Reviewerly/exHarmony huggingface dataset:
Robust Object Pose Tracking for Augmented Reality Guidance and Teleoperation

&lt;p&gt;For many augmented reality guidance, teleoperation, or human-robot interaction systems, accurate, fast, and robust 6 degree of freedom object pose tracking is essential. However, current solutions easily lose tracking when line-of-sight to markers is lost. In this paper we present a tracking system which matches or improves on current methods in speed and accuracy, achieving 1.77 mm and 1.51 degrees accuracy at 22 Hz. Reflective markers are segmented in infrared images using contour detection before using the known marker geometry to perform point correspondence and pose computation using novel approaches. At the same time, a new square root unscented Kalman filter is introduced which improves accuracy and flexibility by tracking the markers themselves rather than the computed pose, and enables fusion of an external inertial measurement unit. This reduces noise and makes the tracking robust to brief loss of line-of-sight. The algorithms and methods are described in detail with pseudo-code for ease of reproduction. The system is implemented in simulation and on a Microsoft HoloLens 2 using Unity for ease of entry and integration into graphical projects. The code is made available open source. Tests of the system are described, and the results analyzed .&lt;/p&gt;
Are Model Explanations Useful in Practice? Rethinking How to Support Human-ML Interactions

Share on Are Model Explanations Useful in Practice? Rethinking How to Support Human-ML Interactions Author: Valerie Chen Machine Learning, Carnegie Mellon University, USA Machine Learning, Carnegie Mellon University, USA 0009-0007-2783-0265View Profile Authors Info & Claims AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and SocietyAugust 2023Pages 942–944https://doi.org/10.1145/3600211.3604726Published:29 August 2023Publication History 0citation56DownloadsMetricsTotal Citations0Total Downloads56Last 12 Months56Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Optimal Multi-Target Path Planing Control for Multiple Mobile Robot System

In this paper, the problems related to cooperative control for the multiple mobile robot system (MMRS) is presented. The LIDAR sensor is employed to obtain the 2D map of the indoor space. The formation control and the leader-following algorithm are applied to control the MMRS moving in a specific formation. Additionally, this paper considers the scenario that the MMRS has to move to several targets in the indoor space with many obstacles. Therefore, the indoor travelling salesman problem-based Ant Colony Optimization (ACO) is studied in this work to determine the optimal moving path of the MMRS. To prove the effectiveness and merit of the proposed methods, the simulation results are provided in this article.
Ceusia-Breast: computer-aided diagnosis with contrast enhanced ultrasound image analysis for breast lesions

Abstract Background In recent years, contrast-enhanced ultrasonography (CEUS) has been used for various applications in breast diagnosis. The superiority of CEUS over conventional B-mode imaging in the ultrasound diagnosis of the breast lesions in clinical practice has been widely confirmed. On the other hand, there have been many proposals for computer-aided diagnosis of breast lesions on B-mode ultrasound images, but few for CEUS. We propose a semi-automatic classification method based on machine learning in CEUS of breast lesions. Methods The proposed method extracts spatial and temporal features from CEUS videos and breast tumors are classified as benign or malignant using linear support vector machines (SVM) with combination of selected optimal features. In the proposed method, tumor regions are extracted using the guidance information specified by the examiners, then morphological and texture features of tumor regions obtained from B-mode and CEUS images and TIC features obtained from CEUS video are extracted. Then, our method uses SVM classifiers to classify breast tumors as benign or malignant. During SVM training, many features are prepared, and useful features are selected. We name our proposed method "Ceucia-Breast" (Contrast Enhanced UltraSound Image Analysis for BREAST lesions). Results The experimental results on 119 subjects show that the area under the receiver operating curve, accuracy, precision, and recall are 0.893, 0.816, 0.841 and 0.920, respectively. The classification performance is improved by our method over conventional methods using only B-mode images. In addition, we confirm that the selected features are consistent with the CEUS guidelines for breast tumor diagnosis. Furthermore, we conduct an experiment on the operator dependency of specifying guidance information and find that the intra-operator and inter-operator kappa coefficients are 1.0 and 0.798, respectively. Conclusion The experimental results show a significant improvement in classification performance compared to conventional classification methods using only B-mode images. We also confirm that the selected features are related to the findings that are considered important in clinical practice. Furthermore, we verify the intra- and inter-examiner correlation in the guidance input for region extraction and confirm that both correlations are in strong agreement.
Ensuring Federated Ownership Verification with FedBack: A Trigger-Based Watermarking Approach

