Reviewerly/exHarmony huggingface dataset:
Performative prototyping in collaborative mixed reality environments: an embodied design method for ideation and development in virtual reality.

Collaborative Mixed Reality Environments (CMREs) enable designing Performative Mixed Reality Experiences (PMREs) to engage participants’ physical bodies, mixed reality environments, and technologies utilized. However, the physical body is rarely purposefully incorporated throughout such design processes, leaving designers seated behind their desks, relying on their previous know-how and assumptions. In contrast, embodied design techniques from HCI and performing arts afford direct corporeal feedback to verify and adapt experiential aesthetics within the design process. This paper proposes a performative prototyping method, which combines bodystorming methods with Wizard of Oz techniques with a puppeteering approach, using inside-out somaesthetic- and outside-in dramaturgical perspectives. In addition, it suggests an interdisciplinary vocabulary to share and evaluate PMRE experiences during and after its design collaboration. This method is exemplified and investigated by comparing two case studies of PMRE design projects in higher-art education using the existing Social VR platform NEOS VR adapted as a CMRE.
Neural networks and backpropagation

Machine Learning ML is a scientific field which studies a type of algorithms that solve problems without being explicitly programmed for them. Deep Learning DL is a specialization of this field, which includes Artificial Neural Networks (ANNs), henceforth referred to as Neural Networks (NNs), a group of algorithms inspired by neurobiology with a wide range of applications, used in fields such as self-driving cars, forecasting financial data, military applications, computer vision, fault identification in electronic systems, medicine, robotics and more. In this chapter, the concept of NNs is presented, as well as the way they function. Their architecture, the activation and cost functions, as well as the way they are trained and used for inference are introduced and explained in detail. Finally, the problem of overfitting is presented, along with methods proposed to mitigate its effects.
Blockchain based smart marketplace for secure internet bandwidth trading

Internet broadband usage is increasing dramatically as the number of Internet of Things (IoT) devices in smart homes that rely on Internet connectivity for effective communication between them grows. As a result, many network and Internet communication issues arise, including network traffic congestion, insecurity, high connection rates, and excess bandwidth waste. This work aims to create a secure and decentralized smart marketplace (SMP) for a fair, transparent, robust, and less expensive way of trading excess or idle traffic from a smart home by directly paying the owner of the bandwidth. The internet bandwidths may initially come from a direct Tel-com product or from other users (smart homeowners) who decide to sell their excess Internet bandwidth. The research was carried out by integrating a given smart marketplace scenario with blockchain technology using proof-of-authority (PoA) consensus mechanism. The simulation results show that the model is more reliable and appears to be an efficient solution to dealing with excess bandwidth waste and offering safe excess bandwidth trading within a smart city. Besides, the excess bandwidth wastage is controlled at least by 75% by making it available in the SMP for buyers.
A porosity model for medical image segmentation of vessels

A physics-based medical image segmentation method is developed. Specifically, the image greyscale intensity is used to infer the voxel partial volumes and subsequently formulate a porous medium analogy. The method involves first translating the medical image volumetric data into a three-dimensional computational domain of a porous material. A velocity field is then obtained from numerical simulations of incompressible fluid flow in the porous material, and finally a velocity iso-surface provides the surface description of the target object. The approach is tested on CT images of eight patient-specific cases, where cerebral aneurysms, nasal cavities (NC), and an aortic arch (AA) are the objects of interest. In the aneurysm cases, the results are compared against constant greyscale thresholding and manual segmentation. The manual segmentations of the aneurysms are validated by a clinical practitioner. Only a qualitative comparison is available for the NC, and the AA geometries. The results show that the proposed method is effective and capable of extracting the target object in a noisy domain. A sensitivity study is carried out to verify the method's performance with respect to modelling or user choices. The segmentation by the proposed method is also evaluated by performing computational fluid dynamics simulation, including a near-wall flow analysis, to ensure that the segmented geometry and the resulting computed solution are representative and meaningful.
Hybrid Similarity Matrix in Neighborhood-based Recommendation System

Modern hybrid recommendation methods have successfully mitigated the data sparsity and cold-start problems. Existing hybrid neighborhood-based models adopt both the transaction history and profiles of users and items, although each is used separately in different phases of learning the similarity scores and giving recommendations. This paper proposes utilizing both types of information to measure similarity scores between items, creating a more robust hybrid similarity matrix which helps improve the accuracy of the neighborhood-based models. Comprehensive experiments show that our proposed hybrid similarity matrix can boost the accuracy of neighborhood-based systems by 0.77 - 4.46% compared to the earlier related hybrid methods.
An Anonymous Blockchain-Based Authentication Scheme for Secure Healthcare Applications

