Reviewerly/exHarmony huggingface dataset:
CM-Unet: A Novel Remote Sensing Image Segmentation Method Based on Improved U-Net

Semantic segmentation is an active research area for high-resolution (HR) remote sensing image processing. Most existing algorithms are better at segmenting different features. However, for complex scenes, many algorithms have insufficient segmentation accuracy. In this study, we propose a new method CM-Unet based on the U-Net framework to address the problems of holes, omissions, and fuzzy edge segmentation. First, we add the channel attention mechanism in the encoding network and the residual module to transmit information. Second, a multi-feature fusion mechanism is proposed in the decoding network, and an improved sub-pixel convolution method replaces the traditional upsampling operation. We conducted simulation experiments on the Potsdam, Vaihingen and GID datasets. The experimental results show that the proposed CM-Unet required segmentation time is approximately 62 ms/piece, the MIoU is 90.4% and the floating point operations (FLOPs) is 20.95 MFLOPs. Compared with U-Net, CM-Unet only increased the total number of parameters and floating point operations slightly, but achieved the best segmentation effect compared with the other models. CM-Unet can segment remote sensing images efficiently and accurately owing to its lower time consumption and space requirements; the precision of the segmentation results is better than other methods.
Enhancing Rainy Weather Driving: Deep Unfolding Network With PGD Algorithm for Single Image Deraining

Although deep learning enables excellent visual perception performance for autonomous driving, its robustness to heavy weather is still worthy of attention since it is prone to forgetting previously learned information. In this paper, we focus on the deraining task from images based on single images of street scenes to improve the perception of autonomous driving in the rain, in which we degrade the rain image to a clean background image by using a deep unfolding network (DUN) combined with the proximal gradient descent (PGD) algorithm and introducing a gradient estimation strategy and a proximal mapping module. In the gradient descent module, we flexibly perform gradient descent on complex images by selectively replacing the degradation matrix. And in the proximal mapping module, we introduce an internal feature fusion module to fuse each stage’s local and global features to improve feature extraction efficiency, and an inter-stage feature fusion module to fuse each stage with the condensed features of the previous stage to reduce information loss during iteration. Finally, we evaluated our method on a synthetic dataset and also utilized real complex rain images for qualitative analysis. In addition, we combined high-level perception tasks, i.e., target detection and semantic segmentation, for autonomous driving to compare the perceptual effectiveness of autonomous driving before and after removing the rain. Experimental results demonstrate that our model not only outperforms existing efficient rain removal networks and produces a noticeable improvement in visual quality, but also significantly enhances the perception performance of autonomous driving in rainy weather for both the combined target detection task and the semantic segmentation task.
A Two-Stage Pillar Feature-Encoding Network for Pillar-Based 3D Object Detection

Three-dimensional object detection plays a vital role in the field of environment perception in autonomous driving, and its results are crucial for the subsequent processes. Pillar-based 3D object detection is a method to detect objects in 3D by dividing point cloud data into pillars and extracting features from each pillar. However, the current pillar-based 3D object-detection methods suffer from problems such as “under-segmentation” and false detections in overlapping and occluded scenes. To address these challenges, we propose an improved pillar-based 3D object-detection network with a two-stage pillar feature-encoding (Ts-PFE) module that considers both inter- and intra-relational features among and in the pillars. This novel approach enhances the model’s ability to identify the local structure and global distribution of the data, which improves the distinction between objects in occluded and overlapping scenes and ultimately reduces under-segmentation and false detection problems. Furthermore, we use the attention mechanism to improve the backbone and make it focus on important features. The proposed approach is evaluated on the KITTI dataset. The experimental results show that the detection accuracy of the proposed approach are significantly improved on the benchmarks of BEV and 3D. The improvement of AP for car, pedestrian, and cyclist 3D detection are 1.1%, 3.78%, and 2.23% over PointPillars.
A Self-Attention-Based Imputation Technique for Enhancing Tabular Data Quality

Recently, data-driven decision-making has attracted great interest; this requires high-quality datasets. However, real-world datasets often feature missing values for unknown or intentional reasons, rendering data-driven decision-making inaccurate. If a machine learning model is trained using incomplete datasets with missing values, the inferred results may be biased. In this case, a commonly used technique is the missing value imputation (MVI), which fills missing data with possible values estimated based on observed values. Various data imputation methods using machine learning, statistical inference, and relational database theories have been developed. Among them, conventional machine learning based imputation methods that handle tabular data can deal with only numerical columns or are time-consuming and cumbersome because they create an individualized predictive model for each column. Therefore, we have developed a novel imputational neural network that we term the Denoising Self-Attention Network (DSAN). Our proposed DSAN can deal with tabular datasets containing both numerical and categorical columns; it considers discretized numerical values as categorical values for embedding and self-attention layers. Furthermore, the DSAN learns robust feature expression vectors by combining self-attention and denoising techniques, and can predict multiple, appropriate substituted values simultaneously (via multi-task learning). To verify the validity of the method, we performed data imputation experiments after arbitrarily generating missing values for several real-world tabular datasets. We evaluated both imputational and downstream task performances, and we have seen that the DSAN outperformed the other models, especially in terms of category variable imputation.
Distorted Aerial Images Semantic Segmentation Method for Software-Based Analog Image Receivers Using Deep Combined Learning

