Reviewerly/exHarmony huggingface dataset:
Selecting Best Software Vulnerability Scanner Using Intuitionistic Fuzzy Set TOPSIS

Software developers endeavor to build their products with the least number of bugs.Despite this, many vulnerabilities are detected in software that threatens its integrity.Various automated software i.e., vulnerability scanners, are available in the market which helps detect and manage vulnerabilities in a computer, application, or a network.Hence, the choice of an appropriate vulnerability scanner is crucial to ensure efficient vulnerability management.The current work serves a dual purpose, first, to identify the key factors which affect the vulnerability discovery process in a network.The second, is to rank the popular vulnerability scanners based on the identified attributes.This will aid the firm in determining the best scanner for them considering multiple aspects.The multi-criterion decision making based ranking approach has been discussed using the Intuitionistic Fuzzy set (IFS) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the various scanners.Using IFS TOPSIS, the opinion of a whole group could be simultaneously considered in the vulnerability scanner selection.In this study, five popular vulnerability scanners, namely, Nessus, Fsecure Radar, Greenbone, Qualys, and Nexpose have been considered.The inputs of industry specialists i.e., people who deal in software security and vulnerability management process have been taken for the ranking process.Using the proposed methodology, a hierarchical classification of the various vulnerability scanners could be achieved.The clear enumeration of the steps allows for easy adaptability of the model to varied situations.This study will help product developers become aware of the needs of the market and design better scanners.And from the user's point of view, it will help the system administrators in deciding which scanner to deploy depending on the company's needs and preferences.The current work is the first to use a Multi Criterion Group Decision Making technique in vulnerability scanner selection.
A Practical and Secure Certificateless Signcryption for Privacy-Preserving Scheme in Healthcare Management System

As a result of modern progress, the usage of wireless sensor networks (WSNs) in the medical field has gotten considerable interest. WSNs are implemented to gather and transmit patient physiological data in healthcare management systems. Therefore, authentication and patient data privacy are two major challenges in the healthcare system. Thus, some cryptographic mechanisms are applied to secure the transmission of physiological data collected by the wearable sensors using bilinear pairing. However, the bilinear pairing is considered high computational. This paper provides a practical and efficient certificateless signcryption scheme with free pairing to secure communication between the sensors and the remote control system. Also, signcryption is employed to meet the main security requirements such as confidentiality, non-repudiation, integrity, and authentication. Besides, under the Computational Diffie-hellman assumption and the Discrete logarithm problem in the random oracle model, this protocol is shown to meet unforgeability and confidentiality. Finally, as shown in the security analysis, our protocol is secure and practical.
Max-Tree Computation on GPUs

In Mathematical Morphology, the max-tree is a region-based representation that encodes the inclusion relationship of the threshold sets of an image. This tree has proved useful in numerous image processing applications. For the last decade, work has led to improving the construction time of this structure; mixing algorithmic optimizations, parallel and distributed computing. Nevertheless, there is still no algorithm that benefits from the computing power of the massively parallel architectures. In this work, we propose the first GPU algorithm to compute the max-tree. The proposed approach leads to significant speed-ups, and is up to one order of magnitude faster than the current State-of-the-Art parallel CPU algorithms. This work paves the way for a max-tree integration in image processing GPU pipelines and real-time image processing based on Mathematical Morphology. It is also a foundation for porting other image representations from Mathematical Morphology on GPUs.
Research Review of Object Detection Algorithms in Vehicle Detection

Target detection technology is an important part of computer vision technology, and current target detection algorithms have been significantly applied in various fields. As an important research content in the field of intelligent transportation, the research on vehicle detection and recognition based on target detection algorithms has gradually become a research hotspot. This paper summarizes the research on vehicle target detection based on deep learning target detection algorithms, hoping to provide assistance for related research.
Application of Laboratory Information Management System (LIMS) in Coal Mine Electronic Products Testing Laboratory

Based on the new requirements of scientific research experiments and the need of informatization and paperless management, a laboratory management system (LIMS) suitable for coal mine electronic product testing is constructed [1], The system includes many functions such as business acceptance, sample management, transaction management, experimental data management, laboratory resource management, network management and report management, which effectively improves the operating efficiency, management level and work efficiency of laboratories and effectively reduces the operating and management costs of enterprises.
Communication-efficient k-Means for Edge-based Machine Learning

