Reviewerly/exHarmony huggingface dataset:
Planning and Learning using Adaptive Entropy Tree Search

Recent breakthroughs in Artificial Intelligence have shown that the combination of tree-based planning with deep learning can lead to superior performance. We present Adaptive Entropy Tree Search (ANTS) - a novel algorithm combining planning and learning in the maximum entropy paradigm. Through a comprehensive suite of experiments on the Atari benchmark we show that ANTS significantly outperforms PUCT, the planning component of the state-of-the-art AlphaZero system. ANTS builds upon recent work on maximum entropy planning methods - which however, as we show, fail in combination with learning. ANTS resolves this issue to reach state-of-the-art performance. We further find that ANTS exhibits superior robustness to different hyperparameter choices, compared to the previous algorithms. We believe that the high performance and robustness of ANTS can bring tree search planning one step closer to wide practical adoption.
Matching Potential and Passion Leads To Promise

Study of Mathematically Precocious Youth (SMPY) was founded by Julian C. Stanley in 1971 when he met a young boy who displayed extraordinary intellect and related capabilities. The historical and basic science underpinnings behind the current SMPY model of talent development can be found in Lubinski , the educational philosophy in Benbow and Stanley , and the theory in Lubinski and Benbow. The model has now been outlined. J. C. Huber stated that with regard to documenting intellectual property that constitutes a patent, It would be hard to find a field of study where so much effort has been expended in establishing a definition. Ability level clearly impacts the level of achievement. Numerous educational programs have been developed for students who are more mathematically and verbally talented.
AI-Based Evaluation to Assist Students Studying through Online Systems

We shall present artificial intelligence (AI) techniques to evaluate the effectiveness of online learning methods. The techniques presented here are independent of the different online learning models like online classroom, video/audio tutorials, eBooks, etc. where there is no teacher to assist. The commonly accepted system to test a student is through question and answer method. This relies upon a sample-based questions from a subject and collect the answers from students. Mostly this activity is performed at the end of the course. An evaluation by this method is limited to the correctness of the answers and basis on which we infer the student’s knowledge on the subject. In this paper we propose that the student should be assessed the on a continuous basis. This type of assessment is different from testing a student to qualify as ‘Pass’ or ‘Fail’ and assess the student on a graded scale. We will focus on AI techniques to assist to grade the performance of the student in an online learning environment and provide a feedback to improve the learning process. A simple implementation based on Markov Decision Process is present to understand this approach.
Taking the Library of Congress CIP Program into the Future with PrePub Book Link

The Library of Congress Cataloging in Publication (CIP) Program and its sister program the Preassigned Control Number (PCN) Program have provided prepublication metadata to publishers since 1971. For the first three decades, CIP publishers submitted paper applications, and CIP staff manually keyed the information into the Library of Congress’s local cataloging system. Between 1999 and 2003, the Library developed an in-house online CIP and PCN system with separate backend modules, the EPCN Traffic Manager and the ECIP Traffic Manager. In 2017 the Library let a contract to completely overhaul the aging online system and replace it with a custom application on a ServiceNow platform. The intensive design and development phase, followed by the controlled chaos surrounding implementation, resulted in a stable, functional, and state-of-the-art application named PrePub Book Link (PPBL). PPBL brought both the CIP and PCN programs into the future with an overall improved user experience, increased security, advanced account management, dynamic Web forms, enhanced ONIX integration and workflows, and features such as the MARC Editor and the customizable CIP data block generator. As the CIP Program prepares for the next 50 years, continued development of PPBL will enable the CIP and PCN programs to thrive.
Eye movements reveal the role of attention in change blindness for shadows

