Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multitask Learning-Based Neural Network for Defect Detection on Textured Surfaces Under Weak Supervision

Automatic defect detection on textured surfaces has long been one of the hotspots in the computer vision community. Many methods based on deep learning (DL) have been proposed in the past few years although most are semantic segmentation-based methods that require a large number of accurately labeled samples. However, obtaining precise labels is time-consuming and costly. In this article, we propose a novel weakly supervised DL-based method to accurately segment and locate defects on textured surfaces. In the proposed method, we design one encoder for extracting features and two decoders for two related tasks. To ensure that the deep neural network benefits from multitask learning, the two decoders share the unique encoder. The main task is designed to restore the defects on textured surfaces, and the auxiliary task is designed to obtain the region of interest (ROI), which is employed to filter out noise in the main task. Subsequently, we can obtain residual maps by comparing the original images and the restored images. Finally, more accurate results can be obtained through the fusion of residual maps and ROIs. A series of experiments on the public defect detection dataset DAGM showed that our method demonstrates the best performance compared with other state-of-the-art methods.
Fingermark Quality Assessment: An Open-Source Toolbox

Fingermark quality assessment is an important step in a forensic fingerprint identification process. Often done in the scope of criminal investigation, it is performed by trained fingerprint examiners whose quality assessment can be rather subjective. The goal of this work is to develop an automated fingermark quality assessment tool, which would assist the fingermark examiners in their work. In this paper, we present a fast, open-source, and well documented fingermark quality assessment toolbox, which contains more than 20 algorithms for feature extraction, segmentation, and enhancement of fingermark images. We demonstrate the utility of the toolbox by assembling a feature vector and training various baseline machine learning models, capable of predicting the quality of fingermark images with high accuracy. The AFQA toolbox source code is publicly available online.
Deployment and Optimization of Multi-UAV-Assisted Maritime Internet of Things for Waterway Data Collection

Maritime Internet of Things (MIoT) is regarded as one of the important paradigms to realize the Smart Ocean. With the increasing number of vessels, it is significant to ensure navigation safety and prevent traffic accidents. Due to the advantages of fast mobility and flexible deployment, Unmanned Aerial Vehicles (UAV) can be employed to detect and collect data packets from Sensor Nodes (SNs). However, it is extreme challenging for one energy-constrained UAV to complete the data collection task while updating information in time. In this paper, we present a Multi-UA V-Assisted MioT (MUA-MIOT) architecture for collecting navigational data packets which can provide important information support for navigation safety. In addition, the optimal deployment for MUA-MIoT is investigated to achieve data packet collection and information detection. UAVis used as a data collection point to collect data packets from sensor nodes by hovering over them. The Integer Linear Programming (ILP) problem is formulated with the objective of minimizing energy consumption in the MUA-MIoT. Considering a series of constraints such as reliability and connectivity, the optimal deployment is given and then its feasibility and scalability is evaluated in simulations.
Optimal Dimensioning of Power-to-Gas Units in the Context of Integrated Power and Gas Grid Planning in Distribution Grids

This paper presents a concept for optimal dimensioning of power-to-gas units within the context of integrated power and gas distribution grid planning, considering different business models and stakeholder perspectives. For the dimensioning, annual simulations that consider the business models provision of local power grid services, spot market optimization, ancillary services and direct hydrogen sale are performed. The results show that economic viability can be achieved with the multi-use deployment of Power-to-Gas units and that integrated planning can lead to better economic results.
Understanding the Sustainable Competitive Advantages of China’s Private Manufacturing Enterprises: An Empirical Study Based on the Dynamic Capabilities of Supply Chain

This study aims to explore the impact of supply chain dynamic capabilities on the construction of sustainable competitive advantage. This paper takes Chinese private manufacturing enterprises as the research object, and we also use statistical methods such as correlation analysis and hierarchical regression for empirical testing. Research shows that supply chain sensing has a positive impact on supply chain agility and supply chain adaptability; supply chain agility and supply chain adaptability have a positive impact on the sustainable competitive advantage of enterprises; environmental dynamics does not have a significant regulating effect on the relationship between supply chain sensing and supply chain adaptability. This research guides Chinese private manufacturing companies to focus on cultivating supply chain dynamic capabilities to reasonably respond to changes in the market environment, thereby promoting sustainable competitive advantages.
Bloom Filter vs Homomorphic Encryption: Which approach protects the biometric data and satisfies ISO/IEC 24745?

