Reviewerly/exHarmony huggingface dataset:
Falsification of Learning-Based Controllers through Multi-Fidelity Bayesian Optimization

Simulation-based falsification is a practical testing method to increase confidence that the system will meet safety requirements. Because full-fidelity simulations can be computationally demanding, we investigate the use of simulators with different levels of fidelity. As a first step, we express the overall safety specification in terms of environment parameters and structure this safety specification as an optimization problem. We propose a multi-fidelity falsification framework using Bayesian optimization, which is able to determine at which level of fidelity we should conduct a safety evaluation in addition to finding possible instances from the environment that cause the system to fail. This method allows us to automatically switch between inexpensive, inaccurate information from a low-fidelity simulator and expensive, accurate information from a high-fidelity simulator in a cost-effective way. Our experiments on various environments in simulation demonstrate that multi-fidelity Bayesian optimization has falsification performance comparable to single-fidelity Bayesian optimization but with much lower cost.
Decentralized Leader-Follower Visual Cooperative Package Transportation using Unmanned Aerial Manipulators

This paper is concerned with the problem of cooperative transportation of payloads by teams of unmanned aerial multirotor vehicles equipped with dexterous robotic arms. In order to increase the autonomy of such systems, a leader-follower setting is implemented. The leader UAV dictates the motion and all other follower UAVs attempt to maintain their desired relative pose with respect to the leader. A fiducial marker structure is attached to the leader, which allows all followers to continuously visually track the leader. Each robotic arm is equipped with force-torque sensors at its end-effector for improving the UAVs’ flight stability. Specialized controllers are designed for the aerial manipulator systems, in order to ensure a compliant behavior against forces transmitted through the payload. Simulations are provided using the Gazebo environment in order to validate the proposed control methods.
Intelligent System for Fraud Detection in Online Banking using Improved Particle Swarm Optimization and Support Vector Machine

In the modern day, online banking has become the most popular service used by banking users. Banks collect vast amounts of useful information on their customers and their transactions every second. Financial institutions can't gain the insights they need without first securing and properly analyzing this important data utilizing big data analytic methods. The current business climate places a premium on analyzing massive data sets consisting of diverse data in order to unearth previously unseen patterns, market trends, client preferences, and other business insights. The purpose of this research is to suggest a strategy for employing IPSO-SVM to detect and prevent financial fraud in the digital sphere. This investigation introduces an improved particle swarm optimization of support vector machine (IPSO-SVM) technique model for fraud detection by combining optimized particle swarm optimization (IPSO) and support vector machine (SVM). The proposed approach outperforms other two models such as CNN and SVM.
Blockchain based Authentication for Internet of Things Devices based on Smart Farming

Farmers and other users who are using smart farming technology can access agricultural data through a unified blockchain-based platform. In this way, the transparency, anonymity, and traceability added by the blocks in the blockchain ensure that the correct data can be used when it is needed or in the future. It is necessary to authenticate with the original owner of the cattle to buy or sell cattle, and in some cases, governments also want to use that data in case of theft or accident. With its inherent characteristics, blockchain offers a promising approach for decentralized authentication in IoT networks. To provide efficient, decentralized mutual authentication and privacy protection for IoT users. This study examines the efficient, secure, and decentralized store of information of blockchain. This study also discusses on the cattle farmers who buy and sell animals, authenticate the owner of the cattle, and ensures scalability, interoperability, cost-effectiveness, and simplicity that help farmers to use blockchain-based authentication for their IoT devices.
Revolutionizing Recruitment: A Comparative Study Of KNN, Weighted KNN, and SVM - KNN for Resume Screening

In the recruitment process, continued screening assumes a fundamental part in distinguishing qualified contenders for a specific employment opportunity. A huge issue that scouts face is the tedious course of manual screening of resumes. To resolve this issue, this paper proposes the utilization of three ML calculations, in particular K-Nearest Neighbors (KNN), Weighted K-Nearest Neighbors (WKNN), and Support Vector Machine KNN (SVM KNN), for the mechanized screening of resumes. The dataset was manually created consisting of two segments that incorporate various classes like CA, advocate, engineering, and so forth, and the related resume portrayals. The dataset was utilized to prepare and assess the precision of the calculations. The trial concentrates on showing that Weighted KNN outperforms KNN and SVM KNN with an accuracy of 74%. The strategy can empower selection representatives to smooth out their enrollment interaction and distinguish qualified applicants rapidly and cost-effectively.
Deep Unified Model for Face Recognition based on Convolution Neural Network and Edge Computing

