Reviewerly/exHarmony huggingface dataset:
GMMDA: Gaussian Mixture Modeling of Graph in Latent Space for Graph Data Augmentation

Graph data augmentation (GDA), which manipulates graph structure and/or attributes, has been demonstrated as an effective method for improving the generalization of graph neural networks on semi-supervised node classification. As a data augmentation technique, label-preservation is critical, that is, node labels should not change after data manipulation. However, most existing methods overlook the label-preservation requirements. Determining the label-preserving nature of a GDA method is highly challenging, owing to the non-Euclidean nature of the graph structure. In this study, for the first time, we formulate a label-preserving problem (LPP) in the context of GDA. The LPP is formulated as an optimization problem in which, given a fixed augmentation budget, the objective is to find an augmented graph with minimal difference in data distribution compared to the original graph. To solve the LPP problem, we propose GMMDA, a generative data augmentation (DA) method based on Gaussian mixture modeling (GMM) of a graph in a latent space. The proposed GMMDA has three phases. First, a novel objective is designed to jointly learn a low-dimensional graph representation and estimate the GMM. The learning is followed by sampling from the GMM, and then the samples are converted back to the graph as additional nodes. To uphold label preservation, we designed a minimum description length (MDL)-based method to select a set of samples that produces the minimum shift in the data distribution. Through experiments, we demonstrate that GMMDA can improve the performance of graph convolutional network on CORA, CITESEER and PUBMED by as much as 7.75% 8.75% and 5.87%, respectively, significantly outperforming the state-of-the-art methods.
A Decentralized Multi-Agent Path Planning Approach Based on Imitation Learning and Global Static Feature Extraction

Abstract Multi-agent path planning is a crucial problem in numerous industrial robotic implementations, ranging from smart material transportation, mobile patrolling to automated warehousing. In this paper, we introduce a decentralized multi-agent path planning approach based on imitation learning and global static feature extraction. Our approach employs a convolutional neural network in the information extraction layer to obtain features from local field-of-view observations and expert-planned paths under a global static map. The information aggregation layer then uses a graph attention network to combine feature information from selected neighboring agents. The request-reply based selective communication is also applied in the information aggregation layer to identify appropriate neighboring agents to be included in the graph attention network. Finally, the action output layer translates the aggregated feature information into actions for each agent. Additionally, we develop a strategy switching mechanism that adaptively utilizes expert-planned paths under a global static map to support agents to escape from local traps. The effectiveness of our proposed approach is evaluated in simulated grid environments with varying map sizes, obstacle densities, and numbers of agents. Experimental results demonstrate that our approach outperforms other decentralized path planning methods in success rate and generalizability. Furthermore, our approach is computationally efficient and scalable, making it suitable for real-world applications.
Artificial Neural Network Application for Sepsis Prediction: A Preliminary Study

Sepsis is one of the leading causes of mortality in hospitalized patients. It is very difficult to find the symptoms of sepsis because of their similarity to the symptoms of other diseases. This paper aims to deliver an artificial neural network implementation in medical decisions support. This study tries to predict sepsis and healthy patient based on vital signs such as heart rate, systolic blood pressure, and diastolic blood pressure taken from the MIMIC-III clinical database. There were several extraction processes applied to vital sign signals such as using the statistical tools, discrete wavelet transforms, and Hilbert-Huang Transform. The ANN algorithm predicts the sepsis patient with 96.7% of accuracy. However, based on the medical requirement for artificial intelligent implementation, this result does not satisfy the requirement as the false positive error is 2.9%.
Hybrid Approach for Discovering k-Hamiltonian Paths in a Torus-Enhanced Butterfly Interconnected Network

The significance of interconnection networks extends beyond the semiconductor industry, becoming integral to industrial engineering, particularly in the era of the Internet of Things (IoT). Both sectors share a strategic emphasis on developing sophisticated networks to enhance performance and scalability. Techniques such as Cartesian product fusion have led to the development of novel interconnected networks, addressing the demand for high-speed communication. The novel network topology, including the Torus-Enhanced Butterfly (TREB) network, is developed by the Cartesian product of the Torus (TR) and Enhanced Butterfly (EB) networks and holds unexplored aspects, particularly concerning the existence of Hamiltonian paths. The objective of this study is to devise a methodology for identifying k-Hamiltonian paths in an extensive TREB network. Contributions include theoretical and algorithmic proofs of Hamiltonian path existence using a hybrid approach. This approach divides the large TREB network graph into four EB subgraphs, and from each subgraph, all Hamiltonian paths are sought using the brute force method as a subsolution. Next, a dynamic programming-based algorithm is used to connect the Hamiltonian paths in these four subgraphs into a complete Hamiltonian path in the TREB network. The experiments reveal several properties of the TREB network that can only be derived through algorithmic approaches. These include the exact number of paths, which can be found from a valid permutation arrangement of the EB subgraphs, reaching approximately one billion paths, and the sequence of nodes forming Hamiltonian pathways. Keywords: Hamiltonian paths, torus network, butterfly network, interconnection network, exact algorithms. https://doi.org/10.55463/issn.1674-2974.50.12.14
Submission system for text-mined annotations

