Reviewerly/exHarmony huggingface dataset:
Anomaly Detection in Credit Card Transactions using Machine Learning

Anomaly Detection is a method of identifying the suspicious occurrence of events and data items that could create problems for the concerned authorities. Data anomalies are usually associated with issues such as security issues, server crashes, bank fraud, building structural flaws, clinical defects, and many more. Credit card fraud has now become a massive and significant problem in today's climate of digital money. These transactions carried out with such elegance as to be similar to the legitimate one. So, this research paper aims to develop an automatic, highly efficient classifier for fraud detection that can identify fraudulent transactions on credit cards. Researchers have suggested many fraud detection methods and models, the use of different algorithms to identify fraud patterns. In this study, we review the Isolation forest, which is a machine learning technique to train the system with the help of H2O.ai. The Isolation Forest was not so much used and explored in the area of anomaly detection. The overall performance of the version evaluated primarily based on widely-accepted metrics: precision and recall. The test data used in our research come from Kaggle.
Question Generation for Adaptive Education.

Intelligent and adaptive online education systems aim to make high-quality education available for a diverse range of students. However, existing systems usually depend on a pool of hand-made questions, limiting how fine-grained and open-ended they can be in adapting to individual students. We explore targeted question generation as a controllable sequence generation task. We first show how to fine-tune pre-trained language models for deep knowledge tracing (LM-KT). This model accurately predicts the probability of a student answering a question correctly, and generalizes to questions not seen in training. We then use LM-KT to specify the objective and data for training a model to generate questions conditioned on the student and target difficulty. Our results show we succeed at generating novel, well-calibrated language translation questions for second language learners from a real online education platform.
Robust, an Earthquake Early Warning System in the Lower Rhine Embayment, Germany

&amp;lt;p&amp;gt;The Lower Rhine Embayment in western Germany is one of the most important areas of earthquake recurrence north of the Alps, facing a moderate level of seismic hazard in the European context but a significant level of risk due to a large number of important&amp;amp;#160;industrial infrastructures. In this context, the project ROBUST aims at designing a user-oriented hybrid earthquake early warning and rapid response system where regional seismic monitoring is combined with smart, on-site sensors, resulting in the implementation of decentralized early warning procedures.&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;One of the research areas of this project deals with finding an optimal regional seismic network arrangement. With the optimally compacted network, strong ground movements can be detected quickly and reliably. In this work simulated scenario earthquakes in the area are used with an optimization approach in order to densify the existing sparse network through the installation of additional decentralized measuring stations. Genetic algorithms are used to design efficient EEW networks, computing optimal station locations and trigger thresholds in recorded ground acceleration. By minimizing the cost function, a comparison of the best earthquake early warning system designs is performed and the potential usefulness of existing stations in the region is considered as&amp;amp;#160;will be presented in the meeting.&amp;lt;/p&amp;gt;
LibGuides: State Library of Pennsylvania Virtual Farm Show Booth 2021: POWER Library and more free online services!

Welcome to the State Library's Virtual Farm Show Booth! Here you will find links to story times, a short video about how to find your ancestors, Rare books and preservation tips, Makerpace STEM activities, and more! Pennsylvania's online library and links to even more free resources!
Credit Point Calculation Model of Functional Position(Case Study of Credit Point Designing for Education Management Quality Assessor at the First Level in Formal Education Institution)

This study aims to obtain the credit point calculation model for proposing a new functional position within government agencies. The result of the study shows that the calculation phase of the credit point of new functional Position, after the entire procedures for preparing the academic manuscript, preparation of the activity item matrix, validation, and the formulation of the implementation framework is conducted then analysing the calculation with the steps: (1) preparation of activity item matrix, (2) distribution of workload questionnaires and workload analysis, (3) recap tabulation of overall calculation result, (4) analysis of work volume and average time, (5) validation of calculation result, and (6) calculation of credit point. From the result of the study, it is known that the credit point for item 1, that is compiling a plan for assessing the education management  get a credit score of 2.210, while for item 2, which is designing parameters for measuring the education management quality gets credit score of 2.605.
Efficient Model for Video Integrity through blockchain

