Miracle-dz/UniFAR-FTUs huggingface dataset:
Hot-n-Cold: Mapping the Syscall Attack Surface Using Thermal Side Channels

As we increasingly rely on digital technologies, cyber security is of paramount importance. While computing systems offer numerous advantages, they also introduce unwanted security risks. In High Performance Computing, security risks have largely been ignored in the name of high performance [1]. Nevertheless, ensuring security and privacy of computations and data is essential [2]. Linux operating systems, running on all Top500 HPC systems, use kernel and user modes to implement security, which prevents unauthorized access to critical kernel functions. System calls connect the two modes. Therefore, they were frequently attacked, as reported in over 100 Common Vulnerabilities and Exposures (CVEs) in the last 7 years [3]. Combining static and dynamic syscalls analysis [4] [5] has recently been shown to be imperative for creating a syscalls whitelist, to minimize the potential attack surface they introduce. This work introduces a novel dynamic analysis technique, Hot-n-Cold, to detect anomalies in the Linux commands’ behavior by monitoring the CPU temperature. We use Hot-n-Cold to map the syscall attack surface on a local HPC system. Hot-n-Cold can be extended and applied to detect, in real-time, an anomaly that may facilitate creation of an attack. The results on two Linux frequently used commands (ls & chmod) show a positive correlation of up to 80% between the original Linux command and a version augmented with syscalls from CVEs. This work shows the importance of security in HPC, and motivates further research into studying and designing security mechanisms that preserve high performance.
Enhancing DETR with Attention-Based Thresholding for Efficient Early Japanese Book Reorganization

Analysis of early Japanese books is an essential clue for researching the history and culture of the period, however, the books are written in pre-modern Japanese called Kuzushiji, which is difficult to read unless one is an expert. Kuzushiji by OCR has been popular in recent years, although recognition of Kuzushiji by end-to-end object detection is difficult with conventional CNN-based models such as YOLO. This paper uses the Transformer-based object detection model DETR, which outperforms CNNs in the field of object detection and improves DETR performance using threshold processing. The threshold processing for Self-Attention can reduce redundant dependencies between each patch and accelerate DETR training. In this experiment, DN-DAB-DETR-R50 with thresholding achieved +2.0%F1 in total and up to +7.3%F1 per book compared to the vanilla model and prove the efficiency of the thresholding. However, threshold processing increases computational costs slightly, and future work is improving the problem by patch pooling according to attention weights using its sparsity with thresholding.
Modeling a GaN Transistor and its Impact on Conducted Emission up to 300 MHz

Accurate transistor models in the wide frequency range are essential for the prediction of electromagnetic interference. This paper investigates the applicability of a silicon power MOSFET behavioral model to GaN transistors and shows which parameters are important for modeling high-frequency emissions. In the tested case we show that GaN modeling is feasible up to 300 MHz using datasheet information and a single measurement. In addition, a simplified power MOSFET model is proposed that requires only three lumped element values and an ideal diode. We also investigate the impact of the layout by 3D simulations, the impact of two DC voltage levels, and we compare the simulated results to measurements.
Simulation of Time Delay Characteristics of TSN-based Intelligent Substation Timing Overcurrent Protection Service

With the proliferation of complex control services, intelligent substations have put forward the requirements of high reliability and low latency for the transmission of protection business data. TSN proposes solutions for the high-reliability transmission of various services in intelligent substation. In order to study the applicability of intelligent substation control services and TSN based bearing technology, this paper analyzed the necessity of using TSN-based technology to carry the substation timing overcurrent protection service, established a TSN-based transmission framework, and studied the transmission delay characteristics of the service with OMNeT++ as the simulation platform. The simulation results show that the transmission delay and delay jitter of the timing overcurrent protection service carried by TSN-based bearer network are low, and the packet transmission quality optimization effect based on TSN frame preemption mechanism is better under traffic explosion, which verifies the feasibility of combining the time-sensitive services and TSN technology in intelligent substations.
What to predict from Twitter Data?

In the last decade, Twitter data has become one of the most valuable research sources for many areas including health, marketing, security, and politics. Researchers prefer Twitter data since it is completely public and can be easily downloaded using Twitter APIs. The recent intensive use of Twitter data makes it difficult for researchers to follow or analyze its research. In this paper, we summarize most of the predictable patterns, aspects, and attitudes from Twitter data and analyze the performance and feasibility of the algorithms used. Moreover, we describe the current popular Twitter datasets used in various domains and applications. Current challenges and research gaps are discussed, and some recommendations are given for future works from different perspectives.
Visual Monitoring of Swarms of Industrial Robots

With the increasing automation of industry and the introduction of robotics in every step of the production chain, the problem of safety has become acute. The article proposes a solution to the problem of safety in production using a visual control system for the fleet of loading automated guided vehicles (AGV). The visual control system is built as towers equipped with cameras. This approach allows to be independent of equipment vendors and allows flexible reconfiguration of the AGV fleet. The cameras detect the appearance of a loading robot, identify it and track its trajectory. Data about the robots’ movements is collected and analyzed on a server. A prototype of the visual control system was tested with the Duckietown project.
Global analysis of runs of annual precipitation and runoff equal to or below the median: run length

The investigation of fluctuations of wet and dry years has a long history in the climatology and hydrology literature. In this, the first of two papers investigating runs of consecutive dry years, the lengths (persistence) of dry runs are investigated. In the second paper the magnitude/intensity and severity (length × magnitude) of dry runs will be investigated. Consecutive dry years are associated with drought, which is a significant physical and economic phenomenon that imposes great stress on ecosystems and societies. Run lengths of consecutive years equal to or below the median were analysed for 3863 precipitation and 1236 runoff stations from around the world. Run lengths were found to be similar across all continents and Köppen climate zones, expect for tropical and arid North Africa (Sahel), which showed a distinct bias toward longer run lengths than any other region of the world. Generally, the run length observed in annual runoff was found to be similar to that observed in annual precipitation for the same location. Both annual precipitation and runoff data were found to be well described by the lag‐one autoregressive (AR(1)) model or by white noise. The influence of the El Niño–southern oscillation on run lengths was not observed to be significant. The presence of decadal and multi‐decadal oscillations was weakly observed in the results of the precipitation runs analysis. The faintness of the decadal and multi‐decadal oscillation signal may be due to the sample sizes not being long enough and/or the runs analysis not being sensitive enough to detect their presence. Copyright © 2004 Royal Meteorological Society
Deep Densely-Connected Residual Learning for Multispectral Image Demosaicing

Multispectral filter array (MSFA) based multispectral image (MSI) demosaicing is to generate fully defined spectral images at different spectral channels from one snapshot raw mosaicked input. However, it is a very challenging problem, as estimating unknown values from a highly under-sampled input is likely to cause inaccurate artifacts. To address this issue, we propose an end-to-end deep densely-connected residual learning network for snapshot MSI demosaicing. Our proposed method does not require any initial interpolation step, which reduces the computation complexity and computation memory. To avoid vanishing-gradient problem and guarantee efficient information flow for image reconstruction, we apply global residual learning and local residual learning in our proposed architecture. Moreover, stacked densely-connected blocks with skip connections are adopted to make full use of multi-level information extracted from raw mosaicked image. Experimental results show that our proposed model outperforms other state-of-the-art demosaicing methods.
An Analytical Approach Towards the Prediction of Undefined Parameters for the National Institutional Ranking Framework

The National Institutional Ranking Framework is a methodology adopted by the Ministry of Education, Government of India for ranking Higher Education Institutions (HEIs) in India. The NIRF ranks HEIs based on five key parameters - Teaching, Learning & Resources, Research and Professional Practice, Graduation Outcome, Outreach & Inclusivity, and Peer Perception. These parameters are each composed of multiple components that are calculated based on the data provided by the respective HEIs. While the NIRF provides well-defined functions for some of these components, others are undefined. Understanding how these functions are computed would assist stakeholders in more effective planning and decision-making for further improving their NIRF scores. This work studies various regression machine learning models and identifies the best-fitting model for approximating these undefined functions. All experimental work is performed on real NIRF data publicly available on the NIRF website.
Enhancing Depression Detection: Employing Autoencoders and Linguistic Feature Analysis with BERT and LSTM Model

