Reviewerly/exHarmony huggingface dataset:
Classification model for NAVTEX navigational warning messages based on adaptive weighted TF-IDF

NAVTEX is a crucial marine safety information broadcasting system for ensuring the safe navigation of ships, which plays a significant role in ship safety. However, the current manual reading and subject classification of NAVTEX suffer from low efficiency and accuracy. To enhance the processing efficiency of maritime safety information (MSI) and promote information and communication, achieving automated MSI classification with high confidence in the accuracy of the results becomes imperative. In the context of machine learning, this study proposes an adaptive weight TFIDF method to address the aforementioned challenges. The primary objective is to optimize the weights of keywords with prominent classification features in NAVTEX. Experimental results demonstrate that the adaptive weight-based TFIDF algorithm significantly improves the classification outcomes for NAVTEX. By enhancing the accuracy and efficiency of MSI classification, this approach facilitates the automation of NAVTEX analysis and promotes the reliability of the generated classification results.
Multi-resolution network based image steganalysis model

Recently, many steganalysis approaches improve their feature extraction ability through adding convolutional layers. However, it often leads to a decrease of resolution in the feature map during downsampling, which makes it challenging to extract weak steganographic signals accurately. To address this issue, this paper proposes a multi-resolution steganalysis net (MRS-Net). MRS-Net adopts a multi-resolution network to extract global image information, fusing the output feature map to ensure high-dimensional semantic information and supplementing low-level detail information. Furthermore, the model incorporates an attention module which can analyze image sensitivity based on different channel and spatial information, thus effectively focusing on areas with rich steganographic signals. Multiple benchmark experiments on the BOSSBase 1.01 dataset demonstrate that the accuracy of MRS-Net significantly improves by 9.9% and 3.3% compared with YeNet and SRNet, respectively, demonstrating its exceptional steganalysis capability.
Research on Stable Cross-Domain Detection Method Based on Attention Mechanism

When traditional convolutional neural network algorithms are applied to extract target features from a single environment in the source domain during cross-domain detection, these features are different from the main features of the object, causing undesirable effects when used for target domain detection. Combined with YOLOv5 and the attention module, this paper proposes a stable cross-domain detection method based on attention mechanism, which applies the attention mechanism to enhance the main features of the object and weaken the environment features to achieve the extraction of the main features of the object and improve the stability of the algorithm in cross-domain detection. Furthermore, this paper uses unmixed source domain information for training and two target domain data that are different from the source domain distribution for detection. Introduction of the SE attention module is demonstrated by comparison experiments to improve the accuracy by 39.54% compared to the classical Faster R-CNN. After normalization, the coefficient of variation is reduced by 83.52% compared to the classical Faster R-CNN and by 8.48% compared to YOLOv5s, indicating the improved detection stability. Experimental outcomes show that this method outperform the comparison method in cross-domain stability and are of strong application.
An Empirical Study on Hybrid Scheduling Algorithms in Cloud Computing

With the flourishment of Internet technology, cloud computing has become the most expedient way to get tasks done without purchasing costly machines. From small businesses to well-established organizations are all availing the cloud services. As millions of users are connected to the cloud, the demand for cloud resources is increasing day by day. This is the reason, the maintenance of streamlined workflow, communication, productivity, service delivery, resource availability, computational cost, load balancing, and performance has become a critical challenge for cloud service providers. To optimally utilize cloud resources, many resource scheduling algorithms exist. In this paper, our contribution is twofold. Firstly, a critical analysis of the heuristics, metaheuristics, and hybrid resource scheduling algorithms in cloud computing is presented by targeting throughput, response time, makespan, load balance, energy efficiency, cost, and resource utilization over the period 2012–2022. Secondly, we discuss the workflow of cloud resource allocation and scheduling techniques and we provide an easy and concise view of the underlying model adopted by each cloud architecture. Our experimental evaluations aim to present the suitability and appropriateness of different resource optimization algorithms in cloud computing.
Deep Attention-guided Multi-scale Feature Pyramid Network for Object Detection

In light of the shortcomings of the feature pyramid network (FPN) in preserving semantic information during adoption and its limited model receptive field, we present a novel approach for improving object detection accuracy in the form of a deep attention-guided multi-scale feature pyramid network (DM-FPN). Our approach introduces a new attention mechanism after the shallow features, which allows us to selectively focus on objects within the image while reinforcing crucial information contained within. Additionally, we introduce a new multi-scale feature fusion module in the highest layer features that expands the receptive field and fully extracts the semantic information to enhance object recognition. Importantly, our DM-FPN model is highly versatile and can be integrated into a variety of models. The experimental results show that for 640×640 input, we achieved 81.6% Mean Average Precision (mAP) on the PASCAL VOC2007 test set, and its mAP improved by 4.9% compared with the original you only need look once version 7 (YOLOv7).
Quantization and Deployment Study of Classification Models for Embedded Platforms

Deep learning models find extensive applications across various domains. However, their large number of parameters, high storage requirements, and computational overhead pose challenges for deploying these models on resource-constrained embedded devices. This study focuses on addressing this issue by exploring techniques to optimize and deploy lightweight models on embedded devices. The approach involves optimization and adjustment of the model, followed by model conversion, quantization, and quantization calibration, aimed at reducing model size and improving inference speed. Notably, improvements are made to the quantization calibration algorithm to mitigate accuracy loss caused by model quantization. The experimental results demonstrate that light quantization significantly reduces model size, facilitating storage on embedded devices. Although there is a slight reduction in accuracy, the inference speed is substantially improved, enabling real-time human face recognition in video scenarios.
Improved Stereo Matching Algorithm Based on Sparse Window

This study presents a sparse window-based stereo-matching algorithm that enhances the accuracy and efficiency of the semi-global matching algorithm. Unlike traditional methods, this algorithm processes pixel areas based on their texture features, resulting in more efficient encoding. The proposed approach systematically samples pixels within the original encoding window to reduce the number of pixels involved in the process. Additionally, using the FAST feature detection method distinguishes texture areas and applies different encoding processes for each area to obtain the feature encoding of the center pixels. Experimental results show that compared with traditional semi-global stereo matching algorithms, our proposed sparse window-based algorithm improves processing speed by 0.06 seconds and reduces average error by 10.92%.
Maximising Server IT Energy Efficiency through Optimal Hardware and Software Configuration

This paper analyses the impact of memory configuration and BIOS power profile on the energy efficiency of a Data Centre server undertaking a range of benchmarks that mimic real world application. It highlights some easy “wins” for achieving these goals, as well as some more specialized improvements that can be made through server profile customization.
Applying and Exploring Supervised Contrastive Regression for Vision-Based Steering Prediction

Self-supervised contrastive learning focus more on classification tasks since the samples from different classes are easy to be distinguished based on the similarity calculation of contrastive loss functions. However, because the continuity nature of regression tasks where samples and labels don't have their associ-ated discreet classes, self-supervised contrastive learning methods face challenges to calculate the similarity between samples for regression tasks. Steering prediction (i.e., a regression task) is a crucial module for self-driving vehicle and U AV autonomous navigation, which is responsible to control the movement orientation. This paper applied the Supervised Contrastive Re-gression (SupCR) method for the vision-based steering prediction. To the best of our knowledge, the SupCR has not been explored in the areas of outdoor self-driving and U AV indoor navigation. The SupCR also obtains better regression performances compared to the bench-mark supervised learning model and other contrastive learning methods. The experiments explore the reasons for better performances of the SupCR according to the visualizations of representation distributions and attention heatmaps.
Online Hierarchical Conformance Refinement Planning for Autonomous Robots

Planning is a fundamental reasoning ability required by any general-purpose autonomous robot that will be deployed into real-world domains. This paper presents our ongoing work towards a novel domain-independent planning paradigm, called Hierarchical Conformance Refinement (HCR), capable of rapid online partial-planning (that is planning partly interleaved with execution), which aims to maximize productivity and minimize down-times of robots. HCR planning achieves this by employing two core concepts involved in human decision making; abstract reasoning and problem decomposition. Our approach combines the expressive power of Answer Set Programming (ASP) with the speed of hierarchical refinement planning. Together, these allow large and complex problems with long plan lengths, to be broken down and solved as many smaller and more manageable partial-problems, in a general way. This exponentially reduces execution latency and total planning times, for the trade-off of small losses in plan quality. Simulated experiments ran on an extension of the blocks world domain show that HCR planning reduced overall planning time by between 43.8 to 97.6 %, and execution latency time by 87.1 to 98.7%, over classical ASP based planning, but could increase plans lengths by 0.0 to 9.0% over the minimum possible length, from our easiest to hardest problems tested.
Dynamic Scene Semantic Information Guided Framework for Human Motion Prediction in Proactive Human-Robot Collaboration

