Reviewerly/exHarmony huggingface dataset:
Quantum Fourier Transform‐Based Arithmetic Logic Unit on a Quantum Processor

Abstract This study proposes and construct a primitive quantum arithmetic logic unit (qALU) based on the quantum Fourier transform (QFT). The qALU is capable of performing arithmetic ADD (addition) and logic NAND gate operations. It designs a scalable quantum circuit and presents the circuits for driving ADD and NAND operations on two‐input and four‐input quantum channels, respectively. By comparing the required number of quantum gates for serial and parallel architectures in executing arithmetic addition, it evaluates the performance. It also execute the proposed quantum Fourier transform‐based qALU design on real quantum processor hardware provided by IBM. The results demonstrate that the proposed circuit can perform arithmetic and logic operations with a high success rate. Furthermore, it discusses in detail the potential implementations of the qALU circuit in the field of computer science, highlighting the possibility of constructing a soft‐core processor on a quantum processing unit.
The Development of a Web-based Library Management System using RFID Technology

A library is a non-profit repository that stores a valuable collection of human knowledge and information. Next, RFID stands for Radio-Frequency Identification, which uses RFID tags to gather digital data. The main research objectives are to design, develop and test a Web-based Library Management System using RFID Technology. The Waterfall model was employed in the development of this system which requires each phase of work to be completed before going on to the next. It is easy to comprehend and use. The proposed system worked effectively for handling book borrowing and return tasks and minimized errors that may occur while utilising manual library management. Future improvements that may be made to the project to make it more effective are based on the book tracing and future security issues such as theft detection.
Managerial Implications for Adopting Advanced Biometric Authentication: A Case Study on Optimizing Keystroke Dynamics Using ISKKC

This paper offers a technology management lens for deploying and optimising advanced biometric authentication systems, utilising the Integrated SVM-KNN Keystroke Classifier (ISKKC) model as a case study. The ISKKC system employs an innovative machine learning stack, including regression classifiers, k Nearest Neighbors (KNN), and Support Vector Machines (SVM), to amplify the accuracy of keystroke dynamics, especially in multi-lingual settings. Utilising a comprehensive research design, we gathered and analysed data from 60 participants who are native speakers of either Malay or Chinese. The study's findings substantiate that ISKKC surpasses standalone authentication models in various performance metrics such as accuracy, precision, recall, and specificity. For technology managers, these results translate into strategic advantages, including enhanced system reliability, lower risk of security breaches, and higher overall Return On Investment (ROI). The study also explores managerial considerations such as the ease of ISKKC's integration into existing technology infrastructures, the costs associated with its implementation, and the strategic alignment with organisational objectives. Furthermore, we discuss how the ISKKC model can contribute to a sustainable competitive advantage by offering superior authentication security, facilitating compliance with regulatory standards and customer trust in an increasingly digital ecosystem. This research aims to guide technology managers to make informed decisions on adopting and optimising biometric authentication systems, emphasising the operational and strategic advantages conferred by machine learning algorithms like those incorporated in ISKKC.
An Application of the Dijkstra’s Algorithm Model in Finding Shortest Traveling Time of Ambulance to Hospital Kuala Lumpur

Traffic congestion has become a widespread issue in many well-developed areas. This issue has mainly caused problems for ambulance services. This study was conducted to solve this issue. One of the factors of this problem is due to the increase in population. It is well known that Kuala Lumpur is a heavily populated area. Thus, this results in traffic congestion around Hospital Kuala Lumpur (HKL). The main goals of this study are to determine the shortest path between two nodes and to reduce the amount of time an ambulance needs to drive from the hospital to the accident scene. This study guarantees the Dijkstra's Algorithm method's functional efficacy within a 5-kilometer radius of HKL. As additional elements need to be taken into account, there are crucial procedures that need to be taken when using Dijkstra's Algorithm to guarantee that the time and distance computations can be approximated accurately. There are certain implicit consequences that may be detected from this project once the planning has been decided. Patients can therefore get prompt medical attention.
The Development of Sports Facilities Booking System