Depression is an abstinent health ailment that affronts several people globally, affecting a consistent decline in mood resulting in a trivial impact on their emotions. The article centers around the utilization of BERT based features and Autoencoders to identify depression from textual input, while also exploring gender identity variations. The study emphasizes the importance of feature engineering for text data obtained from the DAIC_WOZ benchmark dataset. By employing BERT embeddings, which capture the semantic meaning of text, in combination with Autoencoders and additional quantitative characteristics such as PHQ-8 participant feedback, assessment of absolutist word usage, and gender details, the model’s performance is significantly improved. We conduct trials with BERT embeddings that encode the textual meaning to extract text characteristics. These features are subsequently combined using Autoencoders. Our method surpasses the performance of the baseline models, achieving an accuracy of 95.5%. Additionally, the mean absolute error (MAE) is measured at 3.75 and the root mean squared error (RMSE) at 5.02, demonstrating the exceptional performance for binary depression classification. These discoveries reveal the capacity of machine learning in mental health investigation, especially when considering gender disparities.
Mental Health Disorder Detection Using Machine Learning and Deep Learning Tehniques

In the year 2023 mental illness has become a topic that just can't be ignored. According to “Our World In Data” more than 792 million people suffer from some type of mental illness, that means over 10.7% of the global population suffer from mental illness. Data Science is the most popular technology in the world today, and it's not going away anytime soon. There are many organizations that use data science technologies to solve the mental health problem. There are prediction models built with the help of machine learning and deep learning algorithms. The fact is many developers and medical professionals don't understand the difference between machine learning mental health models and deep learning mental health models. There are different technologies used to solve different metal health problems. This project not only tries to address and solve some of the mental health issues with the help of Machine Learning and Deep Learning techniques but also differentiate between the models built with the help of Machine Learning and Deep Learning Techniques. In this project 4 machine learning models have been used to solve the mental health problem of whether the individual gets the medical attention he/she needs. The techniques used are Logistic Regression, K-Neighbors Classifier, Decision Tree Classifier and Bagging, On the deep learning side of we things a CNN model is built that determines whether the individual is depressed or not depressed.
Disturbance Analysis and Solution in High Precision Magnetic Torquer Testing

High-precision magnetic torquer is the core actuator of satellite attitude control, and its ground test effect and control accuracy are very important to satellite attitude control. In this paper, a magnetic torquer with 2‰ resolution and better than 1% output accuracy is designed for a satellite, and the disturbance problem in the test is analyzed, and it is found that the system stability problem is brought while the accuracy is improved. The generation mechanism is that the main compensation parameters are close to the bias signal, and the main device parameters are greatly affected by temperature, which leads to the system instability under high temperature conditions and affects the reliability of the product. A solution to the disturbance problem has been proposed for this phenomenon, that is, by adjusting the hardware structure of the driving circuit and the current sampling method, the system stability problem is solved efficiently, and the accurate and reliable operation of the satellite attitude control is guaranteed.
Semantic Classification and Segmentation of Archaeological Sites Based on a Fusion of Object Detector and 3DEF

In this paper, an original method of semantic objects classification and segmentation data was proposed, which uses 2d and 3d data models. This method has been tested for the task of classifying archaeological sites. The information about the semantic characteristics of archaeological objects was obtained using state-of-art methods for semantic segmentation of the environment, such as YOLO and 3DEF. These methods have been improved by observing an archaeological site from several viewing points. In order to improve the accuracy and performance of the procedure for detecting archaeological sites, the Bayes formula which allow to use collaboratively detector YOLO and 3DEF semantic segmentation method was used. A comparative analysis with various combinations of 2D and 3D detection methods was carried out, the results of computer simulation were presented and discussed.
Algorithm Design of Environment Map Construction for Inspection Robots Based on Data Fusion

Inspection robots are a hot research topic in the field of mobile robots, and the construction of environmental maps is a prerequisite for robot inspection. However, the existing LIDAR-based map building algorithms can cause inaccurate measurements due to distortion and dithering during operation. In this paper, a loosely coupled LiDAR-IMU fusion scheme is designed at the front which can processing the pre-integration information of the IMU, then the distortion of motion in the laser point cloud will be remove, thus, the alignment accuracy of two adjacent point clouds is improved. The back-end uses a tightly coupled fusion approach to obtain the positional information based on error-state Kalman filtering by jointly optimizing the residual terms of the IMU with the residual terms of the laser point cloud matched with the local map. The results are tested on the open-source dataset KITTI, and the environmental maps with better performance are obtained.
Increasing Detection Rate of False Data Injection Attacks Using Measurement Predictors