Abstract Federated learning (FL) has become an emerging distributed framework to build deep learning models through the collaborative efforts from multiple participants without sharing training data across the engaged parties. However, during the whole life cycle of FL models, many unreliable participants may gain access and expose risks to them. Therefore, from the perspective of copyright protection, FedBack, a backdoor watermarking method based on trigger pre-processing is proposed to verify federated ownership on client side. First, clients embed their backdoor watermark information to the global model naturally at model training stage. Subsequently, federated ownership can be preliminarily verified through client detection and gain an outpouring of support through server mediation, without disclosing model privacy. Moreover, Trigger Pre-processing Algorithm is innovatively proposed to study watermark performance by changing watermark levels. Experimental results show that watermark detection rate of any level is high enough to credibly prove ownership verification result, with p-value providing more statistical evidence. This protection mechanism is deployed under two DNN architectures to prove watermark fidelity, significance and robustness. Overall, FedBack can credibly support the ownership verification result, with negligible impact on primitive classification tasks. It is also applicable to various federated training settings and robust to various model attacks.
Person Re-identification Using Soft Biometrics

Abstract Pedestrian characteristics like bags, gender, clothes, or short hair might affect in identifying people in video surveillance. Due to variation in poses, illumination, background, and camera views, the fundamental problem in pedestrian attribute detection is the significant variation in visual manifestation and position of attributes. In this work, we use soft biometrics and appearance features to match people across cameras. The Convolutional Neural Network (CNN), the effective feature representation named Local Maximal Occurrence (LOMO), and the hier-archal Gaussian descriptor algorithm (GOG) are all being utilized to increase the performance of re-identification (re-ID). On the other hand, we applied soft biometric (SB) attributes to improve our system’s performance. The SB attributes are identified, and attribute-based distance scores are calculated between pairs of images (probe, gallery). The results based on soft biometric attributes can be used to define probe and gallery topics at a high level. We applied then the Random Forest (RF) clas-sifier as the primary classifier and a metric learning approach named Cross-view Quadratic Discriminant Analysis (XQDA) to compute the similarity between the probe and the gallery. The experimental results of our approach are evaluated on the VIPeR dataset. The results demonstrate that the SB approach gives promising potential and superior performance to its conventional counterpart methods (without soft).
Progressively Growing of Least Squares Generative Adversarial Networks

&lt;p&gt;In the past decade, generative models have seen exponentially use in the world of computer vision. One architecture that has consistently contributed to this domain is generative adversarial networks. These networks can produce outstanding results and very realistic appearing images. They do not however come without their downfalls, as they tend to be extremely unstable when training for resolutions beyond 64x64. As a result, several solutions have been proposed to combat stability and other issues found during training such as a lack of variation in images produced. The first set of solutions focus on using a variety of different loss functions such as the Wasserstein distance loss function or the least squares loss function. While other solutions propose altering the architecture used or even the training methodology which the networks undergo. To build upon the success of other solutions this paper will propose an architecture which grows during training to allow for high resolution images to be produced. This solution will combine the efforts of multiple other ones while also contributing novel changes to the GAN architecture. As an outcome, this report will showcase the new proposed approach and its ability to produce comparable results to other state-of-the-art solutions. &lt;/p&gt;
H-BNN: FPGA-based binarized convolutional neural network for cloud detection on satellite payload

This paper proposes an FPGA-based binarized convolutional neural network (H-BNN) for cloud detection on a satellite payload. By utilizing 1-bitwise weights and activations, the proposed approach reduces computational complexity and memory requirements, making it an efficient solution for classifying cloud regions in near-infrared images captured by satellite camera sensors. The proposed H-BNN model and hardware implementation approach are more suitable for satellite (a) payload hardware computing and address the challenges posed by traditional Convolution Neural Network models with full precision configuration., and achieved an accuracy of over 90% and 22 Frames Per Second(FPS) on FPGA.
The Level of Reading Comprehension Skills ‎Included in Readiness to Read Activities in ‎Science Textbooks of Middle Schools

The study aimed to investigate the level of reading ‎comprehension skills included in Readiness to Read activities in ‎science textbooks for middle school, 1442H edition, using a ‎descriptive approach based on content analysis design. An ‎assessment tool was prepared, including 14 skills distributed ‎among three levels of reading comprehension skills: direct, ‎inference, and critical comprehension. Validity and reliability ‎were established‏.‏‎ Results showed reading comprehension skills ‎were included in all 36 Readiness to Read activities (68.7%). ‎The direct level of reading comprehension skills has the highest ‎inclusion rate (83.9%), then the inference level (67.4%), and ‎finally the critical level (54.4%). Results indicated no statistical ‎difference in inclusion according to grade. Based on the results, ‎a recommendation was made to reconsider the mechanism of ‎including reading comprehension skills in Readiness to Read ‎activities through diversity in the design of activities. A ‎readability study of readiness-to-read activities in middle school ‎science textbooks was proposed.‎ ‎(Keywords: Reading Comprehension Skills, Direct ‎Comprehension Level, Inference Comprehension Level, Critical ‎Comprehension Level, Inclusion Level, Readiness to Read ‎Activities, Science Textbooks, Middle Schools)‎
The Effectiveness of Using Microsoft Teams ‎Software in Light of the Corona Virus (COVID-19) ‎Pandemic Challenges among Science Teachers in ‎Bani Kanana District Public Schools

