Reviewerly/exHarmony huggingface dataset:
Artistic Visualisation of Personal Data: A Case Study of Digital Scheduler

The rapid development of digital technology has made it possible to manage analogue information digitally. Various analogue information is currently digitalised, and people conveniently use the information with applications in their smartphone. Among many, digital scheduler is one of the popular applications. However, digital scheduler still maintains the traditional look of paper calendars although it has been radically developed in terms of function and usage. And previous studies indicate artistically-visualised data made users to be engaged in daily context. Therefore, our study explores how the look and feel of digital scheduler can be better designed to deliver integrated and engaged calendar experience with daily life. For the study, an online survey and a design workshop were conducted with ten designers who were familiar with digital scheduler. The result shows that various elements can be used for artistic visualisation of their schedules to converse digital schedule in a visually engaged form. As a case study, the findings could provide design practitioners with a useful source of information in the artistic visualisation of digitalised personal data.
Image Dehazing via Weighted Guided Filtering with Removing Lighting Effects

In fog images with the influence of light sources, the light source can easily introduce halo that does not exist in foggy days, which will affect the accuracy of atmospheric light value and transmittance estimation. To solve this problem, a simple and easy dehazing algorithm is proposed to dehaze fog images affected by light sources. Firstly, the light source area is determined in CIELab color space based on super pixel segmentation. Then the light attenuation factor based on distance measurement is introduced to eliminate the influence of light sources. Finally atmospheric light value is estimated in the unit of superpixel block, and weighted guidance filtering is used to iteratively optimize the transmittance to obtain the dehazing result map under the influence of light sources, which improves the efficiency and accuracy. The restoration experiment is carried out on the SOTS dataset and images affected by light sources. Comparing with existing mainstream algorithms subjectively and objectively, the restoration results of the proposed algorithm has lower NIQE and BRISQUE value. And these results have the advantages of high definition, low noise, rich texture details and high color restoration.
Digital image clustering and colour model selection in content-based image retrieval (CBIR) approach for biometric security image

In this paper, we mainly discuss about the Digital Image Clustering in Content-Based Image Retrieval (CBIR) approach for biometric security. The main focus in this paper is to collect and gather as much as information about the technique for feature extraction in order to reduce the semantic gap problem in Content-Based Image Retrieval (CBIR) to be implemented in biometric (face recognition) process. Several general features of Content-Based Image Retrieval (CBIR) have been explained and discussed in this paper such as colour features, texture features, and shape features. From the preliminary study conducted earlier, colour features has been selected for the implementation of features extraction. In terms of biometric image features extraction implementation, image features of facial image will be extracted and stored in image feature database. Next, the query image will be processed in the same way as images in the database. Then, the matching is carried out on the feature database. On top of that, the common algorithm of colour features has been discussed as well to select the suitable colour model to be used. Based on the finding, the Red Green Blue (RGB) has been chosen as the selected colour model. The summarization regarding performance study of Content-Based Image Retrieval (CBIR) algorithm based on RGB colour features selection has been included in this paper. The purpose of that performance study is to analyze the advantages and limitations of implementing the RGB colour model in the Content-Based Image Retrieval (CBIR) technique. Other than selection of the general features of Content-Based Image Retrieval (CBIR), the selection and nomination of the clustering algorithm is very important in order to provide a greater Content-Based Image Retrieval (CBIR) system. Several common algorithms such as K-means, Isodata, and KHM from the Partitional Clustering Algorithm has been discussed in detail in this paper.
Autonomous RC Cars for Control Research and Education: Implementation of Virtual Potential Based Navigation and Platooning

Autonomous and collaborative vehicles not only are seen as a possible solution to reducing congestion and traffic related fatalities. They also provide an excellent multi-domain test bench for engineering education at undergraduate and graduate level. Yet, the use of real scale platforms for experimental educational activities bears prohibitive costs and complexity. While several small scale autonomous platforms have been developed in recent years to address this issue, still they require a significant investment of time and money, which is not always ideal for undergraduate education. Furthermore, none of the available platforms are specifically developed for platooning experiments. In this paper, we detail the results of an undergraduate student's project where a pair of relatively low-cost, off-the-shelf small scale RC cars have been used to implement and test a well known platooning algorithm from the literature. Furthermore, a Virtual Potential Field (VPF) based lateral controller has been included in order to allow the cars to navigate a prescribed closed-circuit track. Self-location of each car has been obtained via a ceiling-mounted motion capture system. Results have shown that, even using a relatively low sampling rate of 10 Hz, accuracies in the order of 1 cm can be obtained when platooning at 0.5 m/s along a circuit of 4 by 3 m. As further improvements to the platform, apart from higher sampling rates for the control law, the inclusion of onboard perception is being explored, in order to eliminate the need for an external motion capture system.
Promise or Peril? When Human Efficacy Meets AI Capability Augmentation

