Reviewerly/exHarmony huggingface dataset:
Can Attention Be Used to Explain EHR-Based Mortality Prediction Tasks: A Case Study on Hemorrhagic Stroke

Stroke is a significant cause of mortality and morbidity, necessitating early predictive strategies to minimize risks. Traditional methods for evaluating patients, such as Acute Physiology and Chronic Health Evaluation (APACHE II, IV) and Simplified Acute Physiology Score III (SAPS III), have limited accuracy and interpretability. This paper proposes a novel approach: an interpretable, attention-based transformer model for early stroke mortality prediction. This model seeks to address the limitations of previous predictive models, providing both interpretability (providing clear, understandable explanations of the model) and fidelity (giving a truthful explanation of the model's dynamics from input to output). Furthermore, the study explores and compares fidelity and interpretability scores using Shapley values and attention-based scores to improve model explainability. The research objectives include designing an interpretable attention-based transformer model, evaluating its performance compared to existing models, and providing feature importance derived from the model.
TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-based Memory Network

Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials based on longitudinal patient electronic health records (EHRs) and eligibility criteria of trials. However, they either depend on trial-specific expert rules that cannot be generalized or perform matching more generally with a black-box model where the lack of interpretability makes the model results difficult to be adopted.
Respon Guru Terhadap Penggunaan Aplikasi Wordwall dalam Pembelajaran Bahasa Indonesia

Learning media as a liaison between teachers and students. Learning media has an important role in the learning process, especially in the delivery of material (basic competence). Seeing, the Covid-19 pandemic is not yet over, learning media must be chosen appropriately so that learning objectives can still be achieved optimally. Integrating technological advances by utilizing them in learning, especially for teachers in designing multimedia and interactive learning media is urgently needed. Therefore, this study focuses on the teacher's response to the use of the wordwall application as a support for learning Indonesian at school. The purpose of this study is to find out, analyze, and describe the teacher's response to interactive learning media assisted by wordwall applications as a support for learning Indonesian. This study uses a qualitative descriptive research procedure. The results of the research are known from the distribution of questionnaires on the analysis of teacher needs in the use of learning media, it is known from the ten questions given that the average answer is yes by 79.2% and no by 20.8% related to the use of technology-based learning media, knowledge, constraints or the obstacles encountered and the application of the wordwall application in learning Indonesian. Thus it can be concluded that the use of learning media, especially the wordwall application as a technology-based interactive media is needed by teachers in carrying out Indonesian language learning activities.
Partial Clustering Ensemble

Clustering ensemble often provides robust and stable results without accessing original features of data, and thus has been widely studied. The conventional clustering ensemble methods often take the full multiple base partitions as inputs and provide a consensus clustering result. However, in many real-world applications, full base partitions are hard to obtain because some data may be missing in some base partitions. To tackle this problem, in this paper, we propose a novel partial clustering ensemble method, which takes the partial multiple base partitions as inputs. In this method, we simultaneously fill the missing values in the base partitions and ensemble them by fully considering the consensus and diversity. Moreover, to address the unreliability issue in the partial data scenario, we seamlessly plug it into a self-paced learning framework. The extensive experiments on benchmark data sets demonstrate the effectiveness and efficiency of the proposed method when handling incomplete data.
An Anchor-Free Lightweight Object Detection Network

Existing anchor-free object detection methods have achieved some amazing results, but these methods are relatively complex and the inference speed is also slow. In this paper, an anchor-free lightweight object detection network is proposed. The proposed method effectively overcomes the limitation of detection model by anchor-free mechanism, and the lightweight backbone network reduces the computational cost. In addition, the proposed small object enhancement module can enhance the focus on small objects, which improves the detection capability of small objects. Besides, a label assignment strategy is proposed to determine the prominent feature, and a center correction mechanism is introduced to make the predicted bounding box closer to the ground truth to further improve the detection accuracy. Extensive experiments are conducted on MS COCO and Pascal VOC datasets, and the results demonstrate that the proposed method achieves better results than the existing detection methods on detection accuracy by increasing 0.5% on the MS COCO dataset, and has a detection accuracy increase of 0.4% on the Pascal VOC dataset, which proves the superiority of the proposed method.
Fuzzy SHmath.Mbio-transform generalization and application to skin cancer imaging (distributed diseases)

Fuzzy integral transformations are an important methodology for solving the relevant fuzzy boundary (initial) value issues that arise from solving fuzzy differential and integral equations. In this study, the way we classify the-for the corresponding-order fuzzy derivative, fuzzy Shmath.mbio.M-transform. Studies of this process are conducted on the assumption of substantial generalized Hukuhara differentiability. When comparing the suggested transform to another transform, the fuzzy Laplace transform, the effectiveness of the proposed transform in extracting melanoma edges was shown to be much higher. The accuracy and efficiency of the proposed transformation may be enhanced even more. statistical parameters were used to corroborate the findings.
The fiducial-Bayes fusion: A general theory of statistical inference

