Reviewerly/exHarmony huggingface dataset:
Optimization and Manipulation of Contextual Mutual Spaces for Multi-User Virtual and Augmented Reality Interaction

Spatial computing experiences are physically constrained by the geometry and semantics of the local user environment. This limitation is elevated in remote multi-user interaction scenarios, where finding a common virtual ground physically accessible for all participants becomes challenging. Locating a common accessible virtual ground is difficult for the users themselves, particularly if they are not aware of the spatial properties of other participants. In this paper, we introduce a framework to generate an optimal mutual virtual space for a multi-user interaction setting where remote users’ room spaces can have different layout and sizes. The framework further recommends movement of surrounding furniture objects that expand the size of the mutual space with minimal physical effort. Finally, we demonstrate the performance of our solution on real-world datasets and also a real HoloLens application. Results show the proposed algorithm can effectively discover optimal shareable space for multi-user virtual interaction and hence facilitate remote spatial computing communication in various collaborative workflows.
Attention Aggregation Encoder-Decoder Network Framework for Stereo Matching

In the stereo matching networks based on deep learning, current cost aggregation networks lack the means to aggregate cost volume to the utmost extent. Therefore, different from the standard encoder-decoder structures, we propose an attention aggregation encoder-decoder network framework for stereo matching that contains three modules. Specifically, we design a sub-branch and cross-stage aggregation encoding module, which aggregate context information of different sub-branches and cross-stages to achieve the mutual utilization of different deep cost volumes. Meanwhile, we introduce a three-dimensional attention recoding module to obtain the robust discriminative cost volume through recalibrating the high-level semantic information of the sub-branches. In addition, we construct a stepwise aggregation decoding module to decode the cost volume via the stepwise fusion upsampling strategy, which further enhances the learning ability of the network model. The experimental results on Scene Flow and KITTI benchmark datasets show that the proposed network framework is superior to other similar methods in aggregating information.
Experimental optical encryption scheme for the double random phase encoding using a nonlinear joint transform correlator

We present a new experimental system for optical encryption using a nonlinear joint transform correlator (JTC) to implement the optical security technique of the double random phase encoding (DRPE). The DRPE, which is usually performed in a 4f-processor, encodes an image into a noisy distribution (encrypted image) by using two random phase masks (RPMs). In our experimental setup, the input plane of the JTC is fully encoded in phase and this plane contains two non-overlapping data functions (images). In the encryption step, the phase-encoded image to be encrypted and the two RPMs are placed in the input plane of the JTC with the purpose of obtaining the needed intensity distributions at the output plane to compute the encrypted image. The optical and numerical transformations are performed in the Fourier domain. A nonlinear operation is introduced to modify the joint power spectrum (JPS) of the JTC in order to reproduce exactly the same results of the DRPE. The experimental optical encryption scheme based on a three-step JTC is implemented by using an optoelectronic setup. The input plane of the JTC is optically implemented by means of a phase-only spatial light modulator (SLM). The decryption process is performed using a virtual optical system. Experimental and numerical results of the optical encryption and simulated decryption systems are presented, in order to show the feasibility of the proposed security system.
Tuning high flow concurrency for MPTCP in data center networks

Abstract In the data center networks, multipath transmission control protocol(MPTCP) uses multiple subflows to balance traffic over parallel paths and achieve high throughput. Despite much recent progress in improving MPTCP performance in data center, how to adjust the number of subflows according to network status has remained elusive. In this paper, we reveal theoretically and empirically that controlling the number of concurrent subflows is very important in reducing flow completion time (FCT) under network dynamic. We further propose a novel design called MPTCP_OPN, which adaptively adjusts the number of concurrent subflows according to the real-time network state and flexibly shifts traffic from congested paths to mitigate the high tail latency. Experimental results show that MPTCP_OPN effectively reduces the timeout probability caused by full window loss and flow completion time by up to 50% compared with MPTCP protocol.
I-nteract: A Cyber-Physical System for Real-Time Interaction With Physical and Virtual Objects Using Mixed Reality Technologies for Additive Manufacturing

This paper presents I-nteract, a cyber-physical system that enables real-time interaction with real and virtual objects in a mixed reality environment to design 3D models for additive manufacturing. The system has been developed using mixed reality technologies such as HoloLens, for augmenting visual feedback, and haptic gloves, for augmenting haptic force feedback. The efficacy of the system has been demonstrated by generating a 3D model using a novel scanning method to 3D print a customized orthopedic cast for human arm, by estimating spring rates of compression springs, and by simulating interaction with a virtual spring in a mixed reality environment.
An Improved Beetle Antennae Search Algorithm and Its Application on Economic Load Distribution of Power System

