Reviewerly/exHarmony huggingface dataset:
Fast algorithm for intra prediction mode of 360 degree panoramic video based on VVC

Authors:
raw_author_name: Tiantian Wang
raw_affiliation_string: Harbin Univ. of Commerce (China)
raw_author_name: Xuesong Jin
raw_affiliation_string: Harbin Univ. of Commerce (China)

Abstract: The Joint Video Exploration Team published the Versatile Video Coding standard in 2020. The standard improves the efficiency of coding for 360-degree panoramic video. However, it also causes an increase in coding complexity, especially in the selection of the intra prediction mode. Therefore, in order to reduce the time required for intra prediction mode decision in the VVC standard, this paper proposes a fast decision algorithm for intra prediction modes from the perspective of reducing the number of candidate modes in the intra prediction modes by exploiting the relationship between the gradient direction and the angular prediction modes of the Sobel operator. Experimental results show that this algorithm can save an average of 6.27% of coding time compared with VTM11.0, while causing an average increment of BDBR of only 0.198% and an average reduction of BDPSNR of only 0.009dB.
Performance Analysis of Quantum Federated Learning in Data Classification

Authors:
raw_author_name: Hyunsoo Lee
raw_author_name: Soohyun Park

Abstract: Federated learning is a method with the advantage of allowing various institutions to create a global model by sharing model parameters without sharing the data they possess. Also, with the advent of the era of quantum computing, efforts to combine traditional machine learning algorithms and quantum computing are gaining momentum. In this paper, we intend to discuss quantum federated learning (QFL) methods. We examine the fundamentals of quantum computing and the structure of quantum neural networks, and introduce the methods of quantum federated learning. QFL demonstrated a potential for practical application in the real world, achieving up to 92% performance in the MNIST data classification task.
ADAPTATION OF REY AUDITORY VERBAL LEARNING TESTFOR INDONESIA: ITS VALIDITY AND RELIABILITY

Authors:
raw_author_name: Augustina Sulastri
raw_affiliation_string: Faculty of Psychology, Soegijapranata Catholic University, Indonesia
raw_author_name: Lucia Widhianingtanti
raw_affiliation_string: Faculty of Psychology, Soegijapranata Catholic University, Indonesia

Abstract: Rey’s Auditory Verbal Learning Test (RAVLT) assesses different as- pects of verbal memory and learning. This test is often used in clinical, educational, and selection contexts. We adapted the Geffen et al’ ver- sion for Indonesia and here we present sychometric properties. We tested the validity via its seven recall trials (A1 to A7), based on theore- tical constructs (e.g. the presence and recency effect) via analyses of variance and factor analyses and the test’s reliability via e.g. correlation analyses and Cronbach’s alpha. The objective of the research was to study the psychometric properties of Rey Auditory Verbal Learning Test which were adapted from Geffen et al’s to Indonesian version.Two groups of healthy participants with varying ages (16-80) and edu- cation (from Elementary school to postgraduate alumni) included 492 persons for the validation, and 50 for the test-retest reliability part of the study with a 6–14-day interval. We tested the validity via its seven recall trials, based on theoretical constructs, and factor analyses. The test’s reliability via Cronbach’s alpha test, and test-retest.The recall scores increased over trials A1 to A5. The expected position effect, primacy, and recency were found for A1 to A5. Factor analysis that the Rey Auditory Verbal Learning Test revealed three memory con- structs. The word “ibu (mother)” caused an anomaly in the position effect of the 15 words. Both Cronbach’s Alpha and the test-retest corre lations increased from low A1 to satisfying A7. Carry-over results were found in the retest.The Indonesian version of the Rey Auditory Verbal Learning Test is valid based on the recall related to the position of the words, the three clusters describing the memory of all seven trials. The score of relia- bility is only moderate for the recall score, it might be due to the test- retest procedure.
Steady-state charging of quantum batteries via dissipative ancillas

Authors:
raw_author_name: F. H. Kamin
raw_affiliation_string: Department of Physics, University of Kurdistan, P.O. Box 66177-15175, Sanandaj, Iran
raw_author_name: S. Salimi
raw_affiliation_string: Department of Physics, University of Kurdistan, P.O. Box 66177-15175, Sanandaj, Iran

