Reviewerly/exHarmony huggingface dataset:
Fast Coflow Scheduling via Traffic Compression and Stage Pipelining in Datacenter Networks

Big data analytics in datacenters often involve scheduling of data-parallel jobs. Traditional scheduling techniques based on improving network resource utilization are subject to limited bandwidth in datacenter networks. To alleviate the shortage of bandwidth, some cluster frameworks employ techniques of traffic compression to reduce transmission consumption. However, they tackle scheduling in a coarse-grained manner at task level and do not perform well in terms of flow-level metrics due to high complexity. Fortunately, the abstraction of coflow pioneers a new perspective to facilitate scheduling efficiency. In this paper, we introduce a coflow compression mechanism to minimize the completion time in data-intensive applications. Due to the NP-hardness, we propose a heuristic algorithm called Fastest-Volume-Disposal-First (FVDF) to solve this problem. For online applicability, FVDF supports stage pipelