Reviewerly/exHarmony huggingface dataset:
Implementation of Appropriate Technology Polycrystalline Photovoltaic as New Renewable Alternative Energy in Public Street Lighting (PJU) at Pakuniran Probolinggo Area

Public road lighting (PJU) in the Pakuniran area, Probolinggo, East Java, more precisely in Gondosuli Village, is not sufficient. An environment without street lighting will be like a dead zone and can occur in many cases of crime, and traffic accidents and will hurt social life at night. Therefore, the community service team at the State University of Malang (UM) through student service provided a solution to reaching the road by utilizing new, renewable energy from polycrystalline solar panels to turn on the lights at night. The results of observations and site surveys show that several points require PJU installation with polycrystalline technology. The implementation carried out in Gondosuli Village was carried out in several stages, namely: observation, preparation of PJU designs, training and maintenance, performance testing, and evaluation tools. This activity, which was completed within 2 months, resulted in 5 PJUs being placed at several points with minimal lighting conditions and received a positive response from the community.&#x0D; Keywords: Public Street Lighting, Polycrystalline Photovoltaic, Gondosuli Village
Named Entity Recognition in Electronic Health Records: A Methodological Review

Objectives: A substantial portion of the data contained in Electronic Health Records (EHR) is unstructured, often appearing as free text. This format restricts its potential utility in clinical decision-making. Named entity recognition (NER) methods address the challenge of extracting pertinent information from unstructured text. The aim of this study was to outline the current NER methods and trace their evolution from 2011 to 2022.Methods: We conducted a methodological literature review of NER methods, with a focus on distinguishing the classification models, the types of tagging systems, and the languages employed in various corpora.Results: Several methods have been documented for automatically extracting relevant information from EHRs using natural language processing techniques such as NER and relation extraction (RE). These methods can automatically extract concepts, events, attributes, and other data, as well as the relationships between them. Most NER studies conducted thus far have utilized corpora in English or Chinese. Additionally, the bidirectional encoder representation from transformers using the BIO tagging system architecture is the most frequently reported classification scheme. We discovered a limited number of papers on the implementation of NER or RE tasks in EHRs within a specific clinical domain.Conclusions: EHRs play a pivotal role in gathering clinical information and could serve as the primary source for automated clinical decision support systems. However, the creation of new corpora from EHRs in specific clinical domains is essential to facilitate the swift development of NER and RE models applied to EHRs for use in clinical practice.
COMEX: A Tool for Generating Customized Source Code Representations

Learning effective representations of source code is critical for any Machine Learning for Software Engineering (ML4SE) system. Inspired by natural language processing, large language models (LLMs) like Codex and CodeGen treat code as generic sequences of text and are trained on huge corpora of code data, achieving state of the art performance on several software engineering (SE) tasks. However, valid source code, unlike natural language, follows a strict structure and pattern governed by the underlying grammar of the programming language. Current LLMs do not exploit this property of the source code as they treat code like a sequence of tokens and overlook key structural and semantic properties of code that can be extracted from code-views like the Control Flow Graph (CFG), Data Flow Graph (DFG), Abstract Syntax Tree (AST), etc. Unfortunately, the process of generating and integrating code-views for every programming language is cumbersome and time consuming. To overcome this barrier, we propose our tool COMEX - a framework that allows researchers and developers to create and combine multiple code-views which can be used by machine learning (ML) models for various SE tasks. Some salient features of our tool are: (i) it works directly on source code (which need not be compilable), (ii) it currently supports Java and C#, (iii) it can analyze both method-level snippets and program-level snippets by using both intra-procedural and inter-procedural analysis, and (iv) it is easily extendable to other languages as it is built on tree-sitter - a widely used incremental parser that supports over 40 languages. We believe this easy-to-use code-view generation and customization tool will give impetus to research in source code representation learning methods and ML4SE. The source code and demonstration of our tool can be found at https://github.com/IBM/tree-sitter-codeviews and https://youtu.be/GER6U87FVbU, respectively.
Communication Topologies for Decentralized Federated Learning

Conventional federated learning aims at enabling clients to contribute to a global training process while keeping their own data local. However, as the number of devices on the network increases, it can no longer be assumed that there is a central entity with sufficient bandwidth or computing resources to handle the volume of requests. Hence, in this paper, we consider implementing federated learning with different topologies in a network without a central entity. We compare hierarchical and decentralized topologies with varying degrees of interconnectivity. In our experiments, we use 50 clients with small CNNs and MNIST, FashinMNIST or Cifar10 datasets. Our results show that models in a decentralized network can achieve similar performances as models in a centralized network if the topology is carefully chosen. We relate the accuracy of the models to the estimated communication overhead by considering the number of communication connections required for a given topology. These results indicate that cluster topologies can leverage similarities of data distributions and mitigate the communication effort without sacrificing performance. In addition, we present a simple method to estimate the information transfer performance of a topology without empirical testing.
Analysis of the Machine Learning Methods Effectiveness for Morphological Classification of Anemia

