Reviewerly/exHarmony huggingface dataset:
An Automatic and Rapid Knowledge Graph Construction Method of SG-CIM Model

The current enterprise-level data model-based knowledge resource classification, intelligent search, and cross-domain knowledge integration and representation are still in their infancy. Existing data models are mainly constructed based on UML, with a relatively complex structure and lack of management or business personnel, making it difficult to establish unified consistency analysis and verification rules. Now based on the data of the existing SG-CIM model of the State Grid Internet Department, a rapid graph construction method is proposed. It mainly constructs a logical rule-based method by combing the logical model with the physical model, which has initially realized the requirements of automatic traceability, made up for the shortcomings of model design, and strengthened the control of data model consistency verification and application implementation. Finally, it can be used to fully support the company's various analysis and recommendation applications based on the unified knowledge graph.
ERIC: Extracting Relations Inferred from Convolutions

Our main contribution is to show that the behaviour of kernels across multiple layers of a convolutional neural network can be approximated using a logic program. The extracted logic programs yield accuracies that correlate with those of the original model, though with some information loss in particular as approximations of multiple layers are chained together or as lower layers are quantised. We also show that an extracted program can be used as a framework for further understanding the behaviour of CNNs. Specifically, it can be used to identify key kernels worthy of deeper inspection and also identify relationships with other kernels in the form of the logical rules. Finally, we make a preliminary, qualitative assessment of rules we extract from the last convolutional layer and show that kernels identified are symbolic in that they react strongly to sets of similar images that effectively divide output classes into sub-classes with distinct characteristics.
Low bit-rate compression of underwater image based on human visual system

Image plays an irreplaceable role compared with the text and sound in the underwater data collection and transmission researches. However, it suffers from the limited bandwidth of the underwater acoustic communication which cannot afford the large image data. Compressing the image data before transmission is an inevitable process in the underwater image communication. As usual, the natural image compression methods are directly applied to the underwater scene. As we all know, underwater image has different degradation from the natural one due to the optical transmission property. Low illumination in underwater will cause more seriously blurring and color fading than that in the air. It is a great challenge to decrease the bit-rate of the underwater image while preserving the compressed image quality as much as possible. In this paper, the Human Visual System (HVS) is taken into account during the compressing and the evaluating stages for the underwater image communication. We present a new methodology for underwater image compression. Firstly, by taking the human visual system into account, the chrominance perception operator is proposed in this paper to neglect the imperceptible chrominance shift which is widely exited in the underwater imaging to improve the image compression rate. Secondly, depth of field(DOF) of underwater image is usually shallow and most of the usable image has targets in it. An ROI extraction algorithm based on Boolean map detection is then used for the underwater image compression so as to reduce the bitrate of the compressed image. Furthermore, the underwater image is grainy and low contrast, that means the degradation happens in some regions of the image would not be perceived. Just notice difference(JND) sensing algorithm based on the spatial and frequency domain masking feature of HVS is also considered in the image processing. By combining the three aspects above, hybrid wavelet and asymmetric coding are used together to promote the underwater image compression, so that the image can have better quality and less redundancy. Experiments show that the proposed method can make full use of the inherent characteristics of underwater images, and maximize the visual redundancy of underwater images without reducing the visual perception quality of reconstructed images.
Comparing Static and Dynamic Weighted Software Coupling Metrics

Coupling metrics are an established way to measure software architecture quality with respect to modularity. Static coupling metrics are obtained from the source or compiled code of a program, while dynamic metrics use runtime data gathered e.g., by monitoring a system in production. We study weighted dynamic coupling that takes into account how often a connection is executed during a system’s run. We investigate the correlation between dynamic weighted metrics and their static counterparts. We use data collected from four different experiments, each monitoring production use of a commercial software system over a period of four weeks. We observe an unexpected level of correlation between the static and the weighted dynamic case as well as revealing differences between class- and package-level analyses.
Lightweight Image Encryption: A Chaotic ARX Block Cipher

Nowadays, lightweight cryptography attracts academicians, scientists and researchers to concentrate on its requisite with the increasing usage of low resource devices. In this paper, a new lightweight image encryption scheme is proposed using the Lorenz 3D super chaotic map. This encryption scheme is an addition–rotation–XOR block cipher designed for its supremacy, efficacy and speed execution. In this addition–rotation–XOR cipher, the equation for Lorenz 3D chaotic map is iteratively solved to generate double valued signals in a speedy manner using the Runge–Kutta and Euler methods. The addition, rotation and diffusion sequences are generated from the double valued signals, and the source pixels of the 8-bit plain test images are manipulated with the addition, rotation and diffusion of the bytes. Finally, the cipher images are constructed from the manipulated pixels and evaluated with various statistical as well as randomness tests. The results from various tests prove that the proposed chaotic addition–rotation–XOR block image cipher is efficient in terms of randomness and speed.
Public-PEZ Cryptography