Aerial images are important for monitoring land cover and land resource management. An aerial imaging source which keeps its position at a higher altitude, and which has a considerable duration of airtime, employs wireless communications for sending images to relevant receivers. An aerial image must be transmitted from the image source to a ground station where it can be stored and analyzed. Due to transmission errors, aerial images which are received from an image transmitter contain distortions which can affect the quality of the images, causing noise, color shifts, and other issues that can impact the accuracy of semantic segmentation and the usefulness of the information contained in the images. Current semantic segmentation methods discard distorted images, which makes the available dataset small or treats them as normal images, which causes poor segmentation results. This paper proposes a deep-learning-based semantic segmentation method for distorted aerial images. For different receivers, distortions occur differently, and by considering the receiver specificness of the distortions, the proposed method was able to grasp the acceptability for a distorted image using semantic segmentation models trained with large aerial image datasets to build a combined model that can effectively segment a distorted aerial image which was received by an analog image receiver. Two combined deep learning models, an approximating model, and a segmentation model were trained combinedly to maximize the segmentation score for distorted images. The results showed that the combined learning method achieves higher intersection-over-union (IoU) scores than the results obtained by using only a segmentation model.
Defect Identification of Power Line Insulators Based on a <scp>MobileViT</scp>‐Yolo Deep Learning Algorithm

Abstract Power line insulator defect identification usually suffers from complex backgrounds, small defect target sizes, and inconspicuous defect features. Traditional identification methods based on image processing, image analysis, and pattern classification have many limitations in solving the aforementioned problems. In recent decades, deep learning classification methods have gradually replaced traditional identification methods in the task of power line insulator defect identification. To accurately identify the locations of insulator defects, this paper proposes an insulator defect detection algorithm using an improved lightweight YOLOv4‐tiny network (ILYTN). First, the CBL (Conv‐BN‐LeakyReLU) modules of the backbone network are replaced with MobileViT blocks to enhance the feature extraction capability of the backbone network. Second, coordinate attention (CA) is introduced in the feature fusion part to improve the network's ability to focus on the location of defects. Finally, an EIOU (efficient intersection over union) loss function, instead of the original CIOU loss function, is used so that the convergence speed of the network can be improved. To verify the effectiveness of the proposed algorithm in this paper, the mViT‐yolo algorithm is compared with the mainstream Faster‐RCNN algorithm, SSD algorithm, YOLOv3 algorithm, and YOLOv4‐tiny algorithm. The experimental results show that the algorithm proposed in this paper outperforms all the above algorithms in terms of detection accuracy. Compared with the traditional YOLOv4‐tiny algorithm, the proposed algorithm increases the mean average precision (mAP) by 1.64%, the average precision (AP) of missing insulator defects by 0.32%, and the average precision (AP) of broken insulator defects by 4.96%. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
A Generic Approach Towards Location-Aware Business Process Execution

Locally distributed processes include several process participants working on tasks at different locations, e.g., craftspeople working on construction sites. Compared to classical IT environments, new challenges emerge due to the spatial context of a process. Real-time location data from Internet of Things (IoT) devices consumed through BPM technology can help businesses to implement more efficient and effective processes. Those advantages, however, have only been touched by small parts of existing research, while the architecture and implementation of executable location-aware processes area only ever been vaguely considered. Therefore, we introduce and present a non-exhaustive list of concepts for the use of location data for BPM as well as a location-aware approach for process execution and present a multi-layer system architecture based on standard BPM technology.
A New Hybrid PSO-JAYA Algorithm for Function Optimization

A simple yet powerful population based algorithm (PSO-JAYA) is proposed with the combination of Particle Swarm Optimization (PSO) and JAYA optimization algorithms. The idea is to synthesize the exploration abilities of PSO and JAYA into hybrid PSO-JAYA, which incorporates both algorithms’ strengths. Benchmark test functions are used to compare PSO-JAYA with other well-known optimization algorithms.
Blockchain-Enabled Banking Services and Customers’ Perceived Financial Well-Being: A Structural Nexus

