Reviewerly/exHarmony huggingface dataset:
Implementing Blockchain Technology: Way to Avoid Evasive Threats to Information Security on Cloud

The research paper is about the dangers to information security and a solution to it by the use of the blockchain implementation in the data security. Blockchain security implementations were mostly witnessed in sensitive information systems, like the financial database. Furthermore, in this research paper, Threats to computers and information systems are highlighted and analyzed and the solution to them well elaborated. The cloud computing raised many security concerns, that had to be addressed and solutions found in curbing the information insecurity. The paper was majorly based on the step by step solution to the threat to the information system by blockchain technology.
Lyceum: An efficient and scalable ecosystem for robot learning

We introduce Lyceum, a high-performance computational ecosystem for robot learning. Lyceum is built on top of the Julia programming language and the MuJoCo physics simulator, combining the ease-of-use of a high-level programming language with the performance of native C. In addition, Lyceum has a straightforward API to support parallel computation across multiple cores and machines. Overall, depending on the complexity of the environment, Lyceum is 5-30x faster compared to other popular abstractions like OpenAI's Gym and DeepMind's dm-control. This substantially reduces training time for various reinforcement learning algorithms; and is also fast enough to support real-time model predictive control through MuJoCo. The code, tutorials, and demonstration videos can be found at: this http URL.
Deep learning based target detection algorithm for motion capture applications

Abstract Motion capture technology is the use of external devices to perform data recording and posture reproduction of the displacement of human structures. Deep learning algorithms are playing an increasingly important role in motion capture technology as the technology involves data that can be directly understood and processed by computers in terms of dimensional measurements, positioning of objects in physical space, and orientation determination. This paper presents an application of a convolutional neural network system, YOLO-V4, in the field of motion capture. YOLO-V4 system weight files are small and do not require high hardware requirements. It can also be implemented in PyTorch so that it can be deployed on mobile devices, enabling edge devices to run these models as well, relieving the space constraint of immovable signal capture devices and providing the advantages of high accuracy and high detection rate.
A Framework For Electronic Health Record Using Blockchain Technology

Blockchain technologies are gaining massive impel in the present digital era. The blockchain is a data structure in which the consequent data is appended to the existing data and is maintained by all the participants of the network. The participants are also called as nodes, where the nodes in the blockchain have an agreement in common on the order of the blocks which contains several transactions based on the tool or platform used by the application. Hence, the blockchain is considered as ordered chronological transactions. The health industries are currently focusing on the blockchain technology to maintain the health data in a more secure, confident and decentralized, as in the current record management system they unable to manage the privacy and integrity. The proposed work is to frame an architecture for the Electronic Health Record (EHR) using blockchain technology. The proposed system is a twofold process where first we focus on the block creation and secondly secure the data using Elliptic Curve Cryptography (ECC) Algorithm.
Gradient-Based Adversarial Image Forensics

Adversarial images which can fool deep neural networks attract researchers' attentions to the security of machine learning. In this paper, we employ a blind forensic method to detect adversarial images which are generated by the gradient-based attacks including FGSM, BIM, RFGSM and PGD. Through analyzing adversarial images, we find out that the gradient-based attacks cause significant statistical changes in the image difference domain. Besides, the gradient-based attacks add different perturbations on R, G, B channels, which inevitably change the dependencies among R, G, B channels. To measure those dependencies, the $$3^{rd}$$ -order co-occurrence is employed to construct the feature. Unlike previous works which extract the co-occurrence within each channel, we extract the co-occurrences across from the $$1^{st}$$ -order difference of R, G, B channels to capture the inter dependence changes. Due to the shift of difference elements caused by attacks, some co-occurrence elements of the adversarial images have distinct larger values than those of legitimate images. Experimental results demonstrate that the proposed method performs stable for different attack types and different attack strength, and achieves detection accuracy up to 99.9% which exceeding state-of-the-art much.
Blind Interleaver Parameter Estimation From Scant Data

A method for blind estimation of interleaver parameter was recently reported which made additional data from a limited amount of received data.However, the process of making additional data creates undesirable linearity which degrades estimation performance.Promise for improved estimation therefore lies in enhancing blind estimation of interleaver parameter without making additional data.In this paper, we propose an improved method to blindly estimate interleaver parameter under the condition of scant data.We first generate a matrix by using the received data.From this matrix we then make square submatrices and obtain their rank deficiency distribution.Finally, we estimate the interleaver parameter by comparing the rank deficiency distribution of the square submatrices and that of random binary matrices.Through computer simulations, we validate the proposed method in terms of detection probability and the number of false alarms.Simulation results show that the proposed method works better than the conventional method given scarce received data.
Experiential learning with virtual reality: animal handling training

