Miracle-dz/UniFAR-FTUs huggingface dataset:
HGTDG: An Interpretable Heterogeneous Graph Transformer Framework for Cancer Driver Gene Prediction

Accurately predicting cancer driver genes remains challenging due to the increasing size and complexity of cancer genomic data. In this study, HGTDG is proposed, a heterogeneous graph transformer framework for predicting cancer driver genes and exploring downstream tasks. The framework includes a heterogeneous graph construction module that constructs a gene-protein heterogeneous network based on KEGG pathways and the protein-protein interactions from the STRING database. In addition, the framework introduces a novel heterogeneous graph transformer module that uses multi-head attention mechanisms for gene node embedding. The transformer module can capture dedicated representations for genes and edges. Finally, the generated gene embeddings are fed into the classification module to classify genes into driver and non-driver genes. The experiment results show that HGTDG outperforms the state-of-the-art methods regarding the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC).
Mapping the dominant forest tree distribution using a combined image classification approach in a complex Eastern Mediterranean basin

Abstract A land use/cover (LUC) classification strategy based on an unsupervised k-means, object-based and expert knowledge classification technique was cross-checked using Landsat satellite datasets along with ancillary data for mapping dominant forest tree species of the Göksu River Basin in the Eastern Mediterranean Region. Eight dominant forest tree species were classified as juniper (Juniperus excelsa Bieb), Taurus fir (Abies cilicica Ant. & Kotschy), Turkish pine (Pinus brutia Tenore), black pine (Pinus nigra Arnold), cedar (Cedrus libani Rich), oak (Quercus pubescens Schwarz and Quercus cerris Pall), stone pine (Pinus pinea L.) and plane (Platanus orientalis L.). The results of the combined classification approach (CCA) were compared with a traditional maximum likelihood classifier (MLC) for a better understanding of the benefits of the CCA. Kappa values of the CCA and the MLC were derived as 0.77 and 0.44 respectively.
Finite-Time Output Feedback Stabilization for High-Order Stochastic Nonlinear Systems With Unknown Output Function

This study investigates the problem of finite-time output feedback stabilization in probability for a class of stochastic high-order nonlinear systems with unknown output function. By combining the homogeneous domination techniques, backstepping method and sign function, we obtain the output feedback controller that includes state controller and recursive reduced-order observer. Subsequently, we present an analytical method to deal with the unknown output function within the stochastic high-order nonlinear system such that the designed output feedback controller can make the closed-loop system globally finite-time stabilization in probability when the unknown output function belongs to the maximum open sector $\Omega $ . The numerical simulation results show that the proposed scheme is feasible.
Credit-based congestion pricing : a policy proposal and the public s response I

Credit-based congestion pricing (CBCP) is a novel strategy proposed here. A revenue-neutral policy where road tolls are based on the negative externalities associated with driving under congested conditions, its generated tolls are returned to all licensed drivers in a uniform fashion, as a sort of driving ‘‘allowance’’. Essentially, the ‘‘average’’ driver pays nothing, while frequent long-distance peak-period drivers subsidize others, in effect paying them to stay off congested roads. In order to anticipate initial public response to a CBCP policy, 500 individuals completed a detailed survey regarding perceptions of, and likely travel reactions to, such a policy. Weights were developed to correct for survey biases in gender, age and household income. Analytical results suggest that 25% support this new strategy, and support is strongly related to familiarity with the concept of congestion pricing. Respondent estimates of congested travel times to work or school are almost double the uncongested times. Values of travel time vary greatly across respondents, as also trip flexibility. Those without children, younger respondents, and those with fewer vehicles appear more willing to support such a policy and more likely to modify their travel behaviors. Older people and full time employed people were willing to pay higher 0965-8564/$ see front matter 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.tra. 2005.02. 014 q Presented at the 10th International Conference on Travel Behaviour Research (Lucerne, Switzerland, 10–14 August 2003) and at the Theory and Practice of Congestion Charging Conference (Imperial College, London, 18–20 August 2003). * Corresponding author. Tel. : +1 512 471 0210; fax: +1 512 475 8744. E-mail addresses: kkockelm@mail.utexas. edu (K.M. Kockelman), sukumar@mail.utexas. edu (S. Kalmanje). 672 K.M. Kockelman, S. Kalmanje / Transportation Research Part A 39 (2005) 671–690 CBCP tolls to drive during peak hours and so were men as compared to women. The survey results corroborate the potential of a CBCP policy to alleviate congestion and generate benefits across income groups and traveler types. 2005 Elsevier Ltd. All rights reserved.
Machine Learning and Deep Learning Techniques for Predictive Modeling of Marine Ecosystem – A case of Flic en Flac Region, Mauritius

