Reviewerly/exHarmony huggingface dataset:
The Application of Machine Learning in Teaching Evaluation of University Courses

Online course evaluation based on students' perception is one of the common tools to evaluate teacher's performance.In this paper, four different classification technologies, including Decision Tree algorithm, Support Vector Machine, Bayes, and Random Forest, are used to classify the data set of students' curriculum evaluation.The results show that all these four classifier models have high classification performance, among them the random forest classifier is the best in terms of accuracy, precision, recall, and F1 value.In addition, the importance of each evaluation feature for each classifier model is analyzed to further improve the evaluation index system.The result of the analysis shows that students' evaluation on courses mainly depends on students' interest in courses.This research verifies the validity of machine learning model in curriculum evaluation and higher education mining.
The Emotional Aspects of Computer-Mediated Communication in Human Relationships

At the core of communication is the sharing and exchange of information.In contemporary age, media of communication have various forms and states, especially the network medium.Computer Mediated Communication (CMC) which provide a new medium to human communication, is a powerful tool that has changed the ways of people's daily life, work, and learning.CMC is a generic term now commonly used to describe text-based and visually interactive communication by means of computers and networks, it has transformed social and cultural interactions by continuously defining and redefining digital communities' identities, relationships and their commonalities.This article reviews the language-based strategies that computer-mediated communication (CMC) users employ to transmit emotions in the absence of nonverbal cues.
Introduction to Hybrid algorithms

Algorithms are the building blocks of the systems industry created by human civilization. From automation to a wide range of simple tasks to predicting values from existing databases visually patterns, algorithms are found almost everywhere. We have successfully created solutions to some of the most complex problems with using algorithms. However, there is still a category of problems that can be solved by the user information. Hybrid algorithms deal with certain types of problems. Hybrid algorithms play an important role in improving search engine algorithms. Consolidation aims to combine the benefits of each algorithm to build a hybrid algorithm, while at the same time trying minimize any major damage. In general, the effect of hybridization can often do something improvement depending on the speed of calculation or accuracy. This chapter considers the concepts, types, and motives behind the existence of a mixture algorithms. Types of integrated algorithms, description of each type, different categories as well segmentation algorithms are the main topics of discussion in this chapter.
Research in Area of Tacit Knowledge: Literature Review on Tacit Knowledge

Knowledge is one of the valuable resources for organizational success and competitive advantage.The characteristics, types, and usage of knowledge is determining the potential of an organization.The types of knowledge are tacit and explicit.Following deductive research approach this paper presents meta-analysis of the use of tacit knowledge in the transport sector.The paper investigated and identified the areas and amounts of studies on tacit knowledge integration to show the gap work and the way forward.Even if from 2000 up to 2019 E.C Knowledge Management (KM) researchers published on the average 10.65 publications yearly, however, this literature survey confirmed that there is no publication is found tacit knowledge integration in the transport sector.
Pengaruh Pendapatan Orang Tua dan Motivasi Belajar Terhadap Hasil Belajar Mata Pelajaran Ekonomi

This study aims to determine how the influence of parents' income and learning motivation on economic learning outcomes in class XII IPS SMAN 3 Pariaman. This type of research is descriptive associative research using SPSS. The population in this study were all students of class XII IPS totaling 87 students. The data used are primary data and secondary data. The data collection technique is by using a questionnaire. The sampling technique used in this study is total sampling with a total of 87 respondents. The analytical method used is path analysis using SPSS. To test the research hypothesis, it was conducted using data analysis through validity and reliability tests, normality tests, heteroscedasticity, path analysis, F tests and t tests. The results of this study indicate that: (1) There is a positive and significant effect of parents' income on students' learning motivation, (2) There is a positive and significant influence of parents' income on learning outcomes. 3) There is no significant effect of student learning motivation on learning outcomes. (4) Indirectly, parental income has no significant effect on student learning outcomes through learning motivation. Keywords: parents' income, learning motivation, learning outcomes
Enhancing a Session-Hijacking Attack Using Session Security On a Browser with Disposable Credentials Using Otc

Many web applications are vulnerable to session hijacking attacks due to the insecure use of cookies for session management. The most recommended defense against this threat is to completely replace HTTP with HTTPS. However, this approach presents several challenges (e.g., performance and compatibility concerns) and therefore, has not been widely adopted. In this paper, we propose “One-Time Cookies” (OTC), an HTTP session authentication protocol for improving session hijacking features, easy to deploy and resistant to session hijacking. OTC’s security relies on the use of disposable credentials based on a modified hash chain construction. Our experiments demonstrate the ability to maintain session integrity with a throughput improvement over HTTPS and a performance approximately similar to a cookie-based approach. In so doing, we demonstrate that one-time cookies can significantly improve the security of web sessions with minimal changes to current infrastructure.
Haze Mitigation and Visibility Restoration in Foggy Conditions for Vehicles

