Reviewerly/exHarmony huggingface dataset:
Navigating in 3D Uneven Environments Through Supervoxels and Nonlinear MPC

Navigating uneven and rough terrains presents difficulties, including stability, traversability, sensing, and robustness, making autonomous navigation in these terrains a challenging task. This study introduces a new approach for mobile robots to navigate uneven terrains. The method uses a compact graph of traversable regions on point cloud maps, created through the utilization of supervoxel representation of point clouds. By using this supervoxel graph, the method navigates the robot to any reachable goal pose by utilizing a navigation function and Nonlinear Model Predictive Controller (NMPC). The NMPC ensures kinodynamically feasible and collision-free motion plans, while the supervoxel-based geometric planning generates near-optimal plans by exploiting the terrain information. We conducted extensive navigation experiments in real and simulated 3D uneven terrains and found that the approach performs reliably. Additionally, we compared resulting motion plans to some state-of-the-art sampling-based motion planners in which our method outperformed them in terms of execution time and resulting path lengths. The method can also be adapted to meet specific behavior, like the shortest route or the path with the least slope route. The source code is available in a GitHub repository. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/NMBURobotics/vox_nav.
A Detection Algorithm Based on Improved Faster R-CNN for Spacecraft Components

Due to the increasing need of science and technology, more and more spacecraft are in space. Once the spacecraft reaches its useful life, it will become floating junk in space. Active space debris removal technology is an effective method to solve space garbage. With the rapid iteration of deep learning methods, target detection technology in the field of computer vision has made a breakthrough. In this paper, a Faster R-CNN model based on RegNet as the main trunk network is proposed, which can reduce the loss of key information caused by R-CNN sampling and effectively improve the target detection effect. We will collect the photos of space spacecraft as training sets and verification sets sent to the network for iterative training. The test results of the data set in this paper show that: With RegNet as the main backbone network, the Faster R-CNN model has better test results than other target detection algorithms under the same parameters. Compared with other backbone networks added to Faster R-CNN, the mAP value is improved by at least 9.6%. Compared with other advanced target detection algorithms, mAP value increased by at least 26.3%. When processing low Earth orbit target identification tasks, the Faster R-CNN model based on RegNet as the main trunk network has a strong recognition ability for the image targets whose color is similar to the background color, relatively fuzzy and small scale.
AI-Based Public Policy Making: A New holistic, Integrated and “AI by Design” Approach

Public policy development is becoming more and more challenging for public administrations and the recent events (first of all the Covid-19 Outbreak) demonstrated how an evidence-based approach is crucial for managing critical situations with short responses, fast adaptation, and citizens' support. The ultimate vision of data-driven policy making entails the use of Artificial Intelligence (AI) as a means of increasing the efficiency of the policy development and management process (i.e. going beyond development to adaptation and optimization) and boosting a more responsive, adaptive, intelligent, and citizen-centric governance. The cross-industry standard process for data mining, known as CRISP-DM, is the most widely-used open standard process model that describes common approaches used by AI experts. The paper presents how the H2020 project AI4PublicPolicy leverages this model to promote a new holistic, integrated, and “AI by design” approach for public policy making, providing the Virtualized Policy Management Environment (VPME) and the blueprints for organizational transformation. Moreover, the paper presents a practical use and validation of this approach in a real-life public policy making case.
Application Research of Digital Film Music Post-test System under the Background of Big Data

This paper firstly studies the digital audio technology of film and television and then, according to the functional model of the software design. Each function model in the software is explained in detail. Especially, the difficulty of the key parts of the software is analyzed, and the concrete implementation scheme of the software is given. According to the rules of film music beat perceived by human ears and the observation of time-frequency characteristics of film music signal. A method to determine the lowest sample rate for accurate bpm detection of film music in a certain bpm range is obtained. The theory and algorithm of the beat signal are obtained by analyzing the beat signal. This paper presents an effective method of film beat extraction which is suitable for digital disc player and other devices. Finally, the system is tested and analyzed. This paper verifies the feasibility of the system.
A quantum-inspired probabilistic prime factorization based on virtually connected Boltzmann machine and probabilistic annealing