Human motion prediction plays an important role in proactive human-robot collaboration. However, current research ignores scene semantic information, especially the dynamic information, which is closely related to future human motion, and therefore the prediction is not accurate enough. To address the above issue, we propose a dynamic scene semantic information guided framework for human motion prediction in proactive human-robot collaboration. We first ascertain the collaboration space depending on the gaze point and recognize the object category. After obtaining the static information, we detect object affordance, which represents the property of objects, and estimate object 6-DoF pose to gain the dynamic information with object manipulation analysis. Then, we design a diffusion sampling strategy to obtain diverse future human motion results. Simultaneously, to ensure the reliability of the prediction, a step-by-step driven ST-GCN with regulatory mechanism is devised. The results of experiment demonstrate the effectiveness of our method in human-robot collaboration on an assembly task.
Data-Driven Prediction of Polymer Intrinsic Viscosity with Incomplete Time Series Data

Prediction of polymer intrinsic viscosity is very important for stabilizing the quality of polyester fibers. Data-driven prediction has been applied to solve engineering problems. However, incomplete data are all too frequent to a phenomenon in the workshop due to signal interference, human error, and other reasons. This paper proposes a data-driven approach for polymer intrinsic viscosity prediction with incomplete series data. First, a time series data generative adversarial network (TSDGAN) is presented to fill in the missing data, in which Attention LSTM serves as the generator and CNN as the discriminator. Next, An Informer model is introduced for polymer intrinsic viscosity prediction with the generated time series data. According to the experimental results under different missing rates, the proposed approach achieves better results than traditional and some state-of-the-art time-series prediction methods.
Automatically-Grown Optimal Neural Controller

Designing an optimal controller for practical systems is rewarding yet challenging due to its intricate dynamics, nonlinearity, and time-varying characteristics. This paper proposes an automatic method to design an optimal neural network controller, AutoNN, for practical applications. The AutoNN grows the neural architecture for minimal structural size, with a genetic algorithm to train the discrete structural and continuous weight parameters. This enables the neural controller to generate its required structure adaptively from the distinct characteristics of the plant being controlled, ultimately achieving optimal control. Further, AutoNN alleviates the labour-intensive process of manually adjusting hyperparameters through iterative experimentation. The efficacy of AutoNN is evaluated in designing a nonlinear controller for a complex heating, ventilation, and air conditioning system. The AutoNN approach and its performance are compared with present training methods for neural controllers, such as those based on multilayer perceptron, recurrent, and long short-term memory neural networks. AutoNN achieves a more compact structure with higher control performance than them and the widely used traditional controller, PID.
An Autonomous Vehicle Platform for Parcel Delivery

Due to its affordability, adaptability, and ease of deployment, autonomous freight vehicles are gaining popularity and offering creative solutions for last-mile distribution, transport from the nearest distribution hub to final destination. The design of a small, autonomous vehicle that can operate in both indoor and outdoor settings named “sidewalk robot” is the purpose of this study. The developed vehicle applies modular design. Its steering chassis integrates the ROS2 framework with autonomous algorithms like Cartographer mapping and Navigation2, using odometers, an IMU, LiDAR, and cameras for ambient awareness. The vehicle's navigation abilities based on the real-time map are encouraging. The testing results show the vehicle's promising potential, which provide technological guidance for projects of a similar nature.
Industrial Image Anomaly Detection Method Based on Improved MAE

This study explores an improved Masked Autoencoder (MAE) model for anomaly detection in common industrial scene images. Industrial image anomaly detection, as an important research topic in computer vision, can detect abnormal data deviations from normal expected behavior and ensure the normal operation of various systems. In actual industrial scenarios, the scarcity of abnormal samples, the cost of labeled data, and the lack of prior knowledge about anomalies make unsupervised learning methods widely used in the field of image anomaly detection. However, most unsupervised learning methods currently require the application of large-scale datasets. In order to make unsupervised learning applicable to small-scale industrial image datasets, we replaced the backbone network with Swin Transformer based on the Masked Autoencoder method, while improving its mask strategy and optimizer. The effectiveness and advantage of the proposed method are demonstrated through an experimental comparison with existing model, the results show that this method is better than MAE model pretrained on ImageNet, and outperforms other unsupervised learning models in multiple categories.
Software Effort Estimation Using Deep Learning and Fuzzy Modelling

Building a successful software application with high quality needs a better software process model. One of the factors that impact software development process is estimating the most likely human effort needed to accomplish software project. Machine learning and statistical algorithms have been widely used to build such estimation model. But a little attention has been paid to the applicability of Deep Convolutional Neural Network to better estimate software effort. One reason that hinder using deep learning is that most of the software datasets contains samples in the form of vector, not a matrix. To handle this challenge and reduce uncertainty in software measurement, we use Fuzzy modelling and Fuzzy c-means clustering to build a convenient data form for each instance. Simply, we used Fuzzy c-means to partition samples in the dataset into different clusters then map these cluster on each feature dimension using the concept of Fuzzy membership function. This process helps yielding a matrix representation for each data sample, which can fit as input to the deep learning model. The proposed model has been evaluated over multiple benchmark datasets obtained from PROMISE repository. The results are promising and in general more adequate than many popular estimation models. However, there is a need to investigate the impact of changing number of clusters and features on the proposed model.
Autonomous Robotic Inspection System for Drill Holes Tilt: Feasibility and Development by Advanced Simulation and Real Testing