Highways are supposed to be of paramount importance infrastructure for any nation. But, potholed laden roads, poor weather conditions or man-made errors results in road crashes, and the ones due to fog cause greater severity to life. To address this problem, we have proposed a model in which the images captured by cameras embedded in the vehicles are processed at real time to restore some visibility. The proposed solution is to display improvised dehazed images of the road scenario to help drivers anticipate potential collisions based on wavelet-based image processing. The future scope of this study is applicable in Advanced Driver Assistance Systems in fog dominant areas. To evaluate the model’s performance fairly and objectively, the model is trained with two image datasets with different visibility ranges. The comparative study and quantitative evaluation exhibit the proposed method can obtain very good defogging image with relatively fast speed.
Visual Cryptographic Approach for Authentication of Social Media Contents

The security of the images is a wide concern in multimedia world. The watermarking of the images is of more interest as to provide the copyrights to it. The delicate images are those images which carries soaring amount of important data that describes an image. Since the utilization of delicate pictures for data trade is expanding, watermarking for these pictures is developing territory of research. The delicate images are mostly victim of tampering during its transmission therefore watermarking of the images is important as to provide copyrights to it. This paper wraps concise research about some of the enhanced trends in digital image watermarking. It will be useful for the researcher to use proper watermarking scheme for their application. A novel model of security for social media content using Visual cryptography is also proposed in our paper. Biometrics of a person is a unique feature of identification. This model will be based on ear pattern as a biometric feature to authenticate the image. The model is tested for its extraction of watermark and its works satisfactorily.
Preface

Welcome to 2022 12th International Conference on Applied Physics and Mathematics (ICAPM 2022), which should be held in Singapore during February 18-20, 2022. Due to global travel restrictions because of the COVID-19 pandemic, the conference was organized as an online meeting. Despite this, despite the different time zones of participants, despite some unavoidable technical hazards the conference matched by far the expectation of researchers and specialists to be a place of discussions, a vitrine of the new achievements in the field of applied Physics and Mathematics. It has been successfully held in 11 consecutive years since 2011, respectively. In the past five years, ICAPM 2017, ICAPM 2018, ICAPM 2019, ICAPM 2020, and ICAPM 2021 Conference Proceedings have been archived into IOP Journal of Physics: Conference Series, indexed by ISI Web of Science (Conference Proceedings Citation Index (CPCI), Scopus, Engineering Information (EI), etc. List of Commitees are available in this pdf.
A Short-Term Tropical Cyclone Intensity Forecasting Method Based on High-Order Tensor (Student Abstract)

Tropical cyclones (TC) bring enormous harm to human beings, and it is crucial to accurately forecast the intensity of TCs, but the progress of intensity forecasting has been slow in recent years, and tropical cyclones are an extreme weather phenomenon with short duration, and the sample size of TC intensity series is small and short in length. In this paper, we devolop a tensor ARIMA model based on feature reconstruction to solve the problem, which represents multiple time series as low-rank Block Hankel Tensor(BHT), and combine the tensor decomposition technique with ARIMA for time series prediction. The method predicts the sustained maximum wind speed and central minimum pressure of TC 6-24 hours in advance, and the results show that the method exceeds the global numerical model GSM operated by the Japan Meteorological Agency (JMA) in the short term. We further checked the prediction results for a TC, and the results show the validity of the method.
Innovative approaches in the training of cadets – future case inspectors for the prevention of destructive behaviors of the younger generation