An overview is presented of a general theory of statistical inference that is referred to as the fiducial-Bayes fusion. This theory combines organic fiducial inference and Bayesian inference. The aim is that the reader is given a clear summary of the conceptual framework of the fiducial-Bayes fusion as well as pointers to further reading about its more technical aspects. Particular attention is paid to the issue of how much importance should be attached to the role of Bayesian inference within this framework. The appendix contains a substantive example of the application of the theory of the fiducial-Bayes fusion, which supplements various other examples of the application of this theory that are referenced in the paper.
Enter the Paraverse: Challenging Assumptions of Live Music in the Metaverse

The Metaverse, a concept and buzzword echoing throughout industry and academic spaces, does not exist. In this paper we theorize about the object that does exist: the Paraverse, the global set of disconnected virtual worlds and virtual world platforms working independently to advance virtual world technology and culture. We present a novel system within the Paraverse named Soundscape.social: a multi-user virtual world where participants explore concert spaces containing pre-recorded, synchronous performances by independent musicians during the early stages of the COVID-19 pandemic. Through its design and implementation, we demonstrate how small teams of independent developers may quickly contribute to the fringes of the Paraverse, and thus play an essential role in driving innovation. We assessed the platform’s ability to foster communal listening experience through virtual ethnography, in-game data collection, and interviews with attendees and performers. Soundscape.social was successful at preventing abusive behaviour despite anonymous communication, respecting participant privacy through minimal in-game data collection, modelling anarchic community through a performance format that challenged hierarchical structures of listening, and fostering a novel understanding of liveness through listener autonomy and a novel asynchronous communication feature of interest we name "virtual graffiti." We further discuss opportunities for future work developing technologies that aid developers looking to create independent Paraverse applications, contributing to the future of virtual worlds and, perhaps, the Metaverse.
Cognitive modeling for understanding interactions between people and decision support tools in complex and uncertain environments: A study protocol

Recent advances in Computational Intelligence Tools and the escalating need for decision-making in the face of complex and uncertain phenomena like pandemics, climate change, and geopolitics necessitate understanding the interaction between these tools and human behavior. It is crucial to efficiently utilize the decision-makers cognitive resources in addressing specific problems.The main goal of this present protocol is to describe the effect that CITs (Computational Intelligence Tools) have on decisions made during complex and uncertain situations. It is an exploratory study with a mixed methodology. Solomon's group experiment design includes a narrative analysis of cognitive features such as integrative complexity (IC), cognitive flexibility (CF), and fluid intelligence (FI). Additionally, measures of neural activity (NA), physiological measures (PM), and eye-tracking data (ET) will be collected during the experimental session to examine the marginal impact of these processes on decision outcomes (DO) and their relation to CIT capabilities. To achieve this objective, 120 undergraduate and graduate students involved in decision-making will participate as subjects. The approximate duration of the study will be 2 years. Strict adherence to the relevant ethical considerations will be maintained during the performance of the experimental tasks.The study will provide valuable information on CITs' effect on decision-making under complex and uncertain contexts. This will help to better understand the link between technology and human behavior, which has important implications. CIT designers can use future results and at the same time, it will be possible to understand cognitive, behavioral, physiological processes, and even the subjective assessment of individuals when they use technological tools to solve a problem.
Social Engineering as a Driving Force for Innovation in Cybersecurity

&lt;p&gt;Cybersecurity measures have long been manipulated, bypassed, and broken. This malpractice points to a facet of human error and innocence, one that is exploited through social engineering. This systematic review highlights the complex and intertwined relationship between the development of new social engineering techniques and the introduction of novel cybersecurity measures. This paper summarizes the history of social engineering, the implications of social engineering, and the use of social engineering to identify preventative solutions to breaches in cybersecurity. This review utilizes common practices, theoretical thinking, and case studies to provide a complete picture of the aforementioned topics. More research must be done and more discussion should take place to determine the future of social engineering’s role in the field of cybersecurity. &lt;/p&gt;
Development of an Image Encryption Algorithm using Latin Square Matrix and Logistics Map

