Reviewerly/exHarmony huggingface dataset:
Design and Development of Collaborative AR System for Anatomy Training

Background: Augmented Reality (AR) incorporates both real and virtual objects in real-time environments and allows single and multi-users to interact with 3D models.It is often tricky to adopt multi-users in the same environment because of the devices' latency and model position accuracy in displaying the models simultaneously.Method: To address this concern, we present a multi-user sharing technique in the AR of the human anatomy that increases learning with high quality, high stability, and low latency in multiple devices.Besides, the multi-user interactive display (HoloLens) merges with the human body anatomy application (AnatomyNow) to teach and train students, academic staff, and hospital faculty in the human body anatomy.We also introduce a pin system to share the same knowledge between multiple users.A total of 5 groups have been considered for the study and the evaluation has been performed using two parameters: latency between the devices and position accuracy of the 3D objects in the same environment.Results: The proposed multi-user interaction technique is observed to be good in terms of 3D object position accuracy and latency between the devices.Conclusion: AR technology provides a multi-user interactive environment for teaching and training in the human body anatomy.
Expecting the unexpected: Goal recognition for rational and irrational agents

Contemporary cost-based goal-recognition assumes rationality: that observed behaviour is more or less optimal. Probabilistic goal recognition systems, however, explicitly depend on some degree of sub-optimality to generate probability distributions. We show that, even when an observed agent is only slightly irrational (sub-optimal), state-of-the-art systems produce counter-intuitive results (though these may only become noticeable when the agent is highly irrational). We provide a definition of rationality appropriate to situations where the ground truth is unknown, define a rationality measure (RM) that quantifies an agent's expected degree of sub-optimality, and define an innovative self-modulating probability distribution formula for goal recognition. Our formula recognises sub-optimality and adjusts its level of confidence accordingly, thereby handling irrationality—and rationality—in an intuitive, principled manner. Building on that formula, moreover, we strengthen a previously published result, showing that "single-observation" recognition in the path-planning domain achieves identical results to more computationally expensive techniques, where previously we claimed only to achieve equivalent rankings though values differed.
RelightGAN: Instance-level Generative Adversarial Network for Face Illumination Transfer

Face illumination perception and processing is a significantly difficult issue especially due to asymmetric shadings, local highlights, and local shadows. This study focuses on the face illumination transfer problem, which is to transfer the illumination style from a reference face image to a target face image while preserving other attributes. Such an instance-level transfer task is more challenging than the domain-level one that only considers the pre-defined lighting categories. To tackle this problem, we develop an instance-level conditional Generative Adversarial Networks (GAN). Specifically, face identifier is integrated into GAN learning, which enables an individual-specific low-level visual generation. Moreover, the illumination-inspired attention mechanism is conducted to allow GAN to well handle the local lighting effect. Our method requires neither lighting categorization, 3D information, nor strict face alignment, which are often employed by traditional methods. Experiments demonstrate that our method achieves significantly better results than previous methods.
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution

Recent deep learning based single image super-resolution (SISR) methods mostly train their models in a clean data domain where the low-resolution (LR) and the high-resolution (HR) images come from noise-free settings (same domain) due to the bicubic down-sampling assumption. However, such degradation process is not available in real-world settings. We consider a deep cyclic network structure to maintain the domain consistency between the LR and HR data distributions, which is inspired by the recent success of CycleGAN in the image-to-image translation applications. We propose the Super-Resolution Residual Cyclic Generative Adversarial Network (SRResCycGAN) by training with a generative adversarial network (GAN) framework for the LR to HR domain translation in an end-to-end manner. We demonstrate our proposed approach in the quantitative and qualitative experiments that generalize well to the real image super-resolution and it is easy to deploy for the mobile/embedded devices. In addition, our SR results on the AIM 2020 Real Image SR Challenge datasets demonstrate that the proposed SR approach achieves comparable results as the other state-of-art methods.
Reservoir computing dissection and visualization based on directed network embedding