CCTV, communication, and alarm systems use face recognition technologies. Face detection in photos is a popular topic in science for practical reasons and because it challenges computer-generated vision systems. The variety of shooting situations (position, lighting, hairdo, emotion, backdrop, etc.) and face traits requires versatility. Deep learning-based image identification methods beat machine learning methods in efficiency and information processing. Modern computer systems have major authentication issues. Internet-connected smart devices are producing more data every day. A new model is needed to handle its vast data output. Deep learning and edge computing process vast volumes of data with high precision. Many trust facial recognition systems. SIFT and accelerated robust features are used in traditional facial recognition algorithms (SURF). This paper presents a convolutional neural network-based face identification and recognition solution that outperforms established methods. Tagged photographs of people taken in the outdoors teach the face-recognition algorithm (LFW). The suggested system had 99.1% accuracy on test data.
Fake Product Restriction using BlockChain

Recently, blockchain has become more popular as it fosters trust between untrusting participants. This paper uses blockchain technology to combat the sale of counterfeit products. In today’s global marketplace, fake goods detection is a problem that is becoming more and more significant. The growth of fake products can harm consumers, cause brand reputation damage, and cost real firms money. The usage of blockchain technology is one potential fix for this issue. Blockchain is a distributed ledger technology that makes it possible to trace transactions securely and openly. Blockchain can aid in preventing the creation and sale of counterfeit goods by creating an unchangeable and impenetrable record of the supply chain. Smart contracts can be used in this situation to automate the verification process and make sure that only genuine goods are sold through legitimate channels. This essay will examine the drawbacks and potential advantages of using blockchain technology to identify counterfeit goods.
Suspicious Activity Monitoring System using Machine Learning

In this world, the activity of crime and suspicious events increase day by day. Humans cannot continuously monitor the suspicious activities that happen in the daily life. In order to avoid manual monitoring, the automatic monitoring system of suspicious activities comes into play. The main aim of this proposed activity monitoring system is to identify and alert the users when suspicious activities are detected, using machine learning. In this research, YOLO is used to detect various suspicious activities like chain snatching, etc. This work aims in helping humans by saving their time and is also cost efficient.
Blood Cancer Detection and Classification using Auto Encoder and Regularized Extreme Learning Machine

Leukaemia is considered as the most common type of blood cancer. The length of time one requires to recover from this disease is determined by how quickly it is recognized and treated. Leukaemia develops when the bone marrow generates an abnormally large amount of white blood cells. Since images of human blood samples are studied under the microscope, haematologists employ techniques such as microscopic colour imaging, image segmentation, grouping, and classification to diagnose patients as fast as possible. Microscopy may identify blood cancer in visible and immature white blood cells in a variety of methods. Early and accurate diagnosis of leukaemia is critical for clinicians to properly treat patients. After the input image is provided, the gaussian filter is used to perform image enhancement, and the Wiener filter is used in image Deblurring, Adaptive K-Means clustering technique, Colour feature, Texture feature, GLCM and Hausdroff are also used, and lastly the Model is trained using AE and RELM. The proposed system outperforms conventional models such as RELM and ELM, which generates an accuracy of 98.45%.
W-IRL: Inverse Reinforcement Learning via Wasserstein Metric

In the control applications based on reinforcement learning, profound professional knowledge and engineering experience are required to set the reward function manually. Therefore, to reduce the design difficulty of a reward function and popularize reinforcement learning in the control domain, it is necessary to introduce inverse reinforcement learning (IRL), which uses an expert data-driven method to construct a reward function instead of a manually designed reward. However, since the control field often faces complex task scenarios involving high-dimensional continuous space, insufficient expert data may lead to problems when using the IRL algorithm, such as difficult training, slow convergence, and poor control effect. In order to solve these problems, this paper proposes an inverse reinforcement learning algorithm based on the Wasserstein metric (W-IRL). Initially, aiming at the problem of hard training and slow convergence, this paper uses the Wasserstein metric as a loss function to alleviate the gradient disappearance and explosion caused by high-dimensional spatial data training. Additionally, to improve the IRL control effect, this paper adopts an adversarial approach by using the reward function as the discriminator and the control strategy as the generator, and adopts the idea of state marginal matching (SMM) in the training process to improve the stability of the model. The experiments are conducted in four Mujoco simulation environments with high-dimensional space and a small amount of expert data. The results prove that the method proposed by this article outperforms mainstream IRL in control performance, robustness, and training stability.
PIC4rl-gym: a ROS2 Modular Framework for Robots Autonomous Navigation with Deep Reinforcement Learning

