Reviewerly/exHarmony huggingface dataset:
KAHOOT! AND USERS’ VIEWS OF ISLAMIC JUNIOR HIGH SCHOOL IN YOGYAKARTA

The student motivation can be created from a novel learning experience such as implementing a modern game application to access the competence. This study identified students’ teaching and learning process in term of assessment emphasizing on a gamification approach construction surrounding to a student response category, called Kahoot! application games. The main objective of this analysis was to discover the influence of Kahoot! on motivation, engagement, studying outcome, and attitudes concerns to ICT integration in future learning experiences. Quantitatively, the study conducted to 28 students belonging to the 9th grade student of an Islamic Junior High School in Yogyakarta, Indonesia. The data collected by using a likert scale questionnaire survey whose validity was tested with r-table score 0,05. We used Kahoot! Games as an application tool in this research. This study has shown that a significant proportion of students are generally positive towards gamification approach used in educational activities, in general, and especially Kahoot! application games. Kahoot! contributed to more student engagement and enthusiasm, better learning performance, and more desire and willingness to try Kahoot game application in the world of learning experiences.This study recommended to all educators in every level of education to elevate the way of teaching to the new era of technology so the student was more engage to learn in teaching learning activities. This study also recommended to all students in Junior High School, Senior High school and University level. They can learn something more enthusiastic by this game.The originality of this study is that this study focus on Junior High School students in Indonesia, whereas the way in teaching and learning in Indonesia is definitely different with the other abroad students such curriculum and even the teacher way in teaching and the students’ learning. Moreover, this study also focus on how the student views’ in applying this game.
DATA CLUSTERING ON TEACHING AND LEARNING EVALUATION APPLICATION OVER PANDEMIC ERA

SIEPEL is an evaluation application for teaching and learning process in University of Bengkulu. It is mandatory for every student to fill the questionnaire before they can see the marking value for each subject each semester. This survey was designed to meet the requirements and expectations of students as educational service for customers. This data is very important to improve the quality of teaching and learning process for further policy and decision maker. However, the analysis of the data remains an open question as the size and the distribution of the data is become some issues to process the analysis. Here, we showed the new approach to analysis the data using K-Means Clustering to see the better distribution and understanding over the evaluation data. This paper used elbow method to find the best number of clusters to be implemented on the algorithm approach which results in four clusters of satisfaction values (unsatisfied, less satisfied, satisfied, and very satisfied). The result of this analysis was published based on website system to show the visualization of analysis. Furthermore, this research showed that the average value of evaluation result for 4 semester was very satisfied 6.50%; satisfied 43.89%; less satisfied 44.26%; and not satisfied 5.36%. The value of vary satisfied students was dropped from 20.47% to 0.12% by 2 years and the value for less satisfied was increased from 27.64% to 66.32%. This term was happened because of the pandemic era and the change on the process of learning and teaching on University of Bengkulu.
IMPLEMENTATION OF THE NAIVE BAYES CLASSIFIER ALGORITHM FOR CLASSIFICATION OF COMMUNITY SENTIMENT ABOUT DEPRESSION ON YOUTUBE

Depression is a disease that knows no age, gender and social status. WHO states that more than 264 million people suffer from depression, people with depression will continue to grow if public knowledge about mental health is still low, especially in Indonesia. This can be known from the way the community responds to a case. This study aims to determine public sentiment towards people with depression by classifying comments using the Niave Bayes Classifier (NBC) algorithm and adding the Term Frequency-inverse Document Frequency (TF-IDF) method as a feature extraction method. Sentiment used as data is obtained from YouTube comments on several news media accounts such as tvOneNews, Kompas TV, Tribunnews, Official iNews, VIVACOID, CNN Indonesia and Tribun Jateng, so that 4783 data are obtained with training data of 3826 and 957 testing data. This sentiment was analyzed by giving three classes, namely positive, neutral and negative. The results of the sentiment analysis were dominated by positive sentiment of 93.31%, followed by negative comments of 6.68% while neutral sentiment was 0%, and the accuracy of the NBC Algorithm was 84.11%.
Simple and Rigorous Proof Method for the Security of Practical Quantum Key Distribution in the Single-Qubit Regime Using Mismatched Basis Measurements

Quantum key distribution (QKD) protocols aim at allowing two parties to generate a secret shared key. While many QKD protocols have been proven unconditionally secure in theory, practical security analyses of experimental QKD implementations typically do not take into account all possible loopholes, and practical devices are still not fully characterized for obtaining tight and realistic key rates. We present a simple method of computing secure key rates for any practical implementation of discrete-variable QKD (which can also apply to measurement-device-independent QKD), initially in the single-qubit lossless regime, and we rigorously prove its unconditional security against any possible attack. We hope our method becomes one of the standard tools used for analysing, benchmarking, and standardizing all practical realizations of QKD.
Learning to solve arithmetic problems with a virtual abacus