In a change blindness paradigm, participants are presented with two images (alternating or side-by-side) and are asked to detect a change created with digital image manipulation. A recent study has suggested that changes in cast shadows are more difficult to detect than changes in objects (Ehinger, Allen, & Wolfe, 2016). Research has also suggested that when judging faces, observers show reduced attention to cast shadows (Hermens & Zdravkovic, 2015). A plausible reason for change blindness for shadows is therefore that observers fail to attend the relevant areas in an image. We here test this hypothesis by measuring eye movements while observers freely view the images from Ehinger et al. (2016). To compare change blindness in the same set-up we also asked participants to perform a change detection task where we presented images side-by-side and balanced the number of object and shadow changes. During free viewing, observers paid little attention to the relevant shadows and objects, but significantly more often fixated the relevant shadows than objects. During the change blindness task, changes in shadows were more easily detected than changes in objects. Attention during free viewing of individual images, however, did not predict change detection performance. The results suggest that during free viewing observers do not automatically discard cast shadows, and that change blindness may not be directly related to visual attention during free viewing.
Digital Humanities in Oman: Logistics, Challenges and Opportunities

This paper focuses on the emergence, place and role of the digital humanities (DH), particularly in the Arab world, with reference to Oman, a country whose post-oil economy relies vastly on the development of national skills and expertise. It traces the development of DH globally and considers the challenges in Oman, focusing on the state of digitalisation of the economy and knowledge through both public and private development. By exploring the possible immediate and far-reaching goals of a DH program in a country that requires drastic changes to meet the economic and social needs of its citizens and residents in a twenty-first century, post-Covid-19 economy, this study looks at the extent to which institutions are primed to build upon existing scaffolding to achieve digital goals. While focusing on pedagogical outcomes and strategies, this study suggests that DH is imperative to the effective implementation of the Omani national vision 2040 in terms of facilitating knowledge creation and nation-building through heritage preservation and that the establishment of a Digital Humanities Research Centre would be essential toward promoting interdisciplinary work in a sustained and effective manner. © 2021, Addleton Academic Publishers. All rights reserved.
ELLIPTIC CURVE DIGITAL SIGNATURE ALGORITHM

Elliptic curve cryptography has been a remarkable development in the history of cryptography thanks to the properties provided by the implementation of elliptic curve cryptography. Elliptic curve cryptography has proven to be adaptable given its broad presence across various electronics of diering sizes and capabilities. Elliptic curve cryptography is known for utilizing the discrete logarithm problem, modern algebra, and elliptic curves that have encouraged continued research into elliptic curve cryptography in its advantages and restrictions. The current comprehension of elliptic curves as well as continuing advancements and employments of elliptic curve cryptography have yielded the elliptic curve digital signature algorithm (ECDSA) which has become an alternative to the primary Digital Signature Algorithm. TheECDSA provides advantages of elliptic curve cryptography to the function of the digital signature algorithm to authenticate and protect transmissions between involved parties. Such can be explored in the securing of bitcoinrelated transactions where the completely electronic, decentralized currencywould face a multitude of cyber threats and require safeguards to remaintrustworthy.
ANALISIS PENGARUH DISTORSI HARMONISA TERHADAP NILAI RUGI-RUGI SERTA PENURUNAN DAYA MAMPU PADA TRANSFORMATOR-2 GIS SPINMILL

Harmonics is a problem that arises due to the use of non-linear load on the electric power system which causes distortion in the sine wave form. One of the effects of harmonic distortion can increase in losses and decrease the ability to power the distribution transformer. In this Thesis, the author tries to analyze the harmonics contained in transformer-2 with a capacity of 60 MVA at the GIS Spinmill PT. PLN (Persero) UP2D Banten, as well as the effect of harmonics on the decrease in capability and the increase in losses of the transformer. From the results of harmonic data processing, the highest THDv value is 2.81%, THDv value is still according to the standard (Vrms ≤ 60 kV: 5%), and the highest THDi value is 12.23%, the current harmonic distortion value has exceeded the IEEE standard 519-2014 (SCratio 50-100: 12%). The impact of the harmonics affects the decrease in the ability of the transformer with the highest value of 2,036%, and the highest increase in the loss of the transformer is 6.49 kW. Passive filter type single tuned is designed to reduce the THDi value especially in the 5th and 7th order with a reactive power capacity is 1,077.54 kVAR.
6 Fractal tools in combating terrorism and money laundering