We consider the problem of computing the <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="lu-ieq2-3144595.gif"/></alternatives></inline-formula>-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine learning computation to nearby edge servers. <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="lu-ieq3-3144595.gif"/></alternatives></inline-formula>-Means computation is fundamental to many data analytics, and the capability of computing provably accurate <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="lu-ieq4-3144595.gif"/></alternatives></inline-formula>-means centers by leveraging the computation power of the edge servers, at a low communication and computation cost to the data sources, will greatly improve the performance of these analytics. We propose to let the data sources send small summaries, generated by joint dimensionality reduction (DR), cardinality reduction (CR), and quantization (QT), to support approximate <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="lu-ieq5-3144595.gif"/></alternatives></inline-formula>-means computation at reduced complexity and communication cost. By analyzing the complexity, the communication cost, and the approximation error of <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="lu-ieq6-3144595.gif"/></alternatives></inline-formula>-means algorithms based on carefully designed composition of DR/CR/QT methods, we show that: (i) it is possible to compute near-optimal <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="lu-ieq7-3144595.gif"/></alternatives></inline-formula>-means centers at a near-linear complexity and a constant or logarithmic communication cost, (ii) the order of applying DR and CR significantly affects the complexity and the communication cost, and (iii) combining DR/CR methods with a properly configured quantizer can further reduce the communication cost without compromising the other performance metrics. Our theoretical analysis has been validated through experiments based on real datasets.
Feature Aligned Ship Detection Based on RepPoints in SAR Images

Ship detection in synthetic aperture radar (SAR) image is an important and challenging task. A number of methods based on deep learning have been proposed for the ship detection task and achieved remarkable performance. However, existing methods still suffer from some problems, such as feature misalignment, multi-scale, small target missing, etc., which lead to low detection accuracy. To solve these problems, we propose a feature-aligned anchor-free ship detection algorithm based on the RepPoints method. The model consists of a feature pyramid networks (FPN) backbone, a feature alignment module, and a refinement detection head. Specifically, the FPN backbone is used to extract multi-scale features, then the feature alignment module is used to perform coarse-grained location prediction. After that, the deformable convolution (DConv) is used for the fine-grained feature alignment based on the coarse prediction. Finally, a score map and refined location prediction are generated on the aligned feature map. Experimental results on the publicly available SAR image dataset SSDD demonstrate the effectiveness of the proposed method.
Application of Object Detection and Tracking Technologies for Unmanned Aircraft Vehicles

Analyze the UAV’s video and image contents, design the detection algorithm, obtain the carplate content with carplate location adopting SVM and character recognition utilizing ANN, submit the evidence collection to the UAV’s command and control (C2) system, and offer the warning and decision information to the user in the C2 process; Design the moving object tracking method, realize the automatic detection and tracking of vehicles through the image matching and moving target location. The method presented in this paper is to improve the level of UAV’s intelligence and application abilities.
Multi-User Voicefilter-Lite via Attentive Speaker Embedding

In this paper, we propose a solution to allow speaker conditioned speech models, such as VoiceFilter-Lite, to support an arbitrary number of enrolled users in a single pass. This is achieved via an attention mechanism on multiple speaker embeddings to compute a single attentive embedding, which is then used as a side input to the model. We implemented multi-user VoiceFilter-Lite and evaluated it for three tasks: (1) a streaming automatic speech recognition (ASR) task; (2) a text-independent speaker verification task; and (3) a personalized keyphrase detection task, where ASR has to detect keyphrases from multiple enrolled users in a noisy environment. Our experiments show that, with up to four enrolled users, multi-user VoiceFilter-Lite is able to significantly reduce speech recognition and speaker verification errors when there is overlapping speech, without affecting performance under other acoustic conditions. This attentive speaker embedding approach can also be easily applied to other speaker-conditioned models such as personal voice activity detection (VAD) and personalized ASR.
The Relationship Between Digital Transformation and Quality of UAE Government Services Through Machine Learning

Digital transformation has become necessary for institutions and bodies seeking to develop and improve their services to beneficiaries, and this study aims to discover The Relationship Between Digital Transformation using machine learning and Quality of UAE Government Services and requirements for digital transformation in the UAE, according to the literature, digital transformation has become necessary for UAE institutions and bodies that seek to develop and improve their services to beneficiaries, and there is a trend towards digital transformation in UAE government institutions. It is possible to take advantage of new digital technologies to create innovative and high-quality services in them, and thus enhance the services provided, which calls for research on the current and expected effects of digital transformation and how to benefit from it as well. Further discussions and implications are also presented in the study.
Detecting Impersonators in Examination Halls Using AI