The goal of this study was to develop a robust image cryptographic scheme based on Latin Square Matrix and Logistics Map, capable of effectively securing sensitive data. Logistics mapping is a comparatively strong chaos system which enciphers with an unpredictability that significantly reduces the chance of deciphering. Additionally, the Latin square matrix stands out for its uniform histogram distribution, thereby bolstering its encryption's potency. The consequent integration of these algorithms in this study was therefore grounded in the scientific rationale of establishing a strong and resilient cypher technique. The study provides a new chaos-based method and extends the application of the probabilistic approach to the domain of symmetric key image encryption. Permutation and substitution approaches of image encryption were deployed to address the issue of images volume and differing sizes. The issue of misplaced pixel positions in the image was also adequately addressed, making it an effective method for image encryption. The hybrid technique was simulated on image data and evaluated to gauge its performance. Results showed that the algorithm was able to securely protect image data and the private information associated with them, while also making it very difficult for unauthorized users to decrypt the information. The average encryption time of 184(μs) on seven (7) images showed that it could to be deployed for real-time systems. The proposed method obtained an average entropy of 7.9398 with key space of 1.17x1077 and an average avalanche effect (%) of 49.9823 confirming the security and resilience of the developed method.
Exploration of The Magnetic Rocks Potential of Mount Penanggungan: A Study of Myth, History, and Its Implications for Educators and Mountaineers

This study explores the potential of magnetic stones on Mount Penanggungan and their relation to history and myths circulating in the community and climbers. This research is important to do in order to minimize the rate of injury caused by fatigue for climbers on this mountain. In addition, aspects of physics and history can be implications for educators to deliver material on the subject. This research uses a qualitative design with the type of case study—data collection methods through observation, interviews, and document studies. The results of this study show that some rocks on Mount Penanggungan, especially when approaching their peak, have a magnetic field anomaly up to 4 times larger than the magnetic field on the earth’s surface, characterized by its darker color or cave walls. Based on the mythical and historical review, the role of magnetic stones underlies the reason ancient people performed hermitage on rocks and in Mount Penanggungan. This study implies that it can be information for climbers that rocks with a strong magnetic field magnitude can accelerate the relaxation of the body. In addition, educators can integrate this research’s findings, including physical phenomena and historical/mythical aspects on Mount Penanggungan, into learning.
Analisa Kesalahan Dalam Berbicara dan Berbahasa pada Diskusi Anak Sekolah Dasar Kelas III dengan Konsep Morfologis di Madrasah Ibtidaiyah Ali Imran Medan

This article aims to provide an understanding of the proper use of language in class III Madrasah Ibtidaiyah students at Madrasah Ibtidaiyah Ali Imran Medan, where the equivalent of most children uses the wrong language in morphological studies. The research method used is a qualitative descriptive research method by observing elementary school children. The result is that many grade III students of MI use standard language mixed with everyday language so that the pronunciation is not beautiful when applied, students are also used to using regional accents so that when using Indonesian it feels foreign, the purpose of this research is to help students be able to speak according to good and correct terms. &#x0D; Keywords : childrens, Language, Indonesian
Heralded quantum multiplexing entanglement between stationary qubits via distribution of high-dimensional optical entanglement

Quantum entanglement between pairs of remote quantum memories (QMs) is a prerequisite for realizing many applications in quantum networks. Here, we present a heralded protocol for the parallel creation of quantum entanglement among multiple pairs of QMs placed in spatially separated nodes, where each QM, encoding a stationary qubit, couples to an optical cavity and deterministically interacts with single photons. Our protocol utilizes an entangled photon pair encoded in the high-dimensional time-bin degree of freedom to simultaneously entangle multiple QM pairs, and is efficient in terms of reducing the time consumption and photon loss during transmission. Furthermore, our approach can be extended to simultaneously support spatial-temporal multiplexing, as its success is heralded by the detection of single photons. These distinguishing features make our protocol particularly useful for long-distance quantum communication and large-scale quantum networks.
Geological Paleontological Code Matching Method Based on Edit Distance Algorithm

In the field of geology and paleontology, digital code matching is an important technology. It can effectively improve work efficiency and shorten retrieval time. In this paper, the method of geological paleontological code matching is studied by using the edit distance algorithm, in order to further improve the accuracy of paleontological code matching. In this paper, the matching of geological paleontological codes is studied by using experimental methods and data comparison. The experimental data show that the improved edit distance algorithm has a higher precision in paleontological code matching, which can reach 75%, but its results are not stable enough, so the algorithm needs to be further improved.
State Component of the National Cooperative Geologic Mapping Program (DOI)

Federal Grants & ContractsVolume 47, Issue 21 p. 4-4 Grants Alerts State Component of the National Cooperative Geologic Mapping Program (DOI) First published: 04 October 2023 https://doi.org/10.1002/fgc.33280Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume47, Issue215 October 2023Pages 4-4 RelatedInformation
APPLICATION OF THE WEIGHTED PRODUCT METHOD FOR DETERMINING ASSISTANCE OF JOINT BUSINESS GROUP

