Reviewerly/exHarmony huggingface dataset:
Distributed Photovoltaic Short-Term Power Prediction Based on Multi-Point Correction of Autoformer and POPtree

With the continuous expansion of photovoltaic grid-connected capacity, precise prediction of photovoltaic power has become essential for ensuring the secure and stable operation of new energy power systems. This paper presents a distributed photovoltaic short-term power forecasting method, utilizing Autoformer and POPtree multi-point correction to overcome challenges associated with insufficient or inaccurate weather forecast data, thus enabling accurate predictions. The methodology begins with the MIC algorithm, which calculates feature correlation coefficients, helping to identify highly correlated features that are then fed into the Autoformer model for prediction, yielding preliminary long sequence prediction results. Next, a point-by-point prediction approach based on the tree model trains multiple lightGBM models, producing multi-point power predictions. Finally, by setting a weighted threshold <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> , the corresponding prediction results from different models are combined using a hybrid weighting method to derive the ultimate photovoltaic short-term power prediction outcomes. Through a case analysis of photovoltaic output data in a specific station area, the results demonstrate that the proposed model achieves high-quality short-term photovoltaic output predictions even without relying on weather forecasting data. This method proves effective in meeting the demands of accurate photovoltaic power forecasting in practical scenarios.
FBT: Medical Intent Recognition Model Based on FreeLB Adversarial Training

Intent recognition is an important research direction in natural language processing. In this paper, taking BERT-TextCNN, an intention recognition model in deep learning, as the benchmark model, and improving the model for the problem that the model is easy to recognize wrongly in the face of samples containing small perturbations or interference from samples containing noise, and the problem that the model does not consider the classification results of BERT, an intention recognition model based on FreeLB adversarial training FBT (FreeLB BERT TextCNN) is proposed. FreeLB adversarial training is added to the BERT-TextCNN model, and feature splicing is used to integrate the recognition results of BERT and TextCNN respectively. The proposed model is compared with the benchmark model for experiments, and the experiments demonstrate that the improved model can effectively improve the recognition effect of the model.
Requirement Elicitation using Natural Language Processing

—This paper is the outcome of the research conductedto investigate the affective requirement engineering techniquesproposed and used for developing software projects. We haveassessed traditional methods and proposed an approach thatcovers various aspects for generating a successful project. AnNLP-based model is designed that takes input from the user andgives the output in the form of a text document after processingit. We have set a 62% similarity index to achieve the maximumrequirements of the required system. These requirements, inreturn, help the developers to develop the product with morefunctionality and productivity.
Quaternion and Split Quaternion Neural Networks for Low-Light Color Image Enhancement

In this paper, two multilayer quaternion feedforward neural network models are presented. While the first model is based on the quaternion algebra, the second uses the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">split quaternion</i> algebra. For both quaternion neural networks, a learning algorithm is derived using the extended Kalman filter. In addition, to analyze the performance of these two neural network models, they were applied to recover bright images from obscured images. After the neural network processing, the original scene has a better brightness. The quaternion neural network enhances the color images in the RGB color space, and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">split quaternion</i> neural network uses images in the HSV color space. Therefore, from the results, we can see that the neural network using the HSV color model shows advantages unpublished before, which are not shown by the neural network using the RGB color model. This work shows a novel neural network processing that can be used advantageously for practitioners interested in working using the HSV color model.
Metropolitan Single-Photon Distribution at 1550 nm for Random Number Generation

Quantum communication networks are used for QKD and metrological applications. We present research connecting two nodes ≈ 20 kilometers apart over the municipal fiber network using semiconductor quantum dots emitting at 1550 nm.
Chip Based Controllable Entanglement Distribution Network

In this work, we demonstrate a controllable entanglement distribution network chip based on silicon photonics with 3 subnets and 24 users and present its ability of reconfigurable network topology experimentally.
Analisis Kemampuan Berhitung Materi Bilangan Kelas VII di SMP Negeri 1 Konawe