The beetle antennae search algorithm (BAS) is a new heuristic algorithm proposed in 2017.BAS is a simulation optimization algorithm based on beetle antennae, which determine the direction of flight by sensing the smell of food and thereby find the food.The BAS algorithm has been proven to have better optimization speed and precision when searching for solutions to low-dimensional problems.However, when solving high-dimensional problems, the algorithm easily falls into a local optimum.To improve the search ability of BAS, this paper introduces inertia weight, which leads the algorithm to perform a global search in early stages and a local search in later stages, which greatly improves the optimization precision of the algorithm.Economic load distribution (ELD) is a typical optimization problem in power systems.This paper analyses the problem of ELD and its mathematical model and then uses a 3-machine 6-node example to apply the improved BAS algorithm to ELD.Finally, the improved BAS algorithm is compared with PSO and other algorithms on the basis of the optimization results of test functions.The conclusion is drawn that the improved BAS algorithm has advantages in dealing with ELD problems.
Sequence Triggered Hardware Trojan in Neural Network Accelerator

With the rapid development of deep learning techniques, the security issue for Neural Network (NN) systems has emerged as an urgent and severe problem. Hardware Trojan attack is one of the threatens, which provides attackers backdoors to control the prediction results of NN systems. This paper proposes a sequence triggered hardware Trojan. Normal images but with specific sequence are used to trigger the hardware Trojan and let attackers fully control the prediction results. This kind of trigger is not only robust to image pre-processing, but also unrecognizable by human beings. In comparison with existing hardware Trojan design, it is more practical and less hardware overhead. The experiments on MNIST, CIFAR100, and ISLVRC show that the proposed hardware Trojan is rarely triggered in normal working status while the hardware cost is reduced by 19X.
Stochastic Hamiltonian Gradient Methods for Smooth Games

The success of adversarial formulations in machine learning has brought renewed motivation for smooth games. In this work, we focus on the class of stochastic Hamiltonian methods and provide the first convergence guarantees for certain classes of stochastic smooth games. We propose a novel unbiased estimator for the stochastic Hamiltonian gradient descent (SHGD) and highlight its benefits. Using tools from the optimization literature we show that SHGD converges linearly to the neighbourhood of a stationary point. To guarantee convergence to the exact solution, we analyze SHGD with a decreasing step-size and we also present the first stochastic variance reduced Hamiltonian method. Our results provide the first global non-asymptotic last-iterate convergence guarantees for the class of stochastic unconstrained games and for the more general class of stochastic games that satisfy a bilinear condition, notably including some non-convex non-concave problems. We supplement our analysis with experiments on stochastic and sufficiently games, where our theory is shown to be tight, and on simple adversarial machine learning formulations.
Optimization under Uncertainty to Reduce the Cost of Energy for Parabolic Trough Solar Power Plants for Different Weather Conditions

Renewable energy use can mitigate the effects of climate change. Solar energy is amongst the cleanest and most readily available renewable energy sources. However, issues of cost and uncertainty associated with solar energy need to be addressed to make it a major source of energy. These uncertainties are different for different locations. In this work, we considered four different locations in the United States of America (Northeast, Northwest, Southeast, Southwest). The weather and cost uncertainties of these locations are included in the formulation, making the problem an optimization-under-uncertainty problem. We used the novel Better Optimization of Nonlinear Uncertain Systems (BONUS) algorithm to solve these problems. The performance and economic models provided by the System Advisory Model (SAM) system from NREL were used for this optimization. Since this is a black-box model, this adds difficulty for optimization and optimization under uncertainty. The objective function and constraints in stochastic optimization (stochastic programming) problems are probabilistic functionals. The generalized treatment of such problems is to use a two-loop computationally intensive procedure, with an inner loop representing probabilistic or stochastic models or scenarios instead of the deterministic model, inside the optimization loop. BONUS circumvents the inner sampling loop, thereby reducing the computational intensity significantly. BONUS can be used for black-box models. The results show that, using the BONUS algorithm, we get 41%–47% of savings on the expected value of the Levelized Cost of Electricity (LCOE) for Parabolic Trough Solar Power Plants. The expected LCOE in New York is 57.42%, in Jacksonville is 38.52%, and in San Diego is 17.57% more than in Las Vegas. This difference is due to the differences in weather and weather uncertainties at these locations.
An Embarrassingly Simple Baseline to One-shot Learning