The study aimed to identify the effectiveness of using Microsoft Teams software in light of the Corona Virus (COVID-19) ‎pandemic challenges among science teachers in the Bani Kinana District public schools. The study used a descriptive analytical ‎research design.To achieve the objectives of the study, a questionnaire was developed, which consisted of five domains (technical ‎effectiveness, educational content effectiveness, test effectiveness, interaction with students effectiveness, and assessment ‎effectiveness) the effectiveness of the Microsoft Teams software among science teachers in government schools in the Bani ‎Kanana District was found to be average. Also, there were statistically significant differences in the domains (technical ‎effectiveness, educational content effectiveness, test effectiveness, interaction with students effectiveness, and assessment ‎effectiveness) attributed to gender. There were statistically significant differences in the test effectiveness domain due to ‎experience, and the differences favored those with experience. In all fields, the differences favored women (10 years or more).The ‎study concluded with a set of recommendations, the most important of which were the most important of which was that the ‎Ministry of Education take its responsibility to pay attention to the use of Microsoft Teams software in public schools in light ‎of the Corona pandemic, and boost it because of its impact on improving teachers’ performance.‎ ‎(Keywords: E-learning Management System, Microsoft Teams Software, Science Teachers, Corona Pandemic, Jordan)‎
Pengembangan Multimedia Pembelajaran Interaktif Menggunakan Aplikasi Articulate Storyline

The purpose of this study id to find out how to develop interactive learning multimedia using the articulate storyline application, to find out the validity and practicality of interactive learning multimedia in class VIII at indonesian subject at MTsN 6 Padang. The results of the material validity test are in the "Very Valid" category with a percentage of 88,42%, the media validation results are in the "Very Valid" category with a percentage of 95% so that flipbook-based learning media products can be tested. Furthermore, the results of the product practicality test are in the "Very Practical" category with a percentage of 87%, so that interactive learning media products are suitable for use by VIII grade islamic high school students as learning media.
A Comparison Study On Reading Comprehension Between Male And Female Students

This study was aimed at finding out the difference on reading comprehension between male and female students at the eleventh grade of State Senior High School 1 Bangkinang Kota. The research design was proactive ex post facto research. The population and sample was the eleventh social science students. The number of sampling was selected by using stratified disproportional technique. Inferential analysis “Independent Sample T-Test” technique was used to measure the difference on reading comprehension between male and female students and descriptive analysis was used to know the dominant factors that influence reading comprehension for male and female students. Based on the research findings, it was found mean score of male students was 61.87 and were categorized into enough level, then female students was 70.13 and were categorized into good level. Moreover, the sig was 0.012 &lt; a (0.05). Therefore, it could be concluded that Ho was rejected and Ha was accepted, it means there is a significant difference on reading comprehension between male and female students at the eleventh social science class of State Senior High School 1 Bangkinang Kota. Whereas, the factors for male students were social environment (22.8%), talent (21.2%), background knowledge (20.6%), vocabulary (19.6%), and motivation (15.8%). While the factors for female students were motivation (22.2%), social environment (20.9%), background knowledge (20.5%), talent (18.6%), and vocabulary (17.8%).
Regulatory Challenges and Opportunities in Web 3

The novel use of distributed ledger technology (DLT) in the financial sector poses considerable regulatory challenges, such as anonymity, technology neutrality, interconnectedness within the market of virtual assets, as well as with the traditional financial system and new legal risks to regulators around the globe. At the same time, the novelty of DLT for financial uses embodies also significant potential benefits to the financial sector, like innovation, inclusion, and competition. This chapter analyses these challenges and opportunities in detail and subsequently reviews possible regulatory responses at the current stage of virtual asset revolution and financial services innovation. Given the ongoing rapid development in the DLT financial services sphere, the analysis identifies the risk-based regulatory approach as potentially the most universal approach for national/regional regulators with advantages of high flexibility and resource efficiency.
The Added Value of Simulations in 3D Virtual Worlds for Professional Training of First Aid Medical Teams