Secure multiparty computation (MPC) is a cryptographic technique that enables us to evaluate a predetermined function over players’ private inputs while hiding information about the inputs. MPC can be conducted using a “private PEZ protocol,” that uses PEZ candies and a dispenser. Specifically, in a private PEZ protocol, players first fill a predetermined sequence of candies in a dispenser. Then, each player in turn privately pops out a number of candies, wherein the number depends on their private input (without anybody else knowing how many candies pop out). The next candy to be popped out of the dispenser indicates the output value of the function. Thus, private PEZ protocols are fun and useful. One drawback would be that every player must pop out candies from the dispenser secretly, implying that a private PEZ protocol is vulnerable to dishonest players, for example, a player could peep the candies inside the dispenser. To overcome this drawback, we herein propose MPC protocols that do not need private actions such as secretly popping out candies after the setup (although each player rearranges the candies secretly in a setup phase, any illegal actions can be caught). That is, we construct a computational model of “public-PEZ cryptography,” where any protocol within the model can be publicly executed. Especially, the proposed public-PEZ AND protocol, which uses only five candies and two dispensers, is simple and easy for conducting a secure computation of the AND function.
An Intelligence Image Processing Method of Visual Servo System in Complex Environment

In robot visual servoing system, effective detected the region of interest area (ROI) in target images is the first problem should be solved, but the detection is susceptible to an unstructured environment. In this paper, an instance segmentation network algorithm based on Mask Region Convolutional Neural Networks (Mask R-CNN) framework is proposed for ROI image preprocessing. As instance segmentation technology can distinguish complex environmental information, so first with this advantage filter out message such as shape or similar area to target image, then with semantic segmentation technology add special category labels to the filtered images and distinguish different individual instances in similar categories. Finally through the series steps aforementioned, robot vision system can overcome the impact of environmental factors and identify the target image. The proposed method is used to detect target image under five constraints such as occlusion and reflection, result shows the algorithm can effectively deal with challenges that brought by complex constraints, and even can predict the location data of missing information based on some image information. In addition, based on the algorithm proposed in this paper, we used one seven axis robot visual servo platform, executed visual servoing experiment under different unstructured environments, further verifies the effectiveness of our proposed method.
Optimization Of The Decision Tree Algorithm Used Particle Swarm Optimization In The Selection Of Digital Payments

Abstract One of the developments of information technology in Indonesia was the developments in Fintech (financial technology) that made it easy for people to access financial products, facilitated online transactions and also increased financial literacy. The development of fintech occurs when the use of cash was reduced to cashless when making payment transactions so that transactions would be more practical, easy, safe and comfortable. The purpose of this study to improve the quality of decision tree modeling and accuracy in the selection of digital payments. This research was focused on determining the fintech applications that were widely used by the people of Indonesia, namely Gopay or Ovo by comparing the advantages of applications and features of each fintech application. Optimization method of Decision Tree Algorithm (C4.5) and Particle Swarm Optimization by selecting several attributes including the level of ease of use, data security, application trust and convenience, maximum balance increase, discounts, cashback, ease of top-ups, range of existing merchants, return the money and customer complaint services. The results of the development of a decision tree algorithm based on particle swarm optimization provide a good classification and increase the validation value in the selection of digital payments.
GPU-based digital image correlation system for uniaxial and biaxial crack growth investigations

Experiments to describe the crack growth behavior are expensive and complex, while the evaluation depends mainly on the determined crack lengths over the testing period. Digital image correlation (DIC) is well established for crack-length and displacement measurements, but it normally requires sample preparation with speckle paint and interferes with mechanical extensometers. Novel GPU-based DIC system capable of measuring total strain at rates up to 850 Hz and strain-fields overcome these limitations. We present first results of fatigue crack growth experiments with uniaxially und biaxially loaded specimens. The force controlled tests under room-temperature conditions were accompanied by conventional measuring systems comprising a side extensometer, an ACPD-measurement system and the GPU-based DIC system. The results indicate that DIC-measured crack depth correlates well with ACPD crack depth values. Furthermore, the crack-flank displacements derived from DIC-evaluation are in good agreement with 2D-FEM simulations. The GPU-based DIC-System appears as a promising measurement technique for crack growth investigations.
Prototyping Virtual Reality Interactions in Medical Simulation Employing Speech Recognition

