Reviewerly/exHarmony huggingface dataset:
Curved surface path planning of operational cleaning robot based on RGB-D semantic segmentation

Compared to flat ground, objects in indoor environments have various curved surfaces, cleaning these surfaces is a higher-level task than traditional ground cleaning. To accomplish this task, we propose a new method to perform wiping on the curved surface of an object and complete the cleaning work through the end tool of the robotic arm. Our method is divided into two parts: An RGB-D semantic segmentation attention-based Feature Fusion Network (AFFNet), which can effectively fuse the features from the encoder and decoder to improve semantic segmentation accuracy; A path planning algorithm based on point cloud, which can autonomously generate the robotic arm operation path. The experimental results show that AFFNet achieves an mIOU accuracy of 46.24% on SUNRGBD dataset with excellent performance, the robotic arm can complete the curved surface cleaning operation with continuous and smooth path.
Experimental study of an ultrasonic low altitude altimetry system for UAVs

Due to the limited load capacity of small unmanned aerial vehicles (UAVs), micro pressure sensors are generally selected to measure altitude and airspeed. However, micro pressure sensors have relatively large absolute error for altitude measurement when UAVs take off or land. Therefore, in this paper, an ultrasonic module is added to make up for the lack of altitude measurement when UAVs take off or land (altitude is below 10 m). This paper mainly introduced the principle of ultrasonic ranging and tested the validity of ultrasonic ranging data. The test results showed that for some complex situations, ultrasonic ranging data needs to be filtered, and it also showed that Kalman filtering could meet the requirements of unmanned ultrasonic low altitude measurement. Finally, this paper proposed the basic architecture of the UAV height fixing control system in combination with ultrasonic ranging technology.
Robot obstacle avoidance path planning based on improved artificial potential field

In the realm of intelligent agent control, path planning has been one of the most well-liked study subjects. The path planning for obstacle avoidance approach proposed in this study is an enhanced artificial potential field method. By directing the barrier in the robot's direction and combining the boundary repulsion and vector decomposition of the goal direction, the local minimum problem is resolved. Next, the final combined force is delivered to the robot's center and the angle coefficient is added to provide a fair obstacle avoidance effect while taking into account the physical distance between the two sides of the robot and the quantity of extra environmental obstacles. Finally, the enhanced algorithm is used to perform a global path search. The simulation results validate the applicability and effectiveness of the suggested approach.
Model predictive torque control of brushless DC motor based on improved whale optimization algorithm

For the adjustment and design of weight factors in the model predictive torque control (MPTC) of brushless DC motor (BLDCM) is too tedious and full of uncertainties. In this paper, a modified whale optimization algorithm (IWOA) is utilized to de-regulate the parameter weight factors in real time. The algorithm incorporates chaotic mapping and greedy selection strategies, where chaotic mapping enables the algorithm to achieve population diversity in the initial stage, and greedy selection strategy enables the algorithm to update the local in the case of falling into a local optimum, making the algorithm more capable of finding the best and more accurate in optimizing MPTC. In order to verify whether the proposed approach can reduce the speed error and torque fluctuation, by modeling and conducting simulations to verify the torque and speed of the motor, and taking into account the switching frequency loss factor, the results show that the torque pulsation is further reduced, and the speed error is further reduced in the proposed approach compared to other intelligent algorithms, confirming the effectiveness of the proposed approach.
Fault diagnosis of GIS equipment based on voice print recognition and algorithm research

An improved model combining convolutional neural network and bidirectional long and short-term memory network is proposed to realize the intelligent diagnosis of GIS device fault. First, the feature vector extraction of the speech data through wavelet packet decomposition, and the results are used as input to the CNN-BiLSTM model to learn the sound feature relationship of GIS devices. Secondly, it improves the ability of the model to intelligently diagnose faults of GIS devices by optimizing parameters and accelerating convergence. After experimental verification, the proposed model has obvious advantages in GIS equipment fault diagnosis over the traditional fault diagnosis methods.
Making FaceBoxes better and faster by reparameterization

Despite multi-branches network can strengthen the ability of feature extraction, the cost is speed reduction. Thus, in order to improve FaceBoxes’ accuracy and do not reduce speed, we propose Reparameterization-FaceBoxes in this paper that its multi-branches modules are replaced with diverse branch block. We make comparative experiments on WIDERFACE dataset, Reparameterization-FaceBoxes outperforms FaceBoxes with higher accuracy and faster speed, it can increase by 0.9%, 0.8% and 0.4% on Easy, Medium and Hard datasets while its speed increases about 20% for VGA-resolution images.
Based on StyleGAN for face image cartoon style transfer