Anemia is a pathological disorder of the structure of blood cells, in which the concentration of hemoglobin is reduced. At the same time, the number of red blood cells decreases. Anemia is characterized as a secondary disease and is a symptom of other ailments. Within the framework of this study, a medical algorithm for diagnosing anemia is considered, as well as the results of using machine learning methods to detect pathology are studied. The stages of the implementation of a medical algorithm for diagnosing anemia are described, as well as the results of model training based on the linear support vector method, logistic regression, the k-nearest neighbor method, decision tree, naive Bayesian classifier method, and random forest method.
Fly embryo nuclei riding on two-fluid flow

The noninteractive zero-knowledge proof (NIZKP) is underlying a broad spectrum of applications. The major categories of NIZKP are instantiated with deterministic hash functions in the real world. However, the outputs of deterministic hash ...Zero-knowledge proof (ZKP) is a fundamental cryptographic primitive that allows a prover to convince a verifier of the validity of a statement without leaking any further information. As an efficient variant of ZKP, noninteractive zero-knowledge proof (...
A study of multi-intentional interrogative comprehension methods in the field of tourism

The interrogative understanding task is an important subtask in question-and-answer systems, and currently, most of the existing interrogative understanding methods are oriented to single-intent scenarios, which do not match the actual scenarios. Based on this, we propose a joint modeling model PBIU (Prompting Based Interrogative Understanding) based on cueing learning for the interrogative sentence understanding task. Instead of the traditional pre-training-fine-tuning approach, a pre-training-prompting-prediction approach is used to formalize two subtasks into the same form using a generic pre-training model, and different prompts are used to complete different subtasks. At the same time, the model introduces auxiliary subtask slot prediction and learns the relationship between labels, thus improving the effectiveness and generalization of the model. The experimental results on the publicly available datasets MixATIS and MixSnips and the self-built multi-intentional interrogative dataset MixTFQD in the tourism domain validate that the model has better performance in the interrogative sentence comprehension task in the tourism domain, which is of more importance for applying to realistic real-world scenarios of interrogative sentence comprehension tasks.
Digital Transformation of the Management Information System (MIS) at Pondok AnNahdloh, Selangor, Malaysia

The role of information technology in enhancing institutional quality, particularly at Pondok AnNahdloh in Malaysia, cannot be underestimated. Information technology is vital in elevating an institution's standards by improving efficiency, communication, decision-making, security, and innovation. Institutions that effectively embrace information technology can optimize their operations and achieve higher levels of quality, resulting in enhanced competitiveness and desired outcomes. The research's primary objective is to describe the Education Management Information System (MIS) for educational documents at Pondok AnNahdloh in Malaysia, which is up-to-date and integrated. The research methodology involves focus group discussions to identify administrative activities, system application design, system testing, improvement, operator training, limited user testing, launching, and evaluation. Data collection methods include field observations, interviews, and activity documentation. Data analysis follows the theory of Miles and Huberman. The research findings reveal the successful implementation of MIS, leading to up-to-date and integrated educational document data, both in terms of input and processes. Adopting information technology in the academic environment through this system ensures efficient and accurate access, management, and utilization of essential data and information in the educational process. With this system in place, improvements in activity quality are expected, positively impacting community empowerment and national development.
Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation

Machine learning, powered by cloud servers, has found application in medical diagnosis, enhancing the capabilities of smart healthcare services. Research literature demonstrates that the support vector machine (SVM) consistently demonstrates remarkable accuracy in medical diagnosis. Nonetheless, safeguarding patients' health data privacy and preserving the intellectual property of diagnosis models is of paramount importance. This concern arises from the common practice of outsourcing these models to third-party cloud servers that may not be entirely trustworthy. Few studies in the literature have delved into addressing these issues within SVM-based diagnosis systems. These studies, however, typically demand substantial communication and computational resources and may fail to conceal classification results and protect model intellectual property. This paper aims to tackle these limitations within a multi-class SVM medical diagnosis system. To achieve this, we have introduced modifications to an inner product encryption cryptosystem and incorporated it into our medical diagnosis framework. Notably, our cryptosystem proves to be more efficient than the Paillier and multi-party computation cryptography methods employed in previous research. Although we focus on a medical application in this paper, our approach can also be used for other applications that need the evaluation of machine learning models in a privacy-preserving way such as electricity theft detection in the smart grid, electric vehicle charging coordination, and vehicular social networks. To assess the performance and security of our approach, we conducted comprehensive analyses and experiments. Our findings demonstrate that our proposed method successfully fulfills our security and privacy objectives while maintaining high classification accuracy and minimizing communication and computational overhead.
Efficient On-device Transfer Learning using Activation Memory Reduction