Medical simulation has seen the adoption of virtual reality as a tool to expose learners to scenarios otherwise impossible in real life. In medical simulation, patient interactions are practiced employing standardized patients (i.e., actors) and most recently artificial intelligence driven avatars. Typical avatar interactions found in virtual simulators rely on buttons, text boxes, and drop-down menus to enable the interactions. While a similar approach is also employed in virtual reality, virtual keyboard inputs and point and click tasks on graphical user interface items using a mouse can be challenging and lead to increased cognitive loads. In this paper, we outline an application for virtual avatar communication by employing speech recognition and gestures to convey non-verbal cues during an eye fundus examination. Our goal is to understand the face validity of speech and gesture driven interactions. A demo was conducted at an open house were attendees tried the scenario and provided their opinions on the experience. From this preliminary experience, we were able to identify challenges and opportunities when deploying speech-based interactions outside a controlled laboratory space.
An Accountable Decryption System Based on Privacy-Preserving Smart Contracts

Accountability is a fundamental after-the-fact approach to detect and punish illegal actions during the execution of a warrant for accessing users’ sensitive data. To achieve accountability in a security protocol, a trusted authority is required, denoted as judge, to faithfully cooperate with the rest of the entities in the system. However, malicious judges or uncooperative protocol participants may void the accountability mechanism in practice, for example by fabricating fake evidence or by refusing to provide any evidence at all. To provide remediation to these issues, in this paper we propose Fialka, a novel accountable decryption system based on privacy-preserving smart contracts (PPSC). The neutrality that is inherent to a secure blockchain platform is inherited by PPSC which are then used in our approach as an accountable key manager as well as a transparent judge. To the best of our knowledge, we present the first PPSC-based accountable decryption system to increase the transparency of warrant execution with formal definitions and proofs. Furthermore, we provide and evaluate a prototype implementation using the PPSC-enabled platform Oasis Devnet, which additionally demonstrates the feasibility of Fialka.
Extending Ginga-NCL to Specify Multimodal Interactions With Multiple Users

Currently, most declarative languages used to define multimedia documents do not support the specification of an interactive application with multiple users and multimodal interaction. To handle multimodal and multi-user interaction in these languages, the author of multimedia applications must write custom code in an imperative language to be included in the declarative document. To fill in this gap, this work proposes an extension of NCM (Nested Context Language), NCL (Nested Context Language) and Ginga-NCL to provide high-level abstraction multimodal and multi-user interaction specifications. As a proof of concept, we developed a multimedia application that uses multi-user voice interaction.
Unsupervised Cross-Modal Alignment for Multi-person 3D Pose Estimation

We present a deployment friendly, fast bottom-up framework for multi-person 3D human pose estimation. We adopt a novel neural representation of multi-person 3D pose which unifies the position of person instances with their corresponding 3D pose representation. This is realized by learning a generative pose embedding which not only ensures plausible 3D pose predictions, but also eliminates the usual keypoint grouping operation as employed in prior bottom-up approaches. Further, we propose a practical deployment paradigm where paired 2D or 3D pose annotations are unavailable. In the absence of any paired supervision, we leverage a frozen network, as a teacher model, which is trained on an auxiliary task of multi-person 2D pose estimation. We cast the learning as a cross-modal alignment problem and propose training objectives to realize a shared latent space between two diverse modalities. We aim to enhance the model's ability to perform beyond the limiting teacher network by enriching the latent-to-3D pose mapping using artificially synthesized multi-person 3D scene samples. Our approach not only generalizes to in-the-wild images, but also yields a superior trade-off between speed and performance, compared to prior top-down approaches. Our approach also yields state-of-the-art multi-person 3D pose estimation performance among the bottom-up approaches under consistent supervision levels.
A mobile game SDK for remote collaborative between two users in augmented and virtual reality

Abstract Remote collaboration is important in Augmented Reality (AR) and Virtual Reality (VR) that allow users to communicate and interact with each other. Mobile games are gaining its popularity due to its portability and lower in cost. Software Development Kit (SDK) allows developer to speed up the development process. However, most of the collaboration of these technologies only focus on either AR or VR independently. Besides, most AR and VR that had been developed are only available in expensive devices but not in mobile devices. Therefore, this research aims to create a remote collaboration between AR and VR with mobile games. There are four phases have been carried out, analysis phases, development phase, construction phase and evaluation phase. The evaluation has been performed and it is based on the usability and user acceptance. The usability results show the application is accepted by the user. User acceptance results show the application works accordingly. Based on the results, this research has successfully produced a working SDK for remote collaboration between AR and VR with mobile games.
Modelling resilient collaborative multi-agent systems

Abstract Multi-agent systems constitute a wide class of decentralised systems. Their functions are usually carried out by collaborative activities of agents. To ensure resilience of multi-agent systems, we should endow them with a capability to dynamically reconfigure. Usually, as a result of reconfiguration, the existing relationships between agents are changed and new collaborations are established. This is a complex and error-prone process, which can be facilitated by the use of formal reasoning and automated verification. In this paper, we propose a generic resilience-explicit formalisation of the main concepts of multi-agent systems. Based on it, we introduce corresponding specification and refinement patterns in Event-B. Our patterns facilitate modelling behaviour of resilient multi-agent systems in a rigorous systematic way and verification of their properties. We demonstrate the application of the proposed approach by a case study—a smart warehouse system.
Cooperative Localization for Multiple Soccer Agents Using Factor Graphs and Sequential Monte Carlo

This paper addresses the cooperative localization problem for a multiagent system in the framework of belief propagation. In particular, we consider the RoboCup 3D Soccer Simulation scenario, in which the networked agents are able to obtain simulated measurements of the distance and bearing to both known landmarks and teammates as well as the direction of arrival (DOA) of messages received from allies around the field. There are, however, severe communication restrictions between the agents, which limit the size and periodicity of the information that can be exchanged between them. We factorize the joint probability density function of the state of the robots conditioned on all measurements in the network in order to derive the corresponding factor graph representation of the cooperative localization problem. Then we apply the sum-product-algorithm (SPA) and introduce suitable implementations thereof using hybrid Gaussian-Mixture Model (GMM) / Sequential Monte Carlo (SMC) representations of the individual messages that are passed at each network location. Simulated results show that the cooperative estimates for position and orientation converge faster and present smaller errors when compared to the non-cooperative estimates in situations where agents do not observe landmarks for a long period.
Myanmar Sign Language Classification using Deep Learning

Nowadays, the development progress based on Human Computer Interaction (HCI) becomes more and more wider. In the field of Information Technology, HCI based Sign Language Classification System can be used to develop a day to day life of deaf people. There are very little research works in Myanmar sign language recognition and it is still needed to develop. This paper studies component detection using skin color detection and component localization based on Region of Interest (ROI) and sign classification using Convolutional Neural Network (CNN). The main focus of this work is to create a vision-based Myanmar Sign Language Recognition System. Myanmar Sign Language is captured by a webcam as an image and the system is implemented by MATLAB.
Safe, Optimal, Real-Time Trajectory Planning With a Parallel Constrained Bernstein Algorithm

To move while using new sensor information, mobile robots use receding-horizon planning, executing a short plan while computing a new one. A plan should have dynamic feasibility (obeying a robot's dynamics and avoiding obstacles), liveness (planning frequently enough to complete tasks), and optimality (minimizing, e.g., distance to a goal). Reachability-based trajectory design (RTD) is a method to generate provably dynamically feasible plans in real time by solving a polynomial optimization program (POP) in each planning iteration. However, RTD uses a derivative-based solver, which may converge to local minima that impact liveness and optimality. This article proposes a parallel constrained Bernstein algorithm (PCBA) branch-and-bound method to optimally solve RTD's POP at runtime; the resulting optimal planner is called RTD*. The specific contributions of this article are the PCBA implementation, proofs of PCBA's bounded time and memory usage, a comparison of PCBA with state-of-the-art solvers, and a demonstration of PCBA/RTD* on hardware. RTD* shows better optimality and liveness than RTD in dozens of environments with random obstacles.
Application of Quantum Machine Learning to VLSI Placement

Considerable advances in quantum computing with functioning noisy, near-term devices have allowed for the application space to grow as a emerging field for problems with large solution spaces. However, current quantum hardware is limited in scale and noisy in generated data, necessitating hybrid quantum-classical solutions for viability of results and convergence. A quantum backend generates data for classical algorithms to optimize control parameters with, creating a hybrid quantum-classical computing loop. VLSI placement problems have shown potential for utilization, where traditionally heuristic solutions such as Kernighan-Lin (KL) are used. The Variational Quantum Eigensolver (VQE) is used to formulate a recursive Balanced Min-Cut (BMC) algorithm, and we suggest that quantum machine learning techniques can lower error rates and allow for faster convergence to an optimal solution.