Face cartoon style transfer to generate high quality face images has become an art form pursued by many people, but face style transfer generally has problems of incomplete detail information after migration and poor generation quality in some exaggerated styles. In this paper, By proposing a network model based on StyleGAN suitable for face style transfer by improving the StyleGAN generator part, introducing a style restriction module to characterize the color as well as more detailed information, and the overall process uses a progressive image generation strategy to gradually generate highquality style transfer result maps. The results show that the method can not only achieve style transfer in both domains, but also reconstruct low-resolution images to better characterize their features with better visual effects.
E-Learning Paradigm in Cloud Computing and Pertinent Challenges in Models Used for Cloud Deployment

The pedagogical science has improved immensely in each section of learning and education due to adoption of recent developments in Information and Communication Technologies (ICT) domain. Adopting web-based tools has aided to new thrust in imparting education through remote means. The deployment of cloud network is quickly aided to all disciplines of learning and at all levels. A user can access, from any remote location, a data stored over cloud platform using their computers, tablets or smartphones, and can also make perturbations in data on need basis. The method of E-learning becomes seamless when cloud-based techniques are employed. This also helps in cost cutting and cumbersomeness of data processing as the major chunk of services is operated by third party infrastructures. Both cloud-based and conventional E-learning when combined offers a greater advantage to teachers, students and institutes albeit with a clutch that the security becomes a major issue of concern. The present review is an analysis of perks of deploying cloud-based learning and the security concerns that come with it, as observed through various studies.
A surface-normal photodetector as nonlinear activation function in diffractive optical neural networks

Optical neural networks (ONNs) enable high speed, parallel, and energy efficient processing compared to their conventional digital electronic counterparts. However, realizing large scale ONN systems is an open problem. Among various integrated and non-integrated ONNs, free-space diffractive ONNs benefit from a large number of pixels of spatial light modulators to realize millions of neurons. However, a significant fraction of computation time and energy is consumed by the nonlinear activation function that is typically implemented using a camera sensor. Here, we propose a novel surface-normal photodetector (SNPD) with an optical-in–electrical-out (O–E) nonlinear response to replace the camera sensor that enables about three orders of magnitude faster (5.7 µs response time) and more energy efficient (less than 10 nW/pixel) response. Direct efficient vertical optical coupling, polarization insensitivity, inherent nonlinearity with no control electronics, low optical power requirements, and the possibility of implementing large scale arrays make the SNPD a promising O–E nonlinear activation function for diffractive ONNs. To show the applicability of the proposed neural nonlinearity, successful classification simulations of the MNIST and Fashion MNIST datasets using the measured response of SNPD with accuracy comparable to that of an ideal ReLU function are demonstrated.
Mitigating Risks in the Cloud-Based Metaverse Access Control Strategies and Techniques

The advent of the metaverse has revolutionized virtual interactions and navigation, introducing intricate access control challenges. This paper addresses the need for effective access control models in the cloud-based metaverse. It explores its distinct characteristics, including its dynamic nature, diverse user base, and shared spaces, highlighting privacy concerns and legal implications. The paper analyzes access control principles specific to the cloud-based metaverse, emphasizing least privilege, separation of duties, RBAC, defense-in-depth, and auditability/accountability. It delves into identity verification and authorization methods, such as biometrics, multi-factor authentication, and role-based/attribute-based authorization. Advanced access control technologies for the cloud-based metaverse are examined, including SSO solutions, blockchain-based access control, ABAC, adaptive access control, and VMI for isolation. Risk mitigation strategies encompass IDS/IPS, SIEM, and user education programs.
Weapon Equipment Operational Index Evaluation Method Based on Cloud Model

Abstract In response to the evaluation requirements of weapon equipment combat test effectiveness index, a cloud model-based equipment combat test effectiveness index evaluation method was proposed to solve the problems of insufficient data utilization and low credibility of evaluation conclusions in the current effectiveness index evaluation. Based on the cloud model theory, the comprehensive evaluation model of the equipment combat test effectiveness index is designed to evaluate and analyze the effectiveness index, and the effectiveness and feasibility of the new method are verified by comparing cases, which can provide technical support for the improvement of equipment combat test effectiveness evaluation ability.
Research in Learning Technology: making friends and influencing people