Nowadays, continuous monitoring of a patient’s healthcare data has become a critical factor in human well-being. However, with the rapid advancement of wireless technology, doctors and healthcare professionals can monitor the patient’s healthcare data in real time. But to access the confidential patient’s data which is transferred through the open wireless medium, the secure transmission plays an important role. In this work, the privacy and the anonymity of the end-users (patient/doctor) are preserved using an anonymous blockchain-based authentication scheme. Moreover, in this work initially, mutual authentication is performed between the end-users, followed by encryption and decryption of confidential data. In addition, to avoid reauthentication of the patient again during the movement of a patient from one doctor to another, a transfer authentication protocol is performed between the doctors which enhances performance analysis. The security analysis section illustrates the withstanding capability of the proposed work against various vulnerable attacks. Finally, performance investigation of the proposed work reveals a reduction in computational and communication costs when compared to existing related works.
Coarse-to-fine SVD-GAN based framework for enhanced frame synthesis

Frame interpolation and synthesis are growing topics in the field of computer vision. Hence, these topics gained more attention recently where several deep-learning architectures were proposed to enhance the quality of the synthesized frames. In this paper, an efficient handcrafted deep approach is proposed for better frame synthesis. The proposed approach takes advantage of singular value decomposition (SVD) framework and Generative Adversarial Networks (GAN). The proposed approach does not require any computationally expensive feature extraction steps such optical flow techniques and block-based motion compensation techniques. Nonetheless, the SVD components still carry the relevant motion information needed to deal with the challenges such as large motion and occlusion. Thus, the frames are temporally upscaled via SVD based construction procedure where new middle frames are interpolated for further enhancement using a GAN based approach that eliminates most of the visual artifacts. The proposed frame synthesis approach is comprehensively evaluated in different scenarios where its performance is assessed and compared with the state-of-the-art. Our framework outperforms the majority of the deep learning approaches in terms quantitative results in addition to qualitative results where our framework can generate smoother frames with less visual artifacts.
Cross-Project Online Just-In-Time Software Defect Prediction

Cross-Project (CP) Just-In-Time Software Defect Prediction (JIT-SDP) makes use of CP data to overcome the lack of data necessary to train well performing JIT-SDP classifiers at the beginning of software projects.However, such approaches have never been investigated in realistic online learning scenarios, where Within-Project (WP) software changes naturally arrive over time and can be used to automatically update the classifiers.We provide the first investigation of when and to what extent CP data are useful for JIT-SDP in such realistic scenarios.For that, we propose three different online CP JIT-SDP approaches that can be updated with incoming CP and WP training examples over time.We also collect data on 9 proprietary software projects and use 10 open source software projects to analyse these approaches.We find that training classifiers with incoming CP+WP data can lead to absolute improvements in G-mean of up to 53.89% and up to 35.02% at the initial stage of the projects compared to classifiers using WP-only and CP-only data, respectively.Using CP+WP data was also shown to be beneficial after a large number of WP data were received.Using CP data to supplement WP data helped the classifiers to reduce or prevent large drops in predictive performance that may occur over time, leading to absolute G-Mean improvements of up to 37.35% and 48.16% compared to WP-only and CP-only data during such periods, respectively.During periods of stable predictive performance, absolute improvements were of up to 29.03% and up to 41.25% compared to WP-only and CP-only classifiers, respectively.Our results highlight the importance of using both CP and WP data together in realistic online JIT-SDP scenarios.
Design and Implementation of Efficient Quantum Support Vector Machine

Machine Learning (ML) is arguably the most ad-vanced sub-field of Artificial Intelligence (AI). It concerns the study of computer algorithms that can improve automatically by learning from data (experience) without being explicitly pre-programmed. On the other hand, the quantum version of ML, Quantum Machine Learning (QML), forms one of the most crucial and recent quantum computing applications. It uses quantum mechanical principles such as superposition, interference, and tunneling to enable quantum computers to learn from data. Some examples of QML algorithms include Quantum Principal Component Analysis (QPCA) and Quantum Support Vector Machine (QSVM). The QSVM is a robust supervised machine learning algorithm used for classification and regression. Therefore, this paper discusses the design and implementation of an efficient QSVM model. The model&#x0027;s efficiency is achieved using Principal Component Analysis (PCA) on the classical data, then loading the classical data into a quantum computer. Experimental results demonstrate the significance and application of the QSVM model reported in this paper.
Robust Clustering Model Based on Attention Mechanism and Graph Convolutional Network