Acquiring mathematical skills is considered a key challenge for modern Artificial Intelligence systems. Inspired by the way humans discover numerical knowledge, here we introduce a deep reinforcement learning framework that allows to simulate how cognitive agents could gradually learn to solve arithmetic problems by interacting with a virtual abacus. The proposed model successfully learn to perform multi-digit additions and subtractions, achieving an error rate below 1% even when operands are much longer than those observed during training. We also compare the performance of learning agents receiving a different amount of explicit supervision, and we analyze the most common error patterns to better understand the limitations and biases resulting from our design choices.
Solving various NP-Hard problems using exponentially fewer qubits on a Quantum Computer

NP-hard problems are not believed to be exactly solvable through general polynomial time algorithms. Hybrid quantum-classical algorithms to address such combinatorial problems have been of great interest in the past few years. Such algorithms are heuristic in nature and aim to obtain an approximate solution. Significant improvements in computational time and/or the ability to treat large problems are some of the principal promises of quantum computing in this regard. The hardware, however, is still in its infancy and the current Noisy Intermediate Scale Quantum (NISQ) computers are not able to optimize industrially relevant problems. Moreover, the storage of qubits and introduction of entanglement require extreme physical conditions. An issue with quantum optimization algorithms such as QAOA is that they scale linearly with problem size. In this paper, we build upon a proprietary methodology which scales logarithmically with problem size - opening an avenue for treating optimization problems of unprecedented scale on gate-based quantum computers. In order to test the performance of the algorithm, we first find a way to apply it to a handful of NP-hard problems: Maximum Cut, Minimum Partition, Maximum Clique, Maximum Weighted Independent Set. Subsequently, these algorithms are tested on a quantum simulator with graph sizes of over a hundred nodes and on a real quantum computer up to graph sizes of 256. To our knowledge, these constitute the largest realistic combinatorial optimization problems ever run on a NISQ device, overcoming previous problem sizes by almost tenfold.
Opti-CAM: Optimizing saliency maps for interpretability

Methods based on class activation maps (CAM) provide a simple mechanism to interpret predictions of convolutional neural networks by using linear combinations of feature maps as saliency maps. By contrast, masking-based methods optimize a saliency map directly in the image space or learn it by training another network on additional data. In this work we introduce Opti-CAM, combining ideas from CAM-based and masking-based approaches. Our saliency map is a linear combination of feature maps, where weights are optimized per image such that the logit of the masked image for a given class is maximized. We also fix a fundamental flaw in two of the most common evaluation metrics of attribution methods. On several datasets, Opti-CAM largely outperforms other CAM-based approaches according to the most relevant classification metrics. We provide empirical evidence supporting that localization and classifier interpretability are not necessarily aligned.
Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection

Accurate bot detection is necessary for the safety and integrity of online platforms. It is also crucial for research on the influence of bots in elections, the spread of misinformation, and financial market manipulation. Platforms deploy infrastructure to flag or remove automated accounts, but their tools and data are not publicly available. Thus, the public must rely on third-party bot detection. These tools employ machine learning and often achieve near perfect performance for classification on existing datasets, suggesting bot detection is accurate, reliable and fit for use in downstream applications. We provide evidence that this is not the case and show that high performance is attributable to limitations in dataset collection and labeling rather than sophistication of the tools. Specifically, we show that simple decision rules -- shallow decision trees trained on a small number of features -- achieve near-state-of-the-art performance on most available datasets and that bot detection datasets, even when combined together, do not generalize well to out-of-sample datasets. Our findings reveal that predictions are highly dependent on each dataset's collection and labeling procedures rather than fundamental differences between bots and humans. These results have important implications for both transparency in sampling and labeling procedures and potential biases in research using existing bot detection tools for pre-processing.
Evaluation of Requirement Engineering Best Practices for Secure Software Development in GSD: An ISM Analysis

