Reviewerly/exHarmony huggingface dataset:
An Analysis of Heuristics for a Mathematically Incomplete Variant of TicTacToe

We attempt to produce a game-winning heuristic for the mathematically incomplete game Ultimate Tic Tac Toe (UTT). There are several game AI that use Monte Carlo Tree Search to decide moves, however, heuristics offer a faster and computationally cheaper alternative. The mathematical analysis of UTT has not been actively pursued, so we attempt to prove a posteriori. We have decided on a few strategies for playing, and assign different strategies to each player. We play several automated games of UTT, and statistically analyse which games end quickest, and use that data to find optimal strategies for playing. This can be used to produce game heuristics for more complicated games, and produce insight about strategies. The first objective is to specify a framework that can compare heuristics for UTT, and decide an optimal strategy for both players. The second objective is to test the framework with a large amount of data, and produce demonstrable results for UTT. Lastly, to aid further research in this topic, we release our dataset into the public domain.
Asymmetric Image Cryptosystem Based on Chaotic Zone Plate Phase Mask and Arnold Transform

An optical asymmetric cryptosystem built on chaotic zone plate phase mask (CZPPM) has been proposed. Here the pixels of an image are shuffled by employing Arnold transform $$\left( {{\text{AT}}^{\omega } } \right)$$ and is then modulated with the CZPPM featuring in the gyrator Transform domain (GT). This increases randomness and adds chaotic parameters that make the system highly secure. The proposed system strengthens the security of the cryptosystem and does not permit the attacker to retrieve the initial image without the expertise of keys. The robustness of the projected cryptosystem has been investigated and validated based on an extra degree of freedom by simulating on MATLAB 9.9.0 (R2020b), and investigational outcomes have been shown to emphasize the efficacy of the algorithm.
Literature Review on Cloud Application

The software that runs its processing logic is a cloud application. In this the data is stored between two systems: client-side and server-side. End-users local hardware and remote server is also a part where some processing is done. However, most data storage exists on a remote server which is one of the major perk of using cloud application. In some cases a local device with no storage space is built with cloud application. Using web browser cloud application interacts with its users; this facility makes the organizations to switch their infrastructure to the cloud for gaining the benefit of digital transformations. In cloud applications it is easier for the clients to move or manage their data safely and it also provides the flexibility required for the emerging organizations to sustain in the digital market. As the cloud applications are emerged with sophistication many papers were employed on its branches. This research paper emphasizes on the evolution and long-term trends of cloud applications. Findings from the paper enable the enterprise with perplexity to decide on adopting cloud.
Spatio-Temporal Representation Factorization for Video-Based Person Re-Identification

Despite much recent progress in video-based person re-identification (re-ID), the current state-of-the-art still suffers from common real-world challenges such as appearance similarity among various people, occlusions, and frame misalignment. To alleviate these problems, we propose Spatio-Temporal Representation Factorization (STRF), a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID. The key innovations of STRF over prior work include explicit pathways for learning discriminative temporal and spatial features, with each component further factorized to capture complementary person-specific appearance and motion information. Specifically, temporal factorization comprises two branches, one each for static features (e.g., the color of clothes) that do not change much over time, and dynamic features (e.g., walking patterns) that change over time. Further, spatial factorization also comprises two branches to learn both global (coarse segments) as well as local (finer segments) appearance features, with the local features particularly useful in cases of occlusion or spatial misalignment. These two factorization operations taken together result in a modular architecture for our parameter-wise light STRF unit that can be plugged in between any two 3D convolutional layers, resulting in an end-to-end learning framework. We empirically show that STRF improves performance of various existing baseline architectures while demonstrating new state-of-the-art results using standard person re-ID evaluation protocols on three benchmarks.
A trust evaluation scheme of service providers in mobile edge computing

Mobile edge computing (MEC) is a new computing paradigm that brings cloud services to the network edge. Despite its great need in terms of computational services in daily life, service users may have several concerns while selecting a suitable service provider to fulfil their computational requirements. Such concerns are: with whom they are dealing with, where will their private data migrate to, service provider processing performance quality. Therefore, this paper presents a trust evaluation scheme that evaluates the processing performance of a service provider in the MEC environment. Processing performance of service providers is evaluated in terms of average processing success rate and processing throughput, thus allocating a service provider in a relevant trust status. Service provider processing incompliance and user termination ratio are also computed during provider’s interactions with users. This is in an attempt to help future service users to be acknowledged of service provider’s past interactions prior dealing with it. Thus, eliminating the probability of existing compromised service providers and raising the security and success of future interactions between service providers and users. Simulations results show service providers processing performance degree, processing incompliance and user termination ratio. A service provider is allocated to a trust status according to the evaluated processing performance trust degree.
Metafora Konseptual dalam Sastra Anak Virtual Indonesia