Sports Facilities Booking System was developed to upgrade the manual booking system to a web-based system in facilitating bookers and managers of mentioned facilities. Preliminary literature studies done by the German Malaysian Institute, the Universiti Sains Malaysia and the University of Macau on similar projects have been used as guidance during the initial planning and requirements of the projects. The main purpose of this project is to facilitate the sports management of UTHM Pagoh Campus and provide a platform for university community to book facilities easily without any problems. The development of this study is guided by an Iterative model that involves the planning phase, analysis and design, implementation, testing and finally the evaluation phase. The software used in system development is Xampp (Apache and MySQL), Bracket, and phpMyAdmin, while the programming languages used are HTML, PHP, SQL, CSS, and JavaScript. This study focuses on producing a system that is full of functionality and has a premium interface to make it easier for users to interact with the system. It was a challenge to develop a system that could differentiate our products from others, but some minor details like emphasizing booking facilities based on a three-hour concept, where reservation is being divided into 3 separate sessions of three hours managed to cover the differences point needed. In addition, the use of legends to distinguish slot availability and provide a function to view the booking list is the result of this study. With that, the aim of developing, helping and evaluating certain factors in our initial objectives has been achieved.
Oncological Applications of Quantum Machine Learning

Background: Cancer is a leading cause of death worldwide. Machine learning (ML) and quantum computers (QCs) have recently advanced significantly. Numerous studies have examined the application of quantum machine learning (QML) in healthcare and validated its superiority over classical ML algorithms. Objectives: This review investigates and reports the oncological applications of QML. Methods: In March 2023, an electronic investigation of PubMed, Scopus, Web of Science, IEEE, and Cochrane databases was performed. The articles were screened based on titles and abstracts, and their full texts were examined. Results: Initially, a total of 207 articles were retrieved. Thereafter, 9 articles were included in the study, most of which were published from 2020 onwards. The results indicated the implementation of various QML techniques in different aspects of oncology, such as reducing mammography image noise, edge detection of breast cancer, clinical decision support in radiotherapy treatment, and cancer classification. Conclusion: These studies revealed that integrating quantum science with ML can significantly improve patient care and clinical outcomes. Future studies should explore the integration of QC and ML and the development of novel algorithms to enhance cancer prognosis, diagnosis, and treatment planning.
State Estimation for Linear Systems With Quadratic Outputs

This letter deals with the problem of state estimation for a class of systems involving linear dynamics with multiple quadratic output measurements. We propose a systematic approach to immerse the original system into a linear time-varying (LTV) system of a higher dimension. The methodology extends the original system by incorporating a minimum number of auxiliary states, ensuring that the resulting extended system exhibits both linear dynamics and linear output. Consequently, any Kalman-type observer can showcase global state estimation, provided the system is uniformly observable.
Decentralized Energy Trading Using Blockchain Technology: A Novel Approach to Democratizing the Energy Market

The Power sector is one of the largest and most important industries globally, significantly impacting the economy and the environment. However, traditional energy trading models are plagued by a lack of transparency, inefficiency, and centralization. This research paper addresses these problems by proposing a decentralized energy trading platform based on blockchain technology. The platform utilizes smart contracts and peer-to-peer transactions to enable secure, transparent, and efficient energy trading. The proposed platform will facilitate the democratization of the energy market by allowing prosumers (producers and consumers of energy) to trade excess energy directly with each other without the need for intermediaries. The study also explores the potential benefits and challenges of implementing such a platform and its impact on the energy market and the environment.
DYNet: A Printed Book Detection Model Using Dual Kernel Neural Networks

Target detection has always been a hotspot in image processing/computer vision research, and small-target detection is a frequently encountered problem in the field of target detection. With the continuous innovation of target detection technology, people always hope that the detection of small targets can reach the real-time accuracy of large-target detection. In this paper, a small-target detection model based on dual-core convolutional neural networks (CNN) is proposed, which is mainly used for the intelligent detection of books in the production line of printed books. The model is mainly composed of two modules, including a region prediction module and suspicious target search module. The region prediction module uses a CNN to predict suspicious region blocks in a large context. The suspicious target search module uses a different CNN from the above to find tiny targets in the predicted region blocks. Comparative testing of four small book target samples using this model shows that this model has better book small-target detection accuracy compared to other models.
A Reversible Data Hiding Technique for Secure Image Transmission

In this paper we propose a methodology for image to text conversion process, which first transforms colour image to grayscale and then performs image to Unicode translation to transform the pixel intensity values into its corresponding Unicode representation for generating a text file. The paper defines an algorithm for the file format conversion process which hides the identity of the image file format as a text file that can later be retrieved back through its inverse transformation procedure. The algorithm also introduces an optimal divisor identification process that improves the efficiency of the methodology. The encoded text file is then Huffman coded to generate a compressed file, which can be decoded to reconstruct the original grayscale image back without much loss of information. The benchmarks such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) attained are as high as 82.25db and 0.9318. Also the Mean Square Error (MSE) has been reduced to as low as 0.0001 respectively, which indicates that the proposed methodology can very well be applied in those application domains where secure transmission of confidential images are highly essential to maintain the integrity of data.
Robustness of Wave–Particle Duality under Unruh Effect