Recent literature has demonstrated that humans high in self-efficacy tend to resist AI advice despite its superior capabilities. In this study, we question the validity of this finding for a prevalent practice, in which AI capability is dynamically augmented and humans accumulate knowledge and interaction experience with AI. As such, humans high in self-efficacy, compared with those low in self-efficacy, are more likely to recognize AI capability augmentation due to their strong learning ability. We thus posit that this phenomenon, together with the AI's superior capability compared with that of humans, would invoke individuals' active cognitive reasoning by comparing themselves with respect to the AI system. Consequently, they tend to change their attitude to follow AI. We leverage a unique natural experiment of AI capability augmentation in the context of an on-demand food delivery service to verify our conjecture. We obtain several interesting findings: (1) Human riders high in self-efficacy indeed become more compliant with AI advice in the presence of an AI capability augmentation, in both the short and long run. However, (2) riders' short-term food delivery performance significantly decreases. Further mechanism analysis reveals that (3) such decline mainly occurs during an adaptation (learning) window when riders shift their decision-making objective for the delivery sequence from their own to the one recommended by the AI system; in the long run, their delivery performance eventually recovers to a high level. By contrast, (4) riders low in self-efficacy are trivially affected by AI capability augmentation. From a dynamic perspective, our findings shed light on the critical role of cognitive reasoning triggered by high human self-efficacy in human-AI collaboration, which prompts humans to better leverage the wisdom of AI and their own to achieve better collaborative decision-making outcomes.
X-ray Imaging Defect Detection of Transmission Line Strain Clamps Based on a YOLOX Model

Effective detection of internal defects of strain clamps is of vital significance to safe operation of transmission lines, and the X-ray radiographic inspection is a useful method to evaluate the hydraulic crimping quality of strain clamps. This paper presents a method to detect defects in X-ray images of strain clamps using YOLOX algorithm. An X-ray image dataset of strain clamps including 4976 images with 6 types of defects was constructed. The images were preprocessed by Gaussian filtering, histogram equalization, and gamma correction, thus to improve the image quality. The model was trained by the training sample X-ray images combining the Mosaic data augmentation method. The trained YOLOX model was applied to detect the defects in the 498 test sample X-ray images, and the mean average precision (mAP) reached 88.96%. The detection results were also compared to those of other object detection algorithms like SSD, YOLOv3, YOLOv4, etc., which indicates that the YOLOX model has higher precision. This study is helpful to automatically detect the defects of X-ray inspection images of transmission line strain clamps.
Slim Scissors: Segmenting Thin Object from Synthetic Background

Existing interactive segmentation algorithms typically fail when segmenting objects with elongated thin structures (e.g., bicycle spokes). Though some recent efforts attempt to address this challenge by introducing a new synthetic dataset and a three-stream network design, they suffer two limitations: 1) large performance gap when tested on real image domain; 2) still requiring extensive amounts of user interactions (clicks) if the thin structures are not well segmented. To solve them, we develop Slim Scissors, which enables quick extraction of elongated thin parts by simply brushing some coarse scribbles. Our core idea is to segment thin parts by learning to compare the original image to a synthesized background without thin structures. Our method is model-agnostic and seamlessly applicable to existing state-of-the-art interactive segmentation models. To further reduce the annotation burden, we devise a similarity detection module, which enables the model to automatically synthesize background for other similar thin structures from only one or two scribbles. Extensive experiments on COIFT, HRSOD and ThinObject-5K clearly demonstrate the superiority of Slim Scissors for thin object segmentation: it outperforms TOS-Net by 5.9% IoU $$_\textrm{thin}$$ and 3.5% $$\mathcal {F}$$ score on the real dataset HRSOD.
Uncertainty Aware System Identification with Universal Policies

Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments. A common problem associated with sim2real transfer is estimating the real-world environmental parameters to ground the simulated environment to. Although existing methods such as Domain Randomisation (DR) can produce robust policies by sampling from a distribution of parameters during training, there is no established method for identifying the parameters of the corresponding distribution for a given real-world setting. In this work, we propose Uncertainty-aware policy search (UncAPS), where we use Universal Policy Network (UPN) to store simulation-trained task-specific policies across the full range of environmental parameters and then subsequently employ robust Bayesian optimisation to craft robust policies for the given environment by combining relevant UPN policies in a DR like fashion. Such policy-driven grounding is expected to be more efficient as it estimates only task-relevant sets of parameters. Further, we also account for the estimation uncertainties in the search process to produce policies that are robust against both aleatoric and epistemic uncertainties. We empirically evaluate our approach in a range of noisy, continuous control environments, and show its improved performance compared to competing baselines.
Intrinsic Decision based Situation Reaction CAPTCHA for Better Turing Test

