Reviewerly/exHarmony huggingface dataset:
Cooperative Motion Planning for Persistent 3D Visual Coverage With Multiple Quadrotor UAVs

In this paper, we address the multiple quadrotor UAVs trajectory planning optimization problem for large-scale, persistent, high-depth visual coverage tasks in three-dimensional (3-D) terrain environment. To minimize the overall energy expenditure of the UAVs for accomplishing a task, we set up an air-to-ground collaborative system which introduces base stations to hold and recharge UAVs. The system is formulated as an integer programming, and solved by a novel hierarchical reinforcement learning trajectory planning algorithm (RL-TP), in which the paths are obtained by reinforcement learning method, and then the trajectories are obtained by Bézier curve method. Both simulation and physical experiments show that RL-TP can effectively improve the efficiency and persistence of aerial visual coverage task. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —While the multi-rotor UAV has been an important means for field monitoring, it suffers the problem of short battery life a lot. To make it more efficient and persistent, we use multiple UAVs and introduce ground base stations to charge the UAVs. The scenario is formulated as an air-to-ground collaborative system, and the motion planning strategy is to minimize the energy consumption. We propose a hierarchical collaborative coverage reinforcement learning trajectory planning algorithm (RL-TP) to solve it. We carry out both simulation and physical field experiments, and compare RL-TP with other popular methods. The experimental results show that the system is feasible and RL-TP performs well in both time efficiency and energy consumption. In future research, we will introduce unmanned ground vehicles to replace the stationary ground base stations to make the air-to-ground collaborative system more powerful and flexible.
Sortledton: a Universal Graph Data Structure

Despite the wide adoption of graph processing across many different application domains, there is no underlying data structure that can serve a variety of graph workloads (analytics, traversals, and pattern matching) on dynamic graphs with single edge updates updates.
A Comprehensive Study on the Association Between Personality Traits and Software Development

The success of software development is a key determining factor of software quality. It is assumed that different developers differ in their efficiency, however, till now the aspects contributing to this distinction have been scarcely studied. This paper inspected whether personality traits, as the five-factor model, defines, impact performance on a task for software development. The research scrutinizes the software development practices and job profiles present in the market. It also explored how to best map the skill sets (technical, soft skills) with the type of developer and its domain. After data collection from the website, the personality of an individual is identified using Myers–Briggs Type Indicator MBTI.
Survey on Foraging Behavior Algorithms

This paper presents a study of research activities inspired by the foraging behavior of animals. Foraging behavior algorithms are computational models that simulate the behavior of organisms searching for food resources in their environment. Foraging behavior algorithms has made the researcher's to actively do research over few years due to their potential applications in fields such as robotics, optimization, and AI. This survey paper provides an overview in foraging behavior algorithms. It begins by defining the concept of foraging behavior and exploring its importance in the natural world. The paper then reviews different types of foraging behavior algorithms, consisting of ant colony optimization, particle swarm optimization. The paper also discusses various applications of foraging behavior algorithms in fields such as wireless sensor networks, and search and rescue operations. Furthermore, the survey paper examines some of the challenges and limitations of foraging behavior algorithms and provides suggestions for future research directions. Overall, this survey paper highlights the potential of foraging behavior algorithms to improve the efficiency and effectiveness of various computational systems and emphasizes the need for further research in this field to address some of the remaining challenges
Vehicle Counting based on Convolution Neural Network

In recent decades, intelligent transportation systems have drawn a lot of attention. For a traffic control strategy to be successful, accurate and thorough traffic flow information is essential. The main objective in this field is vehicle detection, and two crucial applications are vehicle counting and classification. There are numerous ways to count vehicles, including manual counts, computer vision, pneumatic road tube counting, and inductive loops. But developing a quick and precise approach for estimating traffic volume and vehicle counts is the major goal. The conventional approaches need a lot of time and complexity. Therefore, a computer vision-based virtual detection zone method that is straightforward, quick, and accurate is being used to address these problems. A UA-DETRAC dataset is used to evaluate the suggested approach. Firstly, the zone is made by setting the coordinates in the frame. This can be done by manually plotting the points on the frame. The zone is visualised into the frame using the OpenCV library. Pre-trained YOLOv3 model is used for object detection and classification. The vehicles are classified into five different categories. Sort algorithm is used for vehicle tracking and counting the number of vehicles that pass through the virtual detection zone. The number of vehicles passing through the virtual detection zone in a given time can be used to estimate the traffic volume at the time.
An Analysis of Blockchain and NFT Technologies and their Drawbacks