Video and the maintenance of his integrity is one of the most essential concepts that require effective understanding of the process of video generation and storage. Videos are highly integral as they are useful in providing effective realization of audio and video of a particular scenario that can be revisited again through visual and auditory means. The videos are highly useful to such an extent as they are being used as undeniable evidence in the court of law. But due to the large prominence of various tools and techniques that are being used to make forgeries and achieve manipulations on video realistically this utilization of videos as evidence becomes highly problematic and less straight forward. Therefore, there is a need for an effective approach that can be designed to achieve highly accurate and useful realization of video integrity and its evaluation. For this purpose, the proposed methodology effectively, implements the RSA an encryption along with key generation and block chain formation to achieve effective video integrity evaluation.
Developing Expert System Application to Identify Specific Children Learning Disabilities in Inclusion Schools

This study aims to develop an expert system application for the identification of specific children learning disabilities in Inclusion School. The research method used is development research design (R & D) to test the effectiveness of the use of identification of specific children learning disabilities with expert system application. Application testing was involving 40 teachers in inclusion schools to test expert system applications in identification. Validate results by teachers in inclusion schools were obtained that from aspects of system display, user, accuracy, and system responsibility have an average of 4.20 fall into the category of excellent.  Expert system application development has several features, among others; home menu, analysis, and intervention to follow up learning services, learning media, and classroom embedding for specific children learning disabilities.
Reversible Data Hiding Based on Varying Radix Numeral System

A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article.Using varying radix, variable sum of data may be embedded in various pixels of images.This scheme is made adaptive using the correlation of the neighboring pixels.Messages are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image.A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the image.The size of the embedded data depends on the statistics of the pixel distribution in the cover image.One of the major issues in reversible data embedding, the location map, is minimized because of the interpolation process.This technique, which is actually LSB matching, embeds only the residuals of modulo radix into the LSBs of each pixel.No attacks on this RDH technique will be able to decode the hidden content in the marked image.The proposed scheme delivers a prominent visual quality despite high embedding capacity.Experimental tests carried out on over 100 natural image data sets and medical images show an improvement in results compared to the existing schemes.Since the algorithm is based on the variable radix number system, it is more resistant to most of the steganographic attacks.The results were compared with a higher embedding capacity of up to 1.5 bpp reversible schemes for parameters like Peak Signal-to-Noise Ratio (PSNR), Embedding Capacity (EC) and Structural Similarity Index Metric (SSIM).
Study of Deep Learning Approach for Improving Semi-Supervised Algorithm

When trained on the extensive collections of labeled data, Deep neural networks have demonstrated their potential for a good variety of supervised learning activities to deliver impressive performance. However, it takes a significant amount of money, time, and energy to construct such large data sets. In many practical situations, such tools might not be available, restricting the adoption and implementing many deep learning methods. Deep neural network-based semi-supervised learning implementations increasingly engaged in research to scale down the amount of labeled data needed, either by creating new methods or introducing current semi-supervised deep learning frameworks. To beat the need for giant annotated datasets, for more data-efficient deep learning methods. This article concentrated on a detailed analysis of semi-supervised learning focused on deep learning, accompanied by its description.
Financial Sector Development and Investment in Selected ECOWAS Countries: Empirical Evidence using Heterogeneous Panel Data Method

This study investigated the impact of financial sector development on domestic investment in selected Economic Community of West African States (ECOWAS) countries for the years 1985 to 2017. The study employed the Augmented Mean Group procedure which accounts for country specific heterogeneity and cross sectional dependence, and the Granger non-causality test robust to cross sectional dependence. The result reveals that (1) the impact of financial sector development on domestic investment depends on the measure of financial sector development utilised, (2) domestic credit to the private sector has a positive but insignificant impact on domestic investment in ECOWAS while banking intermediation efficiency (i.e. ability of the banks to transform deposits into credit) and broad money supply negatively and significant influence domestic investment, (3) cross country differences exist on the impact of financial sector development on domestic investment in the selected ECOWAS countries, and (4) domestic credit to the private sector Granger causes domestic investment in ECOWAS. The study recommends cautiousness in terms of the measure of financial development which is being utilised as a policy instrument to foster domestic investment as well as the importance of employing country-specific domestic investment policies in order to avoid blanket policy measures. Also, domestic credit to the private sector should be given priority when forecasting domestic investment into the future.
What does LIME really see in images?