Detecting impersonators in examination halls is very significant to conduct the examination fairly. Recently there were occasions of potential impersonators taking tests instead of the intended person. To overcome this issue, we require an efficient method with less manpower. With the advancement of machine learning and AI technology, we can overcome this issue. In this project, we are developing an AI system where images of students are saved and the model is developed using transfer learning process to get accurate results. If the student is an allowed one, it shows the hall ticket number and name of the student otherwise it appears unknown tag.
Learning the Subsystem of Local Planning for Autonomous Racing

The problem of autonomous racing is to navigate through a race course as quickly as possible while not colliding with any obstacles. We approach the autonomous racing problem with the added constraint of not maintaining an updated obstacle map of the environment. Several current approaches to this problem use end-to-end learning systems where an agent replaces the entire navigation pipeline. This paper presents a hierarchical planning architecture that combines a high level planner and path following system with a reinforcement learning agent that learns that subsystem of obstacle avoidance. The novel &#x201C;modification planner&#x201D; uses the path follower to track the global plan and the deep reinforcement learning agent to modify the references generated by the path follower to avoid obstacles. Importantly, our architecture does not require an updated obstacle map and only 10 laser range finders to avoid obstacles. The modification planner is evaluated in the context of <tex>$\mathbf{F}1/10^{th}$</tex> autonomous racing and compared to a end-to-end learning baseline, the Follow the Gap Method and an optimisation based planner. The results show that the modification planner can achieve faster average times compared to the baseline end-to-end planner and a 94&#x0025; success rate which is similar to the baseline.
A Hybrid Algorithm for Pattern Matching: An Integration of Berry-Ravindran and Raita Algorithms

Due to the quick actions of technology, a complicated and huge volume of data deriving from biological sciences are generated which makes string matching patterns a challenging task. This direction has the aim to make the utilization of an individual algorithm for string searching nearly ineffectual as the number of tries and the number of observations continues to increase. So, the solution is in the grouping of more than one algorithm to create a hybrid algorithm for quicker performance. The proposed hybrid algorithm uses the best features of Raita algorithm and Berry-Ravindran algorithm. The reason for choosing these two algorithms is because they achieve better performance in "number of attempts" and "number of character comparisons" tests. New Hybrid Algorithm (BRR) is able to produce better results by decreasing the effort and character comparisons. The data types used to evaluate performance are English text, DNA, and protein. In number of attempts evaluation, for DNA, English, and protein text datasets, the improvement of the hybrid algorithm was 18%, 50%, and 50% in comparison to Berry-Ravindran algorithm and it was 71%, 74%, and 70% in comparison to Raita algorithm. The results show that regardless of the size of the data used, the mix of algorithms yields better results and improved performance than the original algorithm.
Position-based Hash Embeddings For Scaling Graph Neural Networks

Graph Neural Networks (GNNs) bring the power of deep representation learning to graph and relational data and achieve state-of-the-art performance in many applications. GNNs compute node representations by taking into account the topology of the node’s ego-network and the features of the ego-network’s nodes. When the nodes do not have high-quality features, GNNs learn an embedding layer to compute node embeddings and use them as input features. However, the size of the embedding layer is linear to the product of the number of nodes in the graph and the dimensionality of the embedding and does not scale to big data and graphs with hundreds of millions of nodes. To reduce the memory associated with this embedding layer, hashing-based approaches, commonly used in applications like NLP and recommender systems, can potentially be used. However, a direct application of these ideas fails to exploit the fact that in many real-world graphs, nodes that are topologically close will tend to be related to each other (homophily) and as such their representations will be similar.In this work, we present approaches that take advantage of the nodes’ position in the graph to dramatically reduce the memory required, with minimal if any degradation in the quality of the resulting GNN model. Our approaches decompose a node’s embedding into two components: a position-specific component and a node-specific component. The position-specific component models homophily and the node-specific component models the node-to-node variation. Extensive experiments using different datasets and GNN models show that our methods are able to reduce the memory requirements by 88% to 97% while achieving, in nearly all cases, better classification accuracy than other competing approaches, including the full embeddings.
Analysis of DNN based image watermarking data generation for self-recovery