Due to its qualities that offer unbreakable security and global accessibility, blockchain has grown to become one of the most widely used technologies today. Blockchain technology is revolutionizing the financial and banking sectors, but it isn't only the financial sector; the majority of companies that used online payment systems, faced an issue with transparency or wished to do away with the middleman and also wish to incorporate it. NFT is a further emerging technology that is seen as a subset of the blockchain (NFT stands for the non-fungible token). NFT is merely a digital token that is awarded to any original creation, such as a painting or a song and or a tweet. This study examines the drawbacks of blockchain and NFT as they are faced by several industries, including IoT, banking, music, agriculture, food and supplies, and healthcare.
Lexical diversity as a lens into the classification of Slavic languages: A quantitative typology perspective

Abstract This study proposes a linguistic classification method based on quantitative typology, which leverages a large-scale multilingual parallel corpus to obtain valid language classification result by excluding the influence of covariates such as text genre and semantic content in cross-language comparison. To achieve this, we model the type–token relationships of each Slavic parallel text and calculate the lexical diversity to approximate the morphological complexity of the language. We perform automatic clustering of languages based on these lexical diversity metrics. Our findings show that (1) the lexical diversity metrics can well reflect that the language is located somewhere on the continuum of ‘analytism-synthetism’; (2) the automatic clustering based on these metrics effectively reflects the genealogical classification of Slavic languages; and (3) the geographical distribution of lexical diversity in the region where Slavic languages are spoken shows a monotonic increasing trend from southwest to northeast, which is consistent with the pattern found by previous authors on a global scale. The methodological approach taken in this study is data-driven, with the benefit of being independent of theoretical assumptions and easy for computer processing. This approach can offer a better insight into corpus-based typology and may shed light on the understanding of language as a human-driven complex adaptive system.
Classification and quantification of entanglement through wedge product and geometry

Abstract The wedge product of post-measurement vectors of a two-qubit state gives rise to a parallelogram, whose ‘area’ has been shown to be equivalent to the generalized I-concurrence measure of entanglement. In multi-qudit systems, the wedge product of post-measurement vectors naturally leads to a higher dimensional parallelepiped which yields a modified faithful entanglement measure. Our new measure fine grains the entanglement monotone, wherein different entangled classes manifest with different geometries. We present a complete analysis of the bipartite qutrit case considering all possible geometric structures where three entanglement classes of pure bipartite qutrit states can be identified with different geometries of post-measurement vectors: three planar vectors; three mutually orthogonal vectors; and three vectors that are neither planar and not all of them are mutually orthogonal. It is further demonstrated that the geometric condition of area and volume maximization naturally leads to the maximization of entanglement. The wedge product approach uncovers an inherent geometry of entanglement and is found to be very useful for the characterization and quantification of entanglement in higher dimensional systems.
Chatbots Technology and its Challenges: An Overview

A chatbot is a conversational agent that uses Artificial Intelligence (AI) to interpret the text of the chat using Natural Language Processing (NLP) in particular, instead of making direct contact with a live person, users can make conversation via text or voice. Chatbots are a fast-growing AI trend that involves the use of applications communicating with users in a conversational style and imitating human conversation using human language. Many industries are attempting to include solutions based on artificial intelligence like chatbots to improve their customer service in order to deliver better service to their customers with faster and less expensive support. This paper is a survey of the published chatbots to discover knowledge gaps and indicate areas that require additional investigation and study, starting from history and how it evolves during the past, then chatbots architectures to understand how it works, and to identify application of chatbots in many domains, and finish by chatbots limitations that shorten its lifespan and how can future work improve the chatbot for best performance.
Machine Learning Algorithms for Automotive Software Defect Prediction

The use of machine learning algorithms has increased lately in multiple domains, this includes software engineering. Identifying a suitable methodology to recognize Software defect, will help improvement the quality of the software and save costs, especially in an industry like cars manufacturing. It is important to find suitable and significant measures which are most relevant for finding the defects in a software. This paper contributes towards the decision of adoption of machine learning techniques for the purpose of software defect predictions. In our approach, we considered supervised machine learning algorithms to build our model that aims to predict the occurrence of the software defects.
Automatic patient positioning based on robot rotational workspace for extended reality