In this modern era, web security is often required to beware from fraudulent activities. There are several hackers try to build a program that can interact with web pages automatically and try to breach the data or make several junk entries due to that web servers get hanged. To stop the junk entries; CAPTCHA is a solution through which bots can be identified and denied the machine based program to intervene with. CAPTCHA stands for Completely Automated Public Turing test to tell Computers and Humans Apart. In the progression of CAPTCHA; there are several methods available such as distorted text, picture recognition, math solving and gaming based CAPTCHA. Game based turing test is very much popular now a day but there are several methods through which game can be cracked because game is not intellectual. So, there is a required of intrinsic CAPTCHA. The proposed system is based on Intrinsic Decision based Situation Reaction Challenge. The proposed system is able to better classify the humans and bots by its intrinsic problem. It has been considered as human is more capable to deal with the real life problems and machine is bit poor to understand the situation or how the problem can be solved. So, proposed system challenges with simple situations which is easier for human but almost impossible for bots. Human is required to use his common sense only and problem can be solved with few seconds.
Communication Technologies for Edge Learning and Inference: A Novel Framework, Open Issues, and Perspectives

With the continuous advancement of smart devices and their demand for data, the complex computation that was previously exclusive to the cloud server is now moving toward the edge of the network. For numerous reasons (e.g., applications demanding low latencies and data privacy), data-based computation has been brought closer to the originating source, forging the edge computing paradigm. Together with machine learning, edge computing has become a powerful local decision-making tool, fostering the advent of edge learning. However, the latter has become delay-sensitive and resource-thirsty in terms of hardware and networking. New methods have been developed to solve or minimize these issues, as proposed in this study. We first investigated representative communication methods for edge learning and inference (ELI), focusing on data compression, latency, and resource management. Next, we proposed an ELI-based video data prioritization framework that only considers data with events and hence significantly reduces the transmission and storage resources when implemented in surveillance networks. Furthermore, we critically examined various communication aspects related to edge learning by analyzing their issues and highlighting their advantages and disadvantages. Finally, we discuss the challenges and present issues that remain.
Poker in Virtual Reality

This study examines virtual reality (VR) poker and how the platform affects poker players' experience. Players use a self-customized avatar and other features of a computer platform that differs from in-person poker. Data were collected through observations from in-game poker VR recordings and interviews with five professional poker players. Findings are analysed theoretically through proteus effects, social presence, ecological psychology, magic circle, and liminality. This demonstrates the interactive cues that poker players use when immersed in virtual reality and embodied in a digital avatar. The goals from this research are to learn about the influence avatars have on poker players: if players can still maintain their poker skills and read different cue signs from other players while embodied in an avatar and immersed in VR. This paper also explores the promise of poker in virtual reality and its environment, examples of existing applications, a discussion of the research to date, and also provides a vision for the future.
Remote Sensing Course in Geography Online Education: A MOODLE Learning Materials Adaptation and Optimization Workflow

Learning management systems (LMS) have been evolving since the late 1990s as a category of specialized web-based software for educational content delivery and experience. LMS assumed pivotal importance in learning experiences due to the COVID-19 spread. In this software pool, MOODLE (Modular Object-Oriented Dynamic Learning Environment) is a seasoned open-source LMS framework that has been widely adopted by companies, schools, and the academic community. This work aims to set up the learning materials of the Remote Sensing course theoretical and practical modules of UNITO master’s degree in Geography and Territorial Sciences, on the LMS MOODLE and to adapt it considering the multi-disciplinary approach typical of the Geographic domain. Thanks to inherent LMS features and different plugins available on the platform, some analytical approaches originally optimized for face-to-face didactics, are re-developed to optimize the ingestion, classification, and delivery of learning materials through the LMS, to facilitate comprehension and access to such materials by students in the Remote sensing course.
Heap Patterns for Memory Graph Visualization

Visualizing large memory graphs containing dynamic data structures and nested payload data is crucial when debugging legacy and modern software. However, existing visualization tools primarily focus on aggregating data structures and either rely on hard-coded patterns, generic heuristics, or predicates written in expressive logics. We present a novel heap pattern language for concisely and intuitively describing structural aspects of dynamic data structures and nested payload data. Evaluating a heap pattern on a memory graph yields a set of matching groups of interconnected objects, and analyzing these groups enables the construction of a multi-level hierarchy for memory graph visualization, where groups can individually be (un)folded to the desired level of detail. We have prototypically implemented our heap pattern language in the Memory Graph Explorer tool and illustrate its use for visualizing large memory graphs on real-world and textbook examples. Unlike existing tools, developers can now control the construction of hierarchies using heap patterns to flexibly and locally adjust the level of memory graph abstraction in an interactive graph visualization to highlight the areas demanding attention during debugging.
Comparative Study of Image Degradation and Restoration Techniques