Probabilistic computing has been introduced to operate functional networks using a probabilistic bit (p-bit), broadening the computational abilities in non-deterministic polynomial searching operations. However, previous developments have focused on emulating the operation of quantum computers similarly, implementing every p-bit with large weight-sum matrix multiplication blocks and requiring tens of times more p-bits than semiprime bits. In addition, operations based on a conventional simulated annealing scheme required a large number of sampling operations, which deteriorated the performance of the Ising machines. Here we introduce a prime factorization machine with a virtually connected Boltzmann machine and probabilistic annealing method, which are designed to reduce the hardware complexity and number of sampling operations. From 10-bit to 64-bit prime factorizations were performed, and the machine offers up to 1.2 × 108 times improvement in the number of sampling operations compared with previous factorization machines, with a 22-fold smaller hardware resource.
Research on Online Course Design Strategy Based on Text Analysis of Bullet Screen

Based on the instructional design model ADDIE and the principle of goal-oriented course design, this study uses tools such as Python and GooSeeKer to analyze the high-frequency words, quantity changes and proportions, semantic relationships, and emotional changes in the bullet screen text of the online open course “Grade 9 Mathematics” on Bilibili, using a combined quantitative and qualitative analysis method. It was found that learners can engage in creative thinking regarding course content, exhibit high levels of semantic association during interactions, and provide positive feedback on the course overall. However, there are also issues such as a lack of effective supervision leading to deviation from preset goals and fluctuating levels of learning engagement. Based on the analysis results, a model for online course design is proposed, with recommendations for online course development from four dimensions: course goals, course design principles, technical means, and learning feedback. This study attempts to answer questions such as “how to design effective and learner-satisfactory online courses.”
White-Box and Black-Box Adversarial Attacks to Obstacle Avoidance in Mobile Robots

Advances in artificial intelligence (AI) play a major role in the adoption of robots for an increasingly broader range of tasks. However, as recent research has shown, AI systems, such as deep-learning models, can be vulnerable to adversarial attacks where small but carefully crafted changes to a model's input can severely compromise its performance. In this paper, we present two methods to find adversarial attacks against autonomous robots. We focus on external attacks against obstacle-avoidance behaviour where an attacker — a robot — actively perturbs the sensor readings of a goal-seeking victim robot. In the first (white-box) method, we model the interaction between the victim and attacker as a dynamical system and generate a series of open-loop control signals for the attacker to alter the victim's behaviour. In the second (black-box) method, the assumption that the attacker has full knowledge of the system's dynamics is relaxed, and closed-loop control for the attacker is learnt through reinforcement learning. We find that both methods are able to find successful attacks against the victim robot and thus constitute viable techniques to assess the robustness of autonomous robot behaviour.
Research on k-Means Algorithm Based on Differential Privacy

With the rapid rise in the scope and frequency of use, the shortcomings of clustering algorithms in unsupervised learning in terms of privacy protection are gradually exposed. In order to prevent the privacy leakage problem in the process of data mining, this paper investigates the privacy budget allocation scheme of k-means algorithm based on differential privacy, proposes an optimization scheme of privacy budget allocation based on arithmetic technique, designs experiments to prove its effectiveness, and completes a demonstration system of k-means clustering algorithm optimization based on differential privacy.
A Transferable Adversarial Attack Algorithm Based On Spatial Transformation

Artificial intelligence technology represented by deep learning has been widely used in the real world, and its security has elicited increasing interest. Recent studies have shown that compared to white-box attacks, black-box adversarial attacks pose more serious security threats to deep neural networks. Therefore, evaluating model robustness by studying black-box adversarial attacks has become a research hotspot in the field of deep learning security. However, the existing adversarial attack methods generally suffer from the conflict between attack strength and imperceptibility. To solve this problem, this paper explores adversarial samples that are more in line with human perception of the more transferable intermediate layer features and proposes an intermediate layer adversarial attack algorithm stFA based on pixel spatial transformation. The proposed stFA promotes the model to misclassify adversarial samples as target labels, while encouraging the intermediate-layer feature representation of clean images to move away from the original labels. Extensive experiments have shown that stFA has both perfect imperceptibility and black-box transferability, which improves the success rate of targeted and untargeted attacks by 7.8% and 4.4% compared to stAdv.
Effects of Different Generative Adversarial Networks on the Face Generation Task

