Miracle-dz/UniFAR-FTUs huggingface dataset:
SHRAG: Semantic Hierarchical Graph for Floorplan Representation

Representation learning from a floorplan is a fundamental step for various floorplan-related applications, such as retrieval, reconstruction, and generation. Previous works often use single-layer graphs to model room categories and adjacencies as nodes and edges, which can only represent a floorplan in a coarse level without detailed information such as room shape and door/window locations. Thus, we propose SHRAG, a hierarchical semantic graph for floor-plan representation, with two hierarchical graph modules to semantically encode both coarse and detailed information of a floorplan. First, a Detail Graph Module(DGM) was designed to learn detailed contour and attachable elements embedding for each room. Then, a Global Graph Module(GGM) was applied to concatenate and encode both detailed embeddings from DGM and coarse information including room categories and room relations. Results show that the representation learned from SHRAG achieved SOTA on floorplan retrieval tasks. Further, SHRAG performs more satisfying than other floorplan similarity metrics in a comprehensive user study. We also demonstrate that SHRAG can be easily adapted to real-world complex indoor scenes even with furniture.
THL2H-Ex: An Improved Neighbor Discovery Approach for Wireless Sensor Networks

One of the most difficult issues in wireless sensor networks is neighbor discovery because of the combination of limited power, short duty cycle, and high mobility. The two categories of discovery designs currently in use are pairwise-based and group-based. The later approaches speed up discovery but increase transmission package size or incur excessive energy overhead, making them far from practical. The former suffer from a significant discovery delay. In this paper, we suggest an enhancement to a previously known neighbor discovery protocol (THL2H), which does not impose a cap on the number of additional beacons while rearranging the listening slots. As a result, simulation findings show that the proposed protocol, known as THL2H-Ex, performs better than its counterparts.
Pre-Main-Sequence Binary Stars

The observational study of pre-main-sequence (PMS) binary stars is in many ways a very young field; most PMS binaries known today were discovered in the past decade. Nonetheless, T Tauri stars have been under study for more than a half century, and the serendipitous discovery of visual pairs has always been a by-product of their observation (e.g. Joy & Van Biesbroeck 1944). The acceleration of discovery in recent years has at least two stimuli, one technical and one sociological. First, the frequency of binaries among main-sequence solar-type stars peaks at semimajor axes of order 50 AU, projecting to less than 0.5" at the distance of the nearest star-forming regions. The requisite high­ angular-resolution techniques (and near-infrared detectors) have only recently permitted the major surveys for PMS binaries now coming to fruition. Second, and equally important, the formation and early evolution of binaries has attracted increasing attention from those studying star formation. This can perhaps be attributed to both a growing confidence in our general picture for single-star formation (e.g. Shu et a1 1987) and recognition that however correct our theories of single-star formation may be, the usual product of a star-formation event is a multiple-star system. Until recently, the primary observational constraint on the mechanisms of binary formation and early evolution have been provided by main-sequence (MS) binaries in the solar vicinity, acting as a surrogate for the zero-age-main­ sequence (ZAMS) binary population. As the ultimate product of the binary formation process, ZAMS binaries do supply essential constraints. However,
Intrusion detection based on machine learning and statistical feature ranking techniques

Big data has made it easier for people to live an information-based Internet lifestyle, but it has also created several serious network security issues that make it difficult to use networks regularly. At the moment, intrusion detection systems are mostly used to identify aberrant network traffic. So, utilizing a Machine Learning (ML) technique, we suggested a statistical analysis-based intrusion detection system. In this paper, we suggested an intrusion detection system using the statistical analysis of the ranking method known as the T-test: two samples assuming unequal variances. A substantial amount of network traffic data that includes both malware data and normal traffic data is gathered to be able to identify the pattern of the malware data. Nine different traffic features for both intrusion and normal traffic are extracted and further ranked using the t-test, yielding nine "t" values from which various features were derived. The Naive Bayes machine learning algorithm will then be applied to the 9 features, deleting one feature at a time that has the lowest "t" value to provide 9 alternative accuracy values. After examining the accuracy value, we get to the conclusion that the two features with the lowest value are removed to attain the highest accuracy, i.e., 95.69% with the top 7 features rather than using all 9 features.
Quantitative Analysis of Improvement on Wavefront Aberration Correction with Scattering Preconditioner

Many works effectively improved the aberration correction accuracy by adaptive optics (AO) with a defocus preconditioner, which has been quantitatively demonstrated and the formula of the optimal defocus distance were derived. However, no work has yet quantitatively analyzed the improvement with a scattering preconditioner. Spurred by the gap, we equivalently convert the change of the optical field at the imaging sensor due to scattering to the additional phase on the pupil plane, then derive the model between the scattering screen roughness with the probability of dual wavefront (PDWF) and the proportion of intensity in the cropped area (PICA), respectively. The experimental results show that the correction accuracy is significantly improved as PDWF decreases, and deteriorated after PICA less than 95%, which is consistent with the conclusion under the defocus preconditioner. Furthermore, we propose theoretical guidance on how to select the scattering screen roughness based on the AO parameters to meet the requirements of high accuracy and real-time performance.
Suspicious Permissions Detection of Covid-19 Themed Malicious Android Applications

