Reviewerly/exHarmony huggingface dataset:
Simultaneous Chaos Time-Delay Signature Cancellation and Bandwidth Enhancement in Cascade-Coupled Semiconductor Ring Lasers

Optics chaos has been widely studied and its various applications have also been demonstrated in recent years. To improve the performance of chaos-based applications, several properties of the chaotic signals should be evaluated and enhanced accordingly. In this paper, we consider a chaotic system consisting of three cascade-coupled semiconductor ring lasers (SRLs), where the master SRL is subjected to conventional (parallel) optical feedback, while its output is injected to the intermediate SRL and further to the slave SRL. We focus on the time-delay signature (TDS) and bandwidth of the generated chaos and demonstrate the possibility of the simultaneous realization of TDS elimination and bandwidth enhancement in the parameter space of the injection strength against the frequency detuning. In addition, a proof-of-concept test of randomness is carried out, and it demonstrates that the chaotic properties could be greatly enhanced by the proposed cascade-coupling scheme compared with a single SRL with feedback and thus is suitable for use in high-speed random number generation.
Energy-Efficient Dynamic Virtual Machine Management in Data Centers

Efficient virtual machine (VM) management can dramatically reduce energy consumption in data centers. Existing VM management algorithms fall into two categories based on whether the VMs’ resource demands are assumed to be static or dynamic. The former category fails to maximize the resource utilization as they cannot adapt to the dynamic nature of VMs’ resource demands. Most approaches in the latter category are heuristic and lack theoretical performance guarantees. In this paper, we formulate the dynamic VM management as a large-scale Markov decision process (MDP) problem and derive an optimal solution. Our analysis of real-world data traces supports our choice of the modeling approach. However, solving the large-scale MDP problem suffers from the curse of dimensionality. Therefore, we further exploit the special structure of the problem and propose an approximate MDP-based dynamic VM management method, called MadVM. We prove the convergence of MadVM and analyze the bound of its approximation error. Moreover, we show that MadVM can be implemented in a distributed system with at most two times of the optimal migration cost. Extensive simulations based on two real-world workload traces show that MadVM achieves significant performance gains over two existing baseline approaches in power consumption, resource shortage, and the number of VM migrations. Specifically, the more intensely the resource demands fluctuate, the more MadVM outperforms.
User authentication in a tactile internet based remote surgery environment: Security issues, challenges, and future research directions

Recently, the Tactile Internet (TI) becomes a new era of the Internet. The TI provides ultra-reliable, ultra-responsive and intelligent network connectivity for delivering real-time control and physical haptic experiences from a remote location. The TI provides a different feeling to human–machine interaction by implementing the real-time interactive systems. In this review article, we discuss a generalized authentication model which can be used to perform authentication procedure among different communicating parties in order to secure remote surgery in the TI environment. By using the proposed authentication model, an authentication protocol can be designed so that an authenticated surgeon can use the robot/robotic arms to perform the surgery securely as well as remotely. Since the application is very critical, the important instructions provided by the surgeon to the robot/robotic arms must not be leaked in between the communication during the surgical procedure. To deal with this emerging research area, a secure mutual user authentication mechanism should be provided between a remote surgeon and the robot/robotic arms so that they can communicate securely using the established session key among them. Further, several security issues and challenges for such kind of communication are also discussed in this article. Finally, we discuss few points that need to be considered as future research works that are related to authentication for securing remote surgery in the TI environment.
Design and Development of the Fall Detection System based on Point Cloud

This paper proposed a fall detection system based on point cloud. Kinect is used to get the depth data of living environment of human body, drawing the pixels in human real space coordinates through mapping in depth, height change acceleration of the human point cloud gained are used to determine a potential fall activity.The Arduino microprocessor handles the receiving data in real time and notifies the guardian through the GSM module. The experimental results showed that the system is capable of precisely realizing the effective recognition and alarm for the falling action of timely response and has a high precision detection and effectiveness.
Median Filtering Forensics Using Multiple Models in Residual Domain