Understanding the properties and aspects of the robotic system is essential to a successful medical intervention, as different capabilities and limits characterize each. Robot positioning is a crucial step in the surgical setup that ensures proper reachability to the desired port locations and facilitates docking procedures. This very demanding task requires much experience to master, especially with multiple trocars, increasing the barrier of entry for surgeons in training.Previously, we demonstrated an Augmented Reality-based system to visualize the rotational workspace of the robotic system and proved it helps the surgical staff to optimize patient positioning for single-port interventions. In this work, we implemented a new algorithm to allow for an automatic, real-time robotic arm positioning for multiple ports.Our system, based on the rotational workspace data of the robotic arm and the set of trocar locations, can calculate the optimal position of the robotic arm in milliseconds for the positional and in seconds for the rotational workspace in virtual and augmented reality setups.Following the previous work, we extended our system to support multiple ports to cover a broader range of surgical procedures and introduced the automatic positioning component. Our solution can decrease the surgical setup time and eliminate the need to repositioning the robot mid-procedure and is suitable both for the preoperative planning step using VR and in the operating room-running on an AR headset.
Multi-objective hyperparameter optimization on gradient-boosting for breast cancer detection

The most commonly occurring cancer among women, breast cancer, causes lakhs of deaths annually, which can be prevented by early detection and treatment. Detection can be done by using machine learning models on histopathological images which are affordable, reliable, and accurate. Previous studies in this regard have focused on transfer learning methods combining feature selection using Convolutional Neural Networks (CNNs) and an ensemble of gradient-boosting algorithms. However, none of the state-of-the-art techniques capture the multi-objective nature of Breast Cancer Detection (BCD) and tend to improve a single performance measure such as Accuracy and F1 score, which fail to capture certain essential aspects of the problem as the cost of misclassification varies greatly depending on its type. In this study, a multi-objective hyperparameter optimization technique for Breast Cancer Prediction is proposed by comparing random search, Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Bayesian optimization. This approach is applied to an ensemble of three popular gradient-boosting techniques: extreme gradient-boosting, light gradient-boosting machine and categorical boosting on features obtained from Inception-ResNet-v2 CNN model applied on the benchmark BreakHis dataset to optimize Precision, Recall, Accuracy, and AUC simultaneously. The novel NSGA2-IRv2-CXL model proposed in this study achieves maximum Accuracy of 94.40%, AUC of 98.16, Precision of 95.77%, and Recall of 99.29% for 100 $$\times$$ magnification. The study also establishes trade-offs between performance metrics thereby opening avenues for further research in multi-objective approaches to BCD which can provide a larger view of the strengths and weaknesses of the classification model.
A Hybrid Continual Learning Approach for Efficient Hierarchical Classification of IT Support Tickets in the Presence of Class Overlap

A support ticket describes an issue faced by a system's end-users when they encounter issues with their system. For large-scale IT corpora with hundreds of classes organized in a hierarchy, the task of classifying support tickets is vital to guarantee long-term clients. Due to the complexity of the unstructured nature of human language, text classification is challenging. The task is even harder when classes overlap. In the business world, an efficient and interpretable ML model is preferred over an expensive black-box model. In this paper, we propose a Hybrid Online Offline Model (HOOM) for efficient classification of hierarchical text documents using linear ML models. The experimental results on a private dataset of IT support tickets show that the hybrid model (HOOM) exhibits a promising performance if deployed in a real-world scenario. Furthermore, the hybrid model is anticipated to have a fast inference time given the underlying linear classifiers.
Phone Pick-up Authentication: A Gesture-Based Smartphone Authentication Mechanism

Passcodes, fingerprints and now facial scans, smart-phone authentication is becoming ever faster and faster and easier to use. Providing one time authentication mechanism that operate quickly an integrate ever more seamlessly into their customers' lives, is a concern for all mobile companies. These older methods are however more liable to spoofing and smudging attacks and as a result there is a call for new and improved methods. Gesture authentication looks to be a promising area of research but most gesture based schemes requires a specific input from the user, detracting from the simplicity of verifying oneself. We developed a novel gesture based authentication mechanism capable of recognizing the user based on the motion used to remove a phone from their pocket. Through this we are able to provide a high degree of security, but maintaining the user-friendliness of a fast user authentication approach, like fingerprint scanning. Our method also circumvents issues like wearing gloves or face masks. To prove the viability we collected over 500 samples from 10 users taking a mobile phone out of their pocket. Our models were able to achieve an authentication accuracy of over 93% only using 15 valid samples for training and a identification accuracy of 94%.
An Object Detection Model for Electric Power Operation Sites Based on Federated Self-supervised Learning