Abstract Training in handling laboratory animals is fundamentally imperative to the responsible use of animals in research. Animal welfare topic is underdeveloped in the tertiary education, where instruction is majorly delivered in the format of lecture and group discussion only. Students with limited exposure to the laboratory were inattentive to animal welfare and uncertain how ethics intertwine with science. This paper describes a multi-disciplinary experience in developing and implementing virtual reality (VR) simulation to enhance contextual learning of using animal models in research with digital technology in biomedical science teaching at higher education. The in-house developed courseware consists of student-centred stimulations designed with game elements implemented at the tutorial session. At the first game level, the setting situates at in the preparation room that requires learners to apply the laboratory safety knowledge to wear personal protective equipment. At the second game level, the environment situates at the restricted experimental room to perform hands-on injections on mice. If the learner fails to pick up appropriate safety equipment at the first level, the learner is prohibited from entering the next level. During the simulation, the learner’s interaction is also displayed to the monitor that supports parallel teaching to the larger class. At the debriefing, 3Rs principles were reinforced as a sample framework for performing humane animal research. We illustrate how the hybrid uses of VR technology with gamification, together with didactic pedagogy, offers promise in enforcing working knowledge into better task performance, specifically research skills training. Our experience and students’ feedback show using immersive VR for educational purposes to encourage the learner applying conceptual knowledge in the simulated laboratory setting. Further application of VR in science for vocational training or higher education is feasible to engage students or stakeholders from various disciplines.
Fault-Tolerant Architecture Design for Blockchain-Based Electronics Voting System

With an emergence of distributed ledger technology, decentralization and integrity in information storage offer an opportunity to preserve data in electronic voting systems. An inevitable challenge in e-voting system implementation is the tradeoff between system security and generality in design. Leveraging container technology along with blockchain technology, this paper proposes a system design architecture to deliver data security and agility in system integration. The design was further analyzed and assessed by measuring the utilization of computing resources and conducting performance test as to validate that the aforementioned requirements were met.
Non-central refractive camera calibration using co-planarity constraints for a photogrammetric system with an optical sphere cover

Optical sphere covers are widely used in photogrammetric systems to protect camera systems from external conditions. The spherical refraction can cause severe non-linear distortion, resulting in imprecise and unreliable measurements. In particular the cameras with a relatively thick optical sphere cover (e.g. underground or underwater cameras). However, the conventional perspective camera model are not suitable for such spherical refraction calibration problem. This study presents a novel non-central refractive calibration technique for cameras with an optical sphere cover. Considering the general situation in which the camera optical axis could not pass through the spherical center, a universal spherical refraction geometric model was established with coplanarity constraints. The model introduces modified perspective center and enables the spherical refraction can be integrated into the equivalent collinearity equations, which make it possible to describe explicitly the correspondences between the image points and object coordinates. Moreover, a calibration technique is proposed to estimate relative orientation parameters. With no need to gain explicit knowledge of object space coordinates, the spherical refraction calibration can be performed with known refractive index as well as the radius of sphere cover. Numerical and experimental results demonstrated the proposed spherical refraction calibration model to be both effective and robust, which can achieve high levels of calibration accuracy.
Improving Adversarial Robustness by Enforcing Local and Global Compactness

The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application. Among many developed defense models against such attacks, adversarial training emerges as the most successful method that consistently resists a wide range of attacks. In this work, based on an observation from a previous study that the representations of a clean data example and its adversarial examples become more divergent in higher layers of a deep neural net, we propose the Adversary Divergence Reduction Network which enforces local/global compactness and the clustering assumption over an intermediate layer of a deep neural network. We conduct comprehensive experiments to understand the isolating behavior of each component (i.e., local/global compactness and the clustering assumption) and compare our proposed model with state-of-the-art adversarial training methods. The experimental results demonstrate that augmenting adversarial training with our proposed components can further improve the robustness of the network, leading to higher unperturbed and adversarial predictive performances.
Triaxial accelerometer-based human activity recognition using 1D convolution neural network

Deep learning has been instrumental for human activity recognition (HAR). In spite of its strong potential, significant challenges exist, wherein the real case, deep learning model requires a massive dataset for training. However, existing research require an improvement to classify static and dynamic activity with more significant achievement. To address such challenges, we proposed a model utilizing 1-dimensional Convolution Neural Network (CNN) to classify static and dynamic activity using public dataset. The proposed scheme in this study has been conducted (through experiments), in which the result denotes the state-of-the-art methods, obtaining better performance than others.
The Stratic Defuzzifier for discretised general type-2 fuzzy sets