Median filtering, due to highly non-linear and content-preserving, has widely used in the multimedia security fields, such as anti-forensics, steganography, and steganalysis. In the past decade, many excellent algorithms have been proposed, and have solved median filtering forensics perfectly for images without post-processing. However, it is still a challenging task when the test images are of low resolution and post-processed by lossy JPEG compression. Moreover, in order to meet the requirements of detecting forgery from the massive image database in the social network, a fast and efficient detector is needed. In this paper, we present an improved method for median filtering forensics in challenging problems, such as detecting median filtering from a heavily JPEG compressed image. To do this, we first investigate median filtering traces by a group of diverse residuals. These residuals not only eliminate influence from image contents but also highlight the traces from different aspects. We then construct a feature set on the residuals by incorporating the Markov chain model with the auto-regressive model. These two compensated models effectively reveal different relationships among neighboring residuals. For a fast detector, a series of dimension reduction methods are employed. The experimental results on a composite image database demonstrate that the proposed method outperforms prior arts when detecting median filtering in heavily JPEG compressed images as well as low-resolution images.
A Pilot Study on a Smart Home for Elders Based on Continuous In-Home Unobtrusive Monitoring Technology

This article reports on a pilot study of a smart home for elders (SHfE) designed to continuously monitor senior adults' daily behaviors and the living environment of their residential homes using the application of unobtrusive sensors. SHfE users include older adults, their family members, and healthcare staff.Globally, countries are experiencing the challenges of an increasingly aging population. A healthy environment is essential to support aging in place. By applying information and communications technology to building environments to support health, smart homes may be an option to provide a low-cost, comfortable, and user-friendly living environment for older adults.A pilot study was conducted in a capital city in the Yangtze River Delta Agglomerations in China to verify the feasibility of the SHfE. One female older adult participated in the pilot study, which was conducted from November 2015 to January 2016.The results indicated that the SHfE is a feasible way to analyze the behaviors (e.g., sleeping, cooking, water usage) of the elder and monitor the built environment (e.g., temperature, windows, and doors).The pilot study can be used as a baseline for further comprehensive experiments, case studies, and surveys to gain a better understanding of a smart healthy environment for older adults. On the basis of the current study, several recommendations are put forward for further implementation of the SHfE, including integrating multiple unobtrusive sensing devices; detecting fall accidents; monitoring indoor lighting, noise, and ventilation; remotely controlling electrical appliances; and developing the system with various languages. It is anticipated that the SHfE will be adopted in seniors' residential homes in countries around the world which face an increasingly aging population.
Supervised representation learning for multi-label classification

Representation learning is one of the most important aspects of multi-label learning because of the intricate nature of multi-label data. Current research on representation learning either fails to consider label knowledge or is affected by the lack of labeled data. Moreover, most of them learn the representations and incorporate the label information in a two-step manner. In this paper, due to the success of representation learning by deep learning we propose a novel framework based on neural networks named SERL to learn global feature representation by jointly considering all labels in an effective supervised manner. At its core, a two-encoding-layer autoencoder, which can utilize labeled and unlabeled data, is adopted to learn feature representation in the supervision of softmax regression. Specifically, the softmax regression incorporates label knowledge to improve the performance of both representation learning and multi-label learning by being jointly optimized with the autoencoder. Moreover, the autoencoder is expanded into two encoding layers to share knowledge with the softmax regression by sharing the second encoding weight matrix. We conduct extensive experiments on five real-world datasets to demonstrate the superiority of SERL over other state-of-the-art multi-label learning approaches.
Improved Convolutional Pose Machines for Human Pose Estimation Using Image Sensor Data

In recent years, increasing human data comes from image sensors. In this paper, a novel approach combining convolutional pose machines (CPMs) with GoogLeNet is proposed for human pose estimation using image sensor data. The first stage of the CPMs directly generates a response map of each human skeleton’s key points from images, in which we introduce some layers from the GoogLeNet. On the one hand, the improved model uses deeper network layers and more complex network structures to enhance the ability of low level feature extraction. On the other hand, the improved model applies a fine-tuning strategy, which benefits the estimation accuracy. Moreover, we introduce the inception structure to greatly reduce parameters of the model, which reduces the convergence time significantly. Extensive experiments on several datasets show that the improved model outperforms most mainstream models in accuracy and training time. The prediction efficiency of the improved model is improved by 1.023 times compared with the CPMs. At the same time, the training time of the improved model is reduced 3.414 times. This paper presents a new idea for future research.
Ring Theory-Based Evolutionary Algorithm and its application to D{0-1} KP