The first issue of&nbsp;Research in Learning Technology&nbsp;(RLT) was published in 1993. Over 30 years, the journal has comprised an informal research and development facility for new ideas and practices in technology enhanced learning. This paper takes nine published articles from&nbsp;RLT: the three most downloaded in the period January 2021 – March 2023 (but published at any time); the three most downloaded articles published from January 2021 to March 2023; and the three most cited articles published from January 2018 to March 2023. The aim is to identify different areas of current interest and influence, different areas of practice, and different scholarly approaches. The authors are the journal’s current editorial team. This paper identifies diversity of technology enhanced learning-related subject matter and different approaches, too, but with ongoing interest in efficacy and in the ‘how’ of technology enhanced learning: how technology can be applied to truly enhance learning, comprising an approachable community, generating influence.
A Perspective on the Interplay Between 5G and Quantum Computing for Secure Algorithm and Software Engineering

With the advancement of quantum computing technology, a pressing need arises to assess its potential implications on existing systems and infrastructures. In this paper, we delve into the interplay between quantum computing and 5G technology, with a specific focus on its profound impact on cryptography and the emergence of post-quantum techniques. We analyse the potential vulnerabilities quantum computers pose to conventional cryptographic algorithms employed in 5G networks. Our research investigates the challenges and opportunities that arise at the intersection of quantum computing and 5G, ultimately aiming to contribute to the development of secure and future-proof communication systems.
On Deprecated API Usages: An Exploratory Study of Top-Starred Projects on GitHub

A deprecated Application Programming Interface (API) is one that is no longer recommended to use by its original developers. While deprecated APIs (i.e., deprecated fields, methods, and classes) are still implemented, they can be removed in future implementations. Therefore, developers should not use deprecated APIs in newly written code and should update existing code so that it does not use deprecated APIs anymore. In this paper, we present the results of an exploratory Mining-Software-Repository study to gather preliminary empirical evidence on deprecated API usages in open-source Java applications. To that end, we quantitatively analyzed the commit histories of 14 applications whose software projects were top-starred on GitHub. We found that deprecated APIs usages are pretty widespread in the studied software applications; and only in half of these applications, developers remove deprecated API usages after few commits and days. Also, half of the studied applications mostly use deprecated APIs declared in their own source code, rather than using deprecated APIs that lie in third-party software. Finally, we noted that the introductions and removals of deprecated API usages are mostly the result of changes made by senior developers, rather than newcomer ones.
The Testing Hopscotch Model – Six Complementary Profiles Replacing the Perfect All-Round Tester

Contrasting the idea of a team with all-round testers, the Testing Hopscotch model includes six complementary profiles, tailored for different types of testing. The model is based on 60 interviews and three focus groups with 22 participants. The validation of the Testing Hopscotch model included ten validation workshops with 58 participants from six companies developing large-scale and complex software systems. The validation showed how the model provided valuable insights and promoted good discussions, helping companies identify what they need to do in order to improve testing in each individual case. The results from the validation workshops were confirmed at a cross-company workshop with 33 participants from seven companies and six universities. Based on the diverse nature of the seven companies involved in the study, it is reasonable to expect that the Testing Hopscotch model is relevant to a large segment of the software industry at large. The validation of the Testing Hopscotch model showed that the model is novel, actionable and useful in practice, helping companies identify what they need to do to improve testing in their organization.
Status Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey

Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (RE) can help address many problems when engineering ML-enabled systems. However, the state of empirical evidence on how RE is applied in practice in the context of ML-enabled systems is mainly dominated by isolated case studies with limited generalizability. We conducted an international survey to gather practitioner insights into the status quo and problems of RE in ML-enabled systems. We gathered 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems involving open and axial coding procedures. We found significant differences in RE practices within ML projects. For instance, (i) RE-related activities are mostly conducted by project leaders and data scientists, (ii) the prevalent requirements documentation format concerns interactive Notebooks, (iii) the main focus of non-functional requirements includes data quality, model reliability, and model explainability, and (iv) main challenges include managing customer expectations and aligning requirements with data. The qualitative analyses revealed that practitioners face problems related to lack of business domain understanding, unclear goals and requirements, low customer engagement, and communication issues. These results help to provide a better understanding of the adopted practices and of which problems exist in practical environments. We put forward the need to adapt further and disseminate RE-related practices for engineering ML-enabled systems.
Facilitating Security Champions in Software Projects - An Experience Report from Visma