Technological advancement makes the world a global village. The immense use of software systems has modernized human society in every aspect. Thus, the security parameter is an important element that needs to be considered while developing software systems. Considering the significance of software security, it is important to consider the security practices from the early phase of the software development life cycle (SDLC), i.e., requirements engineering (RE). Hence, this study aims to identify and categorize RE practices important to apply for secure software development (SSD) in a geographically distributed development environment. To study the RE practices concerning SSD, we conducted a questionnaire survey with industrial experts in the global software development (GSD) context. Furthermore, the interpretive structure modeling (ISM) approach was applied to evaluate the relationship between the RE security practice core categories. This paper identifies 70 practices and classifies them into 11 fundamental dimensions (categories) to assist GSD organizations in specifying the requirements for SSD. The ISM results show the “Awareness of Secure Requirement Engineering (SRE)” category has the most decisive influence on the other ten core categories of the identified RE security practices. With the help of empirical evidence and the ISM approach, this work attempts to identify potential security practices and to give a set of secure RE practices that can be used to improve the security of the software development process.
Comparison of Full Factorial and Optimal Experimental Design for Perceptual Evaluation of Audiovisual Quality

Perceptual evaluation of immersive audiovisual quality is often very labor-intensive and costly because numerous factors and factor levels are included in the experimental design.Therefore, the present study aims to reduce the required experimental effort by investigating the effectiveness of optimal experimental design (OED) compared to classical full factorial design (FFD) in the study using compressed omnidirectional video and ambisonic audio as examples.An FFD experiment was conducted and the results were used to simulate 12 OEDs consisting of D-optimal and I-optimal designs varying with replication and additional data points.The fraction of design space plot and the effect test based on the ordinary least-squares model were evaluated, and four OEDs were selected for a series of laboratory experiments.After demonstrating an insignificant difference between the simulation and experimental data, this study also showed that the differences in model performance between the experimental OEDs and FFD were insignificant, except for some interacting factors in the effect test.Finally, the performance of the I-optimal design with replicated points was shown to outperform that of the other designs.The results presented in this study open new possibilities for assessing perceptual quality in a much more efficient way.
Spatial Variation for Iron and Manganese Forms by Using Time Series Analysis Under Different Environments

Six regions in Anbar governorate were selected to study spatial variation of iron and manganese forms using time series analysis under different environments. Al-Baghdadi, Heet, Abu Teban, Ramadi, Khalidiya and Falluja were selected. two pedons was selected under two environments: desert and alluvial soil, toward vertical in to Euphrates River. Total, available, Crystalline free and Amorphous free, for each iron and manganese was measurement as well as Crystalline form for two elements. statistical analysis by using time series was calculated. The results of the research showed the following: Differentiation of models that describe forms of iron and manganese according for different sedimentation environment as well as heterogeneity of spatial reliability and sampling methods. The method of Semi Variance showed its preference in obtaining the samples and describing the variation of iron and manganese forms compared&#x0D; &#x0D;
Teachers’ Efficacy With the Use of Technology in Teaching English

This study aimed to examine the common technology being used by the participants, their perceived efficacy level in using technology, and the challenges they face in using those. The research utilized a descriptive method through purposive sampling technique to select the 30 teachers from different private schools in San Pablo City during the Academic Year 2020-2021. A researcher-made questionnaire was used in gathering data. The respondents identified smartphone as hardware device they commonly used while the software apps commonly used were zoom as videoconferencing software app, web browsers as reference software app, and social networking sites as forum software apps. On the other hand, the respondents perceived that they have high level of efficacy with the use of hardware devices. They also have high level of efficacy in using each type of software apps (videoconferencing software, reference software, and forum software). In addition, the result showed four major challenges they faced in using technology; keeping up with changes, fixing troubleshoots, slow internet connection, and lack of familiarity. The results lead to the development of a guidebook, titled “Hi Teach” which may be put into evaluation and validation process to determine its possible strength to be utilized in different classroom purposes.
Vector Space Morphology with Linear Discriminative Learning

This chapter presents three case studies of modeling aspects of lexical processing with Linear Discriminative Learning (LDL), the computational engine of the Discriminative Lexicon model. With numeric representations of word forms and meanings, LDL learns to map one vector space onto the other, without being informed about any morphological structure or inflectional classes. The modeling results demonstrated that LDL not only performs well for understanding and producing morphologically complex words, but that it also generates quantitative measures that are predictive for human behavioral data.
A 48-node All-to-all Connected Coupled Ring Oscillator Ising Solver Chip