Bloom filter (BF) and homomorphic encryption (HE) are popular modern techniques used to design biometric template protection (BTP) schemes that aim to protect the sensitive biometric information during storage and the comparison process. However, in practice, many BTP schemes based on BF or HE violate at least one of the privacy requirements of the international standard ISO/IEC 24745: irreversibility, unlinkability and confidentiality. In this paper, we investigate the state-of-the-art BTP schemes based on these two approaches and assess their relative strengths and weaknesses with respect to the three requirements of ISO/IEC 24745. The results of our investigation showed that the choice between BF and HE depends on the setting where the BTP scheme will be deployed and the level of trustworthiness of the parties involved in processing the protected template. As a result, HE enhanced by verifiable computation techniques can satisfy the privacy requirements of ISO/IEC 24745 in a trustless setting.
Research on Surface Target Detection Algorithm Based on 3D Lidar

3D Lidar is the key perception module of Unmanned Surface Vehicle (USV). Targets in the background of the water are affected by refracted light. The visual sensor is difficult to detect in special scenes, which affects the autonomous navigation and obstacle avoidance function. This paper proposes a 3D lidar-based VoxelNet detection algorithm for water surface targets. The sparse point cloud data on the water surface is divided into voxel form, and the hash table is input for efficient query, and the feature tensor is extracted through the feature learning layer and input into the convolutional layer to obtain the global target Information to achieve high-precision target detection. Experimental results show that the surface target detection algorithm based on 3D lidar improves 13.6% compared with the visual solution, which provides a more effective technical means for the intelligent process of USV.
Blockchain and AI-Empowered Social Distancing Scheme to Combat COVID-19 Situations

The COVID-19 pandemic situation has proved to be disastrous for humanity throughout the world. However, during this period, people must take precautions for safety purposes. One of the essential steps towards eliminating or reducing the effect of COVID-19 is maintaining social distancing while in public places. Some people are neglecting the social distancing norms while on the move. Still, no surveillance system exists, which monitors the people’s movement for social distancing and securely & efficiently shares the information with the concerned administration department. There also exists no penalty system which forces the people to ensure social distancing. Motivated from the aforementioned facts, in this paper, we present a blockchain and artificial intelligence (AI)-envisioned scheme for monitoring social distancing to combat COVID-19 situations. The proposed scheme uses fast region-based convolutional neural networks (RCNN) and you only look once (YOLO) models for the object (i.e., human) detection through the live video feed captured from the static CCTV cameras as well as lens-equipped drones. Further, the efficient euclidean distance calculation is embedded for calculating the distance between two humans. Blockchain technology ensures the secure and trusted exchange of information between the entities at the physical layer and the administration departments. Blockchain wallets are also used to pay the fine when people do not follow social distance norms. The performance of the proposed scheme is evaluated based on three broad parameters such as (i) human detection and violation identification, (ii) blockchain simulation and analysis, and (iii) network performance comparison. The parameters considered for (i) is confidence score, for (ii) are scalability, hash rate, and simulation interface, and for (iii) are network bandwidth, throughput, packet loss rate, and network latency. By analyzing all the parameters mentioned above, we observe the proposed scheme outperforms the traditional approaches.
A Multi-Terminal HVDC Demonstration Grid in the North-Sea: A Cost-Effective Option

n HVDC grid in the North-Sea is considered to have a high potential to aid the energy transition in Europe.n HVDC grid in the North-Sea is considered to have a high potential to aid the energy transition in Europe.A High investment cost and operational challenges are the major roadblocks in its realisation. This paper proposes a demonstration grid in the North-Sea and shows that it is a financially viable project. A sensitivity analysis corresponding to line capacities and base prices is performed. Recommendations for the construction and operation of such a grid are made from the regulatory stand point.
Absorption of P32 by Cattleya 'Trimos' from Foliar and Root Applications

Cattleya 'Trimos' plants were treated with P32 derived from phosphoric acid both as a foliar spray and drench to the growing medium to study the absorptive capacity of leaves and new and three to four year old roots. Plants were fractionated V2, 2, 12, 24 and 120 hours after application and analyzed for P32 content. Florida Agricultural Experiment Stations Journal Series No. 2812. P32 applied to the foliage was readily ab sorbed by Cattleya 'Trimos' plants and roots three to four years old absorbed as much P32 as younger roots. There was an increase in P32 content in the tissue with time.
Biases and Uncertainties of RF Noise Power Measurements

In this paper, we explore various methods of measuring noise power in RF systems. We discuss some of the systems commonly used to make such measurements, then analyze each system's architecture to obtain the measurement bias and uncertainty for six different combinations of noise models (real and complex noise) and methods of averaging (linear power, magnitude, and logarithmic). Equipped with this knowledge, users can correct for post-measurement biases and determine the number of averages required to yield a desired uncertainty level.
Neural Style Transfer: A Critical Review

