Reviewerly/exHarmony huggingface dataset:
Effects of Landslide on the Livelihood of People at Ghumthang, Sindhupalchok

Landslides are considered as one of the most recurring and common disaster in Nepal. They cause huge loss to infrastructure, property and human casualty. The intensity of landslides in Nepal’s mid hills are rising as one of the most common and devastating disasters. The 2020 Ghumthang landslide in Nepal’s Sindhupalchok District destroyed the houses, lands and infrastructures and impacted livelihoods of the people living in the area. Every monsoon landslides in hilly region of the country lead to impact on the livelihood of the people and this aspect has not been analyzed in depth. The disaster-affected people face the destruction crop, livestock, and land. They also face damages of house, drinking water and other infrastructure,. In addition they face non-economic effects like health and mental stress. This article based on a household survey combines quantitative and qualitative data from 50 respondents. Questionnaire, interviews and focus group discussions were held during field survey to solicit their persctives Secondary sources were consulted and lived experience used to triangulate the survey findings for accuracy. This study reveals multidimensional nature of the impact of landslide on the life of people who live in the affected area.
A Mixed Assessment for the Science Learning via a Bayesian Network Representation

This study explored an alternative assessment model to examine Chemistry learners’ progress. “The Assessment of Problem-Solving in Chemistry Learning” as a model represented students’ mastery of chemistry study. The data were from journaling narratives and analyzed through cognitive task analysis. Based on the analyses, a student model was established, which represents the qualitative information in a structure, and provides a potential framework of the assessment model for the quantitative representation—a Bayesian network assessment model. The student’s performance was assessed via the Bayesian network assessment model, and classified into three categories: low level, middle level, and high level. The mastery level should be at least scored at and above 90.51/100 for Declarative, Procedural, and Strategic Knowledge respectively.
Linearized projector alignment system for structured light illumination systems using the quad target method

Structured light illumination (SLI) systems can measure a target’s two-dimensional height profile. High-quality imaging in SLI systems is typically dependent upon well-focused optical systems that can be difficult to achieve due to the nonlinear nature of defocus. We present the quad target method (QTM), a simple and fast alignment technique that linearizes defocus for SLI projectors. This linear response provides a clear zero crossing along the optical axis to minimize any focus ambiguity and maximize projector focus quality. QTM creates the linearization by sampling the field at two locations along the optical axis using a quad target, which is a diffuse target with two different heights in opposing quadrants. To maximize feedback speed, QTM can estimate the current focus position using only a single image by measuring the contrast of projected sinusoidal fringes on the quad target. QTM was tested using a commercial SLI system and compared with a published phase shifting reconstruction technique. The nonlinear defocus behavior was efficiently linearized as evidenced by focus estimation accuracies over a broad range of QTM variables.
Higher education for all

Universities are one of the oldest human institutions, enduring with essentially the same blueprint for a thousand years. Before the current COVID-19 pandemic, there was much talk about the promise of massive open online courses, distance learning, and other innovations to scale and expand the reach of universities, but with only limited success. Given the experience gained from educating during the pandemic, it is time for educators to ask which innovations can be introduced and, importantly, sustained, to expand the accessibility of higher education to meet the needs of the 21st century.
Developing leadership skills in university students

The authors offer their own program that allows leadership development and provides knowledge about leadership in an integrated way in higher education. The study aimed to test this program’s effectiveness using multitest instruments in the form of a survey. The study was conducted with 196 students of Toraighyrov University in the conditions of representative sampling. The experimental group used the proposed method during the semester, the control group did not. Before and after the semester, the following tests were conducted: diagnostics of leadership abilities (Zharikov and Krushelnitsky), Eysenck’s self-assessment of mental states, and methods for identifying communicative and organizational abilities (COA-1). The obtained results were compared using the Student’s t-test to identify significant differences. In all tests, the experimental group showed statistically significant improvement in all indicators. Similarly, significant statistical differences were also found between the results of pre- and post-test, which allows one to assert that the proposed method contributes to leadership qualities development and reduction of negative emotions in students. Thus, the experimental group scored an average of 26.71 (out of possible 50 points) on the pre-test and 37.01 on the post-test, demonstrating a significant increase in the level of leadership qualities from weakly expressed to strongly expressed. According to the post-test results, the difference between the groups becomes statistically significant for both communicative and organizational tendencies (p = 0.023 and p = 0.027, respectively). In practice, the study results can be used to implement leadership development programs in universities.
Image local structure information learning for fine-grained visual classification