The world is rapidly going digital and data security is the foremost concern. Digital images form a large chunk of this digital data. Therefore, techniques like digital image watermarking are used for assuring image security. In this paper application of DNN based compression algorithm is examined for implementation of color image tampering detection and self-recovery system. The DNN based image compression is used to compress the host images and multiple copies of compressed images are embedded in the host image for self-recovery. Due to the smaller size and better quality provided by DNN based compression algorithm, the watermarked images have high PSNR (peak signal to noise ratio). Similarly, the quality of recovered images is also high. The DNN based algorithm is analyzed for different images and tampering conditions. In the best-case scenario, the algorithm can recover the image from about 88% tampering.
Heterogeneous Defect Prediction through Joint Metric Selection and Matching

Defect prediction is one of the hot topics in software engineering. To relax the restriction on metrics, heterogeneous defect prediction (HDP) arises and aims to conduct the prediction across projects with different metrics. Among existing HDP methods, (1) one type of them construct a common metric space for heterogeneous source and target data by metric matching regardless of removing redundant metrics; (2) the other type of them generally consist of two phases that are conducted individually, i.e., metric selection and matching, which makes the whole process likely suboptimal. To solve these issues, we propose a novel approach Jointly optimizing Metric Selection and Matching (JMSM). Specifically, JMSM employs maximum mean discrepancy to reduce the distribution difference between source and target data while filtering out redundant metrics by introducing the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${l_{2,1}}$</tex> -norm regularization. Experiments on 22 projects from three datasets demonstrate the significant superiority of JMSM over baselines in performance and verify the effectiveness of introducing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${l_{2,1}}$</tex> -norm regularization.
Sensor Fingerprints: Camera Identification and Beyond

Abstract Every imaging sensor introduces a certain amount of noise to the images it captures—slight fluctuations in the intensity of individual pixels even when the sensor plane was lit absolutely homogeneously. One of the breakthrough discoveries in multimedia forensics is that photo-response non-uniformity (PRNU), a multiplicative noise component caused by inevitable variations in the manufacturing process of sensor elements, is essentially a sensor fingerprint that can be estimated from and detected in arbitrary images. This chapter reviews the rich body of literature on camera identification from sensor noise fingerprints with an emphasis on still images from digital cameras and the evolving challenges in this domain.
Routing of vehicles by intelligent algorithms in the matter of transporting goods

The task of route planning for a mobile robot or vehicle has attracted the attention of many research papers. In these writings, it is mostly assumed that the robot has a complete and accurate model of its surroundings before moving. Less attention has been paid to the issue of unknown environments. This is the case for a crawler or robot that has to move towards a target without a map of the area, such as vehicle route planning. Traditional intelligent Hard-NP optimization algorithms occur because this has been proven to be of the type they have not had much success in it. As a result, various innovative methods have been used to solve these problems. In the meantime, evolutionary methods are used as a population-based method with an increasing trend. On the other hand, for routing mobile robots, when the working space of the robot is clear, routing for the mobile robot is done in a global mode and offline. If the specifications of the environment are not known, routing is done locally and on the line by taking information from the sensors. In this research, the problem of routing a mobile robot in an unknown environment with two-dimensional dynamic barriers is addressed. Used off-line. Comparison and evaluation of the simulation results of the proposed evolutionary algorithms indicate that the robot guidance from the obtained path has the optimal criteria, in other words, the selected path length has the lowest cost and is much closer to the shortest possible path length.
Image Hashing Based on SHA-3 Implemented on FPGA

In recent years, in our digital world, images play an essential part in everyday's communication or in remote sensing, so techniques to validate the integrity and correctness of the transmission are required. The most popular technique is hashing. In this article, we focus on the SHA-3 (Keccak) algorithm for hashing images. We use the images of dimensions 256 × 256 pixel for our implementations based on the VHDL. Our implementations were performed on the Intel Arria 10 GX field-programmable gate array (FPGA) and Nios II processor. Also, experimental results such as entropy, number of pixel changing rate (NPCR), and unified averaged changed intensity (UACI), metrics show that the SHA-3 (Keccak) algorithm is reliable, secure and has a high application potential for hashing images. The proposed design aims to improve the criteria of efficiency, security, and throughput. Finally, we augmented our design using the Floating Point Hardware 2 (FPH2) IP Block and we compared the results of our research with other existing architectures.