Abstract: We investigate the steady-state charging process of a single-cell quantum battery embedded in an $N$-cell star network of qubits, each interacting with a fermion reservoir, collectively and individually in equilibrium and nonequilibrium scenarios, respectively. We find an optimal steady-state charging in both scenarios, which grows monotonically with the reservoirs' chemical potential and chemical potential difference, where the high base temperature of the reservoirs has a destructive role in all parameter regimes. We indicate that regardless of the strength of the nonequilibrium condition, the high base chemical potential of the battery's corresponding reservoir can significantly enhance the charging process. On the other hand, a weak-coupling strength can strongly suppress the charging. Consequently, our results could counteract the detrimental effects of self-discharging and provide valuable guidelines for enhancing the stable charging of open quantum batteries in the absence of an external charging field.
The role of perceptual similarities in determining the asymmetric mixed-category advantage.

Authors:
raw_author_name: Roy Luria
raw_affiliation_string: Sagol School of Neuroscience, Tel Aviv University.

Abstract: Considering working memory capacity limitations, representing all relevant data simultaneously is unlikely. What remains unclear is why some items are better remembered than others when all data are equally relevant. While trying to answer this question, the literature has identified a pattern named the mixed-category benefit in which performance is enhanced when presenting stimuli from different categories as compared to presenting a similar number of items that all belong to just one category. Moreover, previous studies revealed an asymmetry in performance while mixing certain categories, suggesting that not all categories benefit equally from being mixed. In a series of three change-detection experiments, the present study investigated the role of low-level perceptual similarities between categories in determining the mixed-category asymmetric advantages. Our primary conclusion is that items' similarity at the perceptual level has a significant role in the asymmetric performance in the mixed-category phenomenon. We measured sensitivity (d') to detect a change between sample and test displays and found that the mixed-category advantage dropped when the mixed categories shared basic features. Furthermore, we found that sensitivity to novel items was impaired when presented with another category sharing its basic features. Finally, increasing the encoding interval improved performance for the novel items, but novel items' performance was still impaired when these items were mixed with another category that shared their basic features. Our findings highlight the significant role low-level similarities play in the asymmetric mixed-category performances, for both novel and familiar categories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
BUSTLE: a Versatile Tool for the Evolutionary Learning of STL Specifications from Data

Authors:
raw_author_name: Federico Pigozzi
raw_affiliation_string: Department of Engineering and Architecture, University of Trieste, Trieste, Italy federico.pigozzi@phd.units.it
raw_author_name: Laura Nenzi
raw_affiliation_string: Department of Engineering and Architecture, University of Trieste, Trieste, Italy lnenzi@units.it
raw_author_name: Eric Medvet
raw_affiliation_string: Department of Engineering and Architecture, University of Trieste, Trieste, Italy emedvet@units.it

Abstract: Abstract Describing the properties of complex systems that evolve over time is a crucial requirement for monitoring and understanding them. Signal Temporal Logic (STL) is a framework that proved to be effective for this aim because it is expressive and allows state properties as human-readable formulae. Crafting STL formulae that fit a particular system is, however, a difficult task. For this reason, a few approaches have been proposed recently for the automatic learning of STL formulae starting from observations of the system. In this paper, we propose BUSTLE (Bi-level Universal STL Evolver), an approach based on evolutionary computation for learning STL formulae from data. BUSTLE advances the state-of-the-art because it (i) applies to a broader class of problems, in terms of what is known about the state of the system during its observation, and (ii) generates both the structure and the values of the parameters of the formulae employing a bi-level search mechanism (global for the structure, local for the parameters). We consider two cases where (a) observations of the system in both anomalous and regular state are available, or (b) only observations of regular state are available. We experimentally evaluate BUSTLE on problem instances corresponding to the two cases and compare it against previous approaches. We show that the evolved STL formulae are effective and human-readable: the versatility of BUSTLE does not come at the cost of lower effectiveness.
On Circular q-Rung Orthopair Fuzzy Sets with Dombi Aggregation Operators and Application to Symmetry Analysis in Artificial Intelligence