A novel machine learning-based approach for the detection of False Data Injection Attacks (FDIAs) is presented. Existing machine learning-based FDIA detection methods exhibit a higher percentage of false negatives that may compromise the power grid's reliability. The proposed approach practically eliminates false negatives through the combination of a measurement classifier and measurement predictors. The detection rate increases drastically by detecting attacked measurement vectors in parallel using these two orthogonal mechanisms. Experimental results obtained using the IEEE 14-bus system support the effectiveness of the presented approach.
A Performance Prediction Model for Structured Grid Based Applications in HPC Environments

Predicting the performance of parallel applications at scale is a challenging problem. We have developed a performance prediction model for structured grid-based scientific applications for High Performance Computing systems. Our model can capture the system complexity and consider computation and communication attributes of application performance on HPC architectures. We have also proposed a methodology for obtaining the realistic value of the model parameters by small-scale sample runs of an application on the target system. We have used our model to predict the performance of an actual application (2D Flood Simulation) and a synthetic application (Game of Life) on the Summit supercomputer at Oak Ridge National Laboratory and Stampede2 at Texas Advanced Computing Center. The experimental results indicate that our model’s predictive performance is acceptable, and our model was able to predict the performance of these two applications on Summit and Stampede2 with more than 90% accuracy.
A Deep Hybrid Learning Approach For Nail Diseases Classification

In medical domains, the appearance of fingernails can provide clues to underlying systemic diseases or nutritional imbalance; the neglection of such clues could lead to unwanted health complications and less chance for recovery. In this paper, a Deep Hybrid Learning (DHL) approach was proposed to detect nail-based diseases, where a Deep Learning (DL) model is used for feature extraction, and a traditional Machine Learning (ML) classifier is used for classification. The aim is to classify three nail diseases: melanoma, beau's nails, and eczema, in addition to healthy nails. Further, the proposed approach is compared to the transfer learning approach, where a pre-trained model is used for feature extraction and classification. The experiment results indicate that the DHL approach is superior to the transfer learning approach. Specifically, the architecture where the DenseNet201 pre-trained model is used for feature extraction and the SGDClassifier is used for classification, as it achieved an accuracy of 94%.
Enhancing Traffic Flow and Safety in Mixed Vehicle Fleets: Mitigating the Influence of Non-Cooperative Vehicles on Autonomous Intersection Management Systems

With the rapid advancement of autonomous vehicle technology, integrating mixed autonomous and non-autonomous vehicles that are not cooperative in vehicular network has become a significant challenge. This paper presents an innovative Autonomous Intersection Management (AIM) system designed to optimize traffic flow and enhance intersection safety in such mixed traffic scenarios. By utilizing vehicle-to-infrastructure (V2I) communication and advanced intersection control algorithms, the AIM system showcases the potential of next-generation vehicular network technologies in revolutionizing intersection management. To evaluate the performance of the AIM system and the impact of non-cooperative vehicles, extensive simulations were conducted using realistic traffic scenarios and a mixed traffic model. The results demonstrate that the proposed system effectively enhances intersection throughput, and ensures safe and efficient operations, particularly in situations involving a high proportion of autonomous vehicles. Additionally, the system’s robustness is demonstrated by evaluating its performance under various traffic flow rates and considering imperfect wireless communication conditions.
Pyrite-coated granite cobbles at Lee Bay, Stewart Island

On the west side of Lee Bay on the northeast coast of Stewart Island, ventifact cobbles of pyrite-coated granite occur on the beach near the high tide mark and appear to be derived from a sand-cemented gravel deposit that forms a low bank at the back of the beach. The pyrite coat (up to 1 mm thick) completely covers the granitic cobbles and is zoned, with an inner zone of fine-grained colloform pyrite and an outer framboidal zone. Framboidal pyrite is typically formed in anoxic sedimentary environments. Subrounded grains of hematite, ilmenite with hematite blebs, magnetite, feldspar, biotite, quartz and zircon are present in the outer framboidal zone, with some ilmenite and hematite grains being partially replaced by pyrite. The assemblage of ilmenite-hematite-magnetite-biotite-zircon is similar both in mineralogy and size range to that found in heavy mineral beach sands. Sulphur isotope values of the pyrite coat are consistent with formation of the pyrite by microbial sulphate reduction of seawater sulphate. The framboidal texture together with the presence of grains of beach sand in the pyrite coating indicate that it was deposited in a low-temperature sedimentary environment.