The malicious actors continuously produce malicious Android applications with a COVID-19 theme in the context of the pandemic. Users frequently grant the necessary permissions to install those phoney apps without paying much attention. Android permissions are essential points of weakness. Major privacy issues often result from this vulnerability. Hackers with malicious intent have viewed the COVID-19 pandemic as an opportunity to conduct malware attacks to profit financially and advance their nefarious goals. Through COVID-19-related content, people are becoming victims of phishing scams. The android malware seen explicitly during the pandemic of Covid-19 is discussed in this study, and we next analyze malware detection methods with a focus on these Covid-19-themed malware mobile applications. This research paper attempts to identify dangerous android permissions and the malware families that erupted during the Covid-19 outbreak.
Long-Distance Heart Sound Detection using 122 GHz CW Radar with 3D Printed High-Gain Antennas

This contribution presents a 122 GHz continuous wave (CW) radar system capable of detecting human heart sounds over a distance of at least five meters. A bidirectional long short-term memory (LSTM) neural network is used to extract precise inter-beat-intervals (IBIs) from the radar data. A small-scale human trial with two persons is carried out, and the resulting accuracy of the system is compared to a gold-standard electrocardiogram (ECG). The proposed system shows a mean absolute error of 5 ms and a standard deviation (SD) of 63 ms compared to the ECG IBIs.
Classification of ECG Signals for Detecting Coronary Heart Diseases Using Deep Transfer Learning Techniques

Deep learning (DL) techniques were applied to ElectroCardioGrams (ECGs) for identifying abnormalities in heart diseases. The application of a convolutional neural network is discussed in this paper to perform the detection of coronary heart diseases by extracting QRS complexes and the error signal features. AlexNet, VGG19, ResNet50, GoogleNet, and NasNetLarge pretrained transfer learning models are adopted using the adaptive moment estimate (Adam) and stochastic gradient descent with momentum (SGDM) optimizers for training, testing, and validation. Here, 3 classes of heart diseases have been considered; namely arrhythmia, cardiomyopathy, and ischemia. The suggested method is aimed at automatically detecting these diseases based on ECG signals collected from MIT-BIH databases. The obtained results show that the Adam optimizer outperforms the SGDM optimizer for the five DL architectures. For AlexNet adopting Adam, the accuracy of detecting Arrhythmias, Ischaemia, and Cardiomyopathy is 98.2%, 95.9%, and 93.5% respectively. ResNet-50 and NasNetLarge with the same optimizer, have 98.0%, 96. 9%, 92.3 %, 97.9%, 95.7%, and 94.0 % accuracy in detecting Arrhythmias, Ischaemia, and Cardiomyopathy respectively. In addition, the subtraction of the QRS complexes from the clean ECG signal computationally outperforms the cutting-edge method based on using the continuous wavelet transform method. The reason is that the wavelet method is computationally expensive compared to the proposed QRS-complex subtraction method that results in integer error samples. So, the average execution time is significantly less.
Signal Strength Balanced Scheduling for Secure Ambient Backscatter Networks

With the development of wireless network technology, backscatter networks that effectively connect IoT devices are attracting attention. The tags used in the backscatter network have a simple structure that leads to low-power and low-cost characteristics. However, due to these features, traditional security methods requiring complex operations cannot be applied to backscatter tags, making them especially vulnerable to simple attacks such as eavesdropping. In this paper, we propose the signal strength balanced scheduling scheme to enhance the security of ambient backscatter networks. With the proposed scheduling method, we intentionally generate the collision signal and utilize it for communications between the reader and tags. We also present a decoding algorithm for interpreting the collision signal successfully. Simulation results demonstrate that the proposed scheme achieves security enhancement and that the decoding algorithm interprets the collision signal with high accuracy.
Design and Implementation of MobileRobot Navigation System Based on ROS Platform

This paper presented a mobile robot navigation system based on ROS (Robot Operating System). The hardware structure of mobile robot control system based on ROS was designed, and the core module control principle of bottom and top control system was introduced. The software of robot control system was designed based on hierarchical design, including upper computer control system and chassis control system. A specific prototype was designed, and the experimental results showed that the system not only could control the robot in various ways, but also had the functions of autonomous positioning, mapping and obstacle avoidance navigation. It had the advantages of high control accuracy, low cost, modular design and easy expansion.
Introduction to MITRE ATT&CK: Concepts and Use Cases