The uncertainty and adverse impact of climate change on marine ecosystems is highly evident through diminishing numbers of fish assemblages and hard corals within lagoons. Hence, modeling and predicting the future climatic conditions and existence of flora and fauna is quite challenging. This research carried out in Flic en Flac lagoon of Mauritius, aimed to investigate the relationship between the pertinent independent variables / key predictors such as Sea Surface Temperature, pH, Ocean Salinity, Chemical Oxygen Demand, Nitrate-Nitrogen, Phosphate and dependent variables such as the hard corals, fish assemblages, algae and abiotic factors. Researchers used these historic data and evaluated predictive modelling using various machine learning and deep learning models, including Random Forest Classifier (RF), Bagging Regressor (BR) AdaBoost Regressor (ABR), Linear Regression (LR), Time Series forecast using Seasonal Auto Regressive Integrated Moving Average (SARIMAX), and Long Short-Term Memory (LSTM). The in-depth data analysis revealed as first finding, a strong association between pH, sea surface temperature and marine community structure. Secondly, by comparing the model evaluation metrics of the considered algorithms, the study also confirmed that SARIMAX has better predictive accuracy over other models. This research concludes that proposed predictive model which is a Combined Multiple Regression Algorithms consisting of Linear Regression, Random Forest Regressor, Support Vectors Regression, Multi-Layer Perceptron Neutral Network Regression, is taken as voting ensemble regressor and is found reliable for predictive modeling as the prediction metrics recommend their combined use. Research findings further established that the hybrid approaches, such as training and evaluating SARIMAX and LSTM on pre-clustered data, may enhance predictive quality.
Research on Chinese Elementary Geometry Text Matching Based on Self-Attention and Bi-LSTM Network

Text matching can assist computers in understanding and processing large amounts of textual data, thereby providing more intelligent and personalized services. Addressing issues such as missing traditional machine learning mathematical pre- training data and incomplete semantic extraction of mathematical formula features, a Chinese mathematical plane geometry text matching model based on self-attention mechanism and bidirectional long short-term memory networks (SABi-LSTM) is proposed. The model constructs a parsing tree for mathematical expressions, divides semantics, and establishes a Bi-LSTM network for feature extraction. The performance and timeliness of the model's network structure are evaluated, optimizing the Bi-LSTM layers and hidden layer nodes of the proposed network to determine the optimal network structure. A self-attention mechanism is introduced for optimizing weight parameters to reduce the impact of noise data. Experimental results indicate that compared to traditional machine learning algorithms, the Bi-LSTM network with the introduced self-attention mechanism improves the accuracy of Chinese mathematical text matching by an average of 0.55%. After further optimizing the construction of word vectors, the recognition accuracy increases by an average of 0.34%, resulting in a final accuracy of 89.49%. The proposed model effectively determines the matching degree of Chinese elementary geometry text.
A Transformer-Based Deep Learning Architecture for Accurate Intracranial Hemorrhage Detection and Classification

Intracranial Hemorrhage (ICH) is a critical medical condition characterized by bleeding within the skull or brain, necessitating rapid and precise diagnosis for optimal treatment and enhanced patient outcomes. This paper introduces an innovative deep learning architecture, specifically the Swin Transformer, for the detection and classification of ICH. The proposed model achieves a remarkable log loss of 0.04372, outperforming traditional convolutional neural network-based approaches. Furthermore, it encompasses a comprehensive desktop application tailored for healthcare professionals, facilitating streamlined ICH assessment. This pioneering approach not only represents a significant advancement in medical imaging but also carries the potential to revolutionize the landscape of ICH diagnosis. The paper aims to bridge the gap between cutting-edge technology and practical healthcare applications, offering invaluable insights that resonate with healthcare practitioners. It is believed that the proposed research findings provide a lucid perspective, empowering healthcare professionals with a powerful tool to enhance their diagnostic capabilities in the critical realm of ICH detection and classification.
A Personalized Restaurant Recommendation Based on Topic Modeling of Online Reviews