This study aims to analyze students' numeracy skills. This type of research is qualitative descriptive, providing an in-depth depiction of students' numeracy skills as the primary focus, utilizing a qualitative approach based on collected data. The research subjects consist of 6 students from the seventh grade of SMP Negeri 1 Konawe, divided into three categories: high numeracy skills (2 subjects), moderate numeracy skills (2 subjects), and low numeracy skills (2 subjects). Subject selection was based on the previous Daily Test (UH) scores. Data collection techniques employed included tests and interviews. Students' numeracy skills are categorized into three levels: high numeracy skills, moderate numeracy skills, and low numeracy skills. These numeracy skills encompass three main aspects: the ability to solve multiplication operations, the ability to solve division operations, and the ability to connect multiplication and division operations. Subjects 1 (S1) and 2 (S2) in the high numeracy skills category successfully met all three aspects. In contrast, Subjects 3 (S3) and 4 (S4) in the moderate category, as well as Subjects 5 (S5) and 6 (S6) in the low category, have yet to achieve all these aspects.
Optimizing the Amount of Production Using Hybrid Fuzzy Logic and Census II

Companies should do planning before the production process. Production planning is expected to avoid excessive or insufficient product stocks that harm the company. This study aims to help a plastic spoon company in Gresik, East Java to determine the optimal amount of production using the Fuzzy method. The input variables used are the amount of demand and supply. However, the amount of demand that fluctuated, especially during the Covid-19 pandemic, made it difficult for the company to estimate the amount of demand in the upcoming production period. Therefore, in this study, the amount of demand is calculated from the results of forecasting with the Cencus II method. The results of the study provide an accuracy of the recommendations for the amount of production of 77% and an accuracy of forecasting results of 82%.
Pengembangan Game Simulasi Proses Pengisian Kartu Rencana Studi

Completing the study plan card (KRS) is one of the most important processes in lectures. The problem that is often faced by first year students when doing KRS is that students do not know the steps for doing KRS. This paper proposes a simulation game as a solution to solve this problem. The focus of this paper is to explain the development process and the results that have been achieved. Game development is done using the ADDIE model. Game evaluation is done by using black box testing. Based on the black box test results, all game functionality can run. Therefore the game can be used for user testing
Post-Training Reverse-Engineering Defense (PT-RED) Against Imperceptible Backdoors

Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students.
Exploration of Moroccan EFL University Students’ Retrospective Knowledge of Cognitive and Metacognitive Reading Strategies in Processing Advanced-level Written Discourse in English

Based on my unpublished Doctoral Dissertation (Defended in 2015) that investigates the impact of explicit reading strategy instruction on Moroccan EFL university learners’ (meta) cognitive reading strategy use and reading achievement, the present exploratory study reflects the extent to which the learners are retrospectively conscious of the (meta) cognitive reading strategies (RSs) that facilitate the assimilation and comprehension of the textual input. It addresses 113 English department university students belonging to two groups (Group 1: N= 50; Group 2: N= 63) studying at the first-semester level. The data were obtained through the dependence on two advanced-level reading comprehension texts (i.e., narrative, expository) which were accompanied by a retrospective questionnaire (RQ). The results evince that, given that the targeted two groups unconsciously used some (meta) cognitive reading strategies (RSs) for attaining the textual comprehension, their retrospective consciousness of the core nature and essence of these metacognitively-oriented strategic moves is typically typified by utmost inadequacy. Finally, some useful recommendations relative to both EFL reading research and pedagogy as well as some unavoidable limitations are presented.
Flexible Classification, Question-Answering and Retrieval with Siamese Neural Networks for Biomedical Texts

Training transformers models on biomedical data has shown promising results. However, these language models require fine-tuning large models on very specific supervised data for each task. In this paper, we propose to use siamese neural models (sentence transformers) that embed texts to be compared in a vector space, and apply them to the biomedical domain on three main tasks: classification, question answering and retrieval. Training is based on articles from the MEDLINE bibliographic database associated with their MeSH (Medical Subject Headings) keywords and optimizes an objective self-supervised contrastive learning function. The representation of the texts (embeddings) obtained by our siamese models can be stored, indexed and used with transfer learning without needing the language models. The obtained results on several benchmarks show that the proposed models can solve these tasks with results comparable to biomedical cross-encoders transformers and offer a several advantages by being flexible and efficient at inference time. (Models and data available: https://github.com/arieme/BioSTransformers.git ).
Mining significant high utility gene regulation sequential patterns