Abstract This paper presents a physics-based Ising solver chip fabricated in a standard 1.2V, 65 nanometer complementary metal oxide semiconductor (CMOS) technology featuring an all-to-all architecture with 48 spins and a highly-uniform coupling circuit with integer weights ranging from -14 to +14. Existing locally-connected architectures suffer from size and digital computation overheads while embedding an arbitrary graph. In contrast, our all-to-all connected design allows any graph with up to 48 nodes to be directly mapped to the hardware. The key idea of the all-to-all architecture is to strongly couple a horizontal oscillator with a vertical oscillator so that each horizontal-vertical oscillator pair intersects with all other pairs in the cross-bar style array. The multi-bit coupling circuit was realized by cascading 1-bit coupler stages comprising a pair of inverters and two transmission gates. Statistical measurements were carried out on the fabricated Ising solver chips for different problem sizes, graph densities, operating temperatures, problem instances, and solution samples. The measured oscillation period, power consumption, and relaxation time for different problems are also reported. Finally, we show that dynamically merged spins can achieve higher weight resolution without hardware changes.
Implicit State and Goals in QBF Encodings for Positional Games (extended version)

We address two bottlenecks for concise QBF encodings of maker-breaker positional games, like Hex and Tic-Tac-Toe. Our baseline is a QBF encoding with explicit variables for board positions and an explicit representation of winning configurations. The first improvement is inspired by lifted planning and avoids variables for explicit board positions, introducing a universal quantifier representing a symbolic board state. The second improvement represents the winning configurations implicitly, exploiting their structure. The paper evaluates the size of several encodings, depending on board size and game depth. It also reports the performance of QBF solvers on these encodings. We evaluate the techniques on Hex instances and also apply them to Harary's Tic-Tac-Toe. In particular, we study scalability to 19$\times$19 boards, played in human Hex tournaments.
Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images

Multiple instance learning (MIL) models have achieved remarkable success in analyzing whole slide images (WSIs) for disease classification problems. However, with regard to gigapixel WSI classification problems, current MIL models are often incapable of differentiating a WSI with extremely small tumor lesions. This minute tumor-to-normal area ratio in a MIL bag inhibits the attention mechanism from properly weighting the areas corresponding to minor tumor lesions. To overcome this challenge, we propose salient instance inference MIL (SiiMIL), a weakly-supervised MIL model for WSI classification. Our method initially learns representations of normal WSIs, and it then compares the normal WSIs representations with all the input patches to infer the salient instances of the input WSI. Finally, it employs attention-based MIL to perform the slide-level classification based on the selected patches of the WSI. Our experiments imply that SiiMIL can accurately identify tumor instances, which could only take up less than 1% of a WSI, so that the ratio of tumor to normal instances within a bag can increase by two to four times. It is worth mentioning that it performs equally well for large tumor lesions. As a result, SiiMIL achieves a significant improvement in performance over the state-of-the-art MIL methods.
S-Boxes Design Based On the Lu-Chen System And Their Application in Image Encryption

Abstract The substitution box (S-Box) plays a fundamental role in cryptographic algorithms. In this article, the Lu-Chen system is used to design a chaotic S-Box. We design two S-Boxes, one based on the rotation algorithm relative to the rows (or columns) and the other based on the Zigzag transformation. The performance of the new S-Boxes is evaluated with the bijective, nonlinearity, strict avalanche criterion (SAC), output bit independence criterion (BIC), differential approximation probability, linear approximation probability, algebraic degree and not having a fixed point and opposite fixed point. The analysis results show that the proposed S-Boxes have suitable cryptographic properties. In addition, an image encryption algorithm based on two generated S-Boxed, and a generalized Lai-Massey structure is presented. Experimental results show that the proposed method has achieved acceptable security.
Attention-based deep learning model for Arabic handwritten text recognition

This work proposes a segmentation-free approach to Arabic Handwritten Text Recognition (AHTR): an attention-based Convolutional Neural Network - Recurrent Neural Network - Connectionist Temporal Classification (CNN-RNN-CTC) deep learning architecture. The model receives as input an image and provides, through a CNN, a sequence of essential features, which are transferred to an Attention-based Bidirectional Long Short-Term Memory Network (BLSTM). The BLSTM gives features sequence in order, and the attention mechanism allows the selection of relevant information from the features sequences. The selected information is then fed to the CTC, enabling the loss calculation and the transcription prediction. The contribution lies in extending the CNN by dropout layers, batch normalization, and dropout regularization parameters to prevent over-fitting. The output of the RNN block is passed through an attention mechanism to utilize the most relevant parts of the input sequence in a flexible manner. This solution enhances previous methods by improving the CNN speed and performance and controlling over model over-fitting. The proposed system achieves the best accuracy of 97.1% for the IFN-ENIT Arabic script database, which competes with the current state-of-the-art. It was also tested for the modern English handwriting of the IAM database, and the Character Error Rate of 2.9% is attained, which confirms the model's script independence.