Grounded in Transformative Service Research (TSR), this study aims to examine the mechanism by which blockchain-enabled banking determines customers perceived financial well-being (FW). We conclude that blockchain features augment information transparency, which in turn determines customers perceived financial well-being. Data was collected through a survey filled by 283 individuals having bank accounts. The contextual setting of the study was provided by commercial banks operating in Pakistan. The primary data was analyzed through PLS-SEM to explore the direct and indirect relationships among blockchain features (efficiency, security, and regulatory compliance), perceived information transparency, and perceived financial well-being. The evidence points to the fact that: (1) Blockchain features are significant in determining customers’ financial well-being; (2) Information transparency mediates the relationship between these features and customers perceived financial well-being; hence, that (3) Bank managers who embrace the challenging task of improving the perceived financial well-being of their customers, should adopt blockchain technology to enhance information transparency, and accordingly to augment the customers’ financial well-being.
An Autonomous Self-learning and Self-adversarial Training Neural Architecture for Intelligent and Resilient Cyber Security Systems

Cybersecurity systems have become increasingly important as businesses and individuals rely more on technology. However, the increasing complexity of these systems and the evolving nature of cyber threats require innovative solutions to protect against cyber attacks. One promising approach is the idea of autonomous self-learning and auto-training neural architectures. Autonomous self-learning refers to the ability of the system to adapt to new threats and learn from past experiences without human intervention. Auto-training, on the other hand, refers to the ability of the system to improve its performance over time by automatically adjusting its parameters and algorithms. This research proposes an autonomous Self-Learning and Self-Adversarial Training (SLSAT) neural architecture for intelligent and resilient cyber security systems. It is an extension of the next-generation Continuous-Time Reservoir Computing (CTRC) that was proposed by the authors recently. The CTRC is a time-series anomaly detection system controlled by time-varying differential equations. It uses Reinforcement Learning (RL) to dynamically fine-tune the reservoir computing parameters in order to identify the aberrant changes in the data. The proposed method in this research improves the CTRC’s architecture by including a Conditional Tabular Generative Adversarial Network (CTGAN). Specifically, including CTGAN allows the SLSAT architecture to generate synthetic data based on the identified abnormalities to improve the model’s performance and adapt to new and evolving threats without manual intervention. This, as proved experimentally, helps the model identify aberrant changes in the data and fend off poison and zero-day attacks.
Performance Analysis of Digit Recognizer Using Various Machine Learning Algorithms

In the field of pattern recognition, researchers are trying to figure out how to make a machine that can accurately recognize and predict handwritten digits. The problem falls into the category of object detection and multi-class classification. Several machine learning (ML) algorithms have been used and optimized to achieve effective prediction results for digit recognition. A generic research question that comes up in this context is the usage of specific ML algorithms for performing this task. The purpose of this work is to build efficient deep learning (DL) algorithms to recognize digits and compare their performance with that of conventional ML algorithms. Two of the most common DL algorithms, convolutional neural network (CNN) and multilayer perceptron (MLP) or artificial neural network (ANN), are used here. A widely used conventional ML algorithm, Support Vector Machine (SVM), which usually provides robust performance in general classification tasks, is also used. The performance of these algorithms is compared and analyzed based on accuracy and test results. The MNIST dataset from Kaggle’s Digit Recognizer competition is used here for training and testing the model. A graphical user interface (GUI) is constructed, in which the implemented ML model can be used to predict real-time user input of handwritten digits.
A privacy threat model for identity verification based on facial recognition

The proliferation of different types of photographic and video cameras makes it relatively simple and non-intrusive to acquire facial fingerprints with sufficient quality to perform individuals’ identity verification. In most democratic societies, a debate has been occurring regarding using such techniques in different application domains. Discussions usually revolve around the tradeoffs between utility (security in access control, mobile phone unlocking, payment processing, etc.), usability or economic gain and risks to citizens’ rights and freedoms (privacy) or ethics. This paper identifies the common aspects of different solutions for identity verification based on facial recognition techniques within different application domains. It then performs a privacy threat modelling based on these common aspects to identify the most critical risk factors and a minimum set of safeguards to be considered for their management.
Texture-driven super-resolution of ultrasound images using optimized deep learning model