Authors:
raw_author_name: Zeeshan Ali
raw_affiliation_string: Department of Mathematics and Statistics, Riphah International University, Islamabad 44000, Pakistan
raw_author_name: Miin-Shen Yang
raw_affiliation_string: Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li, Taoyuan 32023, Taiwan

Abstract: Circular q-rung orthopair fuzzy sets (FSs) were recently considered as an extension of q-rung orthopair FSs (q-ROFSs), circular intuitionistic FSs (Cir-IFSs), and circular Pythagorean FSs (Cir-PFSs). However, they are only considered for some simple algebraic properties. In this paper, we advance the work on circular q-ROFSs (Cirq-ROFSs) in Dombi aggregation operators (AOs) with more mathematical properties of algebraic laws. These include the circular q-rung orthopair fuzzy (Cirq-ROF) Dombi weighted averaging (Cirq-ROFDWA), Cirq-ROF Dombi ordered weighted averaging (Cirq-ROFDOWA), Cirq-ROF Dombi weighted geometric (Cirq-ROFDWG), and Cirq-ROF Dombi ordered weighted geometric (Cirq-ROFDOWG) operators. Additionally, we present the properties of idempotency, monotonicity, and boundedness for the proposed operators. In the context of artificial intelligence, symmetry analysis plays a significant and efficient role that can refer to several aspects. Thus, to compute the major aspect, we identify the multi-attribute decision-making (MADM) technique based on the proposed operators for Cirq-ROF numbers (Cirq-ROFNs) to enhance the worth of the evaluated operators. Finally, we use some existing techniques for comparison to our results to show the validity and supremacy of the proposed method.
PENERAPAN MODEL WATERFALL DALAM PERANCANGAN APLIKASI DIGITAL CUSTOMER RELATIONSHIP MANAGEMENT PRODUK FASHION

Authors:
raw_author_name: Sri Harjunawati
raw_author_name: Taufik Baidawi
raw_author_name: Ida Hendarsih

Abstract: To win business competition and maintain market share, companies are required to be able to adapt to market conditions and use appropriate marketing strategies. Customer Relationship Management (CRM) is a marketing strategy to create and maintain good relationships with customers thereby reducing the possibility of customers moving to competitors. The aim of the research is to analyze and develop web-based CRM in the Marketing Information System at Toko Agung, East Jakarta. The research method used in this research is the Software Development Method using the waterfall model. The result is a web-based CRM system application that can manage customer data, product orders, payments and delivery of goods to customers. The implementation of this web-based digital CRM application is expected to make it easier for customers to select the desired product, place an order, make payments and receive goods without having to come to Toko Agung. For busy customers, this system is more efficient and effective. It is hoped that this convenience will have an impact on repeat purchases and will increase sales volume, which can then increase company profits. The Customer Relationship Management (CRM) application can simplify, speed up and optimize the quality of customer service, especially at Toko Agung, East Jakarta.
The Role of Customer Satisfaction and Awareness as A Measure of Customer Loyalty- A Study on Select Banks

Authors:
raw_author_name: Swathi Sasikumar
raw_author_name: N Sundaram

Abstract: Purpose: The study wants to examine customer loyalty through customer satisfaction and awareness using developed constructs for select Nifty Banks.&#x0D; &#x0D; Theoretical framework: The theoretical framework for the study is to analyze customer loyalty through customer satisfaction and awareness using constructs of General Services (GS), Product Cognizance (PC), Technology Services (TS), and Social Responsibility (SR).&#x0D; &#x0D; Design/methodology/approach: The cluster sampling method was used to collect data from bank customers (n=360) using the self-administered questionnaire. The research used Partial Least Squares – Structural Equation Modelling (PLS-SEM) to test the hypothesis using SmartPLS 4.&#x0D; &#x0D; Findings: The result showed, that there is low customer satisfaction with Technology Services (TS) and that hypothesis was rejected.&#x0D; &#x0D; Research, Practical &amp; Social implications: The study has provided recommendations for banks were discussed to pay more attention to customer fulfilment and awareness which convert satisfaction to customer delight.&#x0D; &#x0D; Originality/value: Due to market sustainability and dynamics, the Indian banking industry has undergone many major changes such as digital banking units (DBU), digital currencies, and social green initiatives. Bank has incorporated many advancements to overcome its rivals. So, the study wants to know the customers are aware of these changes among their banks and whether they are satisfied with products and services.&#x0D; &#x0D; Contribution/Originality: The study wants to rank the select banks of India based on the constructs built, which measure customer loyalty through customer satisfaction and awareness.
Multiple image encryption acting at the RNA level

