Reviewerly/exHarmony huggingface dataset:
Quantum Confinement Detection using a Coupled Schrödinger System

Abstract Machine learning techniques have provided valuable insights into understanding the fundamental principles of physics. Recent studies have demonstrated the ability of deep neural networks (DNNs) to learn a wide range of differential equations (DEs) and classical Hamiltonian mechanics systems. This has led to the emergence of solving the inverse problem associated with partial differential equations using DNNs. In this paper, we propose a novel method for detecting quantum confinement by numerically solving the Schr¨odinger system on manifolds with nonlinear coupling. Our method exhibits remarkable performance in identifying various quantum phenomena. Moreover, our research introduces a promising approach for principled modeling and prediction in quantum processes. By combining deep learning with traditional models, our hybrid model leverages the strengths of both approaches, enhancing computational efficiency and enabling meaningful predictions for complex quantum physical processes. The integration of multiple deep learning principles with quantum physics enables effective handling of large datasets and addresses inverse challenges related to intricate dynamical systems and physical phenomena.
Case Study on Habitat, Heritage, and Handicrafts for Indigenous Tourism Development in the Kuala Kangsar District, Malaysia

This paper aims to highlight the opportunities for developing indigenous tourism development attractions in the Kuala Kangsar district for the expanding inbound and outbound tourist destinations in Malaysia through eco-tourism, cultural heritage, and nature tourism exploration attractions.This will help improve the economic sustainability of the tribal communities.The findings of this study lead the author to the conclusion that the indigenous groups that are located in the districts of Kuala Kangsar are suffering from great poverty as a direct result of their low level of education as well as their long-standing tribal isolation in the interior of West Malaysia, which causes them to be reserved and introverted.In addition to this, it was discovered that 398 families with incomes of less than RM400 per month are considered to be living in extreme poverty and that these families require more than just reforms to the economic system because they own the natural resources.
Heavy Metal Assessment in Sediments of Qua Iboe River Estuary, South-South, Nigeria

Heavy metal assessment in Sediments from Qua Iboe River Estuary, South-South, Nigeria was steered between January, 2022 and December, 2022 with the goal of understanding the route sources of heavy metal concentration within the system. Sediment samples were collected monthly from five stations taking cognizance of areas with high, medium and low human activities along the estuary. The samples collected were analyzed using standard protocol as recommended by APHA. Sediments mean values for wet and dry seasons observe were: Cadmium (0.20±0.04 and 0.19±0.04 mg/kg), Chromium (0.35±0.07 and 0.28±0.05 mg/kg), Copper (0.97±0.15and 10.94±0.14 mg/kg), Iron (130.79±13.69 and 115.29±10.19 mg/kg), Lead (0.56±0.06 and 0.54±0.06 mg/kg), Zinc (19.36±2.24 and 18.71±2.78 mg/kg). Heavy metal concentrations in sediments were below the DPR target / intervention limits with exception of iron without a standard. Analysis of variance and paired sample t-test revealed significant (p = 0.05) spatial variation but no seasonal variations were observed respectively. Values of trace metal recorded in the sediment throughout the study duration when compared to DPR target / intervention limit were below the threshold level as recommended. This suggest minimal anthropogenic activities during the period of studies. Nevertheless, constant monitoring of water bodies at regular interval is strongly recommended.
Disaster Literacy for Dealing with Tidal Floods (Rob) in Tanjung Mas Village North Semarang District, Semarang City

Semarang is one of the areas in Central Java that is often affected by tidal floods. Tanjung Mas Village, North Semarang Subdistrict, experiences threats to tidal floods in the high, medium and low categories, but 89.63% of its area experiences high threats to tidal floods. The aims of the research were (1) to find out the community's readiness in dealing with tidal floods (rob), (2) to find out the form of disaster literacy in the community in dealing with tidal floods (rob). This research is a quantitative research. The research population is the head of the family who lives in the tidal flood affected area of Tanjung Mas Village. The sample used purposive sampling technique, obtained a sample of 39 households residing in RW 14 to RW 16. Data analysis technique using descriptive percentage. The results showed that community readiness in dealing with tidal floods was good, community readiness in the form of (1) providing an understanding of tidal conditions to family members, (2) introducing rescue efforts, (3) prioritizing items that must be saved, (4) coordinating with related parties such as PMI, village head, Karangtaruna / rescue team, (5) understanding early warning systems, (6) planning evacuation needs or staying at home during emergency conditions, (7) increase the height of the house building. The form of literacy in the tidal-affected area of Tanjung Mas Village is interactive and communicative disaster literacy. Quantitatively, the form of basic disaster literacy is 84%, functional disaster literacy is 85%, communicative &amp; interactive disaster 86%, critical disaster literacy 85%. Keywords: Disaster Literacy, Readiness, Rob, Tidal floods.
Explanation of Student Attendance AI Prediction with the Isabelle Infrastructure Framework