On-device transfer learning suggests fine-tuning pretrained neural networks on new input data directly on edge devices. The memory limitation of edge devices necessitates using memory-efficient fine-tuning methods. Fine-tuning involves two primary phases: the forward-pass phase and the backward-pass phase. The forward-pass phase generates output activations, and the backward-pass phase computes gradients and updates the parameters accordingly. Although the forward-pass phase demands a temporary memory to store a layer's input and output activations, the backward-pass phase may require storing the output activations from all layers to compute gradients. This fact introduces the memory cost of the backward-pass phase as the main contributor to the huge training memory demands of deep neural networks (DNNs), which has been the focus of many studies. However, little attention has been made to how the temporary activation memory involved in the forward-pass phase may also act as the memory bottleneck, which is the main focus of this paper. This paper aims to mitigate this memory bottleneck by pruning unimportant channels from layers that require significant temporary activation memory. Experimental results demonstrate how the proposed method effectively reduces peak activation memory and total memory costs of MobileNetV2 by 65% and 59%, respectively, at the cost of 3% accuracy drop.
LPDB: Lightweight Policy-Driven Blockchain With Batch Verification for Rail Transit Systems

Rail transit is a commonly used mode of transportation for daily commuting, but traditional Rail Transit Systems (RTS) typically face significant challenges such as high mobility and low latency. Furthermore, the RTS generates a substantial volume of real-time interactive data, posing difficulties in ensuring user privacy while maintaining practicality. To address these concerns, blockchain as a promising technology, has been leveraged to safeguard data transmission and access. However, public blockchains such as Bitcoin and Ethereum may not be suitable for real-time interactive RTS scenarios due to their lack of flexibility and control. To overcome this, the notion of the Policy-Driven Blockchain (PDB) was introduced, which is a private blockchain with flexible policy control and management mechanisms. PDB can be quickly customized and deployed according to specific business requirements and rules, making it a valuable feature for RTS scenarios. However, their scheme relies on bilinear pairings, which incurs high computational overhead and does not support batch validation. In this paper, we propose a Lightweight Policy-Driven Blockchain (LPDB) scheme to overcome these challenges, the core of which is the design of a Pairing-free Policy-Driven Signature (PFPDS) scheme to better tailored to meet the specific requirements in RTS. In addition, considering the large number of communication messages in RTS, we analyze the advantages of batch verification of signatures. In conclusion, the security of our proposal is validated under the random oracle model, and we evaluate the computational expenses associated with the algorithms via simulations on a laptop. The implementation outcomes demonstrate the practicality and viability of our scheme.
RESEARCH ON APP DEVELOPMENT IN VIRTUAL REALITY

Virtual reality (VR) program research explores dynamic landscapes to create immersive digital experiences. This study leads to the complex process of developing VR applications that focuses on using modern technology, 3D environments, and interactive user interfaces. This study aims to provide insight into the challenges and opportunities in this field by exploring how VR applications can be optimized for a range of industries, from gaming and education to healthcare and education. This emphasizes the importance of creating user-centric applications that push the boundaries of innovation, promising to redefine how we interact with digital content in the virtual world. Ultimately, this research aims to contribute to the growing body of knowledge in the ever-expanding domain of VR application development. Keywords: virtual reality, graphic user interface, frames per second
Modelling the Level of Sunlight by Using Fuzzy Sets

The problem of proper management of electricity consumption in the era of high energy prices is a very important issue. In the case of large enterprises that consume significant a mounts of energy, there is an additional problem of correctly determining the tariff to be contracted. Development of an efficient energy consumption management system based on the use of additional energy sources from photovoltaic panels requires taking into account an effective system that allows to predict the level of insolation in the following hours. The paper presents an innovative model for forecasting the level of insolation based on historical data. Due to the constantly changing climatic conditions, a two-level of fuzzyfication was applied. At the first level, the level of membership to various descriptors describing the length of a given day was determined. On the second level of fuzzyfication, based on historical data, the degree of membership to classes representing different levels of sunshine at a given hour was determined. As part of numerical experiments, selected machine learning tools were used to predict the degree of membership to particular classes based on appropriately selected historical data. The obtained results confirm the very high prediction accuracy of the innovative solution that allows to determine the degree of insolation.
Towards the Interpretation of Multi-Label Image Classification Using Transformers and Fuzzy Cognitive Maps