Abstract : This research is a quantitative analysis and uses descriptive qualitative methods. The data is taken from eight digital children's stories from the Ipusnas collection that can be accessed by children during the pandemic. The theory that used is the conceptual metaphor of Lakoff & Johnson (2004) and Wahab (1991) as the main theory and the image scheme from Cruse and Croft's (2004) theory. The problems that discussed in this study are the types of conceptual metaphors used in children's literature and their image schemes. The results of the analysis found 26 conceptual metaphors with 1 ontological metaphor-nominative structure, 7 ontological metaphors-predicative structure, 9 ontological metaphors-calimative structure; 4 structural metaphors-nominative structure, 1 structural metaphors-predicative structures, 2 structural metaphors-calimative structure; 2 orientational metaphors-calimative structure. The image schemes found are 13 excistence schemes, 3 identity schemes, 3 scale schemes, 5 force schemes, 2 space schemes. Based on this analysis, it appears that the child's ability is stronger in conceptualizing events, emotional activities, and ideas as entities and substances, thoughts, experiences, and processes of abstract things are conceptualized to have a physical nature. This is directly proportional to the image scheme used, namely excistence scheme is more dominant to understand something abstract. Abstrak: Penelitian ini merupakan suatu analisis semantik kognitif dan menggunakan metode kualitatif yang bersifat deskriptif. Data diambil dari delapan cerita anak digital koleksi Ipusnas yang dapat diakses secara daring oleh anak saat pandemi. Teori yang digunakan adalah metafora konseptual dari Lakoff & Johnson (2004) dan Wahab (1991) sebagai teori utama serta skema citra dari teori Cruse dan Croft (2004). Masalah yang dibahas dalam penelitian ini adalah jenis-jenis metafora konseptual yang digunakan dalam sastra anak dan skema citranya. Hasil analisis ditemukan sebanyak 26 data metafora konseptual dengan 1 metafora ontologis berstruktur nominatif, 7  metafora ontologis berstruktur predikatif, 9 metafora ontologis berstruktur kalimatif; 4 metafora struktural berstruktur nominatif, 1 metafora struktur predikatif, 2 metafora struktural berstruktur kalimatif; 2 metafora struktural berstruktur kalimatif. Skema citra yang ditemukan sebanyak 13 skema excistence , 3 skema identity , 3 skema scale , 5 skema force , 2 skema space . Berdasarkan analisis tersebut tampak kemampuan anak lebih kuat dalam mengonseptualisasikan kejadian, aktivitas emosi, dan ide sebagai entitas dan substansi. Pikiran, pengalaman, dan proses hal abstrak dikonseptualisasikan memiliki sifat fisik. Hal tersebut berbanding lurus dengan skema citra yang digunakan yaitu lebih dominan skema citra excistence untuk memahami sesuatu yang abstrak.
Getting to know you: Search logs and expert grading to define children's search roles in the classroom

In this paper, we examine the roles children play when using web search engines in the classroom context by revisiting, not replicating, a seminal work set in the home context. In particular, we describe how we juxtaposed performance indicators inferred from a combination of search logs (collected over two years) and expert grading of completed inquiry assignments to discern emerging search roles among children in primary four and five (aged 9 to 11). In light of the COVID-19 pandemic, we also explore differences when a traditional classroom is replaced by online instruction at home. Lastly, we discuss future research directions that we see as pivotal to advance research in Information Retrieval to and for children. © 2021 CEUR-WS. All rights reserved.
Real-Time Video Tracking Framework With Moving Object Segmentation in Stream Data

Object tracking is an active application-oriented research topic. Analysis of video requires human operators to monitor human activities. This framework should be capable of detecting and tracking moving objects accurately. The unsupervised segmentation of moving objects affects the overall performance of an object tracking framework. The ideal aim of unsupervised segmentation method is to split the image into meaningful objects. In this proposed video tracking framework, initially, segmentation of the moving object with displacement vectors in an unsupervised manner is done. Using the displacement vector as a feature, the input frame segmentation is achieved with the Expectation–Maximization (E–M) technique. In this work, the E–M algorithm initialized with the number of objects present in the frame is obtained by clustering objects using the K-means clustering technique. The resulting objects obtained may contain shadows of the objects. Hence, shadow removal approach is used to remove shadows surrounding objects. Finally, the objects tracked using displacement vectors and the behaviour of objects are analyzed. The experiments on various datasets such as standard videos, surveillance videos, and nonstandard videos demonstrated, outperforms in single and multiple cases.
Fast Symmetry Detection with Deep Learning and GeConv