The role of security practices is increasingly recognized in fast-paced software development paradigms in contributing to overall software security. Security champions have emerged as a promising role in addressing the shortage of explicit security activities within software teams. Despite the growing awareness of general security practices, there remains limited knowledge regarding security champions, including their establishment, effectiveness, challenges, and best practices. This paper aims to bridge this gap by presenting insights from a survey of 73 security champions and 11 interviews conducted within a large Norwegian software house. Through this study, we explore the diverse activities undertaken by security champions, highlighting notable differences in motivations and task descriptions between voluntary and assigned champions. We also reported challenges with onboarding, communication, and training security champions and how they can be better supported in the organization. Our insight can be relevant for similar software houses in establishing, implementing, and improving their strategic security programs.
Web Image Formats: Assessment of Their Real-World-Usage and Performance Across Popular Web Browsers

In 2023, images on the web make up 41% of transmitted data, significantly impacting the performance of web apps. Fortunately, image formats like WEBP and AVIF could offer advanced compression and faster page loading but may face performance disparities across browsers. Therefore, we conducted performance evaluations on five major browsers - Chrome, Edge, Safari, Opera, and Firefox - while comparing four image formats. The results indicate that the newer formats exhibited notable performance enhancements across all browsers, leading to shorter loading times. Compared to the compressed JPEG format, WEBP and AVIF improved the Page Load Time by 21% and 15%, respectively. However, web scraping revealed that JPEG and PNG still dominate web image choices, with WEBP at 4% as the most used new format. Through the web scraping and web performance evaluation, this research serves to (1) explore image format preferences in web applications and analyze distribution and characteristics across frequently-visited sites in 2023 and (2) assess the performance impact of distinct web image formats on application load times across popular web browsers.
Metrics for Code Smells of ML Pipelines

ML pipelines, as key components of ML systems, shall be developed following quality assurance techniques. Unfortunately, it is often the case in which they present maintainability issues, due to the experimentatal nature of data collection and ML model construction. To address this problem, this work in progress proposes initial metrics to measure the presence of code smells in ML pipelines. These metrics reflect good software engineering practices for code quality of ML pipelines.
Recent Advanced in Healthcare Data Privacy Techniques

In recent years, there has been a significant increase in the amount of healthcare data generated by patients, healthcare providers, and medical researchers. This data is often sensitive and contains personally identifiable information, making it critical to protect patient privacy while still enabling the sharing of data for research and healthcare purposes. In this chapter, we review recent advances in healthcare data privacy techniques, the privacy breaches from the unique identification of an individual or single/multiple attributes disclosure to the behavioral advertising privacy breaches. We will discuss the strengths and limitations of each approach and provide examples of how they have been applied in healthcare contexts. We also examine the ethical and legal considerations surrounding healthcare data privacy and the challenges of implementing these techniques in practice. Finally, we explore future directions for healthcare data privacy research.
Evaluating Cluster-Based Synthetic Data Generation for Blood-Transfusion Analysis

Synthetic data generation is becoming an increasingly popular approach to making privacy-sensitive data available for analysis. Recently, cluster-based synthetic data generation (CBSDG) has been proposed, which uses explainable and tractable techniques for privacy preservation. Although the algorithm demonstrated promising performance on simulated data, CBSDG has not yet been applied to real, personal data. In this work, a published blood-transfusion analysis is replicated with synthetic data to assess whether CBSDG can reproduce more complex and intricate variable relations than previously evaluated. Data from the Dutch national blood bank, consisting of 250,729 donation records, were used to predict donor hemoglobin (Hb) levels by means of support vector machines (SVMs). Precision scores were equal to the original data results for both male (0.997) and female (0.987) donors, recall was 0.007 higher for male and 0.003 lower for female donors (original estimates 0.739 and 0.637, respectively). The impact of the variables on Hb predictions was similar, as quantified and visualized with Shapley additive explanation values. Opportunities for attribute disclosure were decreased for all but two variables; only the binary variables Deferral Status and Sex could still be inferred. Such inference was also possible for donors who were not used as input for the generator and may result from correlations in the data as opposed to overfitting in the synthetic-data-generation process. The high predictive performance obtained with the synthetic data shows potential of CBSDG for practical implementation.