Reviewerly/exHarmony huggingface dataset:
Implementation of K-Means Clustering Algorithm for Grouping Traffic Violation Levels in Siak

Traffic offences often occur in different regions, ranging from mild to moderate to severe. The categories of offences include not carrying a Driver's Licence, stnk (Vehicle Number Certificate) or stck (Vehicle Trial Certificate) is invalid, not wearing a seat belt, not turning on headlights during the day and under certain conditions, disobeying traffic signs, disobeying traffic signals. Moderate offences include not having a Driver's Licence, not concentrating while driving and breaking the door of the drawbar. Serious violations include deviating from other vehicles on the road, damaging and interfering with road functions, not insuring one's own responsibility and not insuring staff and passengers. In this study, the K-Means algorithm was used with the aim of obtaining information on data groups of traffic violations based on the time of the incident so that the cause of the traffic violations that occurred in Tasikmalaya City is known. Based on the validation with Davies Bouldin Index metric, 4 clusters were identified which can group the data well. The PerformanceVector results from the assessment of the clusters resulted in 4 clusters with a value of 0.134. Cluster 1 with the most data violations amounting to 74 violations occurred at night, Cluster 2 with the most violations amounting to 16 violations occurred during the day, Cluster 3 with the most violations amounting to 6 violations occurred in the afternoon and Cluster 4 with the most violations amounting to 113 violations occurred in the morning.
On the tree models of mildly dissipative maps

This study examines the tree models of mildly dissipative diffeomorphisms on the disk $\D$. These models are one-dimensional dynamical systems with ergodic aperiodic data as well as some properties of the original dynamics. The focus of this work is their quality as dynamical invariants in both the topological and ergodic sense.
Toward Physically Realizable Quantum Neural Networks

There has been significant recent interest in quantum neural networks (QNNs), along with their applications in diverse domains. Current solutions for QNNs pose significant challenges concerning their scalability, ensuring that the postulates of quantum mechanics are satisfied and that the networks are physically realizable. The exponential state space of QNNs poses challenges for the scalability of training procedures. The no-cloning principle prohibits making multiple copies of training samples, and the measurement postulates lead to non-deterministic loss functions. Consequently, the physical realizability and efficiency of existing approaches that rely on repeated measurement of several copies of each sample for training QNNs are unclear. This paper presents a new model for QNNs that relies on band-limited Fourier expansions of transfer functions of quantum perceptrons (QPs) to design scalable training procedures. This training procedure is augmented with a randomized quantum stochastic gradient descent technique that eliminates the need for sample replication. We show that this training procedure converges to the true minima in expectation, even in the presence of non-determinism due to quantum measurement. Our solution has a number of important benefits: (i) using QPs with concentrated Fourier power spectrum, we show that the training procedure for QNNs can be made scalable; (ii) it eliminates the need for resampling, thus staying consistent with the no-cloning rule; and (iii) enhanced data efficiency for the overall training process since each data sample is processed once per epoch. We present a detailed theoretical foundation for our models and methods' scalability, accuracy, and data efficiency. We also validate the utility of our approach through a series of numerical experiments.
Multilayered Network Models for Security: Enhancing System Security Engineering with Orchestration

ABSTRACT Security engineering approaches can often focus on a particular domain—physical security, cyber security, or personnel security, for example. Yet, security systems engineering consistently faces challenges requiring socio‐technical solutions to address evolving and dynamic complexity. While some drivers of this complexity stem from complex risk environments, innovative adversaries, and disruptive technologies, other drivers are endogenous and emerge from the interactions across security engineering approaches. In response, INCOSE's Systems Security Working Group identified the need to better coordinate “disparate security solutions [that] operate independently” as one of eleven key concepts in their IS21 FuSE Security Roadmap. From this perspective, this need for “security orchestration” aligns with the perspective that security is a property that emerges from interactions within complex systems. Current efforts at Sandia National Laboratories are developing a systems security engineering approach that describes high consequence facility (HCF) security as a multidomain set of interacting layers. The result is a multilayered network (MLN)‐based approach that captures the interactions between infrastructure, physical components, digital components, and humans in nuclear security systems. This article will summarize the MLN‐based approach to HCF security and describe two preliminary results demonstrating potential benefits from incorporating interactions across disparate security solutions. Leveraging the logical structure of networks, this MLN model‐based approach provides an example of how security orchestration provides enhanced systems security engineering solutions.
INSTAGRAM FOR WRITING PRACTICE: STUDENTS’ PERCEIVED USEFULNESS AND EASE OF USE