GCN-based clustering schemes cannot interactively fuse feature information of nodes and topological structure information of graphs, leading to insufficient accuracy of clustering results. Moreover, the deep clustering model based on graph structure is vulnerable to the attack of adversarial samples leading to the reduced robustness of the model. To solve the above two problems, this paper proposes a robust clustering model based on attention mechanism and graph convolutional network (GCN), named AG-cluster. This model firstly uses graph attention network and GCN to learn the feature information of nodes and the topological structure information of graphs, respectively. Then the representation results of the above two learning modules are interactively fused by the interlayer transfer operator. Finally, the model is trained end-to-end using a self-supervised training module to optimize the clustering results of the model. In particular, an efficient graph purification defense mechanism (GPDM) is designed to resist adversarial attacks on graph data to improve the robustness of the model. Experimental results show that AG-cluster outperforms the other four benchmark methods, specifically, AG-cluster improves 7.6% in Accuracy and 11.5% in NMI compared to the best benchmark method. Besides, the new model still shows higher robustness and stronger transferability under multiple attacks.
Real-Time Indian Sign Language Recognition System using YOLOv3 Model

Sign Language is a language which helps deaf and mute people for communication with hearing people. The aim of Indian Sign Language recognition (ISLR) is to understand the meaning of signs of speech impaired or hearing impaired person in the Indian region to interact with the society. This paper proposes for ISLR system in real-time based on the YOLOv3 Model and used in conjunction with Darknet-53 convolutional neural network. The system has been tested in real-time with 16 different signs for images and 7 signs for videos. The proposed model was labeled the sign language datasets in YOLO format. The sign language images are captured by the webcam for static sign language recognition and videos are recorded for dynamic sign language recognition. We achieved the accuracies for static and dynamic signs as 95.7% and 93.1%, respectively. The experimental results show that the proposed system can recognize both static and dynamic ISL signs effectively in real time with reduced computational time.
Guided Anchoring Cascade R-CNN: An intensive improvement of R-CNN in Vietnamese Document Detection

Along with the development of the world, digital documents are gradually replacing paper documents. Therefore, the need to extract information from digital documents is increasing and becoming one of the main interests in the field of computer vision, particularly reading comprehension of image documents. The problem of object detection on image documents (figures, tables, formulas) is one of the premise problems for analyzing and extracting information from documents. Previous studies have mostly focused on English documents. In this study, we now experiment on a Vietnamese image document dataset UIT-DODV, which includes four classes: Table, Figure, Caption and Formula. We test on common state-of-the-art object detection models such as Double-Head R-CNN, Libra R-CNN, Guided Anchoring and achieved the highest results with Guided Anchoring of 73.6% mAP. Besides, we assume that high-quality anchor boxes are keys to the success of an anchor-based object detection models, thus we decide to adopt Guided Anchoring in our research. Moreover, we attempt to raise the quality of the predicted bounding boxes by utilizing Cascade R-CNN architecture, which can afford this by its scheme, so that we can filter out as many confused bounding boxes as possible. Based on the initial evaluation results from the common state-of-the-art object detection models, we proposed an object detection model for Vietnamese image documents based on Cascade R-CNN and Guided Anchoring. Our proposed model has achieved up to 76.6% mAP, 2.1% higher than the baseline model on the UIT-DODV dataset.
Blind quality assessment of authentically distorted images

Blind image quality assessment (BIQA) of authentically distorted images is a challenging problem due to the lack of a reference image and the coexistence of blends of distortions with unknown characteristics. In this article, we present a convolutional neural network based BIQA model. It encodes the input image into multi-level features to estimate the perceptual quality score. The proposed model is designed to predict the image quality score but is trained for jointly treating the image quality assessment as a classification, regression, and pairwise ranking problem. Experimental results on three different datasets of authentically distorted images show that the proposed method achieves comparable results with state-of-the-art methods in intra-dataset experiments and is more effective in cross-dataset experiments.
Electromagnetic Pulse (EMP): A Study of General Trends, Simulation Analysis of E1 HEMP Coupling and Protection Strategies

Electromagnetic pulse (EMP) is considered as an element of electronic warfare (EW). EMP is an energy pulse that can damage electronic components. EMP events are considered as high impact low probability (HILP) events. In the current scenario, civil and military infrastructure are equally vulnerable to EMP attack whereas the risk of damage can be avoided by hardening the critical infrastructure. This paper will provide a study on EMP and its impact on modern society. To visualize the impact, simulation of E1 HEMP coupling on different cables is being performed using FEKO. The paper also includes protection strategies to harden vulnerable systems against EMP attack.
Virtual hands in VR

We use our hands every day: to grasp a cup of coffee, write text on a keyboard, or signal that we are about to say something important. We use our hands to interact with our environment and to help us communicate with each other without thinking about it. Wouldn't it be great to be able to do the same in virtual reality? However, accurate hand motions are not trivial to capture. In this course, we present the current state of the art when it comes to virtual hands. Starting with current examples for controlling and depicting hands in virtual reality (VR), we dive into the latest methods and technologies to capture hand motions. As hands can currently not be captured in every situation and as constraints stopping us from intersecting with objects are typically not available in VR, we present research on how to synthesize hand motions and simulate grasping motions. Finally, we provide an overview of our knowledge of how virtual hands are being perceived, resulting in practical tips on how to represent and handle virtual hands.
ConMan: A Connection Manipulation-based Attack Against Bitcoin Networking