n many computer vision applications, it is relevant to know the orientation of the object relative to its symmetry axis. While humans are generally very good at evaluating the reflectional symmetry and relative orientation of objects, constructing a robust symmetry detection pipeline working out of the box is still a challenging task in computer vision. This is particularly the case for irregular objects with skewed geometry and irregular shapes. In this paper, two approaches are presented for symmetry and object orientation detection based on traditional computer vision and Deep Learning. First, an algorithm is presented that allows fast computation of 2-axis reflectional symmetry of contour points and the definition of a radial convex hull, GeConv. The algorithm is tested on 1000+ image dataset from the wind tunnel and flight test experiment.
An effective method for clustering-based web service recommendation

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, availability, etc. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, QoS preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services' functional relevance such as non-useful pertinence, recorded client intrigue importance, potential client intrigue significance, etc. Additionally, to further improve the performance of this approach, we designed Web service Graph Construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.
Choosing an Instant Messaging App: Security or Convenience? Comparison between Whatsapp and Telegram

An increase in Instant Messenger (IM) application users makes it easier for people to get connected through social media. Application developers regularly offer updates. However, those updates donâ€™t always satisfy users expectations, instead confuse them. Even users are sometimes hesitant to update the app thus they switch to another messaging platform. This research discusses the analysis and comparison of two IM applications that reached 100 million downloads on Play Store; Whatsapp and Telegram. The analysis process is carried out from the features use in the Instant Messenger application. The data collection procedure are conducted by viewing the traffic. The data acquisition technique is done by using literacy and observation study methods to get full access to smartphones. The results of the analysis are in the form of a comparing table between both IM applications and expected to be future reference for further reseach.Peningkatan pengguna aplikasi Instant Messenger (IM) memudahkan masyarakat untuk terhubung melalui media sosial. Pengembang aplikasi secara teratur menawarkan pembaruan. Namun, pembaruan tersebut tidak selalu memenuhi harapan pengguna, malah membingungkan mereka. Bahkan pengguna terkadang ragu untuk memperbarui aplikasi sehingga mereka beralih ke platform perpesanan lain. Penelitian ini membahas tentang analisis dan perbandingan dua aplikasi IM yang mencapai 100 juta unduhan di Play Store; Whatsapp dan Telegram. Proses analisis dilakukan dari fitur-fitur yang digunakan pada aplikasi Instant Messenger. Prosedur pengumpulan data dilakukan dengan melihat lalu lintas. Teknik akuisisi data dilakukan dengan menggunakan metode studi literasi dan observasi untuk mendapatkan akses penuh ke smartphone. Hasil analisis berupa tabel perbandingan antara kedua aplikasi IM dan diharapkan dapat menjadi acuan untuk penelitian selanjutnya.
Radiometric Characteristics of the Rio do Peixe Basin-Brazil

This paper presents the analysis of radiometric data from an aerial survey carried out in the Rio do Peixe Basin, which is a promising oil-producing region in the state of Paraiba, Brazil. The data collected through gamma spectrometer allowed the preparation of counting maps of Uranium, Potassium and Thorium, allowing to evaluate and compare the radiometric concentrations obtained with the geological and land use maps of the region. The radiometric maps of total and ternary counting illuminated the basin lineaments. The potassium, thorium and ternary maps allow to identify the geological formations of Rio Piranhas, Sousa and Antenor Navarro, contributing to the geological knowledge of the region.
شناسایی ابعاد مدیریت دادههای پژوهشی در کتابخانههای دانشگاهی قطب ۷

Introduction: Managing large amounts of data in the world is very important and ensuring the quality of research to facilitate the sharing of data with other researchers. The purpose of this study was to identify the dimensions of research data management in Qutb 7 university libraries.Materials and Methods: The method of the present study was exploratory mix (qualitative-quantitative). Qualitative data were collected using a structured questionnaire by Delphi-fuzzy method. In the quantitative part with the categories-items resulting from the qualitative part was obtained in the form of a questionnaire. The study population was 127 librarians of medical universities. Research data analysis was used with SPSS software version 22 and confirmatory factor test. It was used to present the structural equation model (SEM) and PLS Smart software.Results: All components were acceptable with mean factor loads higher than 0.6 and Cronbach's alpha coefficient. With 99% confidence, the good fit of the component measurement models was confirmed. The results of t-test in a small part showed that there was a significant difference between the desired situation and the existing situation in the categories of data management in the university libraries of the Ministry of Health and Medical Education, pole 7, and the current situation was lower than average.Conclusion: The current model of data management faces challenges in achieving the ideal goals. Therefore, strengthening the dimensions of data management provides the basis the organization's sustainable goals, which will ultimately lead to the improvement of data management in the desired academic libraries.Keywords: Data management, Research data, Academic library.
Is Attention Always Needed? A Case Study on Language Identification from Speech