The reservoir computing (RC) has recently gained considerable attention in practice and many methods have been developed to study its internal mechanism. However, the specific role played by the reservoir nodes of RC in time series prediction is still to be defined. An interpretable RC model wherein its reservoir network is designated as the directed acyclic network (DAN) is proposed with focus on time series prediction in this paper. In virtue of asymmetric transitivity and hierarchical structure of DAN, we present a directed network embedding method to identify the latent memory property of each node in the DAN. Such memory property is utilized to characterize the roles played by the reservoir nodes on the prediction performance of the RC. Meanwhile, it can also be leveraged to identify the corresponding memory community of DAN. As a result, we demonstrate how the reservoir network structure takes effect on the prediction performance from the perspective of memory community. In addition, two novel hyperparameters with the deterministic meaning are introduced to quantify the influence of the model initialization on the reservoir input so as to facilitate further dissection of the interpretable RC. The experimental results indicate that tuning these hyperparameters, which is explicable in terms of the Taylor expansion of the activation function, serves as an essential step in achieving superior prediction performance. Finally, comparative experiments with some other RC models on various time series benchmarks are also conducted.
Image Matching Algorithm based on ORB and K-Means Clustering

With the rapid development of science and technology, image processing technology plays an important role in the field of computer vision. In order to improve the matching speed and real-time requirements, this paper proposes an image matching algorithm based on ORB and K-means clustering, which can effectively improve the accuracy of image feature point location and the accuracy and efficiency of image feature matching, and reduce the time consumption. The algorithm uses sub-pixel interpolation to optimize the traditional ORB algorithm, which improves the accuracy and characteristics of clustering calculation.
Using Blockchain and Smart Contract for Traceability in Agricultural Products Supply Chain

The current supply chain of agricultural products is a complex ecosystem involving multiple stakeholders, which makes it difficult to verify several important characteristics, such as the origin of products, the growth stage and quality of crops. In this paper, we propose a method to efficiently execute agricultural products transaction by using consortium blockchain and smart contract, so as to track agricultural products in the whole supply chain. Our solution eliminates the need for trusted centralized authorities and agents, and provides trusted transaction records to improve the integrity, reliability and security of transactions. The proposed solution focuses on the use of smart contracts to manage and control all interactions and transactions between all participants in the supply chain. All transactions are recorded and stored in the immutable ledger of the blockchain, thus providing a high level of transparency and traceability to all supply chain ecosystems in a secure, credible, reliable and efficient manner.
Industry Use Cases on Blockchain Technology

Digital transformation strengthens the interconnection of companies in order to develop optimized and better customized, cross-company business models. These models require secure, reliable, and traceable evidence and monitoring of contractually agreed information to gain trust between stakeholders. Blockchain technology using smart contracts allows the industry to establish trust and automate cross-company business processes without the risk of losing data control. A typical cross-company industry use case is equipment maintenance. Machine manufacturers and service providers offer maintenance for their machines and tools in order to achieve high availability at low costs. The aim of this chapter is to demonstrate how maintenance use cases are attempted by utilizing hyperledger fabric for building a chain of trust by hardened evidence logging of the maintenance process to achieve legal certainty. Contracts are digitized into smart contracts automating business that increase the security and mitigate the error-proneness of the business processes.
Generative adversarial networks for data augmentation and transfer in credit card fraud detection

Augmenting a dataset with synthetic samples is a common processing step in machine learning with imbalanced classes to improve model performance. Another potential benefit of synthetic data is the ability to share information between cooperating parties while maintaining customer privacy. Often overlooked, however, is how the distribution of the data affects the potential gains from synthetic data augmentation. We present a case study in credit card fraud detection using Generative Adversarial Networks to generate synthetic samples, with explicit consideration given to customer distributions. We investigate two different cooperating party scenarios yielding four distinct customer distributions by credit quality. Our findings indicate that institutions skewed towards higher credit quality customers are more likely to benefit from augmentation with GANs. Relative gains from synthetic data transfer, in the absence of feature set heterogeneity, also appear to asymmetrically favour banks operating on the lower end of the credit spectrum, which we hypothesise is due to differences in spending behaviours.
General Reuse-Centric CNN Accelerator

This article introduces the first <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">general reuse-centric accelerator</i> for CNN inferences. Unlike prior work that exploits similarities only across consecutive video frames, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">general reuse-centric accelerator</i> is able to discover similarities among arbitrary patches within an image or across independent images, and translate them into computation time and energy savings. Experiments show that the accelerator complements both prior software-based CNN and various CNN hardware accelerators, producing up to 14.96X speedups for similarity discovery, up to 2.70X speedups for overall inference.
Multi-spectral Facial Landmark Detection

