Reviewerly/exHarmony huggingface dataset:
Penerapan Model Pembelajaran Discovery Learning untuk Meningkatkan Aktivitas dan Hasil Belajar Siswa MAN 1 Lombok Timur

This study aims to improve student learning outcomes and activeness through the application of discovery learning models. This research method uses classroom action research which consists of 2 cycles. Each cycle consists of 4 (four) stages, namely planning, implementing, observing and reflecting. The subjects of this study were students of class XII MIA-1 MAN 1 East Lombok. The research instrument used observation sheets, documentation, and tests. The data analysis technique of this research is using descriptive analysis. The results of this study indicate that in the first cycle, the percentage of classical completeness of learning outcomes and the average level of activeness of students was 18% and 76%, respectively. Whereas in cycle II, the percentage of classical completeness of learning outcomes and the average level of activeness of students was 76% and 90%, respectively. This proves that, the application of discovery learning learning models can improve the results and learning activeness of class XII MIA-1 MAN 1 East Lombok, especially three dimensional material.
Proposal for a Secure Data Sharing and Processing in Cloud Applications for Healthcare Domain

Information Technology (IT) services have become an inherent component in almost all sectors. Similarly, the health sector has been recently integrating IT to meet the growing demand for medical data exchange and storage. Currently, cloud has become a real hosting alternative for traditional on-permise software. In this model, not only do health organizations have access to a wide range of services but most importantly they are charged based on the usage of these cloud applications. However, especially in the healthcare domain, cloud computing deems challenging as to the sensitivity of health data. This work aims at improving access to medical data and securely sharing them across healthcare professionals, allowing real-time collaboration. From these perspectives, they propose a hybrid cryptosystem based on AES and Paillier to prevent the disclosure of confidential data, as well as computing encrypted data. Unlike most other solutions, the proposed framework adopts a proxy-based architecture to tackle some issues regarding privacy concerns and access control. Subsequently, this system typically guarantees that only authorized users can view or use specific resources in a computing environment. To this aim, they use eXtensible Access Control Markup Language (XACML) standard to properly design and manage access control policies. In this study, they opt for the (Abbreviated Language for Authorization) ALFA tool to easily formulate XACML policies and define complex rules. The simulation results show that the proposal offers simple and efficient mechanisms for the secure use of cloud services within the healthcare domain. Consequently, this framework is an appropriate method to support collaboration among all entities involved in medical information exchange.
Newton Optimization on Helmholtz Decomposition for Continuous Games

Many learning problems involve multiple agents optimizing different interactive functions. In these problems, the standard policy gradient algorithms fail due to the non-stationarity of the setting and the different interests of each agent. In fact, algorithms must take into account the complex dynamics of these systems to guarantee rapid convergence towards a (local) Nash equilibrium. In this paper, we propose NOHD (Newton Optimization on Helmholtz Decomposition), a Newton-like algorithm for multi-agent learning problems based on the decomposition of the dynamics of the system in its irrotational (Potential) and solenoidal (Hamiltonian) component. This method ensures quadratic convergence in purely irrotational systems and pure solenoidal systems. Furthermore, we show that NOHD is attracted to stable fixed points in general multi-agent systems and repelled by strict saddle ones. Finally, we empirically compare the NOHD's performance with that of state-of-the-art algorithms on some bimatrix games and continuous Gridworlds environment.
Development of a model for evaluating the effectiveness of innovative startups based on information cycles and using neural networks

An integrated approach to the creation and development of innovative startup projects in the field of information technology is considered. To conduct research, the authors proposed a model of information cycles of startup projects based on the creation of an information model of such projects. At the same time there are dynamic processes of changes in the parameters of the model, which are turbulent in nature and require the use of tools and methods of artificial intelligence for research. The key areas of knowledge of such influence are defined. The mathematical model of processes of management of development of IT startups on the basis of creation and development of a difficult IT product, taking into account influences of environments of the project is constructed, the basic characteristics are allocated and parameters are defined. To do this, the construction of predictive models is proposed to be carried out by modified Demarc trends, the method of self-organization and the neural network. The modeling of the main objective functions of the mathematical model of these processes is performed. The analysis of the received results is carried out and the conclusions are made.
Optimization of IoT-Based Motion Intelligence Monitoring System