Authors:
raw_author_name: Mariem Jarjar
raw_author_name: Abid Abdellah
raw_author_name: Hicham Rrghout
raw_author_name: Mourad Kattass
raw_author_name: Abdellatif Jarjar
raw_author_name: Abdellhamid Benazzi

Abstract: The purpose of this research is to develop a new method for encrypting multiple superimposed or side-by-side images. The process begins by extracting the red, green, and blue channels from each image and converting them into vectors that combine to produce a single image that undergoes an advanced pixel-level Vigenere transform. In the next step, a pseudorandom transition occurs at the nucleotide, followed by a passage to codons for genetic crossover implementation specifically designed for image scrambling. The latter process is controlled by many random tables developed from selected chaotic maps, which ensures a high degree of flexibility and security in the encryption method. To evaluate the effectiveness and security of this innovative multi-image encryption algorithm, extensive simulations were performed using a large number of images randomly selected from the database. The simulation results prove the reliability and robustness of the method.
Experiences in the education of mathematics during the digital curriculum from the perspective of high school students

Authors:
raw_author_name: József Kosztolányi

Abstract: Due to the COVID-19 epidemic, Hungarian schools had to switch to a digital curriculum for an extended period between 2019 and 2021. In this article, we report on the experiences regarding the education of mathematics during the digital curriculum in the light of the reinstated on-site education, all through the eyes of high school students. Distance education brought pedagogical renewal to the lives of many groups. Students were asked about the positives and negatives of this situation. Subject Classification: 97C90
Pengembangan Sistem Cerdas Berbasis Data Mining untuk Meningkatkan Akurasi Prediksi Kebutuhan Obat di Puskesmas Parit Rantang

Authors:
raw_author_name: Khairi Budayawan

Abstract: Effective inventory management is crucial in providing quality healthcare services. Predicting drug needs in the pharmacy warehouse is vital to ensuring adequate availability for patients. This study developed and implemented a prediction system using Artificial Neural Network (ANN) method to forecast drug requirements. Training data comprised drug usage from January 2020 to July 2023, while testing data covered drug usage from August to December 2023. Through several experiments, the best model identified was 12-6-1, with a Mean Absolute Percentage Error (MAPE) of 6.817 and an accuracy of 93.18%. Predictions for Paracetamol drug usage in August were 4603, whereas the actual usage was 4785. This system is expected to enhance drug inventory management efficiency, reduce costs, and improve drug availability for patients.
Enhancing Students’ Participation Through Question and Answer on SMAN 2 Sungai Kakap Kubu Raya

Authors:
raw_author_name: Clarry Sada
raw_author_name: Irfan Agung Wiranto

Abstract: &lt;span lang="EN-US"&gt;The aim of this research is to enhance students’ participation by using question and answer technique. Therefore, the question of the research is how does Question and Answer enhance students’ participation on the 11th grade students of SMAN 2 Sungai KakapKubu Raya in academic year 2021/2022?”. The research was conducted in classroom action research design. The research was conducted in two cycles. On the first cycle, there were only some students who actively participated in the learning activity. On the second cycle, most of the students were very active. The problem about lack of participation was solved. Therefore, it is concluded that question and answer technique is useful in improving students’ participation.&lt;/span&gt;
Tree HR Model of Human Resource Development in Higher Education