Thermal face image analysis is favorable for certain circumstances. For example, illumination-sensitive applications, like nighttime surveillance; and privacy-preserving demanded access control. However, the inadequate study on thermal face image analysis calls for attention in responding to the industry requirements. Detecting facial landmark points are important for many face analysis tasks, such as face recognition, 3D face reconstruction, and face expression recognition. In this paper, we propose a robust neural network enabled facial landmark detection, namely Deep Multi-Spectral Learning (DMSL). Briefly, DMSL consists of two sub-models, i.e. face boundary detection, and landmark coordinates detection. Such an architecture demonstrates the capability of detecting the facial landmarks on both visible and thermal images. Particularly, the proposed DMSL model is robust in facial landmark detection where the face is partially occluded, or facing different directions. The experiment conducted on Eurecom's visible and thermal paired database shows the superior performance of DMSL over the state-of-the-art for thermal facial landmark detection. In addition to that, we have annotated a thermal face dataset with their respective facial landmark for the purpose of experimentation.
Towards suitable description of reference architectures

Due to the increasing size and complexity of many current software systems, the architectural design of these systems has become a considerately complicated task. In this scenario, reference architectures have already proven to be very relevant to support the architectural design of systems in diverse critical application domains, such as health, avionics, transportation, and the automotive sector. However, these architectures are described in many different approaches, such as using textual description, informal models, and even modeling languages as UML. Hence, practitioners are faced with a difficult decision of the better approaches to describing reference architectures. The main contribution of this work is to depict a detailed panorama containing the state of the art (from the literature) and state of the practice (based on existing reference architectures) of approaches for describing reference architectures. For this, we firstly examined the existing approaches (e.g., processes, methods, models, and modeling languages) and compared them concerning completeness and applicability. We also examined four well-known, successful reference architectures (AUTOSAR, ARC-IT, IIRA, and AXMEDIS) in view of the approaches used to describe them. As a result, there exists a misalignment between the state of the art and state of the practice, requiring an engagement of the software architecture community, through research collaboration of academia and industry, to propose more suitable means to describe reference architectures and, as a consequence, promoting the sustainability of these architectures.
Uncertainty-Constrained Differential Dynamic Programming in Belief Space for Vision Based Robots

Most mobile robots follow a modular sense-plan-act system architecture that can lead to poor performance or even catastrophic failure for visual inertial navigation systems due to trajectories devoid of feature matches. Planning in belief space provides a unified approach to tightly couple the perception, planning and control modules, leading to trajectories that are robust to noisy measurements and disturbances. However, existing methods handle uncertainties as costs that require manual tuning for varying environments and hardware. We therefore propose a novel trajectory optimization formulation that incorporates inequality constraints on uncertainty and a novel Augmented Lagrangian based stochastic differential dynamic programming method in belief space. Furthermore, we develop a probabilistic visibility model that accounts for discontinuities due to feature visibility limits. Our simulation tests demonstrate that our method can handle inequality constraints in different environments, for holonomic and nonholonomic motion models with no manual tuning of uncertainty costs involved. We also show the improved optimization performance in belief space due to our visibility model.
An encryption based on DNA and AES algorithms for hiding a compressed text in colored Image

Abstract The vast serious security challenges has created new secured ways of transmitting information and reduce the chance of hackers to use their methods to analyse the confidential information between two parties. The research combines between several stages of encryption techniques: DNA encryption algorithm, GZIP algorithm, AES cryptography and image steganography. We proposed multiplying by a factor along the last stage of DNA encryption, and the outputs of this process are compressed using the GZIP algorithm, where the message is converted to a new form and its size reduced to (75%), then after the message is encrypted using the AES encryption algorithm to increase the level of security. Furthermore, LSB image Steganography technology is utilized to hide the encrypted message in a high-quality image. By combining these four security technologies we obtained good results for image measures such that Lina sample provided (PSNR is equal 67.589, MSE is equal 0.0116, SSIM is equal 1, NPCR score is 0.011630262741374 and UACI score is 4.5608873495583733e-05) and we ensure transferring data without gaps &amp; make it difficult and more complex for the hacker.
A Fast Deep Learning Based Approach for Basketball Video Analysis