We design and implement an intelligent IoT-based motion monitoring system to realize the monitoring of three important parameters, namely, the type of movement, the number of movements, and the period of movement in physical activities, and optimize the system to support the simultaneous use by multiple users. Considering the motion monitoring scenario for smart fit, the framework of an IoT-based motion monitoring system is proposed. The framework contains components such as active acquisition nodes, wireless access points, data processing servers, and terminals. In terms of algorithm optimization, research related to active pattern recognition and periodic calculation methods is conducted. For active pattern recognition, two types of classification algorithms with different complexity are proposed based on Support Vector Machine (SVM) and deep neural networks, respectively, to adapt to scenarios with different computational capabilities. For period calculation, a method based on over-zero detection and wavelet transform is proposed to count the number of actions and calculate the period of each action. In 100 times action cycle calculation experiments, the count statistic calculation method achieves 100% calculation accuracy and the active cycle calculation results are close to the real value, which proves the effectiveness of the cycle calculation method. The system provides a multiuser-oriented communication method and realizes accurate and reliable human movement monitoring, which has a wide application prospect in the fields of physical education and rehabilitation training.
PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation

Dimitris Papadopoulos, Nikolaos Papadakis, Nikolaos Matsatsinis. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop. 2021.
Dense Feature Based Face Recognition from Surveillance Video using Convolutional Neural Network

Face Recognition is a field of identifying the person from the facial features and has wide application range in security, human computer interactions, finance etc. In recent years, many researchers have developed different algorithms to identify the Faces from various illumination variations and Pose variation, but these two problems remain unsolved in Face Recognition (FR) field.&#x0D; The Local Binary Pattern (LBP) has already proved its robustness in illumination variation. This paper proposes a four-patch Local Binary Pattern based FR utilizing Convolutional Neural Network (CNN) for identifying the Facial images from various illumination conditions and Pose variation.
Multi-split Reversible Transformers Can Enhance Neural Machine Translation

Large-scale transformers have been shown the state-of-the-art on neural machine translation.However, training these increasingly wider and deeper models could be tremendously memory intensive.We reduce the memory burden by employing the idea of reversible networks that a layer's input can be reconstructed from its output.We design three types of multi-split based reversible transformers.We also devise a corresponding backpropagation algorithm, which does not need to store activations for most layers.Furthermore, we present two fine-tuning techniques: splits shuffle and self ensemble, to boost translation accuracy.Specifically, our best models surpass the vanilla transformer by at least 1.4 BLEU points in three datasets.Our largescale reversible models achieve 30.0 BLEU in WMT'14 En-De and 43.5 BLEU in WMT'14 En-Fr, beating several very strong baselines with less than half of the training memory.
Online Education Skills of Teachers: Four Axes of Gaps

There is a diversity in the teachers’ digital capabilities of online education skills and many research related this for decades. According to the literature review teachers were being discriminated based on numerous gaps. This paper seeks to assess the existence of gaps of teachers’ online education skills in the four axes: access, age, gender, and language. A research was carried out through an online questionnaire and the results of technology-based online education skills; web-based online education skills, and administrative online education skills were analysed statistically. There was a significant digital convergence among the teachers surveyed, which is important for their ability to deliver effective online courses. To bridge these gaps in the educational world of teachers, in-service training plans should be made, collaboratively with the online education experts and technology experts. This research study has some significant results compared to the studies delivered in the last decade.
Sediment thickness in the Bay of Bengal and Andaman Sea compared with topography and geophysical settings by GMT

Abstract The study presents an analysis of the sediment thickness compared with bathymetric and geophysical settings in the Bay of Bengal and Andaman Sea, Indian Ocean. It uses a combination of the high-resolution data: topographic GEBCO, satellite and marine gravity anomalies, EGM2008 geoid and GlobSed to visualize the correlation between relief, gravity and trends in continent-ocean sediment transport. The results include thematic maps and 3D model showing increased sediment thickness in the Bengal Fan (8,0 to 8,2 km) in NE direction with maximum in Ganges Fan (16,2 km), and southward decrease in the Andaman Sea from Irrawaddy river mouth (6-7 km) to the Strait of Malacca (1-2 km). All maps and 3D model have been plotted by Generic Mapping Tools (GMT) cartographic scripting toolset version 6.0.0.
Artificial Intelligence and Trademark Assessment