Learning agents can optimize standard autonomous navigation improving flexibility, efficiency, and computational cost of the system by adopting a wide variety of approaches. This work introduces the PIC4rl-gym, a fundamental modular framework to enhance navigation and learning research by mixing ROS2 and Gazebo, the standard tools of the robotics community, with Deep Reinforcement Learning (DRL). The paper describes the whole structure of the PIC4rl-gym, which fully integrates DRL agent's training and testing in several indoor and outdoor navigation scenarios and tasks. A modular approach is adopted to easily customize the simulation by selecting new platforms, sensors, or models. We demonstrate the potential of our novel gym by benchmarking the resulting policies, trained for different navigation tasks, with a complete set of metrics.
Research on UAV Ground Target Detection Based on Improved YOLOv7

Aiming at the problems of difficult detection of small targets and high miss rate in ground target detection under UAV high-altitude view, this paper proposes an improved YOLOv7-based UAV ground target detection method. Considering deployment to UAVs, the improved algorithm uses the YOLO-tiny network model as the baseline model to ensure the balance of speed and accuracy, adds the Convolutional Block Attention Module (CBAM) to improve the model's ability to learn important features, and utilizes data augment methods such as mosaic and mixup to effectively improve the detection performance for small targets and effectively reduce the miss detection rate of small targets. The experimental results show that the improved YOLOv7 algorithm has improved in all evaluation indexes, mAP@.5 increased by 3.57% compared with the baseline, Frames Per Second (FPS) decreased by 2 compared with the baseline, but still reaches 69.8, with the ability of real-time detection, and the improved algorithm exhibits good performance in the real test.
Semantic Communications With Variable-Length Coding for Extended Reality

Wireless extended reality (XR) has attracted wide attentions as a promising technology to improve users' mobility and quality of experience. However, the ultra-high data rate requirement of wireless XR has hindered its development for many years. To overcome this challenge, we develop a semantic communication framework, where semantically-unimportant information is highly-compressed or discarded in semantic coders, significantly improving the transmission efficiency. Besides, considering the fact that some source content may have less amount of semantic information or have higher tolerance to channel noise, we propose a universal variable-length semantic-channel coding method. In particular, we first use a rate allocation network to estimate the best code length for semantic information and then adjust the coding process accordingly. By adopting some proxy functions, the whole framework is trained in an end-to-end manner. Numerical results show that our semantic system significantly outperforms traditional transmission methods and the proposed variable-length coding scheme is superior to the fixed-length coding methods.
MMF-GSTIW-PMBM Adaptive Filter for Multiple Group Target Tracking With Heavy-Tailed Noise

High-resolution sensors can monitor and track multiple group targets and provide information support for battlefield situation assessment. However, the tracking accuracy of sensors is significantly impacted by the heavy-tailed noise with unknown characteristics. To solve the multigroup target tracking issue when the statistical characteristics of the noise are unknown, we propose a novel Poisson multi-Bernoulli mixture (PMBM) filter with nested multivariate myriad filter (MMF), which is based on the gamma Student’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> inverse Wishart (GSTIW) mixture distribution of the state variables and is called the MMF-GSTIW-PMBM adaptive filter. First, we model the noise and group target extended states using the Student’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> -distribution and the inverse Wishart (IW) distribution, respectively, and build a group target tracking model based on finite set statistics (FISST) and PMBM filter. Second, the MMF is embedded into the PMBM filter to estimate the characteristics of the innovation distribution and adaptively adjust the noise’s freedom and scale matrix parameters. Finally, to further enhance the adaptive estimation capability of the MMF, the multiwindow weighted fusion technique is employed to optimize the selection of sampling windows. Simulation experiments show that the proposed filter is capable of adaptively estimating the noise characteristic parameters and accurately tracking multiple group targets. It has higher estimation accuracy and is more robust to noise than the gamma Gaussian inverse Wishart (GGIW)-PMBM filter and GSTIW-PMBM filter.
ArEmotive Bridging the Gap: Automatic Ontology Augmentation Using Zero-Shot Classification for Fine-Grained Sentiment Analysis of Arabic Text

