Extraction

This paper proposes methods to obtain difference among subjects by using the degree of reliability of each subject based on the results of fuzzy clustering and multidimensional scaling (MDS).In addition, new fuzzy clustering and MDS, including the weights of reliability scores, are proposed to classify subjects.When we observe data consisting of values of objects with respect to variables, and such data are observed over multiple subjects, capturing the difference among subjects is important in many fields.In this paper, the degree of reliability is obtained through the optimality of convex clustering.Based on this idea, it is shown that the same difference over the subjects can be obtained, regardless of the difference in obtained latent structures, which are the result of dynamic fuzzy clustering and the result of MDS by a numerical example.From this, we show the robustness of the proposed reliability concerning the variety of the obtained latent structures of data.
Mathematical Modelling: Growing Role and Applications

This paper aims at the importance of mathematical modelling, its growing role and its applications. It is a myth that modelling projects progress easily from working through to utilizing, this is scarcely ever the situation. In computer science, the use of modelling and simulating a computer is utilized to fabricate a mathematical model which contains key boundaries of the actual model. The study aims at the use and advantageous application of mathematical modelling in the COVID pandemic such as to understand the transmission of the virus, its solution to prevent the growth of the virus among the population, calculate the infected populations and give a slight idea of the future predicted scenarios. Thus, its role and applicability in the pandemic are discussed by framing a mathematical model case to understand the transmission of the virus at the stage of the beginning of the pandemic. Thus, the study aims to give a basic idea of mathematical modelling, its uses, and its role in recent scenarios.
Social Media in ESL Classroom: Wechat

This study aims to investigate the perceptions of undergraduates regarding the relevance of social media, the WeChat application platform, towards improving reading comprehension skills in English as a Second Language.At the same time, the study wants to find out the influence of social media platform usage on language learning towards improving students' critical thinking skills given its association with the reading comprehension skill.The results show important information on students' use of WeChat for ESL learning intentions.The ease of use and the structural characteristics of these tools engage students in critical thinking; enhance communication, reading, and writing skills while sharing information and modification.As for educators, it is to be noted that students prefer their presence in an online learning community.They need a teacher to guide them and provide helpful feedback related to learning.It is concluded that although using social media provides students with an enriched learning experience, students shared that they need a teacher to plan and execute teaching and learning activities effectively.Consequently, teachers can opt for more reference sources to consider when they design curricula.
Classification of Pneumonia in Thoracic X-Ray images based on texture characteristics using the MLP (Multi-Layer Perceptron) method

One of the diseases that attack the lungs is pneumonia. This disease can attack someone with a weak immune system. Pneumonia is inflammation of the lungs that can be caused by pathogens, such as bacteria, viruses, and fungi. The purpose of this study was to classify fungal pneumonia, bacterial pneumonia, and lipoid pneumonia based on texture characteristics and the MLP method using machine learning WEKA. The method in this study has three stages including pre-processing, extraction of texture features consisting of Histogram and GLCM, and classification using the MLP (Multi Layer Perceptron) method. The results of the texture feature extraction showed that the three types of pneumonia were: lipoid pneumonia with brightness, sharp contrast random distribution on correlation characteristics, bacterial pneumonia with high brightness, high contrast random distribution on energy characteristics, and fungal pneumonia with brightness, sharp contrast, random distribution of homogeneity attributes. The third similarity of pneumonia is in the gray level that collects each other in the middle. The method used in this study resulted in the same accuracy, sensitivity, and specificity, namely 100%. The image classification in this study shows the success of the texture features and the MLP method in classifying pneumonia images accurately so that they can be used as additional tools that make it easier for medical experts. ©2020 JNSMR UIN Walisongo. All rights reserved.
Minimization of time distribution of ballots with Greedy algorithms in Jombang Regency

Travelling Salesman Problem is a problem faced by salesmen in distributing goods by passing all points exactly once. This problem is often encountered in life, not least in the distribution of election ballots from the Komisi Pemilihan Umum Daerah (KPUD) Jombang office to the sub-district office in Jombang Regency. Proper route determination can help to minimize the travelling time between places so that the risk of delaying ballot distribution can be avoided. In determining the solution of Traveling Salesman Problem, a Hamiltonian cycle is required. The Hamiltonian cycle is a closed trail that passes every point exactly one time. The Hamilton cycle can be formed by the Greedy Algorithm. The Greedy Algorithm can quickly determine the next point based on the smallest weight in the form of distance between points. From the problem of ballot distribution in Jombang, the starting point of the route is the office of Komisi Pemilihan Umum Daerah (KPUD) Jombang then through 21 sub-district offices and back to the Komisi Pemilihan Umum Daerah (KPUD) office Jombang. Based on the searching for solutions to minimize the distribution time of ballots in Jombang Regency with Greedy Algorithm, the total distance to pass all existing sub-district offices is 253.1 km with a travel time of 427 minutes or 7 hours 7 minutes.©2021 JNSMR UIN Walisongo. All rights reserved.
Item Analysis Technique for improving Course Outcome Attainment