Right from the beginning, attendance has played an important role in the education systems, not only in student success but in the overall interest of the matter. Although all schools try to accentuate good attendance, still some schools find it hard to achieve the required level (96% in UK) of average attendance. The most productive way of increasing the pupils′ attendance rate is to predict when it is going to go down, understand the reasons—why it happened—and act on the affecting factors so as to prevent it. Artificial intelligence (AI) is an automated machine learning solution for different types of problems. Several machine learning (ML) models like logistic regression, decision trees, etc. are easy to understand; however, complicated (Neural Network, BART etc.) ML models are not transparent but are black-boxes for humans. It is not always evident how machine intelligence arrived at a decision. However, not always, but in critical applications it is important that humans can understand the reasons for such decisions. In this paper, we present a methodology on the application example of pupil attendance for constructing explanations for AI classification algorithms. The methodology includes building a model of the application in the Isabelle Insider and Infrastructure framework (IIIf) and an algorithm (PCR) that helps us to obtain a detailed logical rule to specify the performance of the black-box algorithm, hence allowing us to explain it. The explanation is provided within the logical model of the IIIf, thus is suitable for human audiences. It has been shown that the RR-cycle of IIIf can be adapted to provide a method for iteratively extracting an explanation by interleaving attack tree analysis with precondition refinement, which finally yields a general rule that describes the decision taken by a black-box algorithm produced by Artificial intelligence.
A unified constraint-based approach for plan and goal recognition from unreliable observations

Given a sequence of observations over a plan execution, plan and goal recognition are considered as interchangeable tasks in AI planning. However, strictly speaking, the former tries to identify a plan, and the latter a set of goals, that explain the observations. Both recognition tasks are data-driven, where data comprises the plan observations, and are specially useful in proactive systems. Depending on the source of knowledge about the agents under observation, these tasks are traditionally solved by two different approaches, which require a large plan library or a planning model. In between these approaches, we propose a unified novel constraint-based approach, which distinguishes between the two tasks but is valid for both. We present a formulation, based on Partial Order Causal Link planning, that is compiled from a small plan library, to approximate a model that learns the essential causality of the original planning model. We deal with unreliable observations, which include missing and noisy observations on the real world. Modeling the observations in our formulation is straightforward. The use of the learned model allows us to address a data-driven optimization task to find the plans that most satisfy those observations (plan recognition) and the goals that are sufficiently supported by the causal relationships of the observations (goal recognition). We perform a complete evaluation of our approach in IPC domains under several indicators (accuracy, spread and ROC curves) with varying degrees of partial observability and noise on the observations. We also perform a comparison with other model-based approaches from literature.
Ethereum and Smart Contracts based Healthcare Data Management

This paper delivers a comprehensive report on the successful implementation of an Ethereum and Smart Contract based decentralized web application that is a platform for healthcare institutes to maintain their patient medical records. The system allows multiple healthcare institutes to share patient medical data. The immutability of the data is an essential requirement when handling sensitive data like a patient’s medical history, hence the incorporation of blockchain technology which is a digital edger that keeps track of transactions or information in a secure and transparent way. Key Words: Blockchain, Ethereum, Smart Contracts, Healthcare, Security and Privacy.
Improvement of Vehicle detection and classification performance with Region of Interest

This Abstract-Paper presents an innovative approach to improve the performance of vehicle detection and classification in the context of computer vision. This study proposes the use of Region of Interest (ROI) to identify and process vehicle information in traffic images. The proposed method is based on a combination of deep learning-based object detection algorithms with a more focused ROI-based approach. First, we use state-of-the-art convolution neural networks to perform object detection in traffic images, which involves a visual recognition process to identify vehicle locations. Then, using ROI, we reorder and reduce the dimensions of irrelevant data to remove background areas and increase attention to vehicle objects. This aims to reduce the computational load and speed up the detection and classification processes. We evaluate the proposed approach on a wide traffic dataset and compare the results with conventional object detection approaches without using ROI. The experimental results show that the proposed method outperforms the conventional approach in terms of vehicle detection speed and accuracy. In conclusion, this study succeeded in improving the performance of vehicle detection and classification by leveraging Region of Interest, which allows better focus on relevant regions and reduces computational complexity. These results can make an important contribution to the development of a more efficient and reliable traffic control and transportation system.
Prediksi Ujaran Kebencian Berbasis Text Pada Sosial Media Menggunakan Metode Neural Network