The article presents the experience of the Department of Pedagogy of the Moscow University of the Ministry of Internal Affairs of the Russian Federation named after V.Ya. Kikot in training inspectors for minors.&#x0D; Within the framework of the implemented model of training students by the teaching staff, attention is focused on an integrated approach to the professional training of students, including the prevention of deviant behavior of minors. Complexity in this case is understood as a stable set of the following approaches to education: competence-based (system-activity), culturological, personality-oriented, environmental. Taking into account the orientation of the scientific school implemented by the department "Pedagogy of meaning–life orientations in the system of personnel training for the internal affairs bodies of Russia" (founder - Professor I.V. Ulyanova), the meaning-life orientation approach is also being updated (it includes such aspects as axiological, target, valeological, ethical-aesthetic, gender-psychological, preventive). Practice has shown that it is in the stable totality of the designated theoretical concepts that there is a real opportunity to help the younger generation in harmonious, truly humanistic development. The principle of complexity is revealed in the preparation of cadets for professional activity in the conditions of a rational combination of effective traditional methods, forms of humanistic pedagogy and new organizational technologies.&#x0D; Special attention in the preparation of cadets for the professional activities of the PD inspector is paid to their personal development. It is the strengthening of the moral, humanistic position of the first-year cadet that is noted as a significant innovation position implemented in the educational process.
A Relation-Oriented Model With Global Context Information for Joint Extraction of Overlapping Relations and Entities

The entity relation extraction in the form of triples from unstructured text is a key step for self-learning knowledge graph construction. Two main methods have been proposed to extract relation triples, namely, the pipeline method and the joint learning approach. However, these models do not deal with the overlapping relation problem well. To overcome this challenge, we present a relation-oriented model with global context information for joint entity relation extraction, namely, ROMGCJE, which is an encoder–decoder model. The encoder layer aims to build long-term dependencies among words and capture rich global context representation. Besides, the relation-aware attention mechanism is applied to make use of the relation information to guide the entity detection. The decoder part consists of a multi-relation classifier for the relation classification task, and an improved long short-term memory for the entity recognition task. Finally, the minimum risk training mechanism is introduced to jointly train the model to generate final relation triples. Comprehensive experiments conducted on two public datasets, NYT and WebNLG, show that our model can effectively extract overlapping relation triples and outperforms the current state-of-the-art methods.
Explainable Planner Selection for Classical Planning

Since no classical planner consistently outperforms all others, it is important to select a planner that works well for a given classical planning task. The two strongest approaches for planner selection use image and graph convolutional neural networks. They have the drawback that the learned models are complicated and uninterpretable. To obtain explainable models, we identify a small set of simple task features and show that elementary and interpretable machine learning techniques can use these features to solve roughly as many tasks as the complex approaches based on neural networks.
Comparative Analysis of Clustering Algorithms

Clustering is an unsupervised learning technique in which similar data points are grouped together into groups called clusters. Each cluster contains data points that are similar to each other and dissimilar to datapoints in other groups. Clustering is achieved by various algorithms like K-means, DBSCAN, PAM, Hierarchical clustering etc. Every algorithm has its own drawback which has to be overcome. The most common drawback of algorithms is that they do not work well for large datasets. Accuracy of algorithms is degraded for large datasets. Also, this type of dataset increases processing time of algorithm. We have compared different clustering algorithms on various standard datasets and analyzed their performance with respect to various performance parameters such as time complexity, accuracy and various cluster quality measures. From this analysis it is found that there is a scope to enhance the performance of these algorithms.
Neural Interferometry: Image Reconstruction from Astronomical Interferometers Using Transformer-Conditioned Neural Fields

Astronomical interferometry enables a collection of telescopes to achieve angular resolutions comparable to that of a single, much larger telescope. This is achieved by combining simultaneous observations from pairs of telescopes such that the signal is mathematically equivalent to sampling the Fourier domain of the object. However, reconstructing images from such sparse sampling is a challenging and ill-posed problem, with current methods requiring precise tuning of parameters and manual, iterative cleaning by experts. We present a novel deep learning approach in which the representation in the Fourier domain of an astronomical source is learned implicitly using a neural field representation. Data-driven priors can be added through a transformer encoder. Results on synthetically observed galaxies show that transformer-conditioned neural fields can successfully reconstruct astronomical observations even when the number of visibilities is very sparse.
Deep Spatial Adaptive Network for Real Image Demosaicing

Demosaicing is the crucial step in the image processing pipeline and is a highly ill-posed inverse problem. Recently, various deep learning based demosaicing methods have achieved promising performance, but they often design the same nonlinear mapping function for different spatial location and are not well consider the difference of mosaic pattern for each color. In this paper, we propose a deep spatial adaptive network (SANet) for real image demosaicing, which can adaptively learn the nonlinear mapping function for different locations. The weights of spatial adaptive convolution layer are generated by the pattern information in the receptive filed. Besides, we collect a paired real demosaicing dataset to train and evaluate the deep network, which can make the learned demosaicing network more practical in the real world. The experimental results show that our SANet outperforms the state-of-the-art methods under both comprehensive quantitative metrics and perceptive quality in both noiseless and noisy cases.
Deep Representation Debiasing via Mutual Information Minimization and Maximization (Student Abstract)