These days, numerous online reviews for restaurants are available on the Internet. People often refer to these reviews to gain insights and make decisions about which restaurants they would like to visit. However, sifting through a large number of reviews can lead to confusion due to the abundance of lengthy texts. To address this issue, our study proposed a personalized restaurant recommendation system based on a topic list derived from topic modeling of online reviews. Additionally, we developed a user-friendly web application (web app) that enables users to easily find restaurants based on their preferences, including food type, location, district, and restaurant topic. The initial step involved collecting and preprocessing online reviews of restaurants in Seoul, South Korea. We then utilized Latent Dirichlet Allocation (LDA) to group restaurants according to frequently discussed themes or topics. These generated groups serve as topic descriptions, and each restaurant is assigned to a specific group based on this categorization. However, we recognized that some of the topic descriptions may contain menu names that could be unfriendly or new for certain users. To address this, we integrated OpenAI models through its Application Programming Interface (API) and developed an additional page that allows users to inquire about the full description and view image of the menu in their preferred language. The study is expected to benefit users by offering personalized restaurant recommendations and increasing spending, thus boosting economic activity in the post-pandemic era.
Privacy Analysis for Quantized Networked Control Systems

Quantized signals are widely used in engineering applications. Although quantization can potentially degrade system performances, previous research has demonstrated its usage to preserve privacy of the signals that are quantized. In this paper, we investigate the privacy-preserving properties of two types of quantizers: deterministic and stochastic ones. Specifically, for deterministic quantizers, we demonstrate that an eavesdropper cannot uniquely determine the initial state of a system if the system is Schur stable. Additionally, we propose a necessary condition on the system matrix $A$ to ensure the initial state remains private. For stochastic quantizers, we investigate their differential privacy properties and show that appropriate quantization steps can guarantee differential privacy. However, the quantization step can lead to impreciseness of the quantized signal and we therefore also examine the trade-off between differential privacy and system performance. To optimize the quantization step, we formulate a convex optimization problem, which can be solved efficiently.
Unveiling the Efficacy of Unani Medicine in Female Disorders Through Machine Learning: Current Challenges and Opportunities

In the past decade, the healthcare sector has seen a noteworthy increase in the application of artificial intelligence (AI) technologies across various therapeutic domains. AI-powered technologies have been increasingly used in the context of female diseases within Unani medicine. This study unveils the efficacy of Unani medicine in female diseases through machine learning. This review abides by the preferred reporting items for systematic reviews and meta-analysis extensions for scoping reviews. Studies published after January 1, 2000, were included in 2000 to 2023. A total of 402 full-length articles were searched from the PubMed and Science direct. Two studies from published articles and two from the proposed dataset exclusively focused on the efficacy of Unani medicine, along with one cross-sectional study. This review demonstrates that this research has been conducted using various machine-learning techniques to detect the efficacy of Unani treatment in female disoder. Machine learning was also applied in a cross-sectional study on abnormal vaginal discharge and Mizaj. Machine learning models were used. While research on machine learning models in Unani herbal medicine is in its preliminary stages, the findings from the selected research suggest that ML methods can enhance the services provided by Unani practitioners to their patients. Nevertheless, future advancements should involve the application of diverse ML models across a broad spectrum of Unani treatments.
Wearable Ultrasound Probe for Nerve Localization and Stimulation

Low-intensity focused ultrasound (FUS) is an emerging non-invasive and spatially/temporally precise method for modulating the firing rates and patterns of peripheral nerves. This paper describes custom body-conformal probes used within an image-guided platform for chronic and patient-specific FUS neuromodulation. The probes contain separate ultrasound imaging and modulation arrays realized using piezoelectric transducers assembled on a flexible printed circuit board (PCB). Dualmode probes operating around 4 MHz (imaging) and 1.3 MHz (modulation) were fabricated and tested on tissue phantoms. The resulting B-mode images were analyzed using a template-matching algorithm to estimate the location of the target nerve and then direct the modulation beam towards the target.
Design of Centralized Controller for Efficient Power sharing and Voltage Regulation in DC Microgrid

Direct Current (DC) Microgrid systems have gained popularity due to their advantages over Alternating Current (AC) Microgrid systems, including fewer power conversion control stages and the increasing use of distributed energy resources (DERs) and loads. Voltage regulation and active power sharing are crucial for stable and optimal operation in DC Microgrids since they lack frequency and reactive power. Neglecting proper power sharing and voltage regulation can lead to increased power losses and even blackouts. This paper presents a novel centralized controller without droop control, aiming to achieve precise DC bus voltage regulation and efficient power sharing within a fully isolated DC Microgrid equipped with Photovoltaic-Battery Energy Storage Systems (PV-BESSs) and loads. The proposed non-droop-based centralized control strategy modifies the typical primary controller inner current and outer voltage control loops, while a centralized secondary controller generates power/current reference signals for various distributed generators (DGs) and DC converters to facilitate load sharing. This approach offers the advantage of eliminating conventional droop control and secondary controller. The Simulation results substantiate the efficacy and effectiveness of the proposed centralized control strategy in regulating the DC bus voltage and managing dynamic power variations within the Direct Current (DC) Microgrid.
A Reduced Switch High Step Down Gain Bridgeless Coupled Switched Inductor SEPIC HPF AC-DC Converter for LVEVs Charging Applications