The human face may be the bodily part with the greatest degree of individuality, and it may provide a lot of information. On account of its importance in a wide range of applications, human face has become a popular study topic in the domain of computer vision. Besides the well-known face detection and face recognition task, human face generation technology is also widely concerned because of its various potential applications, including privacy protection, scientific use, entertainment purposes and so on. Generative adversarial network has been a powerful tool to generate human face since its first proposition in 2014 and numerous variants based on GAN were proposed in recent years to improve its stability and capability. With the advancements in generative methods, face generation technology has seen rapid development. In this paper, the author use three representative GAN variants, DCGAN, WGAN-GP and PGGAN to generate human face on the dataset of CelebA-HQ 128x128 and analyze their actual performance. The generated image resembles real human being, however, doesn't reach the level of which the original paper demonstrated because of insufficient computing power and limited running time. In the end, the author pointed out the potential ethical problems of face generation technology and made a prospect for the future of human face generation.
Convolutional Neural Network-Based Novel Framework for Ship Recognition in Poor Visibility Maritime Surveillance Videos

Ship recognition is a central component of the information perception for smart ships. Traditionally, the ship recognition mission is completed by remote sensing or synthetic aperture radar (SAR) or manual calibration via maritime surveillance video. However, the difficulty arises in implementing the mission when it suffers the weather with a poor visibility. The recognition accuracy may deteriorate dramatically in such a hostile environment, especially in the heavy fog environment at sea when using conventional methods. In this case, we develop a novel framework for ship recognition based on a smart recognition system that relies on the dark channel defogging and the neural networks. First, a dark-channel defogging algorithm is utilized to remove the adverse impact of poor visibility on recognition. After that, three different neural networks are used to train the defogged images (Specifically: Faster-RCNN, SSD, YOLOv3). Finally, we present qualitative and quantitative analyses of three different neural network models. The results demonstrate the effectiveness of the proposed method in a hostile environment, especially in fog weather with poor visibility at sea.
Fine-tuning Multilingual Transformers for Hausa-English Sentiment Analysis

Accurate sentiment analysis is greatly hindered by the code-switching phenomena, especially in the setting low resource language such as the Hausa. However, the majority of previous studies on Hausa sentiment analysis have mainly ignored this problem. This study explores the use of transformer fine-tuning techniques for Hausa language sentiment classification tasks using three pre-trained multilingual language models: Roberta, XLM-R, and mBERT. A multilabel sentiment classification was conducted using Python programming language and TensorFlow library, with a GPU hardware accelerator on Google Collaboratory. The Twitter dataset used in this study contains 16849 train and 2677 unlabeled dev and 5303 test unlabeled samples of tweets/accounts, each labelled with positive, negative, and neutral respectively for train set data. The findings demonstrate that the mBERT-base-cased model gets the maximum accuracy and F1-score of 0.73% and 0.73%, respectively, outperforming the other two pre-trained models. The train and validation accuracy graph of the mBERT model shows improvement over time. The study underscores the importance of tailoring the implementation code to meet specific requirements and the significance of fine-tuning pre-trained models for optimal performance.
Current Trend of Software Requirement Engineering Process in IT Small and Medium Enterprises (SMEs)-A Systematic Literature Review

Small and medium-sized businesses or known as SMEs are essential contributors in the software industry. Software Requirement Engineering (SRE) process is an essential part of the software development life cycle. With the advent of technology, the software has become necessary component of business, especially with Small and Medium Enterprises (SMEs). Due to this many challenges faced by IT SMEs in adoption of SRE for successful completion of projects. There have been many factors affecting the progress of SMEs among which few are reported in this SLR. A total of 40 studies are found which are relevant to the research questions and published between 2010 and 2023. The research highlights that SMEs face significant challenges when implementing effective SRE processes due to a lack of resources, experience, and tools. However, the studies propose various solutions to address these challenges, including Agile methodologies, communication enhancement, collaboration among stakeholders, and tools to support SRE processes. The article emphasizes the importance of considering the human elements of SRE, such as team dynamics, training, and motivation, and suggests that SMEs should establish specialized SRE processes that fit their requirements and resources. The article concludes that more research is needed to investigate the effectiveness of SRE practices in SMEs and to provide guidelines and frameworks for improving SRE processes in this significant segment of the software industry. Overall, the research provides helpful insights into the current state of SRE practices in SMEs and highlights the potential for enhancing SRE procedures in this sector.
An Architecture for Risk-Based Authentication System in a Multi-Server Environment

Identity authentication, a vital part of any application access, is also one way for imposters to gain access to an application using various fingerprint authentication technologies. Therefore, because of the lack of security in the authentication architecture, this paper proposes an architecture for a risk-based authentication system using a machine learning model in a multi-server environment. Since the recent study mainly focuses on the multi-server environment and adaptive authentication independently, very little work has been proposed using a multi-server environment for adaptive authentication. The study aims to estimate risk for the user during the initial login process and when the user’s data is extracted enough for prediction in a multi-server environment.
Monocular Person Localization with Lidar Fusion for Social Navigation