Deep representation learning has succeeded in several fields. However, pre-trained deep representations are usually biased and make downstream models sensitive to different attributes. In this work, we propose a post-processing unsupervised deep representation debiasing algorithm, DeepMinMax, which can obtain unbiased representations directly from pre-trained representations without re-training or fine-tuning the entire model. The experimental results on synthetic and real-world datasets indicate that DeepMinMax outperforms the existing state-of-the-art algorithms on downstream tasks.
THEORY OF ACCEPTANCE MODEL UNTUK EVALUASI PERILAKU PENGGUNA SISTEM INFORMASI DI PERGURUAN TINGGI (STUDI KASUS: ASMI CITRA NUSANTARA DI BANJARMASIN)

Various advantages of Information System technology in Universities encourage the use of Information Systems in Universities, until now it has become a necessity no longer a necessity or compulsion for an institution. Utilization of Information Systems in Universities as information centers can be one of the strategies to develop the level of education. This research aims to find out the picture of the successful application of Information System technology in universities as an information center based on user evaluation in the campus environment of ASMI Citra Nusantara Banjarmasin. Evaluation can predict the level of utilization of technology in the future. The method used in the study was a survey with 158 research samples with the Theory of Acceptance Model approach analyzed using the Partial Least Square technique. The results showed that all proposed hypotheses were acceptable so that the variables in the Theory of Acceptance Model could describe user attitudes in information system evaluation. This research can be one of the reviews that shows that the Theory of Acceptance Model as the right model for quantitative-based Information System evaluation research and case studies. This study concludes that the Theory of Acceptance Model is relevant in determining and assessing user behavior regarding the use of technology
The Use of SCRUM Methodology for Final Year Undergraduate Project During Corona Pandemic

Scrum methodology is a traditional agile technology that uses sprints and various forms of meetings to problems and issues during software development and allow a student to practice learning a programming language during design and analysis phases, but it has become difficult to coordinate such physical meetings during teaching and learning in the undergraduate project. Software development team during the COVID- 19 pandemic facing difficult to arrange such meetings. In this paper, we will explain the reasons for the superiority of scrum over traditional methods and the extent of the impact of the Corona pandemic on product quality and customer and employee satisfaction.
Latent Profile Patterns of Affective Attitudes in Math and Science and Their Influential Factors for Elementary and Middle School Students in TIMSS 2019

According to the results of the international academic achievement assessment and previous studies, it is continuously reported that Korean students have high cognitive achievement but very low affective achievement. The purpose of this study is to explore the characteristics of the affective attitudes in math and science and to provide the implications for the improvement of students’ affective attitudes. To this end, multilevel latent profile analysis based on the 3-step method was used. We explored the latent profiles of the affective attitudes in math and science and their influential factors. We used data from 3,886 elementary school students(151 schools) and 3,857 middle school students(168 schools) who participated in TIMSS 2019. The results are as follows. First, for 4th graders, five latent profiles such as ‘negative(3.7%)’, ‘moderate(61.3%)’, ‘math positive(9.8%)’, ‘science positive(17.4%)’, and ‘very positive(7.8%)’ groups were derived. On the other hand, for 8th graders, the negative characteristics of each subject appeared(math negative(5.5.%), science negative(6.2%), moderate(72.2%), positive(11.3%), very positive(4.3%)). Second, in both grades, it was found that ‘sense of school belonging’, ‘instructional clarity’, and ‘solving math problems on your own’ were more related to the positive groups rather than the other groups. Third, for 4th graders, regional scale had an effect on the group classification. Lastly, educational implications for improving the affective attitude in math and science were proposed.&#x0D;
Keynote Speech 1 - Chip-off Forensics: A multi-disciplinary approach

Law enforcement agencies need to develop new reverse engineering methods continuously, which is time and resource-consuming.Reverse engineering is an indispensable method for law enforcement to correctly interpret the system structure, security features, file systems, and other software details for the purpose of evidence acquisition and tool testing.Moreover, law enforcement agencies can hardly compete with new security by default solutions included in mobile devices and operating systems.Chip-off is a destructive technique that entails removing the flash memory chip from the printed circuit board (PCB).Removing the flash memory entails cutting the PCB and grinding the PCB allowing the chip contacts to be exposed.Our research and testing provide the forensic community with an understanding of the capabilities and limitations of Chip-Off reverse engineering techniques.Bio: Dr. Ameer Al-Nemrat -Directory for the Cyber security and Ai