In the various edge cloud devices within the power system, a large amount of discrete data is generated, with the most significant amount being image data captured by each construction site. However, due to distance, privacy protection, transmission loss, and other issues, it is difficult to gather these data together for use, resulting in the waste of valuable data resources. Even if these data are collected, annotating them one by one is challenging, leading to limited training effectiveness for the model. As a result, supervision of electric power operation sites still depends on manual remote viewing of surveillance video, which is inefficient and error-prone. To address these issues, this paper proposes the use of Federated Self-supervised learning for object detection at electric power operation sites. This method effectively utilizes limited visual data in each terminal scene to jointly train a neural network model, improving the model's recognition accuracy under a distributed learning framework, increasing data utilization, and ensuring data security. The proposed approach has the potential to enhance the efficiency of electric power production.
Unsupervised Domain Adaptation for RF-Based Gesture Recognition

Human gesture recognition with radio frequency (RF) signals has attained acclaim due to the omnipresence, privacy protection, and broad coverage nature of RF signals. These gesture recognition systems rely on neural networks trained with a large number of labeled data. However, the recognition model trained with data under certain conditions would suffer from significant performance degradation when applied in practical deployment, which limits the application of gesture recognition systems. In this article, we propose an unsupervised domain adaptation framework for RF-based gesture recognition aiming to enhance the performance of the recognition model in new conditions by making effective use of the unlabeled data from new conditions. We first propose pseudo labeling and consistency regularization to utilize unlabeled data for model training and eliminate the feature discrepancies in different domains. Then we propose a confidence constraint loss to enhance the effectiveness of pseudo labeling, and design two corresponding data augmentation methods based on the characteristic of the RF signals to strengthen the performance of the consistency regularization, which can make the framework more effective and robust. Furthermore, we propose a cross-match loss to integrate the pseudo labeling and consistency regularization, which makes the whole framework simple yet effective. Extensive experiments demonstrate that the proposed framework could achieve 4.35% and 2.25% accuracy improvement comparing with the state-of-the-art methods on public WiFi data set and millimeter wave (mmWave) radar data set, respectively.
Contrastive Self-Supervised Clustering for Specific Emitter Identification

Specific emitter identification (SEI) is crucial for attacking and defending Internet of Things (IoT) devices in untrusted scenarios or battlefield environments. However, existing SEI methods usually require annotation information, which is often unavailable in noncooperative communications and untrusted scenarios. In this article, we propose a signal contrastive self-supervised clustering (SCSC) method for unsupervised SEI applications. First, we propose SCSC with 1-D fingerprint pyramid feature extractor (1D-FPFE) for obtaining hierarchical subtle features of emitter signals. Then, we propose a bit-pulse selection (BPS) strategy and several signal data augmentation methods. By constructing signal positive and negative instance pairs through data augmentation, our approach generates cluster preference representations in a contrastive self-supervised learning manner. Extensive experimental results based on communication burst emitter dataset show that SCSC achieves an accuracy improvement of about 26% over the current best communication signal clustering algorithm. Moreover, SCSC also exhibits good performance and generalization for 30 emitter clustering and few-shot unlabeled signal clustering.
A Data Integrity Verification Scheme for Centralized Database Using Smart Contract and Game Theory

Currently, many companies and institutions use centralized or distributed databases to store massive amounts of data. However, the use of untrusted centralized third-party auditors can result in security issues because these auditors may be malicious and tamper with or delete user data. This poses a significant challenge for ensuring the reliability of the data verification results. Although introducing a third-party auditor can help address this issue, it may also be untrustworthy and collude with the database service provider to forge false data verification results. In this study, we propose a data integrity verification scheme using smart contracts (DIV-SC) to address this challenge in a centralized database environment. Our approach utilizes blockchain technology as a decentralized third-party auditor, ensuring that the information stored on the blockchain is immutable and cannot be tampered with maliciously. In addition, smart contracts deployed on the blockchain can ensure that the verification procedures are correct and are not affected by any malicious party. We also leverage game theory to improve the reliability of multiple verifications, reduce verification time and improve overall performance. Our proposed scheme reduces the total verification time consumption by up to 53.44% while increasing the number of verifiable times by nearly 3.8 times, compared to conventional data integrity verification schemes.
A Customized Kubernetes Scheduling Algorithm to Improve Resource Utilization of Nodes

This article proposes a customized Kubernetes scheduling algorithm to improve resource utilization of nodes in a cluster. The algorithm is optimized over the default algorithm. The custom algorithm solves two problems, that are setting an appropriate Request value and determining the scoring rule. The article also evaluates the performance of the custom algorithm. It can be seen that the utilization rate of node resources is improved, and load balance between nodes in the cluster is better.