Information society imposes globalization and universalization of values. In these circumstances, terrorism, as institutional political violence which attempts to spread morbid fear, becomes a threat not only to the nations but also to politics on the global level. Today, intelligence activities in combatting terrorism encompass financial investigations and money laundering for financing terrorism, which results in broadening of the scope of data to the level that is impossible for human logical evaluation. Technologies development increased the capacity of speed and the amount of data processing, which has enabled the computer experiments and simulations trying to get to more complex planning and forecasting to counterterrorism and “dirty” money transaction, as highly dangerous, complex and variable phenomena. This presentation aims at quotation the wide spectrum of mathematically founded fractal concepts suited to generate computer models of antiterrorist activities. In this sense, the logistic behind the items connected with detecting and recognizing the degree of terroristic threat by comparing fractal structure of people’s faces, fast search through the databases of faces and fingerprints. The speed of searching processes is of vital importance, which promotes the crucial importance of compression and data reduction with preservation of regularity. Especially important are analytic forecasting of missing visual data and forms, to supplement the empirical evidences and records. All these operations are possible with higher degree of knowledge utilization and adaptation of virtual reality in the fight against terrorism and different forms of money laundering. The results indicate that the achievements implementation of the concept of fractals depends on substantial prior knowledge, environmental influences, subsystem integration, decentralization and synchronization, and allows us to come up with similar high information technology models, but not necessarily to enable identification of the authentic features of the various anomalies that result in objectively asocial consequences. In this sense, we conclude that the application of information technology in the fight against terrorism, based on the concept of fractals has its place in the arsenal of antiterroristic prevention.
AI, Biometry and Blockchain Powered User Authentication

User authentication mechanism is a complex subject considering its varying demand. Various technology aspects need to be considered to handle such varying demand. Security, data protection, comfort and convenience are few important criteria which are inherent demand from any good authentication mechanism. In spite of technological advances achieved in various areas, there are still concerns around security, overall user comfort and conveniences. The mechanisms used by fraudsters have seen lots of changes how to steal customer information and misuse it. Customer expectation around whole authentication process is now loaded with expectation of quickest turn-around time. AI, blockchain and biometry powered authentication handles dynamic need of authentication while keeping the user comfort and convenience factor in consideration. This is targeted without compromising the demand of maintaining security level. Artificial intelligence manages the dynamic need of authentication and it targets to be flexible with biometric and non-biometric authentication parameters. Blockchain implementation makes it immutable and strongly safe for handling confidential data of users. Biometric authentication handling makes it secure and non-biometric authentication makes it suitable where security need is not so high.
Application of File Encryption and Decryption Using One Time Pad Algorithm

In maintaining a system security, it is very important to pay attention to that, namely an authentication process. This process is carried out in order to ensure that users who access data or information are users who have the right and authority. In authentication, there are several methods, one of which is using data encryption techniques, namely cryptography. Cryptography is implemented in data and information by coding or hiding the original data so that only certain parts have a key that can access the data or information. This research will apply the One Time Pad (OTP) Algorithm based on android which includes data encryption and decryption which will be designed using Android Studio to carry out encryption of stored data or information. The data will be in the form of ciphertext so that the user gets an access key for the data or information. In making this application, it is hoped that it will be useful in maintaining data security and confidentiality.
MENINGKATKAN PRESTASI BELAJAR PKN KONSEP MENDESKRIPSIKAN PENGERTIAN ORGANISASI MELALUI PENERAPAN METODE SIMULASI PADA SISWA KELAS V SD NEGERI TRUKO 01 UPTD PENDIDIKAN KECAMATAN BRINGIN SEMESTER 2 TAHUN PELAJARAN 2015/2016

