Reviewerly/exHarmony huggingface dataset:
Research on Full-Coverage Path Planning Method of Steel Rolling Shop Cleaning Robot

As a large indoor unstructured environment, the steel rolling shop has many unknown factors that greatly affect the full-coverage cleaning path planning of the cleaning robot. In this paper, a round-trip full-coverage path planning algorithm is improved by using the breadth-first search (BFS) algorithm combined with the improved A* algorithm to complete the full-coverage cleaning task of the steel rolling shop by the cleaning robot. First, the obstacles in the raster map are inflated using the expansion strategy to prevent the cleaning robot from colliding with the obstacles; second, the map is traversed with full coverage by the BFS algorithm; finally, the round-trip full-coverage algorithm is used for path planning based on the traversal information, and when it enters the dead zone location, the improved A* algorithm is used to find the unplanned nodes and plan an optimal path to escape from the dead zone. The simulation results show that the improved round-trip full-coverage path planning algorithm is more efficient than the traditional full-coverage path planning algorithm, and the proposed method can solve the complex problems in unstructured environment and complete the full-coverage cleaning task of the steel rolling shop cleaning robot.
OpenMP Reverse Offloading Using Shared Memory Remote Procedure Calls

The widespread adoption of general purpose GPU programming in high-performance computing (HPC) has led to an increased need for expanded GPU functionality. Currently there are several features that still require host services to be accomplished on the GPU, notably printing and dynamic memory allocation. The OpenMP 5.0 standard includes support for reverse offloading to allow the GPU to execute code on the host. This paper proposes a means of implementing reverse offloading within the LLVM toolchain. The remote procedure call (RPC) is a common method for executing routines on a separate machine. This is adapted here for executing routines on heterogeneous hardware. We claim that shared memory supporting relaxed atomic load and store and acquire/release fences are sufficient to implement remote procedure calls. Furthermore, using reliable shared memory bypasses the usual failure modes associated with networking. In this paper we introduce a generic shared memory RPC library that can be used to implement host services that works on AMDGPU, NVPTX, and x86-64 architectures. We then show how this can be used to implement OpenMP 5.0 reverse offloading at the runtime level. Finally, we examine some of the performance considerations and trade-offs when using this implementation.
Inferring socioeconomic environment from built environment characteristics based street view images: An approach of Seq2Seq method

The street view image (SVI) and the point of interest (POI) are data sources with different modalities and representing different urban environments, respectively. These two types of data may not fully cover a city. Inferring from one type of data to another will enrich our means of sensing cities and facilitate a deeper understanding of the place. Intuitively, by analyzing visual elements of the urban built environment such as buildings, roads, vehicles, and other features captured in photos of their surroundings, we can infer valuable information about the most likely POI types. Traditional approaches are mainly based on probabilistic statistical models. Such models lack a holistic understanding of the scenario and accurate modeling of the non-linear relationship between the built and socio-economic environments. In response, a Seq2Seq framework is proposed to “translate” and create a bridge between those two data. Experiments in Wuhan demonstrated the high performance of our method (accuracy = 0.770, recall = 0.773, f1= 0.771). And this work allows us to better understand the “many-to-many” relationship between the two environments (data), effectively enriching our means of perceiving cities. It also has potential applications in location awareness and visual navigation.
Blockchain-based fairness-enhanced federated learning scheme against label flipping attack

The federated learning technology provides a new method for data integration, which realize the sharing of a global model and prevent the leakage of the user's original data. In order to resist label flipping attack, to ensure the reliability and accuracy of the global model, and to guarantee the fairness of federated learning, we propose a blockchain-based fairness enhanced federated learning scheme. The accuracy of global model and the fairness of the aggregation process are guaranteed by an adaptive aggregation algorithm. The reliability of federated learning process is ensured by recording the entire process of the model training on the blockchain and by using digital signature. The privacy of each participant of federated learning is protected by public key encryption combined with the use of random numbers. Theoretical analysis and experiments show that the scheme can protect the privacy of each participant, mitigate label flipping attack and ensure the reliability and fairness of the entire federated learning process.
Using language-specific input methods and pronunciation rules to improve the guesses of passwords

Password-based authentication has the advantage of easy implementation and no requirement for additional hardware; therefore, it will remain one of the primary identification methods in the near future. However, users tend to choose passwords that are easy to remember, so these passwords may be vulnerable to guessing attacks. Based on the idea that some users are influenced by their native language when choosing passwords, we propose an improved password-guessing method that adds grammars regarding Asian-language input procedures and pronunciation rules to the most updated Probabilistic Context-Free Grammar (PCFG) v4.1. The experimental results show that the improved PCFG v4.1 can increase the success rate of password cracking compared to PCFG v4.1. The improvements range from 2% to 14% for different password datasets. Additionally, we compare the proposed method to many other guessing methods; ours can achieve an excellent performance. Moreover, the characteristics of passwords that are not cracked are analyzed, and we suggest some criteria for more robust passwords.
A hybrid multi-start metaheuristic scheduler for astronomical observations