Smooth social navigation requires not only detection of the people around the robot, but also accurate localization of the people, a process difficult to achieve with a single sensing modality. Hence, literature has focused on various fusion approaches, such as RGB-D, ROI based lidar vision fusion, or Artificial Neural Network (ANN) based lidar vision fusion. However, monocular photogrammetry has always been ignored in the literature. In this work, we propose a fusion approach based on monocular positon estimation and lidar, and show the effectivenes of the approach both on a public dataset and a purpose built dataset with different cameras.
Design and Implementation of a New Spherical Robot with an Elastic Pawl

In order to improve the obstacle-surmounting performance and adaptability of the spherical robot in unstructured terrain, a new spherical robot with an elastic pawl is designed. While retaining the original structural characteristics of the spherical robot, the elastic pawl deformation mechanism and axial expansion mechanism are introduced to enable the robot to switch various motion modes. The parameters of the deformation mechanism are designed through the establishment of a mathematical model, and the kinematic analysis of the obstacle-surmounting process is carried out. ADAMS dynamic simulation software is used to simulate the robot in a variety of typical terrain environments. On this basis, the physical prototype is manufactured and assembled, and the traveling test under various terrains is carried out, verifying the robot structure’s feasibility and practicability.
The Impact of Commercial Chatbots on Customer Relationship Quality: The Mediating Role of Social Perceptions and Psychological Distance*

Purpose—More businesses are choosing to use chatbots instead of traditional human customer support. However, this transition may have significant implications for customer relationship quality. This study aims to comparing the impact of commercial chatbots and human customer service on customer relationship quality and the mediating effects of social perceptions (competence and warmth) and psychological distance. Design/methodology/approach-Using a one factor between-subjects experimental design (service agent types: chatbot vs. human), 296 valid data samples were collected on the Credamo platform to analyze the main effects of service agent types on customer relationship quality and the mediating effects of social perceptions and psychological distance. Findings-The results of this study indicate that compared to human customer service representatives, chatbots have a more significant positive impact on customer relationship quality, including satisfaction, trust, and commitment. Further analysis reveals that perceived competence and psychological distance mediate the effects of service agent types on relationship quality, while perceived warmth does not exhibit mediating effects. Originality/value - This study expands and enriches the research on service agent types by introducing higher-order and mediating psychological process variables such as social perceptions and psychological distance, revealing the specific mechanisms through which service agent types affect customer relationship quality. These findings will contribute to a better understanding of the mechanisms behind service agent type selection and provide practical guidance for optimizing customer relationship quality as enterprises widely adopt commercial chatbots.
An Approach Based on Multi-Entropy and Artificial Intelligence for Communication Modulation Signal Identification

With the increasingly complex electromagnetic influence in the modern communication environment and the explosive growth of communication business data, the modulation recognition system needs to constantly learn new modulation categories and signal characteristics in the process of data increment, and gradually acquire stronger modulation recognition capability. This paper proposes a communication modulation signal recognition method based on multi-entropy and artificial intelligence technology. Firstly, the power entropy, singular spectral entropy, sample entropy, fuzzy entropy, and permutation entropy of the signal are calculated, and a composite entropy feature calculation method based on entropy weight method is proposed to further study the feature extraction of the signal. Finally, it is found that when using neural network for learning, without feature extraction, the recognition accuracy rate is only 70.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , while using the feature extraction method proposed in this paper, the accuracy rate can reach 89.5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , and the recognition speed is significantly improved. In addition, the feature extraction method proposed in this paper can improve all kinds of modulation recognition based on artificial intelligence.
Food Recognition and Classification Based on Image Recognition: A Study Utilizing PyTorch and PReNet

In this study, we address the prevailing gap in food-specific image recognition models by developing an innovative solution using the PyTorch framework and the Predictive REcurrent Neural Network (PReNet). We introduce a tailored food recognition model, constructed upon the robust architecture of convolutional neural networks (CNNs), specifically optimized to enhance accuracy in food classification tasks. Harnessing the power of a comprehensive food dataset, our model significantly surpasses the performance benchmarks set by traditional image recognition models. This study not only advances the academic dialogue in this domain but also facilitates practical applications across health, nutrition, and lifestyle sectors. By demonstrating the formidable potential of the PyTorch framework and PReNet in this unique use-case, we underscore the promising avenues for future exploration and development.