Abstract: Asahan District Social Service is one of the government agencies in Asahan District. This agency is tasked with serving the community and providing the best information for the community, especially in terms of data processing. One of the government programs used to tackle poverty is KUBE. In general, the problems that occur in KUBE are still not optimal and not on target, so that sometimes many residents protest because residents who should receive assistance do not receive assistance and conversely residents who should not have the right to receive assistance from the Joint Business Group (KUBE). By knowing the deficiencies found in the manual calculations carried out by the Asahan District Social Service. So that beneficiaries can be determined according to the criteria determined by the Asahan District Social Service. Then a system was created using the Weighted Product method to carry out accurate calculations in providing assistance made with the PHP programming language with MySQL Database, to create a new computerized decision support which is a good alternative by prioritizing effectiveness and efficiency in determining the provision of assistance to Joint Business Groups (Joint Business Groups). KUBE). The purpose of designing a Joint Business Group assistance decision system can assist Asahan Regency social service employees in determining the granting of Joint Business Groups (KUBE). Keywords: decision support system; KUBE; weighted product (WP) Abstrak: Dinas Sosial Kabupaten Asahan merupakan salah satu instansi pemerintah yang ada di Kabupaten Asahan. Badan ini bertugas untuk melayani masyarakat dan memberikan informasi terbaik bagi masyarakat terutama dalam hal pengolahan data. Salah satu program pemerintah yang digunakan untuk menanggulangi kemiskinan adalah KUBE. Secara umum permasalahan yang terjadi di KUBE masih belum optimal dan belum tepat sasaran, sehingga terkadang banyak warga yang protes karena warga yang seharusnya menerima bantuan tidak mendapatkan bantuan dan sebaliknya warga yang seharusnya tidak berhak mendapatkan bantuan dari Kelompok Usaha Bersama (KUBE). Dengan mengetahui kekurangan yang terdapat pada perhitungan manual yang dilakukan oleh Dinas Sosial Kabupaten Asahan. Sehingga dapat ditentukan penerima manfaat sesuai dengan kriteria yang ditentukan oleh Dinas Sosial Kabupaten Asahan. Tujuan dibuat sistem dengan menggunakan metode Weighted Product untuk melakukan perhitungan yang akurat dalam pemberian bantuan yang dibuat dengan bahasa pemograman PHP dengan Database MySQL, untuk membuat pendukung keputusan terkomputerisasi baru yang merupakan alternatif yang baik dengan mengutamakan efektifitas dan efisiensi dalam penentuan pemberian bantuan Kelompok Usaha Bersama (KUBE). Maka perancangan sistem keputusan bantuan Kelompok Usaha Bersama dapat membantu pegawai dinas sosial Kabupaten Asahan dalam menentukan pemberian Kelompok Usaha Bersama (KUBE). Kata kunci: KUBE; sistem pendukung keputusan; weighted product (WP)
Performance Analysis of Different Machine Learning Algorithms on Credit Card Fraud Detection

Machine learning (ML) is a logical investigation of various algorithms and factual models that PCs utilize to carry out particular operations that are not clearly programmed. This paper aims to statistically analyze different machine learning algorithms, and compare and contrast their performance for credit card fraud detection. Algorithms used are Artificial Neural Networks(ANN), Sample Vector Machine (SVM), and Kth Nearest Neighbour (KNN), Decision Tree, Logistic Regression and Random Forest. All these above mentioned algorithms are compared on basis of performance measures. It is deduced that the random forest algorithm is the best algorithm.
Clinical Teaching sebagai Penangganan Disleksia pada Peserta Didik

Dyslexia is a term that is related to the pattern of learning difficulties of students and is accompanied by characteristic problems in word recognition and accuracy, poor decoding and weak spelling abilities. Dyslexia can be experienced by students at all levels of education. Dyslexia should not prevent students from getting learning like other students. Thus the teacher must find the right subscription to overcome the learning difficulties of dyslexic students. In compiling this article, the authors use qualitative research and focus on literature studies. The author collects through documentation activities, namely collecting all information about dyslexia in books, journals, theses and other reading sources. This study aims to determine the definition of dyslexia, the characteristics of dyslexia and the factors that can affect dyslexia. The author also aims to inform readers about dyslexia subscription assistance which is termed clinical teaching. This article shows that clinical teaching can be one of the subscriptions for dyslexic students to find the weaknesses they face so that teachers and parents can create learning that is in accordance withthe characteristics of dyslexia and they can increase the potential that exists in dyslexic students.&#x0D; Keywords: Dyslexia, Clinical Teaching, Students