The performance of modern algorithms on certain computer vision tasks such as object recognition is now close to that of humans. This success was achieved at the price of complicated architectures depending on millions of parameters and it has become quite challenging to understand how particular predictions are made. Interpretability methods propose to give us this understanding. In this paper, we study LIME, perhaps one of the most popular. On the theoretical side, we show that when the number of generated examples is large, LIME explanations are concentrated around a limit explanation for which we give an explicit expression. We further this study for elementary shape detectors and linear models. As a consequence of this analysis, we uncover a connection between LIME and integrated gradients, another explanation method. More precisely, the LIME explanations are similar to the sum of integrated gradients over the superpixels used in the preprocessing step of LIME.
pix2rule: End-to-end Neuro-symbolic Rule Learning

Humans have the ability to seamlessly combine low-level visual input with high-level symbolic reasoning often in the form of recognising objects, learning relations between them and applying rules. Neuro-symbolic systems aim to bring a unifying approach to connectionist and logic-based principles for visual processing and abstract reasoning respectively. This paper presents a complete neuro-symbolic method for processing images into objects, learning relations and logical rules in an end-to-end fashion. The main contribution is a differentiable layer in a deep learning architecture from which symbolic relations and rules can be extracted by pruning and thresholding. We evaluate our model using two datasets: subgraph isomorphism task for symbolic rule learning and an image classification domain with compound relations for learning objects, relations and rules. We demonstrate that our model scales beyond state-of-the-art symbolic learners and outperforms deep relational neural network architectures.
An Improvement of the Cryptographical Implementation of Coppersmith’s Method

In the late twentieth century, Don Coppersmith published a method to solve polynomial modular equations using lattice theory. This method was generalized by Alexander May in 2009. In 2014, Phi and Nguyen focused on the running time of the Coppersmith’s algorithm and proposed an improvement on the underlying technique (L2) to perform the speedup, then they exploit the matrix structure to reduce the running time even further. In our work, we propose a new implementation of the Coppersmith’s method to improve the running time by finding the suitable lattice dimension experimentally.
고등학교 한문과 연계⋅융합 수업 실천 사례 - 민주시민교육에서 ‘민주주의와 참여’를 대상으로 -

This study was designed as a way to foster creative and integrative learner pursued in the curriculum revised in 2015. It was also designed to explore the possibilities of linking and converging classes with ‘Classical Chinese Characters’ subject. With the aim of the exploration, the association and convergence classes were conducted along with ‘Korean history’ subject and ‘Integrated society’ subject, the purpose of which is to overcome the conservative limitations of ‘Classical Chinese Characters’ subject, and investigate a range of the possibility of expansion of ‘Classical Chinese Characters’ subject. In the field of democratic civic education, linkage and convergence classes were conducted with ‘Korean history’ subject and ‘Integrated society’ subject under the theme of 'Democracy and Participation'. The purpose of the class was made in the connection of the qualifications and capabilities of democratic citizenship pursued in democratic civic education with personality competences pursued in ‘Classical Chinese Characters’ subject. The class was designed to accompany the course assessment in accordance with the achievement standards of the “Classical Chinese Characters and Personality” area. In order to connect the performance of course assessment and divided educational activities into one, the classes were conducted in the intergration of ‘curriculum-class-evaluation-record’ processes. This is to help students grow into the master of life by shifting the center of the class from teachers to students.
E-Pengajian Am Mengenal Pasti Graf In Mobile Learning Application

Pengajian Am is one of the subjects required to pass any semester of the Sijil Tinggi Persekolahan Malaysian exam. Alih Bentuk Komunikasi (Linear Kepada Bukan Linear) is one of the topics in the field. The goal of this project is to examine mobile learning applications in e-Pengajian Am (Mengenal Pasti Graf). The title of this application is E-Pengajian Am (Mengenal Pasti Graf). STPM students are the target users of this initiative. The purpose of this application is to create a mobile learning application for the Pengajian Am graph subject in Semester 2 in an effective way. In addition, this application is planned for STPM students to use sufficient resources for simple learning. This is really important so students are expected to read and practise any of the subjects taught during the semester. E-Pengajian A smartphone learning framework uses the ADDIE model. This model was selected because it has organized elements that are ordered step by step. Analysis, design, development, implementation and evaluation are essential steps of the ADDIE model in order to make mobile learning effective. Overall, this project is very good at helping STPM students learn because the content is easy to understand. The findings of the analysis which have been obtained indicate that they are pleased with it and have made this a useful application.
LibGuides: Music: Guitar for New and Experienced Players: Chords, Scales and Theory

Instructional books, CDs, and DVDs for Guitar in many styles, as well as songbooks and sheet music.
Model-Targeted Poisoning Attacks with Provable Convergence