In this paper, we propose an embarrassingly simple approach for one-shot learning. Our insight is that the one-shot tasks have domain gap to the network pretrained tasks and thus some features from the pretrained network are not relevant, or harmful to the specific one-shot task. Therefore, we propose to directly prune the features from the pretrained network for a specific one-shot task rather than update it via an optimized scheme with complex network structure. Without bells and whistles, our simple yet effective method achieves leading performances on miniImageNet (60.63%) and tieredImageNet (69.02%) for 5-way one-shot setting. The best trial can hit to 66.83% on miniImageNet and 74.04% on tieredImageNet, establishing a new state-of-the-art. We strongly advocate that our method can serve as a strong baseline for one-shot learning. The codes and trained models will be released at http://github.com/corwinliu9669/embarrassingly-simple-baseline.
Bibliometric Survey of Quantum Machine Learning

Quantum Machine Learning (QML) is one of the core research fields in the larger paradigm of Quantum Computing (also known alternatively as Quantum Information). In recent years, researchers have taken deep interest in QML, given the potential time and cost advantages that solutions to real-life problems using QML algorithms provide, in comparison to their classical (or digital) machine learning equivalents. This is still a very new and exciting area of research with new algorithms and their uses being developed almost every other day. Deep research interest in this area has picked up only in the past 5–6 years. Given the background, this paper focuses on studying Scopus and Web of Science databases for the past 6 years (2014–2019) to identify various publication trends in the areas of Quantum Machine Learning. The authors have done an in-depth study of the Scopus and Web of Science publication data pertaining to this area and have come up with interesting insights. The survey covers 276 publications in Scopus and 154 publications in Web of Science. From the Scopus database, it is found that there has been a consistent growth in the number of publications in this period. Four research areas, namely, Physics, Astronomy, Computer Science, and Mathematics, have contributed 68.1% of the research publications. The USA leads the top 10 countries with nearly half (49.2%) of the research publications. A total of 148 patents have been published with 94 of these being published in the last four years (2016–2019). This essentially translates to one patent for every two publications. The Web of Science database, though bringing out 154 publications in the period, shows similar trends across the metrics. We have carried out a comparative study of some of the metrics in Scopus and Web of Science databases. Overall the study identifies the top 10 Institutions, authors, and research journals.
Crime Spatiotemporal Prediction With Fused Objective Function in Time Delay Neural Network

In the criminology area, to detain the serial criminal, the forthcoming serial crime time, distance, and criminal's biography are essential keys. The main concern of this study is on the upcoming serial crime distance, time, and suspect biographies such as age and nationality. In conjunction with having time delays, the dynamic classifier, like Time Delay Neural Network (TDNN) utilized to perform nonlinear techniques-based predictions. The TDNN classifier system, like Back Propagation Through Time (BPTT) and Nonlinear Autoregressive with Exogenous Input (NARX) are two prominent examples. However, BPTT and NARX techniques are unable to identify the dynamic system by using single-activation functions due to producing lower accuracy. Hence, during the training phase, the direct minimization of the TDNN error can further enhance the single activation function. Thus, this work introduces an enhanced NARX (eNARX) model based on the proposed activation functions of SiRBF via fusion of two functions of the hyperbolic tangent (Tansig) and Radial Basis Function (RBF), in the same hidden layer. If a fusion of activation functions can affect the TDNN error minimization, then fusing of the Tansig and RBF functions can produce a precise prediction for crime spatiotemporal. To evaluate the proposed technique and compared it with existing NARX and BPTT, we utilized five time-series datasets, namely, Dow Jones Index, Monthly River flow in cubic meters per second, Daily temperature, and UKM-PDRM datasets namely, “Suspect & Capture” and “Crime Plotting.” The analysis of the results demonstrated that the proposed eNARX produce higher accuracy in comparison to other techniques of NARX and BPTT. Consequently, the proposed technique provides more effective results for the prediction of commercial serial crime
Improving Vaccine Safety Using Blockchain

In recent years, vaccine incidents occurred around the world, which endangers people’s lives. In the technical respect, these incidents are partially due to the fact that existing vaccine management systems are distributively managed by different entities in the vaccine supply chain. This architecture makes it relatively easy to modify or even delete the vaccine circulation data maliciously, which makes tracing problematic vaccine hard and identifying the responsibility for a vaccine accident hard. To solve these issues, this article presents a blockchain-based solution to protect the whole process of vaccine circulation. We first propose a model to supervise the vaccine circulation process by incorporating existing regulatory practices. Then, we propose a blockchain-based tracing system to implement this model. The proposed system takes the blockchain as a global, unique, and verifiable database to store all the circulation data. Through data insertions and queries on the global and unique database, the proposed system achieves the protection of vaccine circulation. We also implement a proof-of-concept prototype of the proposed system. Experimental results confirm that the proposed system is beneficial.
Understanding Stakeholders Needs for Using Blockchain Based Smart Contracts in Construction Industry of Thailand: Extended TAM Framework