Neural Style Transfer (NST) is a class of software algorithms that allows us to transform scenes, change/edit the environment of a media with the help of a Neural Network. NST finds use in image and video editing software allowing image stylization based on a general model, unlike traditional methods. This made NST a trending topic in the entertainment industry as professional editors/media producers create media faster and offer the general public recreational use. In this paper, the current progress in Neural Style Transfer with all related aspects such as still images and videos is presented critically. The authors looked at the different architectures used and compared their advantages and limitations. Multiple literature reviews focus on either the Neural Style Transfer (of images) or cover Generative Adversarial Networks (GANs) that generate video. As per the authors’ knowledge, this is the only research article that looks at image and video style transfer, particularly mobile devices with high potential usage. This article also reviewed the challenges faced in applyingvideo neural style transfer in real-time on mobile devices and presents research gaps with future research directions. NST, a fascinating deep learning application, has considerable research and application potential in the coming years.
LAW-U: Legal Guidance Through Artificial Intelligence Chatbot for Sexual Violence Victims and Survivors

Sexual violence is a severe and chronic occurrence around the world that has not been resolved. The stigmatized nature of sexual violence has forced victims and survivors to accept prejudiced accusations cultivated from discriminatory norms when they are never at fault nor responsible for such violations against their sexuality. LAW-U is an Artificial Intelligence (AI) chatbot that gives legal guidance to survivors of sexual violence by recommending the most relevant Supreme Court decisions to the survivors’ situations. In Thai, “LAW-U” − pronounced similarly to “ ” − means “I will wait for you”, which signifies the chatbot’s unconditional support to the user. 182 Thai Supreme Court cases of sexual violence, relating to Sections 276, 277, 278, and 279 of the Criminal Code, were used to develop Natural Language Processing (NLP) pipelines for LAW-U. Legal experts then generated mock-up dialogs from Supreme Court decisions which became the conversations used to train LAW-U. The computation of the similarity scores and the calculation of percentages of common keywords and keywords’ synonyms were completed to increase the model’s accuracy. When applying the model to the hold-out testing dataset, the accuracy was 88.89% for an exact match between the user’s input and the Supreme Court case − this confirmed that LAW-U was ready for real-life application. LAW-U’s unique design hopes to act as a precedent for other works at home and abroad to perpetuate awareness of sexual violence and eliminate any tolerance against these crimes by empowering sexual violence victims and survivors to reaffirm their inherent rights.
Review on Ultra-Wideband Phased Array Antennas

Ultra-wideband (UWB) phased array antennas are essential for radar, communication, and Electronic Warfare (EW) applications. Systems can operate over wide-band coverage. The high gain systems require wide-band, wide-angle scanning, good radiation pattern, and return loss over a multi-octave bandwidth. This review discusses the problems present in the UWB phased array system, which include mutual coupling, grating lobe rejection, active return loss, gain fluctuation with scanning, and multiple beam generation. In this, the aperture size of the array is limited by the overall gain requirement of the system. On the other hand, low gain systems require a wide-band with a good radiation pattern and axial ratio. Here the matching over wide-band is simpler as it depends on broadband balun configuration. This system has limited bandwidth over scan angles. These antennas are more preferred as wearable antennas in communication applications. This paper mainly discusses the existing UWB phased array antennas such as Vivaldi Arrays, All-metal Arrays, Spiral Arrays, Sinuous Arrays, and Coupled Arrays. This review paper presents a correct direction for selecting the type of antenna in terms of impedance bandwidth, gain, and materials influencing antenna performance, suited for different applications.
Simulations of Statistical Variability in n-Type FinFET, Nanowire, and Nanosheet FETs

Four sources of variability, metal grain granularity (MGG), line-edge roughness (LER), gate-edge roughness (GER), and random discrete dopants (RDD), affecting the performance of state-of-the-art FinFET, nanosheet (NS), and nanowire (NW) FETs, are analysed via our in-house 3D finite-element drift-diffusion/Monte Carlo simulator that includes 2D Schrödinger equation quantum corrections. The MGG and LER are the sources of variability that influence device performance of the three multi-gate architectures the most. The FinFET and the NS FET are similarly affected by the MGG variations with threshold voltage and on-current standard deviations significantly lower (at least 20 %) than those of the NW FET. The LER variability has a negligible influence in the NS FET performance with <inline-formula> <tex-math notation="LaTeX">$\sigma ~{V}_{T}$ </tex-math></inline-formula> values around 12 and 42 times lower than those of the FinFET and the NW FET. The three architectures are equally affected by the RDD (<inline-formula> <tex-math notation="LaTeX">$\sigma {V}_{T}$ </tex-math></inline-formula> = 8 mV) and minimally influenced by the GER (<inline-formula> <tex-math notation="LaTeX">$\sigma {V}_{T} \approx 4$ </tex-math></inline-formula> mV). The variability of NS FETs makes them strong candidates to replace FinFETs.
Adaptive Fuzzy Observer-Based Command Filtered Discrete-Time Control for PMSMs With Input Constraint