This paper implements airy disk smoothing, Poisson noise, Gaussian smoothing, Hanser’s phase term, and Zernike polynomial phase term as degradation techniques on images from the DIV2K dataset. These actions allows the generation of gray-scale degraded images to study the performance of the inverse filter, Wiener filter, and the Richardson-Lucy algorithm as image restoration techniques. Our experiments are conducted on two representative tasks: (i) intense image degradation, and (ii) image restoration from the degraded images. To measure the image degradation and the image approximation to the original image, this paper uses four similarity metrics: global dimensionless relative error of synthesis (ERGAS), mean squared error (MSE), spectral angle mapper (SAM), and visual information fidelity (VIFP). These similarity metrics determine which restoration technique can estimate the original image in more precisely, and enable the analysis of the required conditions for the estimation.
Performance Evaluation of Visual Object Tracking using YOLO Deep SORT with LCF

Most of the problems in object tracking are illumination and increased ID switching because of the occlusion and re-entering. To overcome the problems of object tracking, the proposed system uses the You Only Look Once (YOLO) and Deep Simple Online and Real-Time Tracking (Deep SORT) with Low Confidence Track Filtering (LCF) for object tracking. As a single-stage detector, YOLO performs classification and bounding box regression in one step, it makes faster than most convolutional neural networks. To get better performance for tracking and re-identification of the ID switch, Deep SORT with LCF is applied in this system. When filtering the low average confidence by LCF, the false positive tracks can be reduced. In this paper, YOLOv3 and YOLOv4 are comparatively performed for the dedicated visual tracking system. According to the experiment results for long-term occlusions, YOLOv4 and Deep SORT with LCF can identify more objects tracking than YOLOv3 and Deep SORT with LCF.
HDFU-Net: An Improved Version of U-Net using a Hybrid Dice Focal Loss Function for Multi-modal Brain Tumor Image Segmentation

In the field of brain tumor image analysis, automatic brain tumor image segmentation remains a challenging task due to the varying sizes, shapes, and textures of type of these masses. Deep Learning algorithms have improved the speed and quality of segmentation for certain tasks in medical imaging, especially for Glioma image segmentation. This work present to design and evaluate an algorithm capable of segmenting MRI images. In this regard, U-Net is the most prominent deep network, we recall that it has been the most popular architecture in the medical image segmentation. In this paper, we propose modifications to the U-Net architecture in order efficiently handle a multi-modal MRI input. Based on these modifications, we develop a novel architecture, HDFU-Net as a potential successor to the U-Net architecture by introducing and presenting an improved dice loss that can address the demand for more accurate segmentation in medical images and modify network architecture to improve segmentation performance. The improved dice loss is called Hybrid Dice Focal loss (HDF loss). We then evaluate the presented approach on the BraTS 2020 dataset and discuss the results.
Layered Malicious Nodes Detection with Graph Attention Network in Human-Cyber-Physical Networks

With the advancement of network information technology and smart device technology, cyberspace is gradually evolved into Human-Cyber-Physical Networks (HCPNs). At the same time, the security problems caused by malicious nodes are becoming more and more serious. It is urgent to propose an efficient approach for malicious node detection. In this paper, we apply graph attention network (GAT) to detect malicious nodes layer by layer in HCPN. In addition, we investigate the influence of graph structure features on the detection performance in terms of accuracy, precision, recall, F1-score by comparing with graph convolutional network-based approach. Experimental results show that our approach has better performance as well as stronger generalizability than graph convolutional network-based approach in general.
Deep Q-learning Sampling Based on Advantages

Deep Q-learning (DQN) has shown recent success on a wide range of complicated sequential decision-making issues, especially in the classic control area. However, in most DQN training, the sampling policies, particularly the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> -greedy policy and its derivation rules, produce noise erroneously due to the unsteadiness and unreliability of Q values utilized for searching for optimum actions. We begin with a fundamental hypothesis: complete advantages-based exploitation may be employed to help in successful exploration, and the gap between advantages on outstanding and poor actions tends to extend during the early phases of training before stabilizing. We then present a novel DQN architecture with a superior sampling policy that is totally and directly based on advantage values. This new DQN is named A-sampling DQN, and it requires minor code changes based on the original DQN codes. In most cases, A-sampling DQN and its derivatives outperformed the traditional DQN method.