The focus of research  is on teacher’s effort to improve student learning achievement in Civics learning using simulation method in order to improve  student learning achievement that was previously known to not as expected yet. This research was conducted on 5 th grade  students of Truko 01 elementary school of UPTD Education in District Bringin at second Semester in 2015/2016. The design of research using  Classroom Action Research plan which stars from  making the action plan, carrying out the action in learning, observing, and reflecting the action. The results of the reflection are used to make decision. The research data obtained from  field notes, observations notes, planning documentation, and writing results. The instrument of collection is observation guidelines, field notes, and documentation. Data is analyzed using qualitative  techniques approach, including the stages of data reduction, data exposure, verification, and data conclusions. The test  validity of the data carried is carried out to re-checking (triangulation) with a colleague and students. After completing this research, the author can conclude that the Simulation Method can improve student achievement on 5 th grade students of Truko 01 elementary school  of  UPTD Education in Bringin District in Civics Education. The average value of the learning process of students in the first cycle was 59.6 with a 58% mastery learning. The acquisition of the average value of student learning outcomes in the second cycle, which is 75 and 100% mastery learning.
Optimization of PBFT Algorithm in Cloud Environment and Its Application in Logistics Blockchain Consensus Algorithm

With the rapid development of e-commerce, the logistics industry has also developed rapidly.However, at present, the service transaction system of the logistics industry is highly centralized, the coordination ability is poor, and the customer information security cannot be effectively guaranteed, which affects the information security, automation, and intelligence of logistics enterprise service transactions.The purpose of this article is to study the consensus algorithm of logistics blockchain based on cloud computing.Based on the basic principles of the practical Byzantine consensus algorithm and the logistics blockchain cloud computing model, combined with decentralization and non-repudiation of information security theory, this paper proposes a cloud computing-based logistics blockchain consensus algorithm.Parallel processing functions for design and algorithm analysis.The experimental results show that the test indicators of the algorithm are better than the practical Byzantine consensus algorithm and the optimized MinBFT algorithm.Therefore, this algorithm is an effective blockchain consensus algorithm in logistics service transactions.It has certain application to the logistics industry.Practical significance.In this paper, by testing the security of the algorithm, it is obtained that the attack success rate is 0 when the number of forged nodes differs from normal nodes by 20, and it can be seen that the algorithm has high security performance.
Repeated-root constacyclic codes of length 6<i>l<sup>m</sup>p<sup>n</sup></i>

<p style='text-indent:20px;'>Let <inline-formula><tex-math id="M1">\begin{document}$ \mathbb{F}_{q} $\end{document}</tex-math></inline-formula> be a finite field with character <inline-formula><tex-math id="M2">\begin{document}$ p $\end{document}</tex-math></inline-formula>. In this paper, the multiplicative group <inline-formula><tex-math id="M3">\begin{document}$ \mathbb{F}_{q}^{*} = \mathbb{F}_{q}\setminus\{0\} $\end{document}</tex-math></inline-formula> is decomposed into a mutually disjoint union of <inline-formula><tex-math id="M4">\begin{document}$ \gcd(6l^mp^n,q-1) $\end{document}</tex-math></inline-formula> cosets over subgroup <inline-formula><tex-math id="M5">\begin{document}$ &lt;\xi^{6l^mp^n}&gt; $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M6">\begin{document}$ \xi $\end{document}</tex-math></inline-formula> is a primitive element of <inline-formula><tex-math id="M7">\begin{document}$ \mathbb{F}_{q} $\end{document}</tex-math></inline-formula>. Based on the decomposition, the structure of constacyclic codes of length <inline-formula><tex-math id="M8">\begin{document}$ 6l^mp^n $\end{document}</tex-math></inline-formula> over finite field <inline-formula><tex-math id="M9">\begin{document}$ \mathbb{F}_{q} $\end{document}</tex-math></inline-formula> and their duals is established in terms of their generator polynomials, where <inline-formula><tex-math id="M10">\begin{document}$ p\neq{3} $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M11">\begin{document}$ l\neq{3} $\end{document}</tex-math></inline-formula> are distinct odd primes, <inline-formula><tex-math id="M12">\begin{document}$ m $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M13">\begin{document}$ n $\end{document}</tex-math></inline-formula> are positive integers. In addition, we determine the characterization and enumeration of all linear complementary dual(LCD) negacyclic codes and self-dual constacyclic codes of length <inline-formula><tex-math id="M14">\begin{document}$ 6l^mp^n $\end{document}</tex-math></inline-formula> over <inline-formula><tex-math id="M15">\begin{document}$ \mathbb{F}_{q} $\end{document}</tex-math></inline-formula>.
Website Visitors Forecasting using Recurrent Neural Network Method