Stratification is a feature of the type-reduced set of the general type-2 fuzzy set, from which a new technique for general type-2 defuzzification, Stratic Defuzzification, may be derived. Existing defuzzification strategies are summarised. The stratified structure is described, after which the Stratic Defuzzifier is presented and contrasted experimentally for accuracy and efficiency with both the Exhaustive Method of Defuzzification (to benchmark accuracy) and the α-Planes/Karnik–Mendel Iterative Procedure strategy, employing 5, 11, 21, 51 and 101 α-planes. The Stratic Defuzzifier is shown to be much faster than the Exhaustive Defuzzifier. In fact the Stratic Defuzzifier and the α-Planes/Karnik–Mendel Iterative Procedure Method are comparably speedy; the speed of execution correlates with the number of planes participating in the defuzzification process. The accuracy of the Stratic Defuzzifier is shown to be excellent. It is demonstrated to be more accurate than the α-Planes/Karnik–Mendel Iterative Procedure Method in four of six test cases, regardless of the number of α-planes employed. In one test case, it is less accurate than the α-Planes/Karnik–Mendel Iterative Procedure Method, regardless of the number of α-planes employed. In the remaining test case, the α-Planes/Karnik–Mendel Iterative Procedure Method with 11 α-Planes gives the most accurate result, with the Stratic Defuzzifier coming second.
Accelerated Hardware Implementation of BLAKE2 Cryptographic Hash for Blockchain

Interest in the efficient implementations of hashing algorithms has received even more attention recently due to the growing application-space for blockchain technology. Consequently, a pressing need has arisen to develop high-performance hashing techniques to support these developments. In this paper, an architecture and VLSI implementation of the newest hashing standard, BLAKE2, is presented. The Equihash algorithm serves as a use case for BLAKE2 in Blockchain. An FPGA-based custom computing machine (FCCM) approach is used. This approach is producing an accelerated Equihash hashing-core system which is 5x faster than an ARM Cortex-A9 based system on a ZC706 SoC chip for 0.27 W. Indeed, this is the first BLAKE2 implementation allowing fast execution, and effective substitution of any previous hash families implementations.
Robust High Dynamic Range (HDR) Imaging with Complex Motion and Parallax

High dynamic range (HDR) imaging is widely used in consumer photography, computer game rendering, autonomous driving, and surveillance systems. Reconstructing ghosting-free HDR images of dynamic scenes from a set of multi-exposure images is a challenging task, especially with large object motion, disparity, and occlusions, leading to visible artifacts using existing methods. In this paper, we propose a Pyramidal Alignment and Masked merging network (PAMnet) that learns to synthesize HDR images from input low dynamic range (LDR) images in an end-to-end manner. Instead of aligning under/overexposed images to the reference view directly in pixel-domain, we apply deformable convolutions across multiscale features for pyramidal alignment. Aligned features offer more flexibility to refine the inevitable misalignment for subsequent merging network without reconstructing the aligned image explicitly. To make full use of aligned features, we use dilated dense residual blocks with squeeze-and-excitation (SE) attention. Such attention mechanism effectively helps to remove redundant information and suppress misaligned features. Additional mask-based weighting is further employed to refine the HDR reconstruction, which offers better image quality and sharp local details. Experiments demonstrate that PAMnet can produce ghosting-free HDR results in the presence of large disparity and motion. We present extensive comparative studies using several popular datasets to demonstrate superior quality compared to the state-of-the-art algorithms.
Are Deep Neural Architectures Losing Information? Invertibility is Indispensable

Ever since the advent of AlexNet, designing novel deep neural architectures for different tasks has consistently been a productive research direction. Despite the exceptional performance of various architectures in practice, we study a theoretical question: what is the condition for deep neural architectures to preserve all the information of the input data? Identifying the information lossless condition for deep neural architectures is important, because tasks such as image restoration require keep the detailed information of the input data as much as possible. Using the definition of mutual information, we show that: a deep neural architecture can preserve maximum details about the given data if and only if the architecture is invertible. We verify the advantages of our Invertible Restoring Autoencoder (IRAE) network by comparing it with competitive models on three perturbed image restoration tasks: image denoising, JPEG image decompression and image inpainting. Experimental results show that IRAE consistently outperforms non-invertible ones. Our model even contains far fewer parameters. Thus, it may be worthwhile to try replacing standard components of deep neural architectures with their invertible counterparts. We believe our work provides a unique perspective and direction for future deep learning research.
Creation of a Serious Game for Teaching Code Review: An Experience Report