&lt;p&gt;&lt;strong&gt;Background&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Mining frequent gene regulation sequential patterns in time course microarray datasets is an important mining task in bioinformatics. Although finding such patterns are of paramount important for studying a disease, most existing work do not consider gene-disease association during gene regulation sequential pattern discovery. Moreover, they consider more absent/existence effects of genes during the mining process than taking the degrees of genes expression into account. Consequently, such techniques discover too many patterns which may not represent important information to biologists to investigate the relationships between the disease and underlying reasons hidden in gene regulation sequences.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Results&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;We propose a utility model by considering both the &lt;em&gt;gene-disease association&lt;/em&gt; score and their &lt;em&gt;degrees of expression levels&lt;/em&gt; under a biological investigation. We propose an efficient method called &lt;em&gt;Top-HUGS&lt;/em&gt;, for discoverying &lt;em&gt;significant high utility gene regulation sequential patterns&lt;/em&gt; from a time-course microarray dataset.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Conclusions&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;In this study, the proposed methods were evaluated on a publicly available time course microarray dataset. The experimental results show higher accuracies compared to the baseline methods. Our proposed methods found that several new gene regulation sequential patterns involved in such patterns were useful for biologists and provided further insights into the mechanisms underpinning biological processes. To effectively work with the proposed method, a web interface is developed to our system using Java. To the best of our knowledge, this is the first demonstration for &lt;em&gt;significant high utility gene regulation sequential pattern discovery&lt;/em&gt;.&lt;/p&gt;
Developing Crossword Puzzle As English Learning Media Media To Teach Vocabulary At Madrasah Aliyah Al Khoiriyah Gondanglegi

In order to learn and teach effectively, students need to have a comprehensive knowledge of the English language and understand its meaning. Students must be able to understand language both orally and in writing if they have mastery of vocabulary. The purpose of this writing is to find out whether the application of the media could develop English learning media to teach vocabulary at Madrasah Aliyah Al Khoiriyah Gondanglegi. The instruments used was the Research and Development (R&amp;D) media with 20 students as a trial in applying this method. The results of this study are based on the results of the average percentage of the validation sheet by 2 crossword puzzle learning media experts of 94.8% and is included in the very valid category. Thus, the developed crossword puzzle learning media can be used at Madrasah Aliyah Al Khoiriyah Gondanglegi. Based on the results of the distribution of questionnaires given by students and English teachers to find out the responses of students and teachers to crossword learning media. The percentage of responses given by students is very valid at 81%, and teacher responses with a very valid percentage are 93.3%. Thus, most students and teachers responded in a very valid category to the development of crossword puzzle learning media at Madrasah Aliyah Al Khoiriyah.
The Challenge of Interdisciplinarity at the Intersection of Groundwater Management and Visualization Research

This design study presents an analysis and abstraction of temporal and spatial data, and workflows in the domain of hydrogeology and the design and development of an interactive visualization prototype. Developed in close collaboration with a group of hydrogeological researchers, the interface supports them in data exploration, selection of data for their numerical model calibration, and communication of findings to their industry partners. We highlight both pitfalls and learnings of the iterative design and validation process and explore the role of rapid prototyping. Some of the main lessons were that the ability to see their own data changed the engagement of skeptical users dramatically and that interactive rapid prototyping tools are thus powerful to unlock the advantage of visual analysis for novice users. Further, we observed that the process itself helped the domain scientists understand the potential and challenges of their data more than the final interface prototype.
An RNS-Based Initial Absolute Position Estimator for Electrical Encoders

In digital systems, the Residue Number System (RNS) represents an interesting alternative to the traditional two’s complement representation. Its performance and low-power properties have attracted significant research interest over the years. In this paper, RNS is used to estimate the angular position of a multi-trace electrical encoder (EE), an electro-mechanical device to measure angles at high precision widely used, for example, in antennas on-board satellites. The model of this system presents cyclic characteristics and, consequently, allows efficient use of modular arithmetic for its description. The RNS is applied to EEs equipped with more than two plates, and the absolute angle reconstruction is performed by using the Chinese Remainder Theorem (CRT). Furthermore, the use of RNS allows detection and mitigation solutions for errors due to encoders’ non-idealities and electrical noise. In this noisy context, we provide a detailed analysis of the performance of the system and propose a more robust, flexible, and easy-to-implement solution compared with the traditional methods. The results show that the RNS-based system can attenuate the noise, measure accurately the angles, and improve the overall performance.
Diagnosis and Prognostication of Brain Disorders using Clinically Gathered Electronic Medical Data

This paper aims to use deep learning algorithms for the diagnosis and prognostication of brain disorders, including Alzheimer's disease, using clinically gathered electronic medical data. These algorithms can analyze large amounts of data, including cognitive tests, imaging studies, medical history, and medication use, to identify patterns that are indicative of Alzheimer's disease. This approach has the potential to improve the accuracy and speed of diagnosis and provide more personalized prognostic information to clinicians. Moreover, deep learning algorithms can also identify new biomarkers for Alzheimer's disease, allowing for earlier detection and intervention. While there are still challenges associated with the use of deep learning algorithms, such as the need for large amounts of high-quality data, their potential benefits in the diagnosis and prognostication of brain disorders, particularly Alzheimer's disease. The results of this study demonstrate the promise of deep learning in healthcare and warrant additional study in this field.
Motives for Choosing a Profession and Teachers’ Job Satisfaction

Introduction: Job satisfaction is a function of meeting the employee’s needs and depends on his individual professional expectations and motives for choosing a profession and the extent to which they have been fulfilled in a specific work situation. Thus, individual needs motivating the choice of the teaching profession are an important factor determining the extent to which the current work is beneficial for them. Thus, it is a typical psychological reaction to a professional situation, expressed through affective reactions and cognitive assessments. Job satisfaction plays an important role in the professional development of teachers, their effective performance and gives them a sense of quality of life. Research Aim: The aim of the research was to determine the relationship between the motives for choosing a profession and teachers’ job satisfaction. Method: The study used the method of diagnostic survey, survey technique and questionnaire. Elements of descriptive and correlation statistics (rho-Spearman linear correlation coefficient) were used for the analysis of the research material. Results: The issue of the dependence of job satisfaction on the motives for choosing a profession is the subject of these empirical analyses. The obtained research results confirm the fully adopted assumption of a significant relationship between these variables. Conclusions: When choosing the profession of a teacher, people are guided mainly by internal motivation, i.e. personal interests, willingness to work with children and youth, a specific pedagogical vocation. For a significant number of teachers, an important motive is also the teacher’s convenient working time and job security guaranteed by law. The structure of internal motives shaped in this way fundamentally determines teachers’ satisfaction with their profession.
Cyber Security and People: Human Nature, Psychology, and Training Affect User Awareness, Social Engineering, and Security Professional Education and Preparedness

This work focuses on how machine learning methods may be used to identify threats and provide countermeasures. Security threats and vulnerabilities pose significant difficulties in today’s digital world. Algorithms trained with machine learning can sift through massive volumes of data, look for trends, and spot possible security breaches as they happen. These algorithms can offer preventative security measures and actions because they use sophisticated analytics and predictive models. This abstract delves into the use of machine learning to bolster security, focusing on its potential to improve threat detection and provide implementable suggestions for shoring up overall security.
High-security chaotic dynamical enhanced strategy based on reservoir computing

In this paper, we propose a high-security chaotic dynamical enhanced strategy based on a nonlinear dynamical enhanced chaotic system generated by reservoir computing (RC). The proposed system is applied to an orthogonal frequency division multiplexed passive optical network (OFDM-PON), which gets more complex chaotic behavior by using Sine-Henon cascade mapping (S-Henon). RC generates the chaotic sequences that are used to produce the masking vectors to encrypt the constellation and subcarriers, which can resist the degradation of chaotic dynamics.
Letz Translate: Low-Resource Machine Translation for Luxembourgish

Natural language processing of Low-Resource Languages (LRL) is often challenged by the lack of data. Therefore, achieving accurate machine translation (MT) in a low-resource environment is a real problem that requires practical solutions. Research in multilingual models have shown that some LRLs can be handled with such models. However, their large size and computational needs make their use in constrained environments (e.g., mobile/IoT devices or limited/old servers) impractical. In this paper, we address this problem by leveraging the power of large multilingual MT models using knowledge distillation. Knowledge distillation can transfer knowledge from a large and complex teacher model to a simpler and smaller student model without losing much in performance. We also make use of high-resource languages that are related or share the same linguistic root as the target LRL. For our evaluation, we consider Luxembourgish as the LRL that shares some roots and properties with German. We build multiple resource-efficient models based on German, knowledge distillation from the multilingual No Language Left Behind (NLLB) model, and pseudo-translation. We find that our efficient models are more than 30% faster and perform only 4% lower compared to the large state-of-the-art NLLB model.