In this paper, we investigate Astronomical Observations Scheduling which is a type of Multi-Objective Combinatorial Optimization Problem, and detail its specific challenges and requirements and propose the Hybrid Accumulative Planner (HAP), a hybrid multi-start metaheuristic scheduler able to adapt to the different variations and demands of the problem. To illustrate the capabilities of the proposal in a real-world scenario, HAP is tested on the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (Ariel) mission of the European Space Agency (ESA), and compared with other studies on this subject including an Evolutionary Algorithm (EA) approach. The results show that the proposal outperforms the other methods in the evaluation and achieves better scientific goals than its peers. The consistency of HAP in obtaining better results on the available datasets for Ariel, with various sizes and constraints, demonstrates its competence in scalability and adaptability to different conditions of the problem.
Accuracy analysis for distributed dynamic state estimation in large-scale systems with a cyclic network graph

This paper addresses a distributed dynamic state estimation problem in large-scale systems characterized by a cyclic network graph. The objective is to develop a distributed estimation algorithm for each node to generate local state estimations, based on the coupled measurements and boundary information exchanged with neighboring nodes. Our proposed approach is grounded in the maximum a posteriori (MAP) estimation method, which yields suboptimal results in acyclic network graphs compared with the centralized MAP approach. We extend this approach to systems with a cyclic network graph. Furthermore, we provide an accuracy analysis by deriving bounds for the differences in estimation error covariance and state estimation between the proposed distributed algorithm and the suboptimal centralized MAP method. These bounds apply to a specific category of systems that satisfy certain conditions, including cyclic topology and sparse connections. We demonstrate that these bounds converge asymptotically, with the rate of convergence determined by the loop-free depth of the graph. The loop-free depth of the graph refers to the maximum number of nodes that can be traversed in a cycle without revisiting any node. Finally, we demonstrate the validity of the algorithm through numerical examples.
Context-aware dynamic filtering network for confocal laser endomicroscopy image denoising

Objective.As an emerging diagnosis technology for gastrointestinal diseases, confocal laser endomicroscopy (CLE) is limited by the physical structure of the fiber bundle, leading to the inevitable production of various forms of noise during the imaging process. However, existing denoising methods based on hand-crafted features inefficiently deal with realistic noise in CLE images. To alleviate this challenge, we proposed context-aware kernel estimation and multi-scale dynamic fusion modules to remove realistic noise in CLE images, including multiplicative and additive white noise.Approach.Specifically, a realistic noise statistics model with random noise specific to CLE data is constructed and further used to develop a self-supervised denoised model without the participation of clean images. Secondly, context-aware kernel estimation, which improves the representation of features by similar learnable region weights, addresses the problem of the non-uniform distribution of noises in CLE images and proposes a lightweight denoised model (CLENet). Thirdly, we have developed a multi-scale dynamic fusion module that decouples and recalibrates features, providing a precise and contextually enriched representation of features. Finally, we integrated two developed modules into a U-shaped backbone to build an efficient denoising network named U-CLENet.Main Results.Both proposed methods achieve comparable or better performance with low computational complexity on two gastrointestinal disease CLE image datasets using the same training benchmark.Significance.The proposed approaches improve the visual quality of unclear CLE images for various stages of tumor development, helping to reduce the rate of misdiagnosis in clinical decision-making and achieve computer graphics-assisted diagnosis.
A visually meaningful image encryption algorithm based on P-tensor product compressive sensing and newly-designed 2D memristive chaotic map

Abstract The noise-like visual feature of cipher images produced by using the traditional image encryption technology explicitly reflects the presence of secret information. To overcome this issue, a visually meaningful image encryption algorithm is proposed based on a newly designed 2D memristive chaotic map, P-tensor product compressive sensing (PTP-CS) and discrete Hartley transform (DHT). For concreteness, a new two-dimensional discrete memristive chaotic map is first designed to provide highly unpredictable secret code streams while maintaining low time consumption. Second, the threshold processing and zigzag confusion operations are performed on the discrete wavelet coefficients of the plain image to meet the prerequisites for effective compression. Third, the intermediate secret information is obtained by utilizing PTP-CS in the compression layer. Information entropy and edge entropy are employed to adaptively identify the complex regions that are suitable for embedding due to inconspicuous visual degradation in the carrier image. Finally, the embedding of the secret information in the DHT domain of these regions is accomplished. Security test and performance analysis confirm that our algorithm has the advantage of a high balance between the encryption security and the decryption recovery, and exhibits excellent performance in important indicators such as visual quality, robustness and timeliness.
Optimal latent space for Low-shot Face Recognition