Human-computer interaction remains one of the final frontiers to conquer while held in perspective with the rapid developments and technology growth over recent years.It is an arduous task to convey the true human intent to the machine in order to generate a computerized relevant decision in a certain field.In recent years, focus has shifted to cover fields of study that relate to Sentiment Analysis (SA) to improve and ease the tasks of our daily lives.We Propose ArEmotive (Arabic Emotive), a finegrained sentiment analysis system that is human-independent which can automatically grow its source of information allowing for more precision and a greater dataset each time it is used through ontology augmentation and classification.Our proposed architecture relies on multiple data sources running through certain pipelines to generate a central online repository utilized by any mobile system to access this infobase.This system is important because many researchers in the field of automated ontology alignment and ontology mapping achieved a semi-automated approach to map new ontologies out of old ones or to extend already existing ontologies with data from new ones.ArEmotive identifies fine-grained emotions in text based on a dynamic ontology enriched through ontology alignment, mapping and machine learning assisted classification, resulting in a structure that contributes in: a centralized dataset ever growing to fit the need of the users, a sustainable structure able to allocate new data sources without the need to modify the system , ability to generate appropriate information even with the absence of ''parent'' sources.
YOLOv5s_2E: Improved YOLOv5s for Aerial Small Target Detection

To address the issues of low accuracy in existing small object detection algorithms, an improved network model algorithm called YOLOv5s_2E is proposed.This method first uses the k-means++ clustering algorithm to calculate the prior boxes of the Visdrone dataset.Then, it introduces Soft_NMS and combines it with EIoU to propose the EIoU_Soft_NMS algorithm to replace the non-maximum suppression (NMS) of the original network, improving the detection of objects that are occluded.The bounding box regression loss function uses Focal-EIoU, which speeds up model convergence and reduces loss.Additionally, a detection layer is added to the original detection head to unify the channel numbers, and with the dynamic head framework DyHead, the attention mechanism is integrated with the detector's head to further improve small object detection accuracy.Finally, the system robustness is improved by adjusting the ratio of data augmentation methods Mixup and Mosaic.Experimental results show that the proposed algorithm improves the mAP@0.5,mAP@0.5:0.95 and detection accuracy by 12.6%, 12.2%, and 20.5%, respectively, compared to the previous method on the VisDrone dataset.The parameter size only increases by 4%, and the weight file size increases by only 0.57MB, meeting the accuracy requirements for small object detection.
Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation

Historical interactions are the default choice for recommender model training, which typically exhibit high sparsity, i.e., most user-item pairs are unobserved missing data. A standard choice is treating the missing data as negative training samples and estimating interaction likelihood between user-item pairs along with the observed interactions. In this way, some potential interactions are inevitably mislabeled during training, which will hurt the model fidelity, hindering the model to recall the mislabeled items, especially the long-tail ones. In this work, we investigate the mislabeling issue from a new perspective of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aleatoric uncertainty</i> , which describes the inherent randomness of missing data. The randomness pushes us to go beyond merely the interaction likelihood and embrace aleatoric uncertainty modeling. Towards this end, we propose a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aleatoric Uncertainty-aware Recommendation</i> (AUR) framework that consists of a new uncertainty estimator along with a normal recommender model. According to the theory of aleatoric uncertainty, we derive a new recommendation objective to learn the estimator. As the chance of mislabeling reflects the potential of a pair, AUR makes recommendations according to the uncertainty, which is demonstrated to improve the recommendation performance of less popular items without sacrificing the overall performance. We instantiate AUR on three representative recommender models: Matrix Factorization (MF), LightGCN, and VAE from mainstream model architectures. Extensive results on four real-world datasets validate the effectiveness of AUR w.r.t. better recommendation results, especially on long-tail items.
An Ontology-Based Approach to Reduce the Negative Impact of Code Smells in Software Development Projects

The quality of software systems may be seriously impacted by specific types of source code anomalies. For example, poor programming practices result in Code Smells (CSs), which are a specific type of source code anomalies. They lead to architectural problems that consequently impact some significant software quality attributes, such as maintainability, portability, and reuse. To reduce the risk of introducing CSs and alleviate their consequences, the knowledge and skills of developers and architects is essential. On the other hand, ontologies, which are an artificial intelligence technique, have been used as solution to deal with different software engineering challenges. Hence, the aim of this paper is to describe an ontological approach to representing and analyzing code smells. Since ontologies are a formal language based on description logics, this approach may contribute to formally analyzing the information about code smells, for example, to detect inconsistencies or infer new knowledge with the support of a reasoner. In addition, this proposal may support the training of software developers by providing the most relevant information on code smells. This ontology can also be a means of representing the knowledge on CSs from different sources (documents in natural language, relational databases, HTML documents, etc.). Therefore, it could be a valuable knowledge base to support the struggle of software developers and architects either to avoid CSs or to detect and remove them. The ontology was developed following a sound methodology. The well-known tool Protégé was used to manage the ontology and it was validated by using different techniques. An experiment was conducted to demonstrate the applicability of the ontology and evaluate its impact on speeding up the analysis of CSs.