This paper proposes an adaptive fuzzy observer-based command filtered discrete-time control method for permanent magnet synchronous motors (PMSMs) with input constraint. First, the rotor angular velocity is estimated by using an adaptive fuzzy observer and the unknown nonlinear function is approximated by using fuzzy logic systems (FLSs) in the PMSMs drive systems. Then, compared with the traditional backstepping method, command filtered backstepping method is developed to solve the “complexity of computation” problem. It is shown that all signals of the closed-loop system are semi-globally uniformly ultimately bounded. Finally, the results of the simulation and experiment demonstrate that the new design method can fully consider the influence of input constraint and load disturbance, and improve the tracking performance for PMSMs drive systems.
On the Investigation of Interface Defects of Solar Cells: Lead-Based vs Lead-Free Perovskite

Perovskite solar cells (PSCs) have drawn significant consideration as a competing solar cell technology because of the drastic advance in their power conversion efficiency (PCE) over the last two decades. The interfaces between the electron transport layer (ETL) and the absorber layer and between the absorber layer and the hole transport layer (HTL) have a major impact on the performance of the PSCs. In this paper, we have investigated the defect interfaces between ETL/absorber layer and absorber layer/HTL of calibrated experimental CH3NH3PbX3 lead-based and FASnI3 lead-free PSCs. The influence of the defect interfaces is studied in order to find the optimum value for the maximum possible PCE. While the PCE has not been enhanced considerably for the lead-based, it is boosted from 1.76% to 5.35% for lead-free PSCs. Also, bulk traps were found to have minor role in comparison with interface traps for the lead-free cell while they have a significant impact for the lead-based cell. The results presented in this work would shed some light on designing interface defects of various types of practical PSC structures and demonstrates the crucial impact of the interface defects on lead-free vs lead-based PSCs. All simulation studies are performed by using SCAPS-1D simulator.
A Comparative Study of Faults Detection Techniques on HVAC Systems

Nowadays, machine learning (ML) and artificial intelligence (AI) methods are widely used to improve research outcomes on building energy management, especially when significant levels of data sets are involved. While significant research outputs are continuously generated in the domain of energy consumption prediction, same levels of activities are not replicated within the premise of energy-based fault detection and diagnosis. The amount of faulty data used in modelling is critical during energy-based fault detection. Therefore, this paper aims to present a comparative study for different unsupervised fault detection approaches applied for heating, ventilation, and air conditioning (HVAC) systems based on limited faulty data points. Three methods - isolation forest, OCSVM and LSTM autoencoders were selected. The models were trained and tested on fault-free data points and two HVAC faults under different severities. All models were evaluated for precision of detection, which revealed that LSTM outperformed all other techniques for all faulty points, its precision fluctuating around 80% on the average. Isolation forest did better with small faulty data points but its precision decreased with rising data points. OCSVM exhibited a different pattern whereby precision increased with increasing faulty data points until 96 points was reached, after which a regressive trend was observed. The results and details of the simulated mosque building is presented in the paper.
Context-Aware Deep Markov Random Fields for Fake News Detection

Fake news is a serious problem, which has received considerable attention from both industry and academic communities. Over the past years, many fake news detection approaches have been introduced, and most of the existing methods rely on either news content or the social context of the news dissemination process on social media platforms. In this work, we propose a generic model that is able to take into account both the news content and the social context for the identification of fake news. Specifically, we explore different aspects of the news content by using both shallow and deep representations. The shallow representations are produced with word2vec and doc2vec models while the deep representations are generated via transformer-based models. These representations are able to jointly or separately address four individual tasks, namely bias detection, clickbait detection, sentiment analysis, and toxicity detection. In addition, we make use of graph convolutional neural networks and mean-field layers in order to exploit the underlying structural information of the news articles. That way, we are able to take into account the inherent correlation between the articles by leveraging their social context information. Experiments on widely-used benchmark datasets indicate the effectiveness of the proposed method.
Exploratory Review of Hybrid Fuzzing for Automated Vulnerability Detection

Recently, software testing has become a significant component of information security. The most reliable technique for automated software testing is a fuzzing tool that feeds programs with random test-input and detects software vulnerabilities that are critical to security. Similarly, symbolic execution has gained the most attention as an efficient testing tool for producing smart test-inputs and discovering hard-to-reach bugs using search-based heuristics and compositional approaches. The combination of fuzzing and symbolic execution makes software testing more efficient by mitigating the limitations in each other. Although several studies have been conducted on hybrid fuzzing in recent years, a comprehensive and consistent review of hybrid fuzzing techniques has not been explored. To add coherence to the extensive literature on hybrid fuzzing and to make it reach a large audience, this study provides an overview of key concepts along with the taxonomy of existing hybrid fuzzing tools, problems, and solutions that have been developed in this sphere. It also includes evaluations of the proposed approaches and a number of suggestions for the development of hybrid fuzzing in the future.