Language Identification (LID), a recommended initial step to Automatic Speech Recognition (ASR), is used to detect a spoken language from audio specimens. In state-of-the-art systems capable of multilingual speech processing, however, users have to explicitly set one or more languages before using them. LID, therefore, plays a very important role in situations where ASR based systems cannot parse the uttered language in multilingual contexts causing failure in speech recognition. We propose an attention based convolutional recurrent neural network (CRNN with Attention) that works on Mel-frequency Cepstral Coefficient (MFCC) features of audio specimens. Additionally, we reproduce some state-of-the-art approaches, namely Convolutional Neural Network (CNN) and Convolutional Recurrent Neural Network (CRNN), and compare them to our proposed method. We performed extensive evaluation on thirteen different Indian languages and our model achieves classification accuracy over 98 %. Our LID model is robust to noise and provides 91.2 % accuracy in a noisy scenario. The proposed model is easily extensible to new languages.
PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility

Recent learning-based multi-view stereo (MVS) methods show excellent performance with dense cameras and small depth ranges. However, non-learning based approaches still outperform for scenes with large depth ranges and sparser wide-baseline views, in part due to their PatchMatch optimization over pixelwise estimates of depth, normals, and visibility. In this paper, we propose an end-to-end trainable PatchMatch-based MVS approach that combines advantages of trainable costs and regularizations with pixelwise estimates. To overcome the challenge of the non-differentiable PatchMatch optimization that involves iterative sampling and hard decisions, we use reinforcement learning to minimize expected photometric cost and maximize likelihood of ground truth depth and normals. We incorporate normal estimation by using dilated patch kernels, and propose a recurrent cost regularization that applies beyond frontal plane-sweep algorithms to our pixelwise depth/normal estimates. We evaluate our method on widely used MVS benchmarks, ETH3D and Tanks and Temples (TnT), and compare to other state of the art learning based MVS models. On ETH3D, our method outperforms other recent learning-based approaches and performs comparably on advanced TnT.
ANALYSIS OF SERVICE SATISFACTION LEVEL USING ROUGH SET ALGORITHM

Data mining Is a technique that combines traditional data analysis techniques with algorithms for processing large amounts of data. Data mining can be used to perform data analysis and find important patterns in data. Data mining will be a benchmark or reference for making data mining processing decisions that can be done with the Rough Set method. Rough Set Method is one of the methods above that allows us to make decisions in hotel services because in this method there are formulations or stages of problem mechanics and a Result (decision) of a combination that may occur from the criteria above. From the results (decisions) derived from the processed data mining, it can be used as a reference for decision making. The Rought Set Method is a mathematical technique developed since 1980.
New Vulnerabilities upon Grain v0 Boolean Function through Fault Injection Analysis

Algebraic attacks on stream cipher are very important in cryptography as well as in cryptanalysis. Generally, increasing degree of the equation will make an algebraic attack to the equation hardest. In conducting this analysis, we aim to decrease the degree of the targeted Boolean equation by constructing low degree annihilator equation(s). We adopt the Fault Injection Analysis (FIA) methodology to achieve our objectives. In this study, we found annihilator(s) through FIA (inject with value of one (1)) on Boolean function of selected stream ciphers. With the new injected Boolean functions developed, we proceed to utilize Hao’s method to find new annihilator(s). Then we established new annihilator(s) of Grain v0’s Boolean function. As a result, these newly identified annihilator(s) successfully reduce the complexity of the published Boolean function to guess the initial secret key. It also provides much needed information on the security and vulnerability of these selected stream cipher with respect to FIA.
Nego-Bot: A Human-Robot Negotiation System

In this paper we present a platform composed of a low-cost robot and a multi-agent system that uses deep learning algorithms, whose objective is to establish a negotiation process and persuasively sell items, maximising their price, thus gain. To do this, we have focused on developing an interactive process that is able to interact with humans using a camera, microphone and speaker, to establish all negotiation process without physical contact. This is relevant due to the current COVID-19 situation and arisen issues of human contact. Validation processes with university students have revealed high interest and success in products’ negotiation.