It has become a challenging task to derive information on attainment of cognitive skills by analysing students’ performance. There are certain subjective critical factors such as understanding of basic concepts, quality of teaching, quality of question papers, and the mode of evaluation, which influence of students accomplishment. The authors feel that it is necessary to examine the attributes of end semester examination question papers. It is essential to assess the type of questions posed in the examinations using the statistical techniques and measures. This paper analyse the analysis of quality of questions used in the end semester examinations. The proposed methodology uses ‘item analysis’ technique for improving the course outcome attainment. Statistical results indicate that the question setters may consider the resulted norms while examining the students by standardizing the assessment questions, which in turn yield better academic performance by students. Keywords: Item Analysis, Course Outcome Attainment, Student Performance, Question Paper Quality
Self-assessment of Student Digital Competences in Serbia

The updated Digital Competence Framework, DigComp 2.0, defines five key areas of digital competences that every citizen needs to be able to use ICT in a critical, creative and collaborative manner for business, learning, entertainment, inclusion or social participation. This paper examines digital competences of students in Serbia, and the general aim of the research is to determine how students in Serbia self-assess their digital competences, whether their self-assessment is determined by specific socio-demographic characteristics, such as: university, field of study, year of study, frequency of use of ICT devices in teaching, and everyday activities. Two research instruments were created for the purposes of this research: Questionnaire on Socio-demographic Characteristics, and Digital Competence Assessment Scale, which is based on the European Digital Competence Framework – DigComp 2.0. Students from 22 faculties (N=183) from four administrative districts in Serbia (Moravica, Zlatibor, Južna Bačka and the City of Belgrade District) participated in the research. The research determined that students assess their digital competences as highly developed, both in relation to the scale and its integral subscales, and confirmed that there are significant differences in student self-assessments with regard to independent research variables: university and frequency of use of technical devices (smartphones) in teaching and everyday activities.
Education Process Re-engineering through Spectral Pyramid Framework to Achieve Excellence in Engineering Education

The Global challenges are capturing the interest and focus of Indian Higher Education institutions to update their systems and appraise the criteria of rankings. And it is difficult to justify the Indian Higher education institutions’ unique excellence parameters and its scalability to meet the criteria of rankings, because of its diversity in institutions’ size, nature of courses, disciplines, population, etc. Initially, the local challenges for Indian graduates which include appropriate employment, Career in further Studies, Entrepreneurship opportunities, etc. do not have a unique framework. The other focus area is lack of awareness and expertise in mezzanine technologies like Deep Learning, Artificial Intelligence, Renewable energy, Agricultural Technologies, Machine Learning, Data Science, Block Chain Technologies, Cyber Security, IoT, Virtual Reality, 3D Printing, Robotics, Design Thinking, etc. This paper has focused on these issues to collect the processes and designing a unique framework model to be adapted by higher educational institutions especially technical institutions in India. As the number of technical institutions is rising year on year, the demand is primarily dependent on quality and excellence outcomes. The proposed model is tested with current higher educational functions by implementing it in two higher educational institutions. The results are adaptable and applicable to any of technical higher education institution who are seeking excellence in processes. Keywords— Higher Education, Reengineering, Spectral Pyramid, Total Quality Management.
Growth of Cryptocurrency market in India with special reference to Bitcoins trading and its impact on Economy

With the advancement of communication technologies and knowledge technologies, many activities are changing in our daily lives. The tremendous growth in the number of internet users has led to a new business plan, a digital code to improve the business concept, of marketing, buying and selling. Cryptocurrency represents important and potentially transformative objects electronically in a wide range of networks and applications such as online social networking sites, social media platforms, global platforms and peer networks. In recent years, the use of digital money in various forms like Bitcoin has become very widespread. This paper studies about the prospect of cryptocurrency. It also investigates the confidence of users in the trading of cryptocurrency specially Bitcoin. Also, the paper examines the regulations related to use of the crypto currency in India. In addition, this paper aims to measure the prevalence of the use of cryptocurrencies to obtain a vibrant picture. This paper analysis how different countries have responded to cryptocurrency rules and laws to have a clear impact of India's regulatory laws.
FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild

Image-based age estimation aims to predict a person's age from facial images. It is used in a variety of real-world applications. Although end-to-end deep models have achieved impressive results for age estimation on benchmark datasets, their performance in-the-wild still leaves much room for improvement due to the challenges caused by large variations in head pose, facial expressions, and occlusions. To address this issue, we propose a simple yet effective method to explicitly incorporate facial semantics into age estimation, so that the model would learn to correctly focus on the most informative facial components from unaligned facial images regardless of head pose and non-rigid deformation. To this end, we design a face parsing-based network to learn semantic information at different scales and a novel face parsing attention module to leverage these semantic features for age estimation. To evaluate our method on in-the-wild data, we also introduce a new challenging large-scale benchmark called IMDB-Clean. This dataset is created by semi-automatically cleaning the noisy IMDB-WIKI dataset using a constrained clustering method. Through comprehensive experiment on IMDB-Clean and other benchmark datasets, under both intra-dataset and cross-dataset evaluation protocols, we show that our method consistently outperforms all existing age estimation methods and achieves a new state-of-the-art performance. To the best of our knowledge, our work presents the first attempt of leveraging face parsing attention to achieve semantic-aware age estimation, which may be inspiring to other high level facial analysis tasks. Code and data are available on \url{https://github.com/ibug-group/fpage}.
Dual-Branch Discrimination Network Using Multiple Sparse Priors for Image Deblurring

Blind image deblurring is a challenging problem in computer vision, aiming to restore the sharp image from blurred observation. Due to the incompatibility between the complex unknown degradation and the simple synthetic model, directly training a deep convolutional neural network (CNN) usually cannot sufficiently handle real-world blurry images. An existed generative adversarial network (GAN) can generate more detailed and realistic images, but the game between generator and discriminator is unbalancing, which leads to the training parameters not being able to converge to the ideal Nash equilibrium points. In this paper, we propose a GAN with a dual-branch discriminator using multiple sparse priors for image deblurring (DBSGAN) to overcome this limitation. By adding the multiple sparse priors into the other branch of the discriminator, the task of the discriminator is more complex. It can balance the game between the generator and the discriminator. Extensive experimental results on both synthetic and real-world blurry image datasets demonstrate the superior performance of our method over the state of the art in terms of quantitative metrics and visual quality. Especially for the GOPRO dataset, the averaged PSNR improves 1.7% over others.
Conditional address propagation

Speculative execution is a critical technique in modern high performance processors. However, continuously exposed transient execution attacks, including Spectre and Meltdown, disclosed a large attack surface in mispredicted execution. Current state-of-the-art defense strategy blocks all memory accesses that use addresses loaded speculatively. However, propagation of base addresses is common in general applications and we find that more than 60% blocked memory accesses use propagated base rather than offset addresses. Therefore, we propose a novel hardware defense mechanism, named Conditional Address Propagation, to identify safe base addresses through taint tracking and address checking by a History Table. Then, the safe base addresses are allowed to be propagated to retrieve performance. For remaining unsafe addresses, they cannot be propagated for security. We constructed experiments on cycle-accurate Gem5 simulator. Compared to the representative study, STT, our mechanism effectively decreases the performance overhead from 13.27% to 1.92% targeting Spectre-type and 19.66% to 5.23% targeting all-type cache-based transient execution attacks.
Trusting the trust anchor

The rise in the development of complex and application-specific commercial and open-source hardware and the shrinking verification time are causing numerous hardware-security vulnerabilities. Traditional verification techniques are limited in both scalability and completeness. Research in this direction is hindered due to the lack of robust testing benchmarks. In this paper, in collaboration with our industry partners, we built an ecosystem mimicking the hardware-development cycle where we inject bugs inspired by real-world vulnerabilities into RISC-V SoC design and organized an open-to-all bug-hunting competition. We equipped the participating researchers with industry-standard static and dynamic verification tools in a ready-to-use environment. The findings from our competition shed light on the strengths and weaknesses of the existing verification tools and highlight the potential for future research in developing new vulnerability detection techniques.
EXPERT SYSTEM DEVELOPMENT TO IDENTIFY EMPLOYEE PERSONALITY TYPES USING DEMPSTER SHAFER THEORY