Learning discriminative visual patterns from image local salient regions is widely used for fine-grained visual classification (FGVC) tasks such as plant or animal species classification. A large number of complex networks have been designed for learning discriminative feature representations. In this paper, we propose a novel local structure information (LSI) learning method for FGVC. Firstly, we indicate that the existing FGVC methods have not properly considered how to extract LSI from an input image for FGVC. Then an LSI extraction technique is introduced which has the ability to properly depict the properties of different local structure features in images. Secondly, a novel LSI learning module is proposed to be added into a given backbone network for enhancing the ability of the network to find salient regions. Thirdly, extensive experiments show that our proposed method achieves better performance on six image datasets. Particularly, the proposed method performs far better on datasets with a limited number of images.
Hübner, Fahrbach, Lachner, &amp;amp; Scherer (2022). What predicts students’ future ICT literacy?

Information and communication technology (ICT) literacy is key to solving future societal challenges and to leading an independent and responsible life in an increasingly globalized and digitalized world. Hence, countries around the world have promoted research on students’ ICT skills and their inclusion in curricula. Despite these efforts, little is known about what predicts future ICT literacy, and prior research has almost exclusively conducted cross-sectional studies. In the present study, we addressed this gap by using data from a large-scale longitudinal study of 6,810 students in 311 schools from Germany. These students participated in the National Educational Panel Study over the course of secondary school (in Grades 6, 9, and 12). We were particularly interested in investigating potential spillover effects from skills and dispositions that are inherently fostered in school on ICT development. Thus, we considered a rich set of different types of predictors related to the students’ social, cultural, demographic, and learning backgrounds; their subject-specific motivation; and the composition of their schools, and we assessed the extent to which these variables explained variation in ICT literacy development at two key stages in the educational system (Grades 9 and 12). Our results indicated that prior ICT literacy was the most important predictor of future ICT literacy but more so for the development between Grades 9 and 12. In addition, variables related to the students’ learning background, followed by variables related to the composition of their schools and students’ subject-specific motivation explained future ICT literacy. Overall, the predictors explained between 49% and 67% of the variation in future ICT literacy. Our longitudinal findings suggest that the learning environment and students’ skills and dispositions are crucial for understanding ICT development and are indicative of the deliberate design of ICT interventions.
DEVELOPMENT OF NON-PROFIT ACCOUNTING INFORMATION SYSTEMS

This research is included in information system development research. The output product of this research is an Accounting Information System. The stages used in this study adapt the waterfall model from Pressman. Implementation of web-based system development using MySQL database on PHPMyAdmin. MySQL is one of the Database Management System (DBMS). The backend development of the accounting information system of this non-profit organization focuses on system logic starting from input, process and output. Input financial transactions in cash and bank as well as transactions that affect capital excluding income and costs. Automatically processes the inputted data in the report recapitulation according to the primary key that is set. Cash, bank, and capital transaction inputs are automatically recorded in the cash, bank and capital ledger, income ledger, expense ledger, income details, expense details, cash flow, and year-end reports. The output of the inputted report is a print preview to print the report by connecting to the hardware (printer). The testing procedure in this study was carried out with 2 tests, namely white-box testing, testing of the buttons on the system and black-box testing, testing of functions or processes that occur in the system. The data obtained from the results of filling out a set of instruments in the form of a questionnaire given to experts and users to assess the feasibility of the developed system. The results of the development research state that the accounting information system for non-profit organizations is feasible to be applied. This is evidenced by the results of the validation test of material experts, technology experts, and trials to users who are in the outstanding categories. The average on the material expert validation test shows the outstanding category with a score of 4.8. Technology experts show the outstanding category with a score of 4.78. Meanwhile, the trials on users belong to the very good category with a score of 4.7.
COVID-19 Vaccination and PPKM Policy with the Implementation of the Fuzzy Sugeno Method to Income Classification

This study aims to determine the implementation of Fuzzy Sugeno in classifying textual data obtained from Twitter so as to determine the polarity of public opinion regarding PPKM policies and Covid-19 vaccinations. This study uses primary data via Twitter related to COVID-19 vaccination and PPKM policies in Indonesia starting from February 9, 2021 to January 17, 2022. There are several stages carried out, namely data collection, data pre-processing, data labeling, data weighting. , identification of membership functions, determination of fuzzy sets, formation of classification systems, and evaluation of classification results. The results of this study explain that Fuzzy Sugeno's performance in classifying tweets is quite good with an average accuracy of 89.13%. Meanwhile, public opinion regarding PPKM policies and Covid-19 vaccinations tends to be balanced with 36.92% of tweets classified as positive sentiments, 22.85% negative sentiments, and another 40.23% classified as neutral sentiments. In addition, the fuzzy set that is formed based on the data observation method is very well done because it is able to adjust the frequency of the data in each category. This really helps improve the performance of the built classification system.
Transformation of the system of information and methodological support of the activities of scientific libraries of the Siberian Branch of the Russian Academy of Sciences