ABSTRACTABSTRACTWhile comparing to the additional medical imaging modalities, the low resolution (LR) with poor quality images are obtained because of natural intrinsic imaging characteristics. We proposed a novel deep learning- based super-resolution of ultrasound images that are texture-driven. In this study, Convolutional Neural Networks (CNNs) are used to speed up the process to increase the image quality. Additionally, the Dwarf Mongoose Optimization (DMO) method is used to adjust the parameters of CNN thereby improving the quality of the image resolutions. The textures of the super-resolution images are examined with the Histogram of Oriented Gradients (HOG). The experimental works are handled using python software. Super-resolution per second and PSNR/dB values for the proposed model are 0.289 and 42.340, respectively. This model also offers FSO, GMSD, MAD, and MSSIM values of 0.9712, 0.013, 0.9802, and 0.9832 which is relatively higher than the existing techniques.KEYWORDS: Super resolutionultra sound imagingConvolutional Neural NetworkHistogram of Oriented Gradientslow resolution‌textureCAD diagnosisdeep learningdwarf mongoose optimization Disclosure statementNo potential conflict of interest was reported by the author(s).Availability of data and materialData sharing is not applicable to this article as no new data were created or analyzed in this study.Additional informationNotes on contributorsM. MarkcoM. Markco obtained his bachelor's degree in Computer Science and Engineering from E.G.S Pillay Engineering College Nagapattinam in the year 2009 and obtained Master's Degree in the year 2011from Anna University, Coimbatore, India. He is currently pursuing Ph.D., in Anna University, Chennai, India and working as an Assistant Professor in the Faculty of Computer Science and Engineering at E.G.S Pillay Engineering College Nagapattinam, India. His area of specialization includes Wireless Network, Image Processing and Data mining. His current research interest is Image processing. He is a member of ISTE and IE.S. KannanDr. S. Kannan is a Professor of Electrical and Electronics Communication Engineering, Kings College of Engineering, Pudukottai. He holds MS in Information Technology from Bharathidhasan University at Tiruchirappalli, ME in Software Engineering at Periyar Maniammai College of Technology for women, Anna University Chennai and PhD in Computer Science and Engineering from Manonmaniam Sundaranar University at Tirunelveli. He special interests include Business Intelligence Systems, Data Mining, Wireless Sensor Network and soft computing.
Discrete Adversarial Attack to Models of Code

The pervasive brittleness of deep neural networks has attracted significant attention in recent years. A particularly interesting finding is the existence of adversarial examples, imperceptibly perturbed natural inputs that induce erroneous predictions in state-of-the-art neural models. In this paper, we study a different type of adversarial examples specific to code models, called discrete adversarial examples , which are created through program transformations that preserve the semantics of original inputs.In particular, we propose a novel, general method that is highly effective in attacking a broad range of code models. From the defense perspective, our primary contribution is a theoretical foundation for the application of adversarial training — the most successful algorithm for training robust classifiers — to defending code models against discrete adversarial attack. Motivated by the theoretical results, we present a simple realization of adversarial training that substantially improves the robustness of code models against adversarial attacks in practice. We extensively evaluate both our attack and defense methods. Results show that our discrete attack is significantly more effective than state-of-the-art whether or not defense mechanisms are in place to aid models in resisting attacks. In addition, our realization of adversarial training improves the robustness of all evaluated models by the widest margin against state-of-the-art adversarial attacks as well as our own.
HWASanIO: Detecting C/C++ Intra-object Overflows with Memory Shading

C/C++ are often used in high-performance areas with critical security demands, such as operating systems, browsers, and libraries. One major drawback from a security standpoint is their susceptibility to memory bugs, which are often hard to spot during development. A possible solution is the deployment of a memory safety framework such as the memory tagging framework Hardware-assisted AddressSanitizer (HWASan). The dynamic analysis tool instruments object allocations and inserts additional check logic to detect memory violations during runtime. A current limitation of memory tagging is its inability to detect intra-object memory violations i.e., over- and underflows between fields and members of structs and classes. This work addresses the issue by applying the concept of memory shading to memory tagging. We then present HWASanIO, a HWASan-based sanitizer implementing the memory shading concept to detect intra-object violations. Our evaluation shows that this increases the bug detection rate from 85.4% to 100% in the memory corruptions test cases of the Juliet Test Suite while maintaining high interoperability with existing C/C++ code.
Nesnelerin İnternetinde Ara Yazılım Protokollerinin Hazır Bulunurluğuna Yapılan Saldırılar: MQTT Üzerine Bir Vaka Çalışması

The Internet of Things (IoT) encompasses a technological ecosystem that improves the daily lives of individuals by increasing productivity, safety, comfort, health and sustainability. In addition, the IoT brings a variety of benefits to many industries, including increased efficiency, productivity and cost savings. However, the proliferation of IoT technologies has revealed many security vulnerabilities, especially in the middleware layer. In this article, we presented possible attacks on availability of middleware layer messaging protocols. In the research, a comprehensive case study was carried out, especially focusing on the MQTT (Message Queuing Telemetry Transport) protocol. We performed Man-in-the-Middle (MitM), Denial of Service (DoS) and Brute-Force attacks in our experimental environment. The effects and results of the attacks made in cases where the connection to the MQTT protocol is made with a user name and password, and when the user name and password are not used are examined. The results of the attacks that emerged in the different scenarios created were evaluated and the precautions to be taken to protect against the attacks were discussed.