Authors:
raw_author_name: Syaiful .Syaiful

Abstract: &lt;p&gt;&lt;span&gt;Research related to human resource development models in the world of education is very important, because the world of education is one of the determinants of the quality of HR assets. This research aims to find the HR model that best suits the development of Human Resources (HR) in higher education. The research uses qualitative methods. The type of research used is R&amp;amp;D (Research &amp;amp; Development). In the first year using the Analysis and Design approach from the ADDIE model (Analysis, Design, Development, Implementation and Evaluation). Data collection was carried out through a literature review study of 90 articles indexed by Scopus, Sinta 1, Sinta 2, and interviews with 15 experts from the academic and professional world. This research was analyzed using the SWOT Analysis and FGD approach. The results of this research found a prototype tree-based HR model and important variables that were mapped to the tree structure, namely root variables, stem variables, and fruit/leaf variables. This research is used to improve the quality of higher education human resources. This has implications for improving the quality of lecturers both in self-development and teaching in higher education, which has an impact on the quality of students and Indonesia's human resource assets&lt;/span&gt;&lt;span&gt;.&lt;/span&gt;&lt;/p&gt;
Efficient Traffic Classification Using Hybrid Deep Learning

Authors:
raw_author_name: Farnaz Sarhangian

Abstract: &lt;p&gt;Network traffic classification is important for QoS, security, and billing. Using a single machine learning classification model suffers from low prediction and classification accuracy, especially for high dimensional datasets with a high sparsity level. Recently, hybrid-deep learning algorithms have shown high efficiency for traffic forecasting and classification. In this research, we suggested two hybrid models – the Convolutional Neural Network (CNN) combined with the Recurrent Neural Network (RNN) models and the Gated recurrent unit (GRU) combined with Long ShortTerm Memory (LSTM) – to improve traffic classification accuracy in terms of the ratio of correct predictions to total input samples. The hybrid CNN-LSTM and CNN-GRU have achieved an accuracy of up to 99.23% and 93.92%, respectively, for binary classification and 67.16% for multiclass classification. We employed Markov Blanket Algorithm to make the reduced feature set selection, which significantly improved classification accuracy and fscore for multi-class scenarios.&lt;/p&gt;
Optimal path for automated pedestrian detection: image deblurring algorithm based on generative adversarial network

Authors:
raw_author_name: Xiujuan Dong

Abstract: The pedestrian detection technology of automated driving is also facing some challenges. Aiming at the problem of specific target deblurring in the image, this research built a pedestrian detection deblurring model in view of Generative adversarial network and multi-scale convolution. First, it designs an image deblurring algorithm in view of Generative adversarial network. Then, on the basis of image deblurring, a pedestrian deblurring algorithm in view of multi-scale convolution is designed to focus on deblurring the pedestrians in the image. The outcomes showcase that the peak signal to noise ratio and structural similarity index of the image deblurring algorithm in view of the Generative adversarial network are the highest, which are 29.7 dB and 0.943 dB respectively, and the operation time is the shortest, which is 0.50 s. The pedestrian deblurring algorithm in view of multi-scale convolution has the highest peak signal-to-noise ratio (PSNR) and structural similarity indicators in the HIDE test set and GoPro dataset, with 29.4 dB and 0.925 dB, 40.45 dB and 0.992 dB, respectively. The resulting restored image is the clearest and possesses the best visual effect. The enlarged part of the face can reveal more detailed information, and it is the closest to a real clear image. The deblurring effect is not limited to the size of the pedestrians in the image. In summary, the model constructed in this study has good application effects in image deblurring and pedestrian detection, and has a certain promoting effect on the development of autonomous driving technology.
Never-Ending Lonely Distancing Dance Graphs

Authors:
raw_author_name: Elizabeth M. Reid

Abstract: Lonely distancing is the idea that people can crave company at the same time they fear too much of it. Taking inspiration from this, we construct a scenario in which k players on an n × n grid jockey to occupy space next to a single companion while maintaining at least a minimum distance from everyone else. The tension behind these conflicting needs—companionship versus isolation—induces a dynamical system on the grid we model with directed graphs. Depending on n and k, players are likely to redistribute themselves into one or more equilibrium states. In very special cases, no such resolution is possible, and the players enter a cycle of perpetual redistribution in what we call a never-ending lonely distancing dance.