In this paper, we propose a new view for designing an evolutionary algorithm by using algebraic theory to solve the combinatorial optimization problem. Using the addition, multiplication and inverse operation of the direct product of rings, we first propose two evolution operators: the global exploration operator (R-GEO) and the local development operator (R-LDO). Then, by utilizing the R-GEO and R-LDO to generate individuals and applying the greedy selection strategy to generate a new population, we propose a new algorithm – the Ring Theory-Based Evolutionary Algorithm (RTEA) – for the combinatorial optimization problem. Moreover, we give a new method for solving the discounted {0-1} knapsack problem (D{0–} KP) by using the RTEA. To verify the performance of the RTEA, we use it and existing algorithms to solve four kinds of large-scale instances of the D{0-1} KP. The computational results show that the RTEA performs better than the others, and it is more suitable for solving the D{0-1} KP problem. Moreover, it indicates that using algebraic theory to design evolutionary algorithms is feasible and effective.
Evolutionary-based tailoring of synthetic instances for the Knapsack problem

The assessment of strengths and weaknesses of a solver is often limited by the diversity of the cases where it is tested upon. As such, it is paramount to have a versatile tool which finds the problem instances where such a solver excels/fails. In this manuscript, we propose to use an evolutionary algorithm for creating this tool. To validate our approach, we conducted several tests on four heuristics for the knapsack problem. Although, the process can be extended to other domains with relatively few changes. The tests cover different sets of instances, both favoring the performance of one heuristic while hindering that of the remaining ones, and vice versa. To further test our evolutionary-based model, we also apply it on a recent approach that combines the strengths of different heuristics to improve its performance (usually referred to as a hyper-heuristic). We show that it is possible to tailor instances in which even this more complex model excels/fails. Throughout our approach, a researcher can test a solver under different kinds of scenarios, delving deeper into the conditions that make it perform well/poorly. Therefore, we recommend using the proposed approach as a means to grasp better insights about strengths and weaknesses of different solvers.
Perception-Preserving Convolutional Networks for Image Enhancement on Smartphones

Although the configuration of smartphone cameras is getting better and better, the quality of smartphone photos still cannot match DSLR camera photos due to the limitation of physical space, hardware and cost. In this work, we present a fast and accurate image enhancement approach based on generative adversarial nets, which elevates the quality of photos on smartphones. We propose the lightweight local residual convolutional network to learn the mapping between ordinary photos and DSLR-quality images. To make the generated images look real, we introduce the perception-preserving measurement error, which comprises content, color, and adversarial losses. Especially, the content loss is constituted of contextual and SSIM losses, which maintains the natural internal statistics and the structure of images. In addition, we introduce the knowledge transfer strategy to ensure the high performance of the proposed network. The experiments demonstrate that our proposed method produces better results compared with the state-of-the-art approaches, both qualitatively and quantitatively. The code is available at https://github.com/Zheng222/PPCN .
Unsupervised Semantic Generative Adversarial Networks for Expert Retrieval

Sources in computer-based collaborative systems such as webpages can help employees to connect and cooperate with each other. It is natural to enable the systems to look not only for documents but also for experts. In this paper, we study the problem of expert retrieval in enterprise corpora: given a topic, also known as query containing a set of words, identify a rank list of candidate experts who have expertise on the topic. To tackle the problem, we propose an unsupervised semantic two-player minimax game, i.e., our unsupervised semantic generative adversarial networks (USGAN). Unlike almost all the previous generative adversarial networks-based algorithms that require ground truth training data, our USGAN is an unsupervised semantic expert retrieval algorithm that consists of a discriminative network and a generative network aiming at capturing the representations of words and experts in an unsupervised way. Candidates that have similar semantic representations to that of the topic are retrieved as relevant to the topic. Our USGAN would provide inspiration on how to extend the standard GAN and its variants by unsupervised ways to address other retrieval tasks where labelled data are missing. Experimental results on public datasets validate the effectiveness of the proposed expert retrieval algorithm.
Cross-Resolution Feature Fusion for Fast Hand Detection in Intelligent Homecare Systems

Fast hand detection plays an important role in Intelligent Homecare systems due to its close association with many human-related tasks, such as human activity and behavior recognition. In the last decade, although hand detection has been studied widely, fast hand detection has remained a challenge. In this paper, we employ a single shot multibox detector (SSD) as the base architecture and propose a novel cross-resolution feature fusion (CFF) approach to add contextual information and semantic information to shallower layers for fast hand detection. Our approach helps improve performance significantly, especially in small instances, owing to the inclusion of two important modules: a narrow atrous spatial pyramid pooling (N-ASPP) module and a richer semantic information generation (RSIG) module. The proposed N-ASPP module employs atrous convolution to capture multiscale context information by adopting different atrous rates. The proposed RSIG module uses a resolution-matching submodule to enlarge an input feature map and a ResNeXt block to exploit richer semantic information. In verification experiments, the proposed 2CFF-SSD model achieved 66.41% average precision (AP) in the Oxford hand test set conducted at 55 frames per second on a GTX 1080 Ti graphics processing unit, which is superior to the original SSD method and many other state-of-the-art methods in terms of accuracy, while maintaining the high speed.
Coherence as a resource for source-independent quantum random-number generation