Currently information technology has helped in various forms of human life. They can communicate with each other through various electronic media, including using social media. The number of social media users is increasing from year to year in Indonesia. However, the development of the use of social media has also resulted in various problems, including hate speech, which will eventually lead to legal consequences. Various methods have been taken to limit the development of hate speech, including by blocking users who write hate speech on social media applications. Limiting the use of social media for hate speech can be more optimally carried out by detecting text-based words that have the potential to become hate speech. This study uses the Neural Network (NN) method to predict words that contain hatespeech on social media with an accuracy rate of 73% better than other methods such as Decission Tree and K-Nearest Neighbor (KNN) which only achieve an accuracy rate of 68.5 %.
Optimasi Penggunaan Jenis Busi, Oli, Dan Campuran Ethanol Bensin Terhadap Peningkatan Suhu Dan Jarak Tempuh Sepeda Motor 4 Langkah Dengan Metode Taguchi

This study will look for optimal results from using the type of spark plug, oil, and gasoline ethanol mixture to increase the temperature and mileage of a 4 stroke motorcycle using the Taguchi method, which this research can also be used as a reference for further researchers. The object of research in this study is a Yamaha Vega ZR 2010 modified injection system. Data collection is done directly, such as the temperature test is carried out with an engine speed of 5000 rpm with a span of 2 minutes each run, while for the mileage test using the full to full method. Based on the results of the research that has been done, the increase in temperature gets an optimal response, namely A1 (Nickel Spark Plug) SNR response (36.2803), B3 (Shell Advance Oil) SNR response (37.8012), C2 (Ethanol gasoline 75%: 25 %) SNR response (36,4388). As for the optimal response to mileage, the factors are A2 (Iridium Spark Plug) SNR response (28.5501), B2 (Federal Racing Oil) SNR response (28.5446), C3 (Ethanol Gasoline 80%:20%) SNR response (28, 5446).&#x0D;
Psychological Features of the Formation of National Musical Representations in Educational Process

National music is a powerful emotional and psychological tool, and the importance of socio-psychological factors in shaping adequate images through its perception is highlighted in the works of Central Asian thinkers. National musical imagination is the transformation of a musical work perceived by a listener as a representative of the nation into artistic information directly in the consciousness of a person. This scientific article highlights the widespread use of scientific ideas and considerations of Central Asian thinkers on the possibilities of emotional and psychological effects of music in contemporary music education providing the effectiveness of teaching.
PEMBELAJARAN MENCERITAKAN KEMBALI ISI TEKS CERITA RAKYAT SECARA TERTULIS DENGAN MENGGUNAKAN MODEL INKUIRI PADA PESERTA DIDIK KELAS VII SMP NEGERI 1 PLERED

This research is motivated by the ability to retell the contents of the story text. Because the learning model did not vary, students' interest decreased, this study focused on the ability to retell the contents of the story text with stages or processes. Therefore, the writer uses the inquiry model to teach retelling of the contents of the story in writing. The purpose of this study was to determine the ability of the writer to plan, implement and evaluate learning to retell the contents of the story; knowing the ability of students in learning to retell the contents of folklore texts in writing through the inquiry model; knowing the effectiveness of the inquiry model in learning to retell the contents of folklore texts in writing; determine the ability of students to learn between the experimental class using the inquiry model and the control class using the lecture method to retell the contents of folklore texts in writing. This study used a quantitative experiment with a quasi design,
Machine Learning

Abstract: Machine learning refers to the study and development of machine learning algorithms and techniques at a conceptual level, focusing on theoretical foundations, algorithmic design, and mathematical analysis rather than specific implementation details or application domains. It aimsto provide a deeper understanding of the fundamental principles and limitations of machine learning, enabling researchers to develop novel algorithms and advance the field. In abstract machine learning, the emphasis is on formalizing and analyzing learning tasks, developing mathematical models for learning processes, and studying the properties and behavior of various learning algorithms. This involves investigating topics such as learning theory, statistical learning, optimization, computational complexity, and generalization. The goalis to develop theoretical frameworks and mathematical tools that help explain why certain algorithms work and how they can be improved. Abstract machine learning also explores fundamental questions related to the theoretical underpinnings of machine learning, such as the trade-offs between bias and variance, the existence of optimal learning algorithms, the sample complexity of learning tasks, and the limits of what can be learned from data. It provides a theoretical foundation for understanding the capabilities and limitations of machine learning algorithms, guiding the development of new algorithms and techniques. Moreover, abstract machine learning serves as a bridge between theory and practice, facilitating the transfer of theoretical insights into practical applications. Theoretical advances in abstract machine learning can inspire new algorithmic approaches and inform the design of real-world machine learning systems. Conversely, practical challenges and observations from realworld applications can motivate and guide theoretical investigations in abstract machine learning. Overall, abstract machine learning plays a crucial role in advancing the field of machine learning by providing rigorous theoretical frameworks, mathematical models, and algorithmic principles that deepen our understanding of learning processes and guide the development of more effectiveand efficient machine learning algorithms.
A Novel Spin Based Logic Circuitry Synthesis Using SS Gate