As networks become more complex and large-scale, the types of attacks become more diverse, and the importance of cyber security is increasing. Therefore, a need for security analysis is growing to classify and analyze several cyber attack types to prepare for the attacks. To solve this problem, MITRE presented ATT&CK as an attack classification model that systematically classifies cyber attackers’ tactics, techniques, and procedures. Therefore, we review MITRE’s ATT&CK in terms of concept and the use cases.
The 2022 Rayong Oil Spill Crisis in Thailand: An Identification for Reliability and Nearly-Real Time Data using Twitter

The oil spill is considered as a crisis that caused a lot of impact on the marine environment. At the beginning of 2022, an oil spill crisis happened in Rayong Province, Thailand. This crisis caused more than 140 barrels of leakage. During the crisis, Twitter was one of the leading social networking services many people shared and posted news related to the situation. In this research, the analysis based on the content from Twitter for defining the reliability and nearly-real-time properties of Twitter has been performed. The cosine similarity has been selected to be the methodology to find the similarity for the Part of Speech (POS) between the contents from verified and non-verified Twitter accounts for proving the reliability property. The results of the analysis show that the contents posted on Twitter platform have timeliness property due to the increasing amount of the tweets posted since the beginning of the crisis. For the reliability property of Twitter, it can be proved by the high similarity value for nouns and adjectives tagged between the contents from verified and non-verified accounts.
Portable Real-Time System for Multi-Subject Localization and Vital Sign Estimation

A real-time non-contact vital sign detection system is developed by utilizing neural network-based detection, multi-object tracking, and direction of arrival (DoA) techniques. The DoA produces a spatial-based image, which is fed into the detector. The detector is a convolutional neural network (CNN), which produces a list potential subject locations. These locations are propagated and associated via a tracking method called BYTE. All of these methods allow the system to accurately localize and track subjects as well as improve the robustness of vital sign estimation for stationary, multi-subject scenarios. We demonstrate that this real-time system produces low error rates of less than 1 and 3 BPM for breathing and heart rate estimations respectively in both single and multi-subject scenarios. All this is done while maintaining an average of 14 FPS on a portable Jetson Xavier NX.
Garment Ideation: Iterative View-Aware Sketch-Based Garment Modeling

Designing real and virtual garments is becoming extremely demanding with rapidly changing fashion trends and increasing need for synthesizing realistically dressed digital humans for various applications. However, traditionally designing real and virtual garments has been time-consuming. Sketch based modeling aims to bring the ease and immediacy of drawing to the 3D world thereby motivating faster iterations. We propose a novel sketch-based garment modeling framework that is specifically targeted to synchronize with the iterative process of garment ideation, e.g., adding or removing details from different views in each iteration. At the core of our learning based approach is a view-aware feature aggregation module that fuses the features from the latest sketch with the thus far aggregated features to effective refine the generated 3D shape. We evaluate our approach on a wide variety of garment types and iterative refinement scenarios. We also provide comparisons to alternative feature aggregation methods and demonstrate favorable results. The code is available at https://github.com/pinakinathc/multiviewsketch-garment.
A Ge-on-Si single-photon avalanche diode operating in Geiger mode at infrared wavelengths

The Ge APD detectors are fabricated on Si by using a selective chemical-vapor deposition (CVD) epitaxial growth technique. A novel processing procedure was developed for the p+ Ge surface doping by a sequence of pure-Ga and pure-B depositions (PureGaB). Then, PVD Al is used to contact the n-type Si and the anode of p+n Ge diode. Arrays of diodes with different areas, as large as 40×40 μm2, were fabricated. The resulting p+n diodes have exceptionally good IV characteristics with ideality factor of ~1.1 and low saturation currents. The devices can be fabricated with a range of breakdown voltages from a minimum of 9 V to a maximum of 13 V. They can be operated both in proportional and in Geiger mode, and exhibit relatively low dark counts, as low as 10 kHz at 1 V excess reverse bias. The dark current at 1 V reverse bias are as low as 2 pA and 20 pA for a 2×2 μm2 and 2×20 μm2 devices, respectively. Higher IR-induced current than that induced by visible light confirms the sensitivity of Ge photodiodes at room temperature. The 25% peak in Id/Iref at an IR-wavelength of 1100 nm in Geiger mode is measured for excess bias voltages of 3 V and 4 V, where Id refers to the photocurrent of the 2×20 μm2 device at different wavelengths, and Iref is the reference photodiode current. The timing response (Jitter) for the APD when exposed to a pulsed laser at 637 nm and 1 V excess bias is measured as 900 ps at full width of half maximum (FWHM).
Reinforcement Learning Approach for Resource Allocation in 5G HetNets