Measuring quantum states provides a means to generate genuine random numbers. It has been shown that genuine randomness can be obtained even with an uncharacterized source by measuring two incompatible bases [Phys. Rev. X 6, 011020 (2016)]. As coherence is the necessary source for generating randomness, we extend the scheme and propose a framework for quantum random number generation with general uncharacterized coherence resource. The previous scheme can be treated as a special case under the framework by considering a nonlinear uncertainty-relation-based coherence witness. Considering general coherence witnesses, we propose a source-independent random-number generation scheme that achieves a higher randomness generation rate. Our paper highlights the close relation between coherence and random-number generation and may shed light on designing general semi-device-independent quantum information processing protocols.
Quality of Service (QoS)-driven resource provisioning for large-scale graph processing in cloud computing environments: Graph Processing-as-a-Service (GPaaS)

Large-scale graph data is being generated every day through applications and services such as social networks, Internet of Things (IoT) and mobile applications. Traditional processing approaches such as MapReduce are inefficient for processing graph datasets. To overcome this limitation, several exclusive graph processing frameworks have been developed since 2010. However, despite broad accessibility of cloud computing paradigm and its useful features namely as elasticity and pay-as-you-go pricing model, most frameworks are designed for high performance computing infrastructure (HPC). There are few graph processing systems that are developed for cloud environments but similar to their other counterparts, they also try to improve the performance by implementing new computation or communication techniques. In this paper, for the first time, we introduce the large-scale graph processing-as-a-service (GPaaS). GPaaS considers service level agreement (SLA) requirements and quality of service (QoS) for provisioning appropriate combination of resources in order to minimize the monetary cost of the operation. It also reduces the execution time compared to other graph processing frameworks such as Giraph up to 10%–15%. We show that our service significantly reduces the monetary cost by more than 40% compared to Giraph or other frameworks such as PowerGraph.
Optimized Modular Multiplication for Supersingular Isogeny Diffie-Hellman

Recent progress in quantum physics shows that quantum computers may be a reality in the not too distant future. Post-quantum cryptography (PQC) refers to cryptographic schemes that are based on hard problems which are believed to be resistant to attacks from quantum computers. The supersingular isogeny Diffie-Hellman (SIDH) key exchange protocol shows promising security properties among various post-quantum cryptosystems that have been proposed. In this paper, we propose two efficient modular multiplication algorithms with special primes that can be used in SIDH key exchange protocol. Hardware architectures for the two proposed algorithms are also proposed. The hardware implementations are provided and compared with the original modular multiplication algorithm. The results show that the proposed finite field multiplier is over 6.79 times faster than the original multiplier in hardware. Moreover, the SIDH hardware/software codesign implementation using the proposed FFM2 hardware is over 31 percent faster than the best SIDH software implementation.
Application of Mixed-Integer Nonlinear Optimization Programming Based on Ensemble Surrogate Model for Dense Nonaqueous Phase Liquid Source Identification in Groundwater

Groundwater contamination source identification (GCSI) is critical for taking effective measures to protect groundwater resources, assess risks, mitigate disasters, and design remediation strategies. Simulation–optimization techniques have been effective tools for GCSI. However, previous studies have applied individual surrogate models when replacing simulation models, rather than making efforts to combine various methods to improve the approximation accuracy of the surrogate model over the simulation model. In this study, the kernel extreme learning machine (KELM) model was proposed to enhance the surrogate model, and to approach GCSI problems, especially those of dense nonaqueous phase liquid-contaminated aquifers, more effectively. In addition, a kriging model and a support vector regression (SVR) model were built and compared with the KELM model, and various ensemble surrogate (ES) modeling techniques were applied to establish four ES models. Results showed that the KELM model was more accurate than the kriging and SVR models; however, the ES models performed much better than the three individual surrogate models. The most precise ES model increased the certainty coefficient (R2) to 0.9837, whereas limiting the maximum relative error to 13.14%. Finally, a mixed-integer nonlinear programming optimization model was established to identify the groundwater contamination source in terms of location and release history, and simultaneously assess aquifer parameters. The ES model developed in this article could reasonably predict the system response under given operation conditions. Replacement of the simulation model by the ES model considerably reduced the computation burden of the simulation–optimization process and simultaneously achieved high computation accuracy.