Abstract Artificial intelligence (AI) has an unparalleled potential for facilitating intellectual property (IP) administration processes, in particular in the context of examining trademark applications and assessing prior marks in opposition and infringement proceedings. Several stakeholders have developed AI-based algorithms that are claimed to enhance the productivity of trademark professionals by carrying out, without human input, (parts of) the legal tests required to register a trademark, oppose it, or claim an infringement thereof. The goal of this chapter is to assess the functionality of the AI tools currently used and to highlight the possible limitations of AI tools to carry out autonomously the legal tests enshrined in trademark law. In fact, many of these tests are rather subjective and highly depend on the facts of the case, such as an assessment of the distinctive character of a mark, whether the relevant public is likely to be confused or whether a third party has taken unfair advantage of a mark. The chapter uses doctrinal research methods and interview data with fourteen stakeholders in the field. It finds that AI tools are so far unable to reflect the nuances of the subjective legal tests in trademark law and, it is argued, even in the near future, AI tools are likely to carry out merely parts of the legal tests and present information that a human will have to assess, taking prior doctrine and the circumstances of the case into account.
A Large-scale Evaluation of Neural Machine Transliteration for Indic Languages

We take up the task of largescale evaluation of neural machine transliteration between English and Indian languages, with a focus on multilin gual transliteration to utilize orthographic sim ilarity between Indian languages.We create a corpus of 600K word pairs mined from parallel translation corpora and monolingual corpora, which is the largest transliteration corpora for Indian languages mined from public sources.We perform a detailed analysis of multilingual transliteration and propose an improved mul tilingual training pipeline for Indic languages.We analyse various factors affecting transliter ation quality like language family, translitera tion direction and word origin.
Curriculum Evaluation: Inputs for Principal’s Instructional Leadership

One of the most important components in education is the curriculum. It undergoes evaluation and development to be responsive to the needs of the times. It adapts to the kind of teachers and learners because teaching and learning are dynamic. The main drivers of the curriculum are the education leaders primarily the principals who are the instructional leaders. The main task of the principal is instructional leadership which means overseeing the teaching and learning process starting with the curriculum. However, the administrative tasks of the principals deprive them of becoming effective instructional leaders. This study aims to present the experiences of the researcher in curriculum evaluation under different principals in a private sectarian school in Manila and seeks to identify how principals can take a strong leadership presence in the challenges in instruction and curriculum evaluation. A critical reflection which is an extension of “critical thinking” and asks us to think about our practice and ideas and then it challenges us to step back and examine our thinking by asking probing questions was used as the method in this study. The researcher looks back into his experiences and identifies the key points in curriculum evaluation vis-à-vis the tasks of instructional leadership. It found out that curriculum evaluation, primarily an integral part of instructional leadership. The principals have to do curriculum evaluation as a collaborative task and process. Moreover, curriculum evaluation with instructional leadership should be giving importance to professional development and mentoring of academic heads and teachers.
Optimization of Reservoir Dam Operation Using Gray Wolf, Crow Search and Whale Algorithms Based on the Solution of the Nonlinear Programming Model

Optimization of Reservoir Dam Operation Using Gray Wolf, Crow Search and Whale Algorithms Based on the Solution of the Nonlinear Programming Model
Exploiting Learned Policies in Focal Search

Recent machine-learning approaches to deterministic search and domain-independent planning employ policy learning to speed up search. Unfortunately, when attempting to solve a search problem by successively applying a policy, no guarantees can be given on solution quality. The problem of how to effectively use a learned policy within a bounded-suboptimal search algorithm remains largely as an open question. In this paper, we propose various ways in which such policies can be integrated into Focal Search, assuming that the policy is a neural network classifier. Furthermore, we provide mathematical foundations for some of the resulting algorithms. To evaluate the resulting algorithms over a number of policies with varying accuracy, we use synthetic policies which can be generated for a target accuracy for problems where the search space can be held in memory. We evaluate our focal search variants over three benchmark domains using our synthetic approach, and on the 15-puzzle using a neural network learned using 1.5 million examples. We observe that Discrepancy Focal Search, which we show expands the node which maximizes an approximation of the probability that its corresponding path is a prefix of an optimal path, obtains, in general, the best results in terms of runtime and solution quality.
An aggregated deep convolutional recurrent model for event based surveillance video summarisation: A supervised approach