Abstract. The number of visitors and content accessed by users on a site shows the performance of the site. Therefore, forecasting needs to be done to find out how many users a website will come. This study applies the Long Short Term Memory method which is a development of the Recurrent Neural Network method. Long Short Term Memory has the advantage that there is an architecture of remembering and forgetting the output to be processed back into the input. In addition, the ability of another Long Short Term Memory is to be able to maintain errors that occur when doing backpropagation so that it does not allow errors to increase. The comparison method used in this study is Backpropagation. Neural Network method that is often used in various fields. The testing using new visitor data and first time visitors from 2018 to 2019 with vulnerable time per month. The computational experiment prove that the Long Short Term Memory produces better result in term of the mean square error (MSE) comparable to those achieved by Backpropagation Neural Network method.
Pipeline of Optimization Techniques for Multi-Level Thresholding in Medical Image Compression Using 2D Histogram

In recent times, medical scan images are crucial for accurate diagnosis by medical professionals. Due to the increasing size of the medical images, transfer and storage of images require huge bandwidth and storage space, and hence needs compression. In this paper, multilevel thresholding using 2-D histogram is proposed for compressing the images. In the proposed work, hybridization of optimization techniques viz., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS) is used to optimize the multilevel thresholding process by assuming the Renyi entropy as an objective function. Meaningful clusters are possible with optimal threshold values, which lead to better image compression. For performance evaluation, the proposed work has been examined on six Magnetic Resonance (MR) images of brain and compared with individual optimization techniques as well as with 1-D histogram. Recent study reveals that peak signal to noise ratio (PSNR) fail in measuring the visual quality of reconstructed image because of mismatch with the objective mean opinion scores (MOS). So, we incorporate weighted PSNR (WPSNR) and visual PSNR (VPSNR) as performance measuring parameters of the proposed method. Experimental results reveal that hGAPSO-SOS method can be accurately and efficiently used in problem of multilevel thresholding for image compression.
Predicting Antonyms in Context using BERT

We address the task of antonym prediction in a context, which is a fill-in-the-blanks problem. This task setting is unique and practical because it requires contrastiveness to the other word and naturalness as a text in filling a blank. We propose methods for fine-tuning pre-trained masked language models (BERT) for context-aware antonym prediction. The experimental results demonstrate that these methods have positive impacts on the prediction of antonyms within a context. Moreover, human evaluation reveals that more than 85% of predictions using the proposed method are acceptable as antonyms.
Deadlock Prevention by Mutual Exclusion Process in Cloud Storage

Nowadays, users want to use cloud storage to remotely access and control resources without deadlock. For deadlock handing, developers used many deadlock handling strategies such as bully algorithm and other algorithms. In our work, we try to manage sessions to create a deadlock-free model by developing a modified version of the bully algorithm. Here in the bully algorithm, we try to manage various sessions by using the nested sleep function. These nested sleep function helps to create a deadlock-free system. This sleep function sleeps process for some milliseconds for completing the previous method. There are used sleep functions with enough time to manage these deadlock handling conditions without more delay.