Social media has grown rapidly and affects our everyday life. It helps many aspects in our life, including students’ life. Instagram, one of the top platforms to use in the world recently, can be brought to the classroom for student’s learning purpose. Therefore, this research aims to specifically investigate students’ perception toward its usefulness and ease for writing practice. The sample was 31 out of 66 students in English Literature Study Program in the Faculty of Languages and Literature of one state university in Makassar. The data were collected by distributing questionnaire and by interviewing half of the participants. The items in the questionnaire were adapted from Davis (1989) theory about Technology Acceptance Model Articles. Supporting data were taken from the result of an interview. This research found that items in both perceived ease of use and perceived usefulness majority received a positive response. Students’ knowledge of Instagram is an advantage in using it as a writing practice medium. The distraction from the learning process is the downside that frequently happened while using it. This indicates that Instagram is a useful media for writing practice.
Swarm Intelligence and Machine Learning Algorithms for Cancer Diagnosis

Machine learning is an artificial intelligence discipline that employs a variety of statistical, mathematical, and computational approaches to allow computers to “literate” through past instances and detect difficult-to-find trends in noisy, large, or complicated data sets. This capacity would’ve been beneficial to medical applications, particularly those which rely on advanced proteomic and genomic measures. As a result, machine learning has been widely utilised in the prediction and characterization of cancer. Furthermore, machine learning has also been used to identify and predict cancer. This technique is particularly fascinating even though it is part of a growing trend toward personalised, anticipatory treatment. During writing the above chapter, experts conducted a thorough examination of the many varieties of machine learning techniques that are employed, the sorts of information which are incorporated, and the effectiveness of these algorithms in prediction, prognosis, and diagnosis. There is a growing reliance on microarray data as well as nutrient biochemical markers, a sturdy prejudice forward into prostate and breast cancer application domains as well as a heavy reliance on “older” advanced technologies like convolutional neural networks (CNNs) as well as more recently developed and perhaps more easy and understandable machine learning based.
Metaphors in Jason Mraz's Song Lyrics (An Ecolinguistic Perspective)

This research was intended to analyze Metaphor in Jason Mraz’s song lyrics based on an Ecolinguistic perspective by Kovecess theory. The objective of this research was “To describe how metaphors delineation realized in Jason Mraz’s song lyrics based on the common source domain and the common target domain”. This research used a descriptive qualitative method. The researcher used the metaphor theory by Kovecess to analyze the data. The source of the data in this research was taken from Jason Mraz’s song lyrics such as The World as I See it, Back to the Earth, and Wise Woman. The data was collected through downloading, reading, classifying, coding, describing, and interpreting the data to get a complete conclusion of the Metaphor in Jason Mraz’s song lyrics based on an Ecolinguistic perspective. The researcher analyzed lyrics that show metaphor based on the common source domain and the common target domain in the lyrics. The result showed that the metaphor delineations were revealed from the analysis process by using the mapping common source domain and common target domain by Kovecess. The researcher found 19 Metaphors include the common source domain and the target domain. It showed the condition of the world that has a remarkable place, the beauty of ecology, our role as a part of nature. The three songs also convey an implicit message about how protecting and loving the environment create harmony and live peacefully because humans and nature have an interrelation.&#x0D; Keywords: metaphor, ecolinguistics, kovecses, sog lyrics, source domain and target domain
Exact electronic states with shallow quantum circuits through global optimisation

Quantum computers promise to revolutionise electronic simulations by overcoming the exponential scaling of many-electron problems. While electronic wave functions can be represented using a product of fermionic unitary operators, shallow quantum circuits for exact states have not yet been achieved. We construct universal wave functions from gate-efficient, symmetry-preserving fermionic operators by introducing an algorithm that globally optimises the wave function in the discrete ansatz design and the continuous parameter spaces. Our approach maximises the accuracy that can be obtained with near-term quantum circuits. Highly accurate numerical simulations on strongly correlated molecules, including water and molecular nitrogen, and the condensed-matter Hubbard model, demonstrate that our algorithm reliably advances the state-of-the-art, defining a new paradigm for quantum simulations featuring strong electron correlation.
Penerapan Metode Snowball Throwing Berbantuan Media Audio Visual dalam Meningkatkan Hasil Belajar IPS Siswa Kelas IV