From algorithms to the bench Text-mining holds the promise of helping researchers to overcome information overload. It is a familiar premise: an avalanche of scientific knowledge is being produced and shared. Teaching machines to “read” might soon be the only manageable way to digest large amounts of information into useful facts.
FAIR-biomed: a browser extension using Europe PMC API

What is FAIR-biomed? FAIR-biomed is a browser extension that aims to facilitate investigative research in biomedicine. It connects users to information stored in specialized databases. For example, when reading an online report, with simple clicks, FAIR-biomed can help you find all the relevant information on biological entities present in that report. For example, a gene, its description, along with its biological functions and interactors.
A Frequency Modulated Continuous Wave LiDAR System Based on Reservoir Computing

A frequency modulated continuous wave (FMCW) light detection and ranging (LiDAR) system based on reservoir computing (RC) is proposed and its reliability is verified through simulations and experiments. Fundamentally different from previous approaches where Fourier analysis is always required to determine distance information, here temporal signal analysis using RC is adopted. The system is robust to nonlinearity in frequency modulation of optical carrier, hence improving the range detection resolution. An intensity-modulated light is injected into a semiconductor laser to deliberately generate a non-ideal nonlinear wavelength scanning light source. The change in distance corresponds to the change in the IF signal over time. The RC classifies the IF signal waveform in the time domain to eliminate the need for linearization, and determines the distance from the classification results. The distance measurement resolution of this method is 1 cm in the 6.1 GHz scanning range, which shows that the method effectively addresses the effect of source nonlinearity due to the scanning wavelength while reducing computational complexity. The method has been demonstrated to reduce calculation cost.
Image Denoising via Multiscale Feature Extraction and Triplet Attention based CNN

Recent advances in deep learning have had a significant impact on the field of noise reduction and other image-processing tasks. One of the most important innovations of deep learning is the use of convolutional layers to extract features from images. However, most convolutional neural networks (CNNs) designed for noise reduction only perform feature extraction at a single scale. Additionally, the attention mechanism, which is commonly used for channel or spatial weighting of features, neglects the interdependence and interaction of features between different dimensions. This study proposes a new CNN network for noise reduction that addresses these limitations. The proposed network uses a triplet attention mechanism and multi-scale feature extraction to capture the interactions and interdependencies between different dimensions of features. Experimental results show that the proposed approach can effectively remove Gaussian noise from grayscale images and achieves state-of-the-art performance in both quantitative and qualitative evaluations.
Hybrid Wheelchair control method with EEG signal and facial Expression

While degrading their mobility, one of the major concerns of elderly/disabled people is affecting their ability to live independently. Assistive technologies for mobility are now designed to improve people’s living conditions. However, improvements are needed to existing mobility assist devices. This article explores the design and control of a smart wheelchair with two sources of control to improve wheelchair user safety. To improve user involvement and safety, the proposed system includes control interfaces with the EEG signal processing application using the EMOTIV Insight EEG headset in addition to facial expressions using a webcam, at the same time defining the state of the user.
Abstract User Goals in Open-Domain Dialog Systems

In task-oriented dialog systems, conversational agents have the means to plan the dialog to accomplish user tasks (e.g., order pizza). In chit-chat systems, there are no such straightforward tasks. Yet, in chit-chat dialogs people still pursue goals, but these goals are more abstract and thus less formalizable. In this work, we describe the development process of two goal-aware prototypes of a chatbot. The first prototype features entirely human-crafted scenarios for seven topic-specific (low-level) goals and a Goal Tracker service that detects these goals and monitors the process of their achievement. The other one combines pre-written utterances with response generation using DialoGPT model to cover the scenarios of four general (high-level) goals. The results show that introducing the concept of goals improves performance of a chit-chat dialog system. Qualitative analysis of conversations with the High-Level goals prototype demonstrates cases where a goal-aware chatbot outperforms the original one.
Strategies for creating inclusive learning for learners experiencing barriers to learning