The ability of humans to learn to classify objects after seeing a very few examples of them in the past, has given rise to the field of Low-shot learning. The idea is to be able to train a deep learning model to differentiate between same and different pairs and then generalise these ideas to evaluate new categories. As shapes, structure and low level visual features of human faces are similar in nature, so we can make use of extensive public face data sets to initially train a deep neural network (DNN), to learn generalised features of human face. We call this face space as Latent Feature Space. Then we demonstrate the use of probablistic interpretation of principal component analysis (PPCA) along with Extreme Learning Machine (ELM) algorithms, as an efficient technique to transform this space for representing our novel dataset classes with limited number of available samples. We avoid any kind of network re-training, while enforcing the network to learn a distance function between images rather than explicitly classifying them. The proposed algorithm couples a deep neural network (DNN) based feature representation with a low-dimensional manifold extraction to address the Low-shot classification and verification problems. We call this low-dimensional subspace as Feature Transformed Latent Space. Also, in addition to providing performance improvements in terms of accuracy, the suggested approach provides significant advantages in terms of memory, computation and speed during classification/ verification tasks, while being agnostic to occlusion, pose, expression and illumination conditions.
Using Artificial Intelligence to Detect Falls

This work aims to apply both traditional machine learning approaches and deep neural networks in human activity recognition. A multi-modal approach is used to identify falls both in a frame as well as across a video. The models use camera data from a single position as well as three-axis accelerometer data to identify falls. This research aims to present possibilities for an easily implementable model using affordable data sources and limit the burden on healthcare staff by mitigating false-positive results. In our first experiment, the traditional machine learning models used returned an accuracy of approximately 98 percent and in our second experiment, the deep learning model had an accuracy of 89 percent but had more difficulty determining if the subject was classified as falling.
Deployment of Proposed EfficientNeXt on NXP i.MX 8M Mini

Convolutional Neural Networks make tasks of computer vision like image classification and object tracking possible. The advances in accelerator hardware have made the progress in neural networks possible. Accelerator hardware is prevalent on desktops and high-end computing systems and may not always be available on low compute devices deployed on the Edge of Internet of Things. The capabilities of neural network need to be ported to hardware that can run without accelerators. Benchmark setting neural networks like EfficientNet are too heavy for deployment on systems with low compute capabilities and can benefit from reduction in their memory footprint and optimized to improve their inference times. To this end we propose the design of EfficientNeXt and demonstrate its inference capabilities with reduced memory footprint (by $\sim$56%), increased accuracy and reduced inference time (by $\sim$30%) on an ARM based device.
Risk Assessment of Cloud Infrastructure and Services for Software Development Startups

Software development start-ups are founded each day, and the use of latest technologies such as cloud infrastructure and cloud services have made it much easier the daily maintenance of the company processes and products. However, contrary to what usually the impression is formed from the success of big start-ups, according to several authors only a small number of start-ups succeed in the market and the main reason why these software start-ups do not survive mostly it is not the idea or competition environment where they are operating but usually it is how these start-ups are managed. Considering the growing number of start-ups and the lack of research in the risk identification and assessment for these types of companies in our region we focus on this research to understand what are the main risks that software development start-ups are facing during a process of using cloud infrastructure and cloud services and how they are handling them to minimize their effect in company daily routine and its sustainability. Thus, the main purpose of this study is to identify to the best possible extent, the main risks that are affecting the normal flow and the outcome of a project in a software development start-up by using the cloud computing platforms. Specifically, through a qualitative research approach through a case-studies, we will explore the risks related to the organization, resources -both human and technology, and internal/ external factors
On the Improvements of Mouse Dynamics Based Continuous User Authentication

Traditional authentication methods are vulnerable when users leave their devices unattended or if their credentials are compromised. In contrast, continuous authentication offers a perpetual strategy for user validation, ensuring that only authorized users access critical information throughout their entire usage. The problem of continuous authentication boils down to a binary classification task: determining whether the usage is legal or illegal. Deep learning presents a promising solution for this problem, although the use of convolutional neural networks (CNNs) in continuous authentication still has room for improvement. In this study, we employ residual learning to train and test a user authentication model. To further enhance the accuracy of the results, we implement a realistic augmentation method and employ a superior image mapping technique compared to existing literature. As a result, we achieve significantly more accurate results than those reported in the referenced studies. On average, our tests yield a False Accept Rate of 0.45 and a False Reject Rate of 0.34, which are 6.5 times better than the referenced studies. These findings demonstrate a substantial improvement in the usability and effectiveness of real-world cybersecurity applications.
A Multi-style Interior Floor Plan Design Approach Based on Generative Adversarial Networks