Industry 4.0 and forthcoming 5.0 introduce process methods that primarily rely on robotic systems to succeed. Robotic systems provide accuracy in inspection tasks over current methods. However, in the aerospace industry the required accuracy is beyond what a standard robotic arm can provide. This paper aims to introduce a simple finite state-controlled robotic system using image processing to guide the motion and evaluate the tilt of a series of drill holes using one of them as reference hole. The system is developed via simulation in RoboDK and then validated in both a simulated and a real setup, using an ABB GoFa robot. The inspection experiment is performed by aligning the camera optical axis to the first drill hole axis. By taking it as a reference, the rest of the holes are measured and compared with the reference one, and to the ground-truth results. Drill hole detection is refined using a proposed circle optimisation algorithm. Results demonstrate the validity of the approach with an error below 0.5 degrees.
An Overview of Path Planning for Autonomous Robots in Smart Manufacturing

With the increasing need for greater flexibility and intelligence of robotic and autonomous systems (RAS) towards smart manufacturing, this paper aims to present a comprehensive overview of path planning approaches to deal with the challenging working environments characterised by uncertainty, complexity and unpredictability and dynamic interactions with humans/objects/machines. Specifically, it will provide a general overview of path planning problem by clarifying the related terminology and methods. A critical review of path planning approaches for autonomous robots is provided alongside with examples of industrial applications. Current challenges and future work are highlighted for researchers aiming to develop advanced RAS towards smart manufacturing.
Cross-Intent Graph Contrastive Learning for Fashion Sequential Recommendation

Sequential recommendation in e-commerce seeks to recommend items to users based on their past purchase preferences and has received much research attention. However, most approaches are so far restricted to modeling the user's long-term intent (user-to-item) and short-term intent (item-to-item) separately, which may extract the user's purchase interest but disregard possible synergistic interplay between the long-term and short-term intents on the user's overall purchase interest. To balance the users' long-term and short-term intents, this paper develops a cross-intent graph contrastive learning framework. By encouraging alignment of graph network representations among multiple intentions, we capture complementary information from different intentions of the same user. We extensively evaluate this framework by conducting experiments on two publicly available fashion datasets. The results demonstrate the effectiveness and efficiency of cross-intent graph contrastive learning.
A Multimodal Attention Tracking in Human-Robot Interaction in Industrial Robots for Manufacturing Tasks

The field of human-robot interaction has seen tremendous growth in recent years, and the use of robots in manufacturing tasks has become increasingly common. However, the success of human-robot interaction is highly dependent on the ability of the robot to understand and adapt to the human operator's actions and attention. In this paper, we propose a novel approach that uses a context-aware natural language interface and position tracker to track the operator's attention and improve interaction with the robot. The system integrates multimodal inputs such as head pose estimation and intent recognition to accurately predict the operator's attention and adjust the robot's behaviors. The proposed approach is evaluated in a manufacturing logistic scenario, and the results show a significant improvement in collaboration and a reduction in errors in task completion. The approach is expected to have broad applicability in industrial manufacturing settings, where it can enhance productivity and efficiency by improving human-robot interaction.
Detecting Worker Attention Lapses in Human-Robot Interaction: An Eye Tracking and Multimodal Sensing Study

The advent of industrial robotics and autonomous systems endow human-robot collaboration in a massive scale. However, current industrial robots are restrained in co-working with human in close proximity due to inability of interpreting human agents' attention. Human attention study is non-trivial since it involves multiple aspects of the mind: perception, memory, problem solving, and consciousness. Human attention lapses are particularly problematic and potentially catastrophic in industrial workplace, from assembling electronics to oper-ating machines. Attention is indeed complex and cannot be easily measured with single-modality sensors. Eye state, head pose, posture, and manifold environment stimulus could all play a part in attention lapses. To this end, we propose a pipeline to annotate multimodal dataset of human attention tracking, including eye tracking, fixation detection, third-person surveil-lance camera, and sound. We produce a pilot dataset containing two fully annotated phone assembly sequences in a realistic manufacturing environment. We evaluate existing fatigue and drowsiness prediction methods for attention lapse detection. Experimental results show that human attention lapses in production scenarios are more subtle and imperceptible than well-studied fatigue and drowsiness.