Surveillance video summarisation is characterised by extracting video segments containing abnormal events from surveillance video footages. Accurate identification of abnormal events from surveillance footages is of paramount importance in surveillance video summarisation. Accordingly, the proposed framework builds an aggregated convolutional recurrent model that can precisely detect the suspicious events in a surveillance footage, by employing a supervised learning which is found to yield better results compared with unsupervised counterparts. The preliminary stage in the model is a multilayer Convolutional Neural Network for frame-level feature extraction followed by stacked bidirectional Gated Recurrent Unit for sequence-level feature extraction and classification. Since the video clips used for training are not implicit to surveillance, a block-based approach for testing on surveillance videos is proposed. The results evaluated on two custom datasets, Streets and Campus, prove that the proposed model produces remarkable results leveraging the properties of bidirectional GRU with supervised learning. Extensive experimental analysis on selection of optimum architecture is conducted which substantiates the significance of stacked bidirectional GRUs over unidirectional ones. Additionally, qualitative results ensure that summaries produced are concise, representative, complete, diverse and informative. Moreover, comparison of the performance of the proposed model with state of the art certainly proves the superiority of the proposed model.
Similarity measure fuzzy soft set for phishing detection

Phishing is a serious web security problem, and the internet fraud technique involves mirroring genuine websites to trick online users into stealing their sensitive information and taking out their personal information, such as bank account information, usernames, credit card, and passwords. Early detection can prevent phishing behavior makes quick protection of personal information. Classification methods can be used to predict this phishing behavior. This paper presents an intelligent classification model for detecting Phishing by redefining a fuzzy soft set (FSS) theory for better computational performance. There are four types of similarity measures: (1) Comparison table, (2) Matching function, (3) Similarity measure, and (4) Distance measure. The experiment showed that the Similarity measure has better performance than the others in accuracy and recall, reached 95.45 % and 99.77 %, respectively. It concludes that FSS similarity measured is more precise than others, and FSS could be a promising approach to avoid phishing activities. This novel method can be implemented in social media software to warn the users as an early warning system. This model can be used for personal or commercial purposes on social media applications to protect sensitive data.
Colour Visual Cryptography (3,3) Scheme

Visual Cryptography is an encryption technique which performs only encryption in cryptography, and it is used to encrypt every visual data. And this cryptography is different and unique in all cryptographic techniques, because of not performing decryption process mechanically, and that is done mechanically. In normal visual cryptography only black and white images are encrypted. In this paper we propose a different type of visual cryptography scheme for colour imagesin CMY format. And it protects the secret of the original image and no other techniques does not decrypt it except our decryption technique.
Interlingual Subtitles to Increase High School Students’ Ability in Understanding Explanation Texts

&lt;p&gt;This paper explores the use of interlingual subtitle as a media in teaching reading skill in one of private high schools in Surakarta. This research was initially conducted as the teachers found problems in teaching genres of texts, especially explanation text. Interlingual subtitle which is placed in videos is offered by the researchers as a solution and expected to help students in understanding the content of explanation text. This research uses classroom action research method with two cycles. The objectives of this research are to find out: 1) the implementation of interlingual subtitle in teaching explanation text, and 2) the impacts of interlingual subtitle on students’s achivements. The subjects of this research are 33 students of social science class and 33 students of natural science class. It is found out that interlingual subtitle can be used as a supplementary material in teaching explanation text. Interlingual subtitle has also contributed positively to the increasing scores of the students during the pre-test to the post-test.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
AN INVESTIGATION AND ANALYSIS OF CYBER SECURITY INFORMATION SYSTEMS: LATEST TRENDS AND FUTURE SUGGESTION

Network protection includes of the strategies and procedures implemented in this paper to avoid and monitor intrusion attempts, infringement, manipulation or violation of a computer system and services accessed by the network. Network protection applies to the different countermeasures put in place to secure or flow through the network and data collected on or through it. Protection for web applications is the method of securing websites and internet resources from multiple security attacks that target weaknesses in an application. Security for web apps is a data management division that primarily deals with the security of websites, web applications and web services. Risks to cyber security include a broad range of allegedly criminal practices on the web. For years, computer security risks against utility assets were identified, cyber-attacks arising from the exploitation of data device weaknesses by unauthorized access users This study is a cyber infrastructure assault, vulnerability and vulnerabilities, including hardware and software devices, networks, business networks, intranets, and cyber intrusion usage. Only big organisations are at risk of cyber-attacks, a widespread misunderstanding. Even then, the targeting of small and medium sized enterprises has seen a large improvement. This is since less-sophisticated cyber defence mechanisms appear to be used by these smaller organisations. As many as 50% of all cyber-attacks hit small companies, wasting $200,000 on average, sufficient to get out of business for less-established entities. an enterprise-grade protection device and awareness of the various forms of cyber threats facing corporations in 2025 to safeguard company from hacking attacks. fight cyber threats successfully and decrease the exposure of enterprise.&#x0D;