Inclusive learning compels all public schools to accommodate all learners and imple­ment curricula that meet their needs. This study explored strategies for creating inclu­sive learning for learners who experience barriers to learning. Most of the literature on inclusive learning in schools has discussed the tension between theoretical approaches and the practical achievements of inclusive learning during teaching and learning. Furthermore, the local literature has demonstrated that educators try to modify and differentiate the existing curriculum with no tangible results for learners experiencing barriers to learning and not comprehending the content of the curriculum. This gene­ric interpretive qualitative case study was conducted in the uMgungundlovu District (KwaZulu-Natal Province, South Africa), using semi-structured interviews with four teachers selected from four primary schools. Observations were conducted during tea­ching and learning, and documents were reviewed. The themes were derived and used through thematic data analysis to yield the study findings and draw conclusions. The study results indicated a shift toward inclusive learning, whereby learners experiencing barriers to learning can learn and better comprehend the curriculum's content through role-play, constructing objects, and drawing. Therefore, learners experiencing barriers to learning felt part of the teaching and learning process and could transform their contextual affordance into actions, which sheds light on their education journey.
Experimental Investigation of an Optical Reservoir Computer with MZI-based Optical Mask

In this paper, we have experimentally demonstrated a novel all-optical masking solution in photonic time stretch reservoir computing to eliminate the need of high-speed signal generator for digital masks. By leveraging the high chirp rate generated through an unbalanced Mach-Zehnder Interferometer (MZI) structure, we achieved a random optical mask in the spectral domain. Optical wavelengths, acting as virtual reservoir nodes, enable spectral mixing within the reservoir layer. Thanks to time-spectrum mapping in the photonic time stretch system, equivalent temporal masking is obtained. The develop system exhibits memory capacity through delayed optical feedback process. A full photonic reservoir computing system is implemented. Two experiments of waveform and digit-spoken signal classification have been carried out. Our experimental results show that MZI-based optical mask enables superior performance in photonic time stretch reservoir computing.
School of Computing Course Slots Distribution System

Every year there will be thousands of students enrolling into universities. Before the starting of every semester, the head of department in all schools have a very important job which is deciding the numbers of classes/slots to be opened for every course and submit them to the Academic Affairs (HEA) office. The number of classes/slots for each course is determined based on the number of students enrolled for the course. Knowing the accurate number of classes/slots to be opened could help in allocating suitable lecture halls for each class/slot. This would then ensure the lecture halls are used to the fullest and the students would have a comfortable learning environment. The traditional method of counting the number of classes/slots manually course by course is rather troublesome and time consuming. Therefore, a web-based School of Computing (SOC) Course Slots Distribution System is developed to help solve the problem. The system aims to distribute the classes/slots for each course in BSc in Information Technology and BSc in Computer Science according to the number of students enrolled for the course. The system is developed using HTML, PHP and CSS. The system was then tested by the representatives of SOC management and evaluated based on user acceptance using an online survey platform – Google Form. The result of the evaluation suggested that the SOC Course Slots Distribution System is usable to assist the head of department of every school in Universiti Utara Malaysia (UUM) to calculate the number of classes/slots to be opened for each course efficiently. The respondents also suggested for the system to have lab allocation as well for further improvement. With the help of this system, the workload of head of departments will greatly decrease.
Explaining the Effectiveness of Machine Learning in Malware Detection: Insights from Explainable AI

We use Explainable Artificial Intelligence (XAI) to understand and assess the decisions made by ML models in Android malware detection. To evaluate malware detection, we conducted experiments using seven datasets. Our findings indicate that it is possible to accurately identify malware across multiple datasets. However, each dataset may have a different collection of features available. We also discuss the implications of incorporating expert-dependent features into the malware detection procedure. Such features have the potential to increase model accuracy by detecting minor indicators of harmful behaviour that automated algorithms may miss. However, because of the necessity for in-depth manual analysis, this strategy increases the resource and time requirements. It also risks adding human bias into the models and raises scaling issues in the continuously developing Android application landscape. Our results suggest that XAI techniques should be used to help malware analysis researchers understand how ML models work, rather than only concentrating on increasing accuracy.
Contrastive Learning Based Human Activity Recognition Using Body Sensors