Heterogeneous Networks (HetNets) have been hailed as a critical technology for 5G communications, allowing for the rapid expansion of mobile traffic. HetNets can increase network capacity and serve additional users by installing small cells inside macrocells. However, resource allocation for such networks becomes more challenging than for conventional cellular networks due to interference between small-cells and macrocells, making it more difficult to provide quality of service. Deep Reinforcement Learning (DRL) has opened the door for applications in resource allocation for 5G HetNets, because of recent breakthroughs in the field. We present a unique resource allocation technique based on DRL that may be used to both small and macro cells. According to the resource allocation process, an autonomous "agent", in our case a cell, makes judgments to determine the appropriate BS to assign to a user, and the optimal number of Resource Blocks that should be allocated, while not needing or waiting for any information.
Detection of Chromosomes in Metaphase Images Using Segmentation Techniques

The process of identifying chromosomes in metaphase images is demanding and it takes a lot of time. Thus, chromosome detection correctly in metaphase images is very important. It is proven that deep learning methods have been effective in object detection. Using a deep learning network, an automatic chromosome detection model is constructed for metaphase chromosome images. The model learns the chromosome images at the pixel level in less time. The detection results of the network verify the positive role of the model. This model of automatic chromosome detection based on deep learning methods may speed up the process of detecting anomalies.
Performance Analysis for Coded Wireless Steganography System with OFDM Signaling

We investigate the performance of a physical layer security technique based on wireless steganography, where or-thogonal frequency division multiplexing (OFDM) signal that carries private information is embedded within the single-carrier (SC) cover signal that broadcasts public information. The legitimate receiver employs successive interference cancellation (SIC) similar to that of power domain non-orthogonal multiple access (PD-NOMA), where it first decodes the public information, and then decodes secret information from the embedded OFDM signal after subtraction of the cover SC signal. For a given power ratio of the two signals, the achievable information rate of embedded data is analyzed, and the trade-off relationship among the two information rates and power ratio is confirmed by the computer simulation in terms of the coded bit error rate (BER) performance. We also evaluate the secrecy capacity if the wiretap channel model is applied to our system.
Fertilizer and Crop Recommendation using IoUT and Machine Learning

Utilization of good and great manures can be bit costly and unsafe in the event that not utilized in a cautious and leveled way in this manner the undertaking centers around the brilliant cultivating method by putting forth an objective to manage the upper layer of soil pictures to deliver a mechanized and soil requested system for scientists for a negligible cost. The key variable for instance, the upper layer of soil is to be instructed of earlier of doing development. It impacts the yield assurance and direct the water transmission property. The project centers around seven fields in the Nazafgarh locale of Delhi, India. The conventional hydrometer testing strategy that reports the degree of sand build up and further more silt and percentage of clay that is available in the soil has been used in the current work. Although this method is incredibly expensive and it is extremely dreary. The curent work considers 35 soil samples from various fields of Najafgarh, Delhi, India. The proposed research also undergo the unsupervised machine learning model that is generating adversarial network. The aim is to produce the large amount of image dataset from the sample soil images that has been captured by the sensors at the time of collection. Among the variety of deep learning algorithms, a convolutional neural network (CNN) has been implemented in the study as it showcase unique properties in working on images.The accuracy achieved for every images has been highlighted and discussed in the further section. The highest accuracy achieved is 91%. It further indicates that the performance of the model reaches to the expectations for the used dataset.
Modelling and Quantification Methods of Voltage Sags and Voltage Unbalanced

First, the features of various power quality disruptions (PQDs) and their standard have been discussed in brief to reveal the most important PQDs. Further, based on huge economic impacts and frequency of occurrences, the modellings and quantification methods of voltage sags and voltage unbalanced have been incorporated to highlight their shortfalls. The R.M. S voltage method of voltage sag characterization is modelled and it is shown that this method provides overestimation in voltage sag duration as well as dependent on the window length of the samples. Also, the model is developed to show the impacts of fault resistance and transformer winding connections on the characteristics of voltage sags. Finally, the quantitative descriptions of degree of voltage unbalanced, namely voltage unbalance factor (VUF) has been presented. It is shown that the degree of voltage unbalanced (VUF) is not sufficient for the accurate prediction of the impacts but condition of unbalanced is also desirable for the accurate assessment of the impacts of the voltage unbalanced.
Design and implementation of a cost-effective microscope for fabrication and imaging

The use of lasers and optical systems for advanced research and demonstrative purposes has traditionally been cost-prohibitive for many researchers. In this note, we present the design and optimization of a low-cost microscopy setup capable of imaging, fabrication or photopolymerization via multiphoton excitation of a photoactivator and the study of processes such as diffusion using fluorescence recovery after photobleaching (FRAP). The setup features a continuous wave (CW) Ar-ion laser, a pulsed Nd3+:YAG laser, an inverted microscope with a CCD camera and appropriate optics. The setup is cost-effective and puts a once-expensive setup within reach of more researchers interested in micron- and sub-micron-scale processes.