Scientific library, being a traditional institution for preservation and transmission of information, has the potential, which implementation can be determined by new models of social impact, depending, among other things, on introduction of innovative forms and methods of functioning. In the context of developing digitalization of resources and communications, scientific and methodological support for the activities of scientific libraries providing profile information to research scientists and specialists is of particular importance.The article presents forms and methods of information and methodological support for the activities of the libraries of the Siberian Branch of the Russian Academy of Sciences (SPSTL SBRAS), carried out by the State Public Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences – the methodologica l center for the libraries of the Siberian Branch of the Russian Academy of Sciences, which are transforming in connection with the processes of science reformation and restructuring the system of scientific institutions, that began in the second decade of the 21st century . Information about the services and products offered by the SPSTL SB RAS, presented in the article, can be used by libraries to improve the information support of scientists and specialists.
Attention and Motor skill improvements in Mild Cognitive Impairment patients using COSMA Application

Serious games have grown rapidly to provide cognitive stimulation for people with cognitive impairment. In this endeavour, we have created the COSMA games. In this two-arm study with patients with mild cognitive impairment (MCI), we investigated information processing speed and cognitive skills with 28 days of use of COSMA games, a brain stimulation gaming platform. We measured reaction times during neuropsychological assessment with the Cambridge Neuropsychological Test Automated Battery and the COSMA games. Results showed that people with MCI who played COSMA for 28 days at home were faster in 48% of the COSMA games and the Control group which played only during the laboratory visits was faster in 20% of the COSMA games at the end of the study. Overall, these outcomes showed evidence that motor skill practice and learning retention are possible in people with MCI and that these skills are still able to improve with the regular practice of the COSMA games.
Improved authenticated elliptic curve cryptography scheme for resource starve applications

Elliptic curve cryptography (ECC) remains the best approach to asymmetric cryptography when it comes to securing communication among communication partners in low-computing devices such as wireless sensor networks (WSN) and the Internet of Things (IoT) due to its effectiveness in generating small keys with a strong encryption mechanism. The ECC cuts down on power use and improves device performance, so it can be used in a wide range of devices that don't have a lot of resources. However, most of the existing ECC implementations suffer from implementation flaws that make them vulnerable to cryptanalysis attacks. In this study, flaws in the existing implementation of ECC are identified. A new scheme where the identified flaws are remedied was developed. The results of the security analysis show that the new scheme is an indistinguishable authenticated adaptive chosen ciphertext attack (IND-CCA3), resistant to malleability and man-in-the-middle attacks (MIMA). The results of comparative security analysis show that the mapping scheme employed in the new scheme maps any blocks of plaintext to distinct points on an elliptic curve, which makes it resistant to all attacks that the existing schemes are vulnerable to without having a negative effect on its encryption and decryption time, throughput, or power consumption.
Automatic model transformation on multi-platform system development with model driven architecture approach

Several difficulties commonly arise during the software development process. Among them are the lengthy technical process of developing a system, the limited number and technical capabilities of human resources, the possibility of bugs and errors during the testing and implementation phase, dynamic and frequently changing user requirements, and the need for a system that supports multi-platforms. Rapid application development (RAD) is the software development life cycle (SDLC) that emphasizes the production of a prototype in a short amount of time (30-90 days). This study discovered that implementing a model-driven architecture (MDA) approach into the RAD method can accelerate the model design and prototyping stages. The goal is to accelerate the SDLC process. It took roughly five weeks to construct the system by applying all of the RAD stages. This time frame does not include iteration and the cutover procedure. During the prototype test, there were no errors with the create, read, update, and delete (CRUD) procedure. It was demonstrated that automatic transformation in MDA can shorten the RAD phases for designing the model and developing an early prototype, reduce code errors in standard processes like CRUD, and construct a system that supports multi-platform.
Scenario modeling and operation path planning of autonomous driving tower cranes