Human resources are an important asset for the company to develop and realize the company's goals. One of the efforts to optimize the capacity of employees is to know their personality. Personality is the form possessed by an individual in behaving and all the characteristics that distinguish one individual from another. Knowing the personality of employees is important for the company and the employees themselves. Because by knowing a person's personality, the company can maximize the potential of employees and can place certain positions that suit the personality of the employee. This study aims to implement dempster-shafer theory on an inference engine in building an expert system to identify employee personality types. Dempster-shafer theory can perform probability calculations so that evidence can be carried out based on the level of confidence and logical reasoning. The system developed is able to identify the personality type of the employee through the nature or symptoms that exist in the employee. In addition, the system can display the results of the diagnosis with an explanation of the personality type, its nature in work and occupations or positions that are suitable for that personality type. Based on the results of the accuracy test obtained from the comparison of expert system diagnoses with the analysis of an expert, the accuracy value reaches 85%.
Efficient Batch Fully Homomorphic Encryption with a Shorter Key from Ring-LWE

Fully homomorphic encryption allows users to use the computing resources of cloud servers for the computation of encrypted data without worrying about data leakage. The fully homomorphic encryption approach has problems with excessive noise and the expansion of the ciphertext dimension after the homomorphic evaluation. The key switching technology effectively solves the problem of the ciphertext dimension expansion. The generated evaluation key is a masked secret key that must be shared between the data owner and the computational entity, so the security must be guaranteed. In the RLWE-based FHE scheme, the efficiency improvement of the key switching depends on the circular security assumption, meaning the security needs to be improved. To solve the above problems, we select the secret key from the noise distribution with variable parameters so that the evaluation key and the initial noise of the encryption scheme are smaller. Specifically, the secret key is replaced after each homomorphic evaluation to ensure the security. We use the “modulus scaling” method to control the noise generated by itself, rather than the BitDecomp technology, which is complex when applied to polynomials. Finally, we combine the packing technology that relies on the polynomial CRT (Chinese remainder theorem) to design a batch-leveled fully homomorphic encryption scheme. We analyze the scheme’s noise, security proof, and specific security parameters. Compared with the FV12 scheme, our scheme is more secure. Compared with the MB18 scheme, our evaluation key size is smaller.
DESIGN OF WEB-BASED VIRTUAL TOURISM INFORMATION SYSTEM AT GEOPARK CILETUH SUKABUMI

Geopark Ciletuh Sukabumi is an earth park that has biodiversity such as geological, social, and cultural elements, to locations that can be used for research and tourism. Tourist destinations in the Ciletuh Geopark include waterfalls, beaches, mountain peaks, islands, cultural tourism, and available biodiversity. Due to the lack of information media about tourist objects in the Ciletuh Geopark, not many tourists visit to enjoy the beauty of natural attractions and local cultural wisdom that is still maintained at the Ciletuh Geopark. Based on these problems, research was carried out to design and build a tourist information system with a virtual tour feature based on the Geopark Ciletuh Sukabumi website. In this study, the author designed the system using the Zachman Framework system development method and was built using CMS WordPress and Page Builder as well as PHP and MySQL programming languages. Testing the Virtual Tourism Information System at the Ciletuh Sukabumi Geopark using Blackbox Testing. The results of the design of a web-based virtual tourist information system at the Ciletuh Sukabumi Geopark can be used as a medium of tourist information for the general public and visitors who will travel to the tourist attraction&#x0D;
IMPLEMENTATION OF K-MEANS ALGORITHM FOR CLUSTERING OF COVID-19 VACCINATION IN EAST JAVA WITH ORANGE

Entering the era of the Covid-19 pandemic, the government has intensively implemented a vaccination program to date. The Covid-19 vaccination program aims to boost the immune system, reduce the risk of transmission, and reduce the severe impact of the virus to achieve group immunity. In its implementation, the Covid-19 vaccination is regulated by the regional government in each province with a policy that requires the Covid-19 vaccination to be vaccinated twice for everyone who meets certain criteria. This study aims to cluster the implementation of vaccination in all areas of East Java province in 2021. The method used in conducting this clustering is the K-Means algorithm. From the results of the study, the results of the division or clustering of regions into three clusters were C1 for the area with the lowest vaccination, namely Pasuruan Regency, C2 for the area with moderate vaccination, namely Kediri City, and C3 for the highest vaccination area, namely Surabaya City. The clustering results obtained based on the K-Means algorithm can be used as input for the East Java Provincial government in evaluating the implementation of the Covid-19 vaccination.