By considering a uniformly accelerated two-level system in an initial superposition state of a qubit, we investigate the loss of coherence induced by the acceleration. In addition, we investigate the impact of acceleration on the complementarity relation in a quantum interferometric circuit or quantum scattering circuit. We present an alternative approach to exploring acceleration effects through examination of quantum coherence decay and degradation in the interference pattern. Our investigations help to provide understanding of the consequences of decoherence induced by the Unruh effect on the wave–particle duality of a uniformly accelerated qubit.
VSA-SD: A Service Discovery Method Based on Vector Symbol Architecture for Low-Cost IoT System Development

In recent years, with the widening applications of the Internet of Things (IoT), more and more perception services (e.g. air quality indicator services, road traffic congestion monitoring services, etc) with different arguments (e.g. data type, source location, creator, etc) will be deployed by dedicated IT infrastructure service providers for constructing customized IoT systems with low cost by subscription. So it is an indispensable step to check whether the required perception services with specified arguments have been available for the constructing IoT through discovery method to reduce the redundancy of service deployment. However, it is a challenging problem to design efficient (i.e. achieving high accuracy and low response delay with low overhead), highly robust, and trustworthy mechanisms for discovering perception services on resource-constrained IoT devices. To solve this problem, we proposed a distributed service discovery method, named VSA-SD, based on the Vector Symbolic Architecture (VSA). This method employs hyperdimensional vectors to describe services in a distributed manner, and measures the degree of service matching by calculating the Hamming distance, thereby achieving service discovery. We implemented VSA-SD in NBUFlow, which is an IoT task construction and offloading test platform, and evaluated its performance through comprehensive experiments. Results show that VSA-SD outperforms the centralized, hybrid, and other distributed service discovery mechanisms in terms of accuracy, response delay, overhead, robustness, trustability, interoperability, and mobility.
ALIH KODE DAN CAMPUR KODE DALAM INTERAKSI PEMBELAJARAN BAHASA INGGRIS DI FAKULTAS PERTANIAN UNIVERSITAS TRUNOJOYO MADURA

The purpose of the research is to describe code choice and code mixing in the interaction of learning English in Agriculture Faculty University of Trunojoyo Madura. The reseach data included the code switching and code mixing of Agriculture students when learning English in the classroom. The data analysis use a contextual approach of Speaking by Dell Hymes. The result of the research showed that code switching used by the students in the Faculty of Agriculture, namely English and Indonesia, switching codes from English into Indonesia and vice versa, meanwhile code mixing from English into Inonesia, and mixed Indonesia into Javanese, mixed Javanese into English.
Few-shot learning based on deep learning: A survey

&lt;abstract&gt;&lt;p&gt;In recent years, with the development of science and technology, powerful computing devices have been constantly developing. As an important foundation, deep learning (DL) technology has achieved many successes in multiple fields. In addition, the success of deep learning also relies on the support of large-scale datasets, which can provide models with a variety of images. The rich information in these images can help the model learn more about various categories of images, thereby improving the classification performance and generalization ability of the model. However, in real application scenarios, it may be difficult for most tasks to collect a large number of images or enough images for model training, which also restricts the performance of the trained model to a certain extent. Therefore, how to use limited samples to train the model with high performance becomes key. In order to improve this problem, the few-shot learning (FSL) strategy is proposed, which aims to obtain a model with strong performance through a small amount of data. Therefore, FSL can play its advantages in some real scene tasks where a large number of training data cannot be obtained. In this review, we will mainly introduce the FSL methods for image classification based on DL, which are mainly divided into four categories: methods based on data enhancement, metric learning, meta-learning and adding other tasks. First, we introduce some classic and advanced FSL methods in the order of categories. Second, we introduce some datasets that are often used to test the performance of FSL methods and the performance of some classical and advanced FSL methods on two common datasets. Finally, we discuss the current challenges and future prospects in this field.&lt;/p&gt;&lt;/abstract&gt;
Finitary Simulation of Infinitary $\beta$-Reduction via Taylor Expansion, and Applications