Abstract Combined with the operation specification of building tower cranes, the three-dimensional path planning technology of tower cranes and the optimization of path points are studied. The improved RRT-Connect algorithm is proposed. By introducing heuristic information in the path search, the path search efficiency and quality of the algorithm are improved. By eliminating invalid points in the path, the control burden of tower crane is reduced, and the optimized lifting point movement path is transformed into tower crane joint movement, and finally the calculation result is uploaded to PLC through LAN. The running trajectory of the tower crane lifting point is visually displayed in the scene model, and the validity and safety of the path are verified. The real tower crane is controlled. The operation is completed and the automatic driving of the tower crane is realized.
A metaheuristic approach for mining gradual patterns

Swarm intelligence is a discipline that studies the collective behavior that is produced by local interactions of a group of individuals with each other and with their environment. In Computer Science domain, numerous swarm intelligence techniques are applied to optimization problems that seek to efficiently find best solutions within a search space. Gradual pattern mining is another Computer Science field that could benefit from the efficiency of swarm based optimization techniques in the task of finding gradual patterns from a huge search space. A gradual pattern is a rule-based correlation that describes the gradual relationship among the attributes of a data set. For example, given attributes {G,H} of a data set a gradual pattern may take the form: "the less G, the more H". In this paper, we propose a numeric encoding for gradual pattern candidates that we use to define an effective search space. In addition, we present a systematic study of several meta-heuristic optimization techniques as efficient solutions to the problem of finding gradual patterns using our search space.
A systematic construction of non-i.i.d. data sets from a single data set: non-identically distributed data

Abstract Data-driven models strongly depend on data. Nevertheless, for research and academic purposes, public data sets are usually considered and analyzed. For example, most machine learning algorithms are applied and tested using the UCI Machine Learning repository. There is a current need for not i.i.d. data sets for distributed machine learning. Recall that i.i.d. random variables stand for independent and identically distributed (i.i.d.) random variables. An example of this need is federated learning. In federated learning, the typical scenario is to consider a set of agents each one with its own data set. Agents are typically heterogeneous and because of that, it is not appropriate to consider that the data of these agents follow the same distributions. In this paper we propose an approach to build non-identically distributed data sets from a single data set for machine learning classification, where we may suppose or not that all instances follow the same distribution. Each device will have only instances of a subset of the classes. The approach uses optimization to distribute the data set into a set of subsets, each one following a different distribution. Our goal is to define an approach for building subsets for training that is as systematic as the approaches used for cross-validation/ k -fold validation.
Anomaly Detection System for Ethereum Blockchain Using Machine Learning

Over the past few years, Blockchain technology has been utilized in various applications to improve privacy and security. Although blockchain has proven its worth as a very powerful technology, research has shown that it is not entirely immune to security and privacy attacks. There was a successful 51% attack on Ethereum Classic back in January 2019 which shows that blockchain still facing security and privacy challenges. This paper aims to develop an anomaly detection solution for the Ethereum blockchain to overcome security challenges using Machine Learning (ML). The proposed solution focuses on using a dynamic approach where the normal operational behaviour of the Ethereum blockchain is used to train ML algorithms and any deviation will be tagged as an anomaly and will be detected by the system. Four ML algorithms including K-Nearest Neighbours (KNN), Gaussian Naive Bayes (GaussianNB), Random Forest, and Stochastic Gradient Descent (SDG) were utilized to train and verify the accuracy of the proposed solution. The experimental results demonstrated that the random forest algorithm provided the best accuracy of 99.84% over other ML algorithms.
Action Plan for a Two Wheeled Mobile Robot Navigation with Controlled Velocity Approach on MATLAB

This paper presents an “Autonomous Navigation” Action plan for a 2 wheeled, single axis rotary mobile robot with controlled (or constant) velocity approach. Through this analysis we will step-by-step understand how we can navigate a mobile robot from a ‘start point’ to a ‘goal point’ with “obstacle avoiding” utility and constant velocity provision (using different controllers). This paper also presents the comparison of different types of controllers used to control the velocity and different algorithms to plan as well as track the path.
Image Caption Generator in Hindi Using Attention

Image Captioning has gained tremendous spotlight in recent years. However, the captioning models generate captions in English language. In this paper, we present an image caption generator for our regional language that is Hindi using Resnet50 and LSTM with attention module. An experimental study is shown highlighting the effect of attention-based learning on generated Hindi captions. Flickr8k dataset in Hindi language is used to validate the performance of proposed work in terms of BLEU score.
On Provability Logic of HA

We axiomatize the provability logic of HA and show that it is decidable. Moreover we axiomatize the preservativity and relative admissibility for several modal logics extending iK4. As a main tool, we also provide some sort of semantics, called provability semantics, for modal logics extending iGL, which is a mixture of usual Kripke semantics and provability in propositional modal logics.