Bitcoin is a representative cryptocurrency system using a permissionless peer-to-peer (P2P) network as its communication infrastructure. A number of attacks against Bitcoin have been discovered over the past years, including the Eclipse and EREBUS Attacks. In this paper, we present a new attack against Bitcoin’s P2P networking, dubbed ConMan because it leverages connection manipulation. ConMan achieves the same effect as the Eclipse and EREBUS Attacks in isolating a target (i.e., victim) node from the rest of the Bitcoin network. However, ConMan is different from these attacks because it is an active and deterministic attack, and is more effective and efficient. We validate ConMan through proof-of-concept exploitation in an environment that is coupled with real-world Bitcoin node functions. Experimental results show that ConMan only needs a few minutes to fully control the peer connections of a target node, which is in sharp contrast to the tens of days that are needed by the Eclipse and EREBUS Attacks. Further, we propose several countermeasures against ConMan. Some of them would be effective but incompatible with the design principles of Bitcoin, while the anomaly detection approach is positively achievable. We disclosed ConMan to the Bitcoin Core team and received their feedback, which confirms ConMan and the proposed countermeasures.
Static Type Inference for Foreign Functions of Python

Static type inference is an effective way to maintain the safety of programs written in a dynamically typed language. However, foreign functions implemented in another programming language are often outside the inference range. Python, a popular dynamically typed language, has a lot of widely used packages which follow the multilingual structure with C/C++ extension modules. Existing deterministic Python static type inference tools which are not based on type annotations can do nothing about these foreign functions. In this paper, we propose a novel method to infer the type signature of foreign functions by analyzing implicit information in the layer of foreign function interface. We design a static type inference system, its evaluation on CPython, NumPy and Pillow shows that our method soundly infers the number and type of arguments for most foreign functions. Our results can further work as a complement to the state-of-the-art Python static type inference tool and enable it to analyze programs with foreign function calls. We catch 48 bugs of mismatch between foreign function declaration and its implementation, which make a parameter-free foreign function take argument of any type. 8 of the bugs we reported have been confirmed and fixed by communities.
Dense view synthesis for three-dimensional light-field displays based on position-guiding convolutional neural network

Dense view synthesis from sparse views is a fundamental and significant research field, which has attracted considerable attention in Three-Dimensional (3D) light-field displays. As we known, capturing stereo views from binocular cameras is always a fast and convenient way in engineering applications. Synthesizing dense high-quality views from stereo views, however, is still a challenging problem. Here, to address this problem, a novel method of dense-view synthesis based on a position-guiding convolutional neural network (CNN) is proposed, which only uses left and right views as input when synthesizing novel virtual views. To handle the occluded areas on left-right views, the depth map of a middle view is exploited with a depth estimation CNN. Furthermore, a normalized position factor is proposed to adjust the depth information by scaling operation for guiding the dense-view synthesis in a view-rectifying CNN. The experiments show that our approach could synthesize high-quality dense views and the SSIM is above 0.94. Besides, the synthesized dense views are presented on our light-field device and perform continuous and reasonable occlusion relations. We think our work can find wide applications of 3D light-field displays in the future.
Hand Landmark-Based Sign Language Recognition Using Deep Learning

John, Jerry Sherif, Bismin V.As human being’s communication is a very crucial part of our life, it is essential for sharing thoughts and ideas for our survival. But unfortunately for the speaking and hearing-impaired minority, it is difficult or impossible to communicate through speech. But most of the common people have no knowledge about sign language and its interpretations. This is one of the major problems faced by these kinds of people during their communications. Getting an interpreter is not easy every time. To solve this issue, a model is developed using neural networks for finger-spelling based on the various hand gestures. In this user-independent model, CNN-based models are trained using a set of image and hand skeleton datasets. The skeleton dataset is created using the hand image dataset and is done to improve the accuracy of the model. By using this model, a speech-impaired person can easily communicate with a person who has no knowledge of it, the model translating the hand gestures to sentences in English. The main issue with the current system is that there are some groups of alphabets, whose sign language symbols look alike. This makes the image-based classification model a bit difficult to correctly classify and the accuracy of the prediction can also be low. This issue can be solved using a hand skeleton classifier. As in the hand skeleton image, the position and structure of each finger can be identified more accurately than from an image. This makes the neural network model to learn better and can classify with higher accuracy of about 98.6.