Originating in Girard's Linear logic, Ehrhard and Regnier's Taylor expansion of $\lambda$-terms has been broadly used as a tool to approximate the terms of several variants of the $\lambda$-calculus. Many results arise from a Commutation theorem relating the normal form of the Taylor expansion of a term to its B\"ohm tree. This led us to consider extending this formalism to the infinitary $\lambda$-calculus, since the $\Lambda_{\infty}^{001}$ version of this calculus has B\"ohm trees as normal forms and seems to be the ideal framework to reformulate the Commutation theorem. We give a (co-)inductive presentation of $\Lambda_{\infty}^{001}$. We define a Taylor expansion on this calculus, and state that the infinitary $\beta$-reduction can be simulated through this Taylor expansion. The target language is the usual resource calculus, and in particular the resource reduction remains finite, confluent and terminating. Finally, we state the generalised Commutation theorem and use our results to provide simple proofs of some normalisation and confluence properties in the infinitary $\lambda$-calculus.
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network

Although 3D point cloud classification neural network models have been widely used, the in-depth interpretation of the activation of the neurons and layers is still a challenge. We propose a novel approach, named Relevance Flow, to interpret the hidden semantics of 3D point cloud classification neural networks. It delivers the class Relevance to the activated neurons in the intermediate layers in a back-propagation manner, and associates the activation of neurons with the input points to visualize the hidden semantics of each layer. Specially, we reveal that the 3D point cloud classification neural network has learned the plane-level and part-level hidden semantics in the intermediate layers, and utilize the normal and IoU to evaluate the consistency of both levels' hidden semantics. Besides, by using the hidden semantics, we generate the adversarial attack samples to attack 3D point cloud classifiers. Experiments show that our proposed method reveals the hidden semantics of the 3D point cloud classification neural network on ModelNet40 and ShapeNet, which can be used for the unsupervised point cloud part segmentation without labels and attacking the 3D point cloud classifiers.
Efficient Constraint Learning For Stream Reasoning

Nowadays, large amounts of data are generated by a wide range of data streams. This data contains valuable knowledge to support decision-making processes in a variety of application domains, such as smart cities, social media analysis, and the Internet of Things. To address the constantly changing data, stream reasoning has emerged as a crucial method for performing logical reasoning on streams of data. This paper focuses on the limitations of existing stream reasoning systems based on Answer Set Programming (ASP). It proposes a novel solution based on Deep Reinforcement Learning (DRL) to handle continuous data streams efficiently. Existing ASP-based stream reasoning systems face challenges in managing their internal state and effectively handling data streams over extended periods. To overcome these limitations, we propose an approach that integrates cache management techniques with DRL and ideas from heuristics developed for the Conflict-Driven Constraint Learning (CDCL) algorithm. DRL is chosen for its ability to outperform traditional Reinforcement Learning (RL) algorithms in managing complex tasks and generalizing learned policies to unseen situations. The research contributions include a DRL-based framework that efficiently manages learned constraints in ASP-based stream reasoning engines. The proposed approach is compared to recent work in the literature, and the results demonstrate its benefits in terms of average cache utilization. The findings showcase the potential of the proposed DRL-based solution for improving the performance and widespread implementation of stream reasoning with ASP engines. By effectively handling the dynamic and continuous nature of data streams.
A Two-stage Approach for Tables Extraction in Invoices

The automated analysis of administrative documents is an important field in document recognition that is studied for decades. Invoices are key documents among the documents available in companies and public services. Most of the time, invoices include data that are presented in tables. These tables must be clearly identified to extract suitable information. In this paper, we propose an approach that combines an image processing-based estimation of the shape of the tables with a graph-based representation of the document. We aim to precisely identify different types of tables, including possible complex layouts. We propose an experimental evaluation using a real case application and a classic dataset.
On data selection for the energy efficiency of neural networks: Towards a new solution based on a dynamic selectivity ratio

In this paper, we address the energy efficiency of neural networks training through data selection techniques. We first study the impact of a random data selection approach that renews the selected examples periodically during training. We find that random selection should be considered as a serious option as it allows high energy gains with small accuracy losses. Unexpectedly, it even outperforms a more elaborate approach in some cases.Our study of the random approach conducted us to observe that low selectivity ratios allow important energy savings, but also cause a significant accuracy decrease. To mitigate the effect of such ratios on the prediction quality, we propose to use a dynamic selectivity ratio with a decreasing schedule, that can be integrated to any selection approach. Our first results show that using such a schedule provides around 60% energy gains on the CIFAR-10 dataset with less than 1% accuracy decrease. It also improves the convergence when compared to a fixed ratio.