Virtual worlds are based on a virtual reality technology, which allows three-dimensional realistic situations within a digital environment. The study framework is based on the Activity Theory and the model of Serious Games. These frameworks emphasize the interrelationships between the virtual worlds’ various components, including user activity, training design, and their formal definition as Serious Games. This study examined the teaching and learning characteristics which take place in three-dimensional virtual world environments (3DVW) and focused on a training pilot for emergency medical personnel of the Israeli First Aid medical teams (IFA). Semi-structured interviews were conducted through videoconferencing with 27 volunteers and employees of the IFA who participated in 3D virtual worlds training simulations. These interviews provided an understanding of their training experience in virtual worlds in general and the various medical simulation experience in particular. The findings showed that learning effectiveness was the main added value in immersive 3D virtual worlds training. The VW simulator has the ability to make learning more efficient and results in improvement and acquisition of professional skills in medical teams with an emphasis on following medical protocols. In addition, the virtual environment can enhance motivation and enjoyment in the context of medical training. Moreover, the virtual environment enables safe practice of complex medical procedures. These findings strengthen the positive trend of 3D virtual worlds technology implementation in medical training.
Segmentation of brain tissue using improved kernelized rough-fuzzy c-means technique

&lt;p&gt;&lt;span&gt;Brain magnetic resonance imaging (MRI) data is a hot topic in the domains of biomedical engineering and machine learning. Without locating anomalies, such as tumors and edema, radiologists and other medical experts cannot effectively recommend or administer therapy for patients. Having three different magnetic resonance techniques (T1 weighted, T2 weighted, and T3 weighted), MRI can produce detailed multimodal scans of different human brain tissues with varying contrast, which can help pinpoint the source of any abnormalities. The cerebrospinal fluid (CSF), white matter (WM), and grey matter (GM) are all components of the brain, and their boundaries are sometimes hazy and difficult to nail down. In light of the problems above, this paper makes an effort to tackle issues like: i) the noise that exists in the brain datasets for MRI, ii) the fuzziness, uncertainty, overlap, indiscernibility of complex brain tissue regions, iii) the inability of traditional unsupervised methods to reliably distinguish between various brain tissue locations, and iv) ineffective performance. We propose some robust techniques by utilise spatial contextual data, a rough set, a fuzzy set, and ultimately a fuzzy set to steer the clustering process in a better direction, allowing it to deal with likely noise, outliers, and other artifacts.&lt;/span&gt;&lt;/p&gt;
“Hate Speech is Like a Weapon… It Destroys People”- Attitudes in Georgian Society towards Hate Speech

It is well-known that language is often employed as a tool for gender oppression. Therefore, feminist and gender studies in linguistics ultimately aim to represent the problem, reflect on it, and propose ways to solve it. Together with technological advancement in communication, a new form of discrimination has arisen- hate speech used against women in electronic media discourse. Due to the nature of social media and the internet, messages containing hate speech can spread in seconds and reach millions of people. However, only a few know about the detrimental effects they may have on the addressee and society. This paper aims to analyse the answers to the anonymous questionnaire conducted on an electronic media platform. The questionnaire aimed to elicit what Georgian people know about hate speech and its detrimental effect on people. Altogether 200 people participated in the survey. As the research revealed, participants have heard of the hate speech, and have even been victims of it at a certain point in their lives. However, they do not know how to identify it. Thus, raising awareness about hate speech is of utmost importance to tackle the issue at hand.
MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing

In recent years, Transformer networks are beginning to replace pure convolutional neural networks (CNNs) in the field of computer vision due to their global receptive field and adaptability to input. However, the quadratic computational complexity of softmax-attention limits the wide application in image dehazing task, especially for high-resolution images. To address this issue, we propose a new Transformer variant, which applies the Taylor expansion to approximate the softmax-attention and achieves linear computational complexity. A multi-scale attention refinement module is proposed as a complement to correct the error of the Taylor expansion. Furthermore, we introduce a multi-branch architecture with multi-scale patch embedding to the proposed Transformer, which embeds features by overlapping deformable convolution of different scales. The design of multi-scale patch embedding is based on three key ideas: 1) various sizes of the receptive field; 2) multi-level semantic information; 3) flexible shapes of the receptive field. Our model, named Multi-branch Transformer expanded by Taylor formula (MB-TaylorFormer), can embed coarse to fine features more flexibly at the patch embedding stage and capture long-distance pixel interactions with limited computational cost. Experimental results on several dehazing benchmarks show that MB-TaylorFormer achieves state-of-the-art (SOTA) performance with a light computational burden. The source code and pre-trained models are available at https://github.com/FVL2020/ICCV-2023-MB-TaylorFormer.