Multi-label image classification is a challenging task in the field of computer vision. Recently, many deep learning approaches have been proposed to deal with this task; however, despite their effectiveness, they lack interpretability, in the sense that they are unable to explain or justify their outcomes. To address this limitation, in this paper, we propose a novel framework for interpretable multi-label image classification, based on Fuzzy Cognitive Maps (FCMs) and Transformers. The introduced framework distinguishes the most representative image semantics and analyzes them using cause-and-effect relationships. It provides understandable classification interpretations, using linguistic terms, in a way compatible to human perception, while being simple and easy to implement. The main contributions of this paper include: a) a novel FCM model enabling the interpretation of the outcomes of transformers; b) the first FCM approach developed to perform multi-label image classification; c) a mechanism to automatically define the weights of the FCM graph and the fuzzy sets needed, thus limiting human intervention in the definition of the FCM structure. Experiments on publicly available datasets demonstrates the effectiveness of the proposed framework, with competitive classification results in comparison to other transformer-based multi-label classifiers, while offering the advantage of interpretability.
Fusion of a Fuzzy Rule-Based Method and Other Decision-Making Models in Injury Prediction Problem in Football

As injury prevention in football is one of the main aspects of physical preparation, it has also become an important issue addressed by researchers and analysts. They use machine learning methods, rule-based decision systems, or statistical analysis, however, taking into account the complexity of injury prediction, the proposed methods still need to improve the effectiveness and explainability of their operation. This study presents one of the approaches aimed at improving the reliability of the decision model result, namely the creation of an ensemble model based on aggregated results of an expert knowledge rule-based model, a fuzzy model, and a machine learning method (XGBoost algorithm). As the component models are designed to be diverse, the ensemble model combines their features to optimize the decision-making process. This fusion of expert knowledge-based system, fuzzy system, and machine learning model aims to enhance the predictive outcomes.
A new tilted aerial robotic platform: Modeling and control

Aerial Manipulators are considered in modern applications due to their essential features that have the maneuverability of Unmanned Aerial Vehicles (UAVs) and abilities of robotics arms. One of the primary challenges encountered by aerial manipulators is their inability to maintain a consistent orientation though the duration of flight. This limits the application of aerial manipulators for movement of fragile and liquid objects that should not change their orientation during the flight. To solve this issue, this study introduced a new Aerial Robot Platform (AR-P) which can decouple all motions by configuring tilting rotors into the structure of the AR-P. The AR-P consists of a Tilted-Rotor Quadcopter (T-RQ) and three linked Aerial Robot Arm (AR-A) that have a gripper to grasp objects. The AR-P with this new mechanism of tilting parts overcomes the problem of manipulation limitations by adjusting the direction of the thrusted rotors according to the exerted forces of the grasped object. The new dynamics model of the AR-P is derived using the Lagrange-d'Alembert approach. A special Multi Input Multi Output (MIMO) Proportional Derivative (PD) controller based on Computed Torque Control (CTC) is designed for the developed AR-P. The method of nonlinear least squares is applied to obtain the optimal gains of the over-looped PD controller. The simulation results showed the ability of the developed CTC to track the required joint angle of each the T-RQ and each link of the AR-A with minimizing steady state errors and without overshooting. The implemented controller showed its robustness against external disturbances in form of wind conditions. On one hand, the controller is tested considering traditional aerial manipulator. On the other hand, the controller is tested by our new tilted aerial robot platform. The comparison results of the flight scenario between the new tilted aerial manipulator and the traditional one approved the advantage of the proposed new tilted AR-P to transport the grasped objects in fixed orientation.
Teacher’s Techniques in Teaching Reading Narrative Text to the Eighth Grade Students of UPTD SMP N 5 Pematang Siantar

This research aims to explore the various techniques employed by English teachers in teaching reading comprehension of narrative texts to eighth-grade students at UPTD SMP N5 Pematang Siantar. The participants in this research were eighth-grade English teachers at UPTD SMP N5 Pematang Siantar. The data were collected from various sources, including classroom observations, interviews, and analysis of teaching materials such as syllabi, textbooks, and lesson plans. The researcher employed a descriptive qualitative research approach. The data was meticulously analyzed, focusing on the teacher’s technique. The findings of this research revealed that teachers frequently used the technique of translating literature passages and employing question-and-answer exercises. This technique enables students to comprehend the content of the reading material, acquire information from the text, and enhance their critical thinking skills through direct engagement with the questions and answers.