The purpose of this study was to determine the improvement of students social science knowledge competence through conventional learning methods with the Snowball Throwing learning method assisted by audio-visual media in fourth grade students of SDN Banjarpanji. This research is included in classroom action research (CAR) with two cycles. The subjects of this study were fourth grade students at SDN Banjarpanji. The number of fourth grade students at SDN Banjarpanji is 13 students. Data collection techniques used in this study were observation, interviews, documentation, and tests. The results obtained from the pre-cycle activities showed that the percentage of classical learning completeness was 30.7%. Cycle I with the percentage of classical learning completeness 38.4%, while in the second cycle the percentage of classical learning completeness is 100%. To compare two paired samples, the researcher used Paired Sample T-Test as data analysis. It can be seen that there is a significant difference between the two learning methods of 0.237. Thus, it can be concluded that the Snowball Throwing learning method assisted by audio-visual media can improve social studies learning outcomes for fourth grade students at SDN Banjarpanji.
AUDITEM: Toward an Automated and Efficient Data Integrity Verification Model Using Blockchain

Data tampering is often considered a severe problem in industrial applications as it can lead to inaccurate financial reports or even a corporate security crisis. A correct representation of data is essential for companies' core business processes and is demanded by investors and customers. Traditional data audits are performed through third-party auditing services; however, these services are expensive and can be untrustworthy in some cases. Blockchain and smart contracts provide a decentralized mechanism to achieve secure and trustworthy data integrity verification; however, existing solutions present challenges in terms of scalability, privacy protection, and compliance with data regulations. In this paper, we propose the AUtomated and Decentralized InTegrity vErification Model (AUDITEM) to assist business stakeholders in verifying data integrity in a trustworthy and automated manner. To address the challenges in existing integrity verification processes, our model uses carefully designed smart contracts and a distributed file system to store integrity verification attributes and uses blockchain to enhance the authenticity of data certificates. A sub-module called Data Integrity Verification Tool (DIVT) is also developed to support easy-to-use interfaces and customizable verification operations. This paper presents a detailed implementation and designs experiments to verify the proposed model. The experimental and analytical results demonstrate that our model is feasible and efficient to meet various business requirements for data integrity verification.
Pengembangan Media Pop Up Book Berbasis Permainan Pada Pembelajaran Tematik Di Sekolah Dasar

This research aims to describe the stages of developing pop up book media, to determine the feasibility of pop up book media, and to determine student responses to pop up book media. The type of research used is R&amp;D with research and development procedures from Borg and Gall which has been modified by Sukmadinata with development steps, namely; (1) Conducting preliminary studies, (2) Product planning, (3) Development of product drafts, (4) Individual trials (5) Revising the results of field trials, (6) Limited trials, (7) Completion of test products limited, (8) Extensive implementation test, (9) Final product improvement. The results of this study indicate that the material experts assessment got an average score of 88% in the "valid" category, the media experts assessment got an average score of 90.83% in the "valid" category, the linguists assessment got an average score of 87.49% in the "valid" category. valid", and the assessment of student responses at SDN Tegal got an average score of 97.20% in the "Very Good" category.
Epistemic Modality in English-Medium Artificial Intelligence Research Articles: A Systemic–Functional Perspective

Epistemic modality is a crucial tool for describing the speaker's judgment—or request for the judgment of the listener—that has gained broad recognition and been widely investigated in academic writing. This research investigates the use of epistemic modality in 50 English-medium artificial intelligence (AI) research articles (RAs) from a systemic–functional perspective. Our research focuses on the frequency and function of the value, orientation, and polarity of epistemic modality in AI RAs. The results reveal that these AI RA writers tend to use both high- and low-value modality with explicitly objective orientation and positive polarity and that the epistemic modality performs the interpersonal function. This indicates that AI RA writers tend to ensure the objectivity of their results and that these writers adopt varying attitudes and hold differing views toward AI technology and thus have not yet achieved consensus. The study furthers the understanding of scholars’ attitudes toward AI statements in recent years.
The Theory of Lateral Thinking and the Development of Informational Scripts