Pervasive sensing and wearable sensor techniques have been increasingly employed to monitor and recognize human activities through body sensors in areas of smart healthcare and manufacturing. However, conventional machine learning or deep learning based human activity recognition (HAR) requires a large amount of labeled data, which is cost-expensive in real-world scenarios. To tackle this issue, we propose a contrastive learning framework for HAR (CL-HAR), which utilizes the generated unlabeled data as the input and explores the supervised features of the unlabeled data under the principle of self-supervised learning. A simple yet effective backbone network as a feature extractor for subsequent activity recognition is proposed. By using a small portion of the labeled samples as the training set which is fed into our learned feature extractor, we build a classifier and use the rest of the data to verify the feasibility and effectiveness of our CL method. Extensive experiments on three benchamark datasets and one real-world dataset demonstrate that CL-HAR can achieve better classification accuracy than compared supervised and semi-supervised methods with less labelled samples, which is of practical use.
First Demonstration of a Bayesian Machine based on Unified Memory and Random Source Achieved by 16-layer Stacking 3D Fe-Diode with High Noise Density and High Area Efficiency

In this work, a Bayesian machine based on unified memory and random source using 16-layer stacking 3D Fe-diode is experimentally demonstrated for the first time. A 3D integration of Fe-diode can provide high area efficiency for storage and ultra-high memory density per unit area (0.26F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /bit) can be realized. Meanwhile, shot noise in Fe-diode is selected as the random source owing to the frequency-independent high noise density compared with other types of noise. A low cost TRNG circuit based on shot noise is then proposed to generate stochastic bit-streams (SBS) with eliminated correlation, validated by ACF and NIST tests. The Bayesian machine is finally implemented by unified memory and random source based on 16-layer 3D Fe-diode array and excellent properties (95.31% MN1ST recognition accuracy, lowest RMSE of 0.026 and 10% noise tolerance) can be successfully achieved.
Advancing High-Performance Large-Scale Quantum Computing with Cryogenic 2D-CMOS

We present the first comprehensive evaluation of 2D-semiconductors (2DS) as a promising materials platform for realizing cryogenic-CMOS interface-electronics to enable highperformance large-scale quantum computing. Our study encompasses ab-initio density functional theory and quantum transport simulations as well as the development of a novel 2D-FET compact model applicable at cryogenic temperatures. Our analyses reveal that the pristine interface, uniform thickness, excellent electrostatics, high density of states, smaller Urbach energy, lower thermal and flicker noise of 2DS w.r.t conventional bulk-semiconductors such as Si, Ge, and GaAs allow them to be employed in cryogenic CMOS FETs with ultra-low supply voltages, minimal device-to-device variation, and unprecedented improvements in energy-efficiency and performance, thereby paving the way for next-generation 2DS based cryo-electronics and large-scale quantum computers.
Implementing Set-Valued k-Modes Clustering Technique to Enhance the Quality of Library Book Recommendation System using Hybrid Semantic Ontology-based model

This research implements the Set-Valued k-Modes (SV-k-Modes) clustering technique on the Hybrid Semantic Ontology-based (HSO) model for the library book recommendation system. Note that HSO originally consisted of three algorithms: Collaborative Filtering (CF) Matrix Factorization, Content-based (CB) ONTO Semantic Similarity (ONTO), and Hybrid. In this case, we cluster the library books before the implementation of the CB ONTO algorithm of HSO such that the calculation of books’ semantic similarities is based on the books of each cluster instead of all books in the system. SV-k-Modes technique consists of three main consecutive algorithms. First is the algorithm for generating initial cluster centers which depends on the density and mutual Jaccard coefficient distance of each book. The second algorithm is for clustering the books into k clusters which computation is based on the dissimilarity or distance measure between the books with their set-valued keywords. Third is the algorithm for updating the centers in the second algorithm which is based on the highest frequency of keywords in the clusters. The second and third algorithms are repeated until convergence. A series of experiments using a real-world library dataset shows the outperformance of our proposed method compared to the original HSO method. The average increase percentage of our proposed method from Top-1 to Top-20 in terms of the Normalized Discounted Cumulative Gain (NDCG) is 1.002%. The increase is not very significant but it confirms that adding the SV-k-Modes technique on the HSO model can increase the performance of the library book recommendation system.
Creating Own AI Datasets from Different Language Sources Efficiently: Advantages, Disadvantages and Best Pracitces to Make Own Datasets

Artificial Intelligence (AI) has become a ubiquitous technology that has the potential to revolutionize many industries. However, AI requires access to vast amounts of data to achieve its full potential. By understanding these data sources and the best practices for working with them, we can unlock the full potential of AI and create groundbreaking solutions that benefit society as a whole. Data sources can be utilized to train AI algorithms, including structured and unstructured data, but finding relevant datasets is challenging. The main problem is the international sources. Most of the datasets are available only in English or other common languages. However these are good sources, but translating them is expensive and time-consuming. This paper shows how to generate own datasets from open-source data with translation.