Code review, a manual inspection of source code by developers other than the author, is a frequently used practice for improving code quality in the software development lifecycle.Employing a tool-based review of code changes has become the norm for a wide variety of open source and industrial systems.Despite its widespread usage and importance, software development practices such as code review are often not addressed in typical Software Engineering education.To address this knowledge gap, we propose to use a serious game approach for teaching code review practices.In this study, we define our learning objectives and design a code review serious game along with its companion quizzes.Then we conduct a small preliminary experiment in order to procure feedback.Using the results of the experiment and participant interviews, we improve our game prototype for integration into a software engineering course while optimizing the initial experiment for student's benefit.We document the process, lessons learned and the future directions of the game.The results we gather indicate that the game is ready to be used in a software engineering course setting.
Interactive Machine Learning of Musical Gesture

This chapter presents an overview of Interactive Machine Learning (IML) techniques applied to the analysis and design of musical gestures. We go through the main challenges and needs related to capturing, analysing, and applying IML techniques to human bodily gestures with the purpose of performing with sound synthesis systems. We discuss how different algorithms may be used to accomplish different tasks, including interacting with complex synthesis techniques and exploring interaction possibilities by means of Reinforcement Learning (RL) in an interaction paradigm we developed called Assisted Interactive Machine Learning (AIML). We conclude the chapter with a description of how some of these techniques were employed by the authors for the development of four musical pieces, thus outlining the implications that IML has for musical practice.
An Alternative Approach for SIDH Arithmetic

In this paper, we present new algorithms for the field arithmetic layers of supersingular isogeny Diffie-Hellman; one of the fifteen remaining candidates in the NIST post-quantum standardization process. Our approach uses a polynomial representation of the field elements together with mechanisms to keep the coefficients within bounds during the arithmetic operations. We present timings and comparisons for SIKEp503 and suggest a novel 736-bit prime that offers a \(1.17\times \) speedup compared to SIKEp751 for a similar level of security.
Variational Diffusion Autoencoders with Random Walk Sampling

Variational autoencoders (VAEs) and generative adversarial networks (GANs) enjoy an intuitive connection to manifold learning: in training the decoder/generator is optimized to approximate a homeomorphism between the data distribution and the sampling space. This is a construction that strives to define the data manifold. A major obstacle to VAEs and GANs, however, is choosing a suitable prior that matches the data topology. Well-known consequences of poorly picked priors are posterior and mode collapse. To our knowledge, no existing method sidesteps this user choice. Conversely, diffusion maps automatically infer the data topology and enjoy a rigorous connection to manifold learning, but do not scale easily or provide the inverse homeomorphism (i.e. decoder/generator). We propose a method ( https://github.com/lihenryhfl/vdae ) that combines these approaches into a generative model that inherits the asymptotic guarantees of diffusion maps while preserving the scalability of deep models. We prove approximation theoretic results for the dimension dependence of our proposed method. Finally, we demonstrate the effectiveness of our method with various real and synthetic datasets.
Minimal Solutions for Panoramic Stitching Given Gravity Prior

When capturing panoramas, people tend to align their cameras with the vertical axis, i.e., the direction of gravity. Moreover, modern devices, e.g. smartphones and tablets, are equipped with an IMU (Inertial Measurement Unit) that can measure the gravity vector accurately. Using this prior, the y-axes of the cameras can be aligned or assumed to be already aligned, reducing the relative orientation to 1-DOF (degree of freedom). Exploiting this assumption, we propose new minimal solutions to panoramic stitching of images taken by cameras with coinciding optical centers, i.e. undergoing pure rotation. We consider six practical camera configurations, from fully calibrated ones up to a camera with unknown fixed or varying focal length and with or without radial distortion. The solvers are tested both on synthetic scenes, on more than 500k real image pairs from the Sun360 dataset, and from scenes captured by us using two smartphones equipped with IMUs. The new solvers have similar or better accuracy than the state-of-the-art ones and outperform them in terms of processing time.
An Improved Hough Transform for Circle Detection using Circular Inscribed Direct Triangle

Considering that the classical Hough transform for circle detection has poor real-time performance because of massive computation and memory consumption caused by accumulative voting in three-dimensional parameter space, this paper presents an improved Hough circle detection algorithm using circular inscribed direct triangle. The algorithm reduces the three-dimensional parameter space needed by classical Hough transform to two-dimensional parameter space by utilizing the geometrical characteristics of circle. By traversing an s pixels only once, the coordinate points which may be centers of candidate circles are voted, and the radius corresponding to the coordinate points whose number of votes exceeds the user-defined threshold is taken out. Finally, the false circles are eliminated, and all real circles in the image are detected. The experimental results show the algorithm has fast speed, high accuracy and good anti-noise performance in contrast with the classical Hough circle detection method.