In a poisoning attack, an adversary with control over a small fraction of the training data attempts to select that data in a way that induces a model that misbehaves in a particular way desired by the adversary, such as misclassifying certain inputs. We propose an efficient poisoning attack that can target a desired model based on online convex optimization. Unlike previous model-targeted poisoning attacks, our attack comes with provable convergence to any achievable target classifier. The distance from the induced classifier to the target classifier is inversely proportional to the square root of the number of poisoning points. We also provide a lower bound on the minimum number of poisoning points needed to achieve a given target classifier. Our attack is the first model-targeted poisoning attack that provides provable convergence, and in our experiments it either exceeds or matches the best state-of-the-art attacks in terms of attack success rate and distance to the target model. In addition, as an online attack our attack can incrementally determine nearly optimal poisoning points.
언택트(Untact) 시대의 소통방안에 대한 소고 - 대학의 실시간 온라인 수업에 관한 평가를 중심으로 -

This paper was written based on the results of conducting a survey on “real-time online classes” attempted in full swing by each university in the 2nd semester of 2020. This attempt was for diagnosing which direction online-based classes should take in the future and examining which aspects should be borne in mind for desirable Chinese character education by reviewing the opinions on real-time online classes that students had encountered for the first time. First, in case of “proportion”, it was found that real-time online classes were carried out in over the majority of subjects with 100% contact-free classes. In the survey related to “platforms,” the use of the “ZOOM” platform was significantly higher in the humanities sector, and the features of “one-on-one chat” and “small meeting room” offered by “ZOOM” were confirmed to be receiving positive assessment. Finally, in “points of improvement,” the absence of communication and the issue of declining concentration levels were confirmed to be demanding most urgent supplementation. As a complementary plan, specific Chinese characters class plans were introduced. As this study is based on the survey content of a limited number of people, there are limitations in the research to conclude its details as the generality. Nevertheless, the study was conducted with the thought that it is necessary to think about the issues that are raised redundantly even within a limited number of people. Follow-up studies will be promised for the future considering the lack of proposing concrete and varied models specialized for Chinese character classes due to the restricted space and time.
Damages for Noneconomic Harm in Intellectual Property Law

This article provides a comprehensive analysis of awards of “non-economic” damages for reputational and emotional harm in intellectual property (IP) law, including trademarks, copyright and moral rights, the right of publicity, and patent law. The article discusses, among other matters, the Second Circuit’s recent decision in Castillo v. GM the European Union’s Intellectual Property Rights Enforcement Directive and its 2016 Liffers decision, which appear to require member states to award, where warranted, non-economic (“moral prejudice”) damages across the full range of IP cases; and some recent arguments in favor of awarding damages for emotional harm in, even, patent infringement actions. Prompted by these and other developments, I argue that courts should recognize reputational harm as a potentially cognizable injury throughout all of the branches of IP law, but that damages for emotional harm should be limited to right of publicity and moral rights matters. In addition, I discuss the various options for providing monetary relief in response to non-economic harm, including awards of general damages, statutory damages, disgorgement of the infringer’s profits, and enhanced or punitive damages; and I conclude with a set of recommendations for crafting awards in a manner that would both vindicate the relevant, cognizable interests of plaintiffs while reducing the risks of arbitrary, uncertain, and potentially over-deterrent relief.
CHILDREN'S CYBER ENVIRONMENT "BAIRN-BALA ROBOTS" AS A SUSTAINABLE TECHNOTRONIC EDUCATIONAL SPACE

This material is a complex panorama, where the main object enters the problem of the intersection of intellectual mesoareals between modern man and representatives of the cyber community with the code name “Bairn-Bala Robots”. The philosophical significance of the phenomenon under consideration was condemned by the latest information and technotronic challenges that crossed the cognitive boundaries of young cyber providers, choosing new platforms of communication infrastructure as an alternative form of free technotronic education. One of the main issues of interpretation is to identify the causal relationship of the attitude of the children's society in the process of developing a project in order to decipher the anthropomorphic type of biomechanical humanoid, thanks to digital products and technology of the modern IT industry. A review of analytical processing of information collection on cyber sciences, automation of electronic computers, electronic control systems and informatization of educational infrastructures showed that the formation of adaptation of technotronic-informational consciousness and behavior of an individual during the last stages of social formation were generated in parallel with the laws of evolutionary development. Thus, the phenomena of the cyber environment were identified as a scientific collection of information, in the role of a robot-poser, which contributed to the emergence of a new ecosystem of educational-technotronic communication "Bairn-Bala Robots".