Abstract: Because of its perceived advantages, reversible logic has attracted a lot of study attention. It introduces commendable characteristics like low power consumption, minimized garbage output, and least propagation delay. Reversible logic integrated circuits (ICs) have been produced utilizing traditional CMOS topology in a number of published research endeavors up to this point. However, because CMOS technology is already experiencing a common scaling down issue, researchers are now focusing on post-CMOS devices for the manufacturing of reversible integrated circuits in the future. This particular research project is focused on related research projects that feature reversible IC design based on Spintronic. The authors first implemented spinoriented conventional reversible logic gates and evaluated their effectiveness in terms of generated garbage values, power consumption, device integrity, and size. Later, in order to understand the benefits of the suggested Single Spin Logic based SS gate, the authors come across an SS gate that is singular in its kind and stick to its spin realization.
OpinionConv: Conversational Product Search with Grounded Opinions

When searching for products, the opinions of others play an important role in making informed decisions. Subjective experiences about a product can be a valuable source of information. This is also true in sales conversations, where a customer and a sales assistant exchange facts and opinions about products. However, training an AI for such conversations is complicated by the fact that language models do not possess authentic opinions for their lack of real-world experience. We address this problem by leveraging product reviews as a rich source of product opinions to ground conversational AI in true subjective narratives. With OpinionConv, we develop the first conversational AI for simulating sales conversations. To validate the generated conversations, we conduct several user studies showing that the generated opinions are perceived as realistic. Our assessors also confirm the importance of opinions as an informative basis for decision-making.
CLASSLA-Stanza: The Next Step for Linguistic Processing of South Slavic Languages

We present CLASSLA-Stanza, a pipeline for automatic linguistic annotation of the South Slavic languages, which is based on the Stanza natural language processing pipeline. We describe the main improvements in CLASSLA-Stanza with respect to Stanza, and give a detailed description of the model training process for the latest 2.1 release of the pipeline. We also report performance scores produced by the pipeline for different languages and varieties. CLASSLA-Stanza exhibits consistently high performance across all the supported languages and outperforms or expands its parent pipeline Stanza at all the supported tasks. We also present the pipeline's new functionality enabling efficient processing of web data and the reasons that led to its implementation.
In-Context Alignment: Chat with Vanilla Language Models Before Fine-Tuning

In this note, we explore inference-time alignment through in-context learning. We consider a vanilla pretrained language model Llama-2 before any fine-tuning and retrieve an average of 9 demonstration alignment examples when the model is prompted to follow chat-style instructions. Compared to direct prompting, the in-context alignment without changing model weights leads to a 7x increase in win-rate w.r.t. the text-davinci-003 model from OpenAI, making the vanilla language model comparable to strong baselines with alignment fine-tuning.
DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds

Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits feature fusion from both modalities for point-wise scene flow estimation. Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling, KNN, and ball query algorithms for local feature aggregation. To mitigate these issues in scene flow learning, we regularize raw points to a dense format by storing 3D coordinates in 2D grids. Unlike the sampling operation commonly used in existing works, the dense 2D representation 1) preserves most points in the given scene, 2) brings in a significant boost of efficiency, and 3) eliminates the density gap between points and pixels, allowing us to perform effective feature fusion. We also present a novel warping projection technique to alleviate the information loss problem resulting from the fact that multiple points could be mapped into one grid during projection when computing cost volume. Sufficient experiments demonstrate the efficiency and effectiveness of our method, outperforming the prior-arts on the FlyingThings3D and KITTI dataset.
Correlating Medi-Claim Service by Deep Learning Neural Networks

Medical insurance claims are of organized crimes related to patients, physicians, diagnostic centers, and insurance providers, forming a chain reaction that must be monitored constantly. These kinds of frauds affect the financial growth of both insured people and health insurance companies. The Convolution Neural Network architecture is used to detect fraudulent claims through a correlation study of regression models, which helps to detect money laundering on different claims given by different providers. Supervised and unsupervised classifiers are used to detect fraud and non-fraud claims.