The aim of this study is to obtain an algorithm to analyze a basketball training in real time. That is, the approach should be able to detect the correct players and scores for each player. For this purpose, we first propose a method to detect the shots and if they are makes or misses. Specifically, we use a deep learning model to detect the position of the hoop and background subtraction to detect the shot. Then, scoring is determined by another neural network using classification. After the determination of the shot, we go back in our buffered images to find the player who sent the shot. The player is detected by YOLOv3-tiny and identified by a deep unsupervised reidentification model. To keep the real-time aim, all models employ mobile networks as base with different parameters and training methods. The experiments were conducted on two training videos. The experiment results show the effectiveness of the current method with over 95% accuracy on scoring identification and up to 91.4% overall accuracy on reidentification of 4 players in real-time.
Aiming at methods’ wider adoption: Applicability determinants and metrics

Numerous computer science methods and techniques have been proposed by the scientific community. However, depending on the domain, only their minor fraction has met wider adoption. This paper brings attention to the concept of applicability — the notion that is well acknowledged in the scientific field but have not been analysed with respect to determinants, metrics and systematisation. The primary objective of the study was to identify applicability determinants and metrics and consolidate them into a taxonomy, based on a systematic process. To achieve the objective, a methodological literature review supported with operationalisation activities were applied. As a result, more than thirty descriptors of applicability were introduced in the proposed applicability taxonomy, and a similar number of metrics has been elicited. Based on them, a questionnaire for evaluation of the method’s applicability was created and applied to the evaluation of a currently developed cybersecurity risk assessment method that aims at broader market uptake. The analysis provided indications on its further developments, but also findings of a more universal character.
Exploring Interoperability of Blockchain Technology and the Possibility of Collaboration with the Existing Information Systems of the Enterprises

Blockchain technology is information technology that provokes research in various scientific fields, including information technology, humanitarian and economic. One of the issues that needs wide discussion is the interoperability of Blockchain technology, both between the individual crypto projects and the ability to integrate and communicate with existing information systems of companies. The interoperability study is necessary due to the fact that the migration to a new information system is associated with high costs related to the development, implementation and training of employees. It is for this reason that it is appropriate to analyze the prerequisites for interoperability between existing information systems and the ability to communicate through blockchain architectures.
Comparing footwear impressions that are close non‐matches using correlation‐based approaches

Abstract Forensic activities related to footwear evidence may be broadly classified into the following two categories: (1) intelligence gathering and (2) evidential value assessment. Intelligence gathering provides additional leads for investigators. Assessment of evidential value, as practiced in the United States, involves a trained footwear examiner evaluating the degree of similarity between a known shoe of interest (together with its test impressions) and footwear impressions obtained from a crime scene, by performing side‐by‐side visual comparisons. However, the need for developing quantitative approaches for expressing similarities during such comparisons is being increasingly recognized by the forensic science community. In this paper, we explore the ability of similarity metrics to discriminate between impressions made by a shoe of interest and impressions made by close non‐matching shoes. Close non‐matching shoes largely share the same design and size. Therefore, the ability to effectively discriminate between them requires considering, either explicitly or implicitly, not only design and size, but also wear patterns and, to some extent, individual characteristics. This type of discrimination is necessary for assessment of evidential value. The similarity metrics examined in this paper are correlation‐based metrics, including normalized cross‐correlation, phase‐only correlation, AvNCC, and AvPOC. The latter two metrics are based on features obtained from a convolutional neural network. Experiments are performed using Everspry impressions, FBI boot impressions, and the West Virginia University footwear impression collection. The results show that phase‐only correlation performs as well as or better than the other metrics in all cases for the datasets we considered.
Key Management Using Elliptic Curve Diffie Hellman Curve 25519

Key management shows an important role in the information transmissions. This paper analyzes the more improved and cost-effective key management scheme with respect to transmission overhead, computation cost. A key management policy is effectually employed for the purpose of generating a distinctive convention key for ample members to promise the security of their prospect communications, and this agreement can be allied in cloud computing for the purpose of assisting protected and skilled data distribution. The Key exchange protocols provides the two or more members to decide on a shared secret key and later be able to used for encrypting a long message. The complexity of Discrete Logarithm Problem (DLP) as basic problem considered in Diffie-Hellman Key Exchange Protocol. The Elliptic curve Diffie-Helman (ECDH) is considered as an expansion to the standard Diffie-Hellman. The ECDH function is ideal for a wide range of cryptographic functions. Key management based on ECDH provides much security on key meanwhile having more amount of storage cost and execution time involving in this. The ECDH Curve 25519 makes much faster in execution and lesser storage cost due its structure which uses much higher prime group of the order 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">252</sup> .