Artificial intelligence is reshaping the process of interior floor plan design, making it more intelligent and automatic. Due to the increasing demand for interior design, this paper proposes a dual-module approach to achieve multi-style floor plan design, which adopts the pix2pix series models, a type of Generative Adversarial Networks (GAN). The proposed approach consists of two modules to generate semantic label images from design sketches and multi-style design drawings from label images. To this end, the key area layout and style texture information are separated through extracting and coding areas’ label. Afterwards, the experimental comparison between dual-module and single-module generation validates the superiority of proposed dual-module generation approach. Finally, the dual-module approach with label coding is verified in multi-style interior floor plan generation experiments. Based on the proposed style evaluation method, the stylization indicators of generated results exceed 0.8, which further denotes the multi-style generation ability of the proposed dual-module approach.
AG-UN-Net: U-shaped and N-shaped network with attention gate for liver segmentation

U-shaped network is the most widely used backbone architecture in the field of biomedical image segmentation in recent years. Meanwhile, it is widely used in segmentation of liver from computed tomography, since these volumes have heterogeneous and diffusive shapes. However, there are considerable performances drop in the case of detecting small region, finer details of surfaces and edges. We analyze this issue and address it by adding N-Net to backbone, where N-shaped network maps data to higher dimensions to segment small region and blurred boundaries. Meanwhile, we replace skip connection with attention gate in U-shaped net. The soft-attention can highlight specific target regions and avoid the model to repeatedly extract similar low-level features. The proposed method is validated on the public Liver Tumor Segmentation Challenge dataset. Experimental results demonstrate the superiority of the proposed method compared with the other advanced methods, where the dice scores of AG-UNet and AG-UN-Net are 96.7% and 97.8%, respectively.
Application of WOA-SVM Based Algorithm in Tumor Cell Detection Research

Early screening of cancer is crucial for patients’ survival time and quality of life. Based on this, this essay suggests a tumor cell detection technique based on a combination of whale optimization technique is proposed and support vector machine. Firstly, tumor cells are categorized using the support vector machine technique; secondly, the support vector machine's parameters have been optimized using the whale optimization technique to continue increasing the model’s accuracy; eventually, a variety of approaches are dreamed up for comparison in order to confirm the success of the suggested approach. The program's findings demonstrate that the previously proposed model's accuracy is around 5% better than that of the conventional support vector machine training regimen, demonstrating the proposed model's cost effectiveness in the field of breast cancer cell proof of identity.
SE-RCN: An Economical Capsule Network

As the Convolutional Neural Networks (CNNs) became more prominent in the field of Computer Vision (CV) their disadvantages gradually became apparent. By sharing transformation matrices between the different levels of a capsule, the Capsule Network (CapsNet) innovated the method of solving affine transformation problems. While the ResNet, it introduces skip connections, which makes deeper networks more powerful and solves the vanishing gradient problem. Fusing the advantageous ideas of CapsNet and ResNet with Squeeze and Excite (SE) block, this paper presents SE-Residual Capsule Network (SE-RCN), a neural network model. In the proposed model, skip connections and SE block take the place of the traditional convolutional layer of CapsNet, reducing the complexity of the network. Based on MNIST and CIFAR-10 datasets, the performance of the model is demonstrated with a substantial reduction in parameters when compared to similar neural networks.
Effects of an Interactive e-Book on Enhancing Nursing Students' Knowledge, Confidence, and Learning Self-efficacy of Nursing Skills

Background: Interactive e-books offer multimedia and interactive features that make them particularly suitable for learning nursing skills and also allow for easy content updating, ensuring that students learn the most current skills based on the latest clinical guidelines. Purpose: To examine the effectiveness of an interactive e-book in increasing nursing students' knowledge, confidence, and learning self-efficacy of performing nasogastric tube feeding. Methods: A randomized controlled trial was conducted with 62 nursing students, with the experimental group using an interactive e-book for learning whereas the comparison group using a printed textbook. Results: The experimental group demonstrated significantly greater knowledge and confidence improvements than the comparison group, but no significant difference was found in learning self-efficacy. The experimental group was significantly more satisfied with the learning materials than the comparison group. Conclusions: Interactive e-books are more effective in enhancing nursing students' knowledge and confidence in performing nursing skills than printed textbooks.