the aim of our research is to study dominant areas of theoretical and em-pirical researches in the paradigm of Creative Psychology, to describe the theory of lateral thinking and the development of informational scripts within it. methods of the research. The following theoretical methods of the research were used to solve the tasks formulated in the article: the categorical method, structural and functional methods, the methods of the analysis, systematization, modeling, generalization.the results of the research. It is showed that the main ways to change and develop information scripts in the paradigm of the theory of lateral thinking are: free, unmotivated regrouping of elements; gaining of information; expansion of the information field, inclusion of random “extra” information in the already known individual of the frame; an abrupt way of developing a certain idea. The principles of lateral thinking are: postponement of the assessment; the accident; the information which jumps in any direction; doubts; future; the opposite of absolute confidence.conclusions. So, distinguishing between vertical and lateral thinking, we emphasize two cardinal, opposite and interrelated areas of the creative process: the association and dissociation of information elements, the creation of stereo-types and their destruction. It is also substantiated the possibility of creating a system of technologies and methods of creative transformation. It is proved that all these were of fundamental importance for the prospects of developing psy-cho-correctional and developmental programs in the Psychology of Creativity. Some current issues of Creative Psychology (CREATIVE PROCESS – RESULT – PER-SONALITY OF THE CREATOR) are identified, the scientific context of personality is determined, the presupposition and dominant guidelines for further search and implementation of our own empirical researches are showed.
Studying the impact of magnitude pruning on contrastive learning methods

We study the impact of different pruning techniques on the representation learned by deep neural networks trained with contrastive loss functions. Our work finds that at high sparsity levels, contrastive learning results in a higher number of misclassified examples relative to models trained with traditional cross-entropy loss. To understand this pronounced difference, we use metrics such as the number of PIEs (Hooker et al., 2019), Q-Score (Kalibhat et al., 2022), and PD-Score (Baldock et al., 2021) to measure the impact of pruning on the learned representation quality. Our analysis suggests the schedule of the pruning method implementation matters. We find that the negative impact of sparsity on the quality of the learned representation is the highest when pruning is introduced early on in the training phase.
Learning Network Architecture for Open-Set Recognition

Given the incomplete knowledge of classes that exist in the world, Open-set Recognition (OSR) enables networks to identify and reject the unseen classes after training. This problem of breaking the common closed-set assumption is far from being solved. Recent studies focus on designing new losses, neural network encoding structures, and calibration methods to optimize a feature space for OSR relevant tasks. In this work, we make the first attempt to tackle OSR by searching the architecture of a Neural Network (NN) under the open-set assumption. In contrast to the prior arts, we develop a mechanism to both search the architecture of the network and train a network suitable for tackling OSR. Inspired by the compact abating probability (CAP) model, which is theoretically proven to reduce the open space risk, we regularize the searching space by VAE contrastive learning. To discover a more robust structure for OSR, we propose Pseudo Auxiliary Searching (PAS), in which we split a pretended set of know-unknown classes from the original training set in the searching phase, hence enabling the super-net to explore an effective architecture that can handle unseen classes in advance. We demonstrate the benefits of this learning pipeline on 5 OSR datasets, including MNIST, SVHN, CIFAR10, CIFARAdd10, and CIFARAdd50, where our approach outperforms prior state-of-the-art networks designed by humans. To spark research in this field, our code is available at https://github.com/zxl101/NAS OSR.
Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We implement five state-of-the-art methodologies for explaining black-box machine learning models (LIME, PermutationSHAP, KernelSHAP, DiCE, CEM) and examine the strengths of each approach on the downstream task of student performance prediction for five massive open online courses. Our experiments demonstrate that the families of explainers do not agree with each other on feature importance for the same Bidirectional LSTM models with the same representative set of students. We use Principal Component Analysis, Jensen-Shannon distance, and Spearman's rank-order correlation to quantitatively cross-examine explanations across methods and courses. Furthermore, we validate explainer performance across curriculum-based prerequisite relationships. Our results come to the concerning conclusion that the choice of explainer is an important decision and is in fact paramount to the interpretation of the predictive results, even more so than the course the model is trained on. Source code and models are released at http://github.com/epfl-ml4ed/evaluating-explainers.
The Use of Pictionary Game in Teaching Vocabulary to Second Grade Students of SMPN 1 Raya

Pictionary game is a fun and enjoyable guessing word game that can motivate students to learn English. This research deals with improving students’ vocabulary by using the Pictionary game for the second-grade students in SMP N 1 Raya. This research aims to determine whether the Pictionary game can increase students’ vocabulary. This study applied Classroom Action Research (CAR). The sample of the research was the second-grade students of SMP N 1 Raya in academic 2021/2022. The writer used two cycles that occur of two meetings. The finding of this study was that the Pictionary game can improve students’ vocabulary. It can be proved from the calculation of the test in both cycles. In the pre-test, the mean of the students’ score was 62,18. In post-test cycle I, the mean of vocabulary score was 72,84 and the mean score in post-test cycle II was 77,09. Based on the data, it could be concluded that there was a significant improvement in students’ vocabulary by using the Pictionary game. Based on the research, it is suggested that English teachers should use the Pictionary game as a technique in teaching vocabulary.