A single-switch modified SEPIC bridgeless coupled switched inductor-based charger, for charging applications of low voltage electric vehicles (LVEVs) is demonstrated in this work. Presented charger achieves higher step-down gain between the AC grid and the DC battery pack output without requirement of a step-down transformer, using a coupled switched inductor structure. The discontinuous inductor current mode (DICM) operation of charger reduces the magnetic volume, thus making it compact. The DICM operation adds advantages of inherent power factor correction (PFC) at the input side, without requirement of additional input sensing circuitry. Added to this, DICM operation facilitates zero current turn-on and zero-current turn off for switches and diodes in the charger, respectively, which reduces switching losses associated with high frequency components. Lastly, the design, operating modes and performance of charger is verified through development of a prototype and results obtained are presented in this work.
Maximum Power Point Tracking of Grid Connected PV System Under Partial Shading Condition Using Grey Wolf Algorithm

To optimize the usage of solar energy, it is an utmost need to run the photovoltaic (PV) panels at their maximum power point (MPP), which may be done by applying maximum power point tracking (MPPT) techniques. However, during partial shading situation p-v curve will have many peaks which makes tracking more challenging. The global MPPT point cannot be properly tracked using conventional MPPT control methods. In this study, a novel MPPT control method called the grey wolf algorithm (GWA) is described, and a levy distribution is derived to improve the convergence of the GWA. This study uses the GWA to examine the system having a single-phase inverter control connected in a partially shaded PV along with single-phase grid-connected system. The system comprises four parallel-connected PV arrays that are partially shaded, a boost converter, an RC filter, and a single-phase inverter connected with the grid. Here, proportional resonant (PR) control is used for inner current control along with Proportional Integral (PI) controller for outer voltage controller. The PR controller can able to track the sinusoidal reference current with zero steady-state error. The overall system is simulated in MATLAB Simulink environment and the simulation result shows that the PR controller can successfully able to track the sinusoidal reference inverter current and reduce the overall harmonics.
A Botnet Data Collection Method for Industrial Internet

Industrial internet security is related to industrial production operations and national economic security. Network intrusion detection dataset in industrial control environment can effectively train detection models and mitigate network attacks, which is an indispensable component for industrial internet security. In this paper, we propose a data collection method for industrial Internet-oriented botnet by classifying traffic packets according to network traffic features and industrial control identification codes, containing the status of industrial control devices. This method can greatly improve the detection accuracy of the model in the industrial internet. To create a botnet incursion dataset in the industrial control environment, we simultaneously employed the existing industrial control devices to gather both industrial control and botnet network traffic. Finally, we used an AI-based model to detect the botnet.
Mutualistic Interactions in Heterogeneous Multi-Agent Systems

This paper presents a collaboration strategy that enables heterogeneous agents, i.e., different capabilities and dynamics, to accomplish tasks by working together. The collaboration between multiple agents is inspired by the ecological concept known as a mutualism, an interaction between two or more species that benefits everyone involved. A collaborative act is made possible through the composition of barrier functions, which allows the heterogeneous agents to work together safely. Moreover, a measure of collaborative potential is established to assess the merit of agents interacting with each other. Furthermore, the collaboration framework is provided for a general multi-agent setting. Finally, the collaboration framework's efficacy is demonstrated in two case studies that necessitate collaboration between the heterogeneous agents to complete their respective tasks.
Multi-Vehicle Multi-Camera Tracking With Graph-Based Tracklet Features

Multi-target multi-camera tracking (MTMCT) is an important application in intelligent transportation systems (ITS). The conventional works follow the tracking-by-detection scheme and use the information of the object image separately while matching the object from different cameras. As a result, the association information from the object image is lost. To utilize this information, we propose an efficient MTMCT application that builds features in the form of a graph and customizes graph similarity to match the vehicle objects from different cameras. We present algorithms for both the online scenario, where only the past images are used to match a vehicle object, and the offline scenario, where a given vehicle object is tracked with past and future images. For offline scenarios, our method achieves an IDF1-score of 0.8166 on the Cityflow dataset, which contains the actual scenes of the city from multiple street cameras. For online scenarios, our method achieves an IDF1-score of 0.75 with an FPS of 14.