Traditionally, the contractual transactions between untrusted parties were generally carried out in a centralized form, requiring a trusted third party to act as a witness, and make them legally binding, enforceable and trustful. However, the process of involving a third party is associated with high expenses and delays. The Blockchain technology has been developed to address these issues, by allowing parties to enter into contractual agreements without involvement of third parties. The contract allows conducting of trusted agreements and transactions between unknown parties without the need of having an enforcement mechanism, trusted third party or legal system. In the construction sector, the smart contracts would help eliminate the slow, expensive and brittle transactions, associated with integrity and transparency issues due to potential records manipulation of the traditional contracts. This study focused on designing a blockchain smart contract adoption model and understanding stakeholders' needs for using blockchain-based smart contracts in construction industry. The study proposed the extended Technology Acceptance Model, which illustrates the constructs around the adoption and use of the blockchain smart contracts in Thailand construction sector. The model suggests that factor such as perceived financial costs, facilitating conditions, trust and readiness has influence on the construct of perceived usefulness (PU) and perceived ease of use (PEOU), which affects the behavioral intention to use the blockchain smart contracts in construction industry of Thailand.
Smart Learning Environments y ergonomía: una aproximación al estado de la cuestión

Educational technology evolves constantly, in line with the innovative technologies we implement, but always catering for the improvement of teaching and learning. For this, Smart Learning Environments (SLE) emerge as an optimal alternative to traditional teaching as, through ergonomics, an inclusive outlook which is bound to enhance the educational experience of every student is provided. The method utilized is based on a systematic review of the existing literature which has allowed us to analyze in depth a final sample of 19 documents after an initial review of 633, being these all the works published between 2013 and 2019. Therefore, the principal objective of the present work is carrying out an analysis of the state of the art in relation to ergonomics, inclusiveness and the SLE. The analysis of results is performed utilizing a semantic network, generated through atlas.ti. v.8, by means of which 3 categories, 10 codes and 33 quotes are extracted. Namely, the results reveal the emerging nature of the thematic line researched and how ergonomics is linked to inclusiveness and stands out as one of the most defining components when designing an educational proposal based on SLE.
A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising

Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining underlying clean images based on a non-local low-rank framework. Firstly, to take advantage of the inherent layering structures of the SBP image, a direction image is obtained and used as a guidance image. Secondly, the robust guidance weight for accurately selecting the similar patches is given. A novel denoising method combining the weight and a non-local low-rank filtering framework is proposed. Thirdly, after discussing the filtering parameter settings, the proposed method is tested in actual measurements of sub-bottom, both in deep water and shallow water. Experimental results validate the excellent performance of the proposed method. Finally, the proposed method is verified and compared with other methods quantificationally based on the synthetic images and has achieved the total average peak signal-to-noise ratio (PSNR) of 21.77 and structural similarity index (SSIM) of 0.573, which is far better than other methods.
Blind Image Quality Assessment for Super Resolution via Optimal Feature Selection

Methods for image Super Resolution (SR) have started to benefit from the development of perceptual quality predictors that are designed for super resolved images.However, extensive cross dataset validation studies have not yet been performed on Image Quality Assessment (IQA) for super resolved images.Moreover, powerful natural scene statistics-based approaches for IQA have not yet been studied for SR.To address these issues, we introduced a new dataset of super-resolved images with associated human quality scores.The dataset is based on the existing SupER dataset, which contains real low-resolution images.This new dataset also has 7 SR algorithms at three magnification scales.We selected optimal quality aware features to create two no-reference, (NR) opinion-distortion unaware (ODU) IQA models.Using the same set of selected features, we also implemented two NR-IQA opinion/distortion aware (ODA) models.The selection process identified paired-product (PP) features and those derived from discrete cosine transform coefficients (DCT) as the most relevant for the quality prediction of SR images.We conducted cross dataset validation for several state-of-the-art quality algorithms in four datasets, including our new dataset.The conducted experiments indicate that our models achieved better than stateof-the-art performance among the NR-IQA metrics.Our NR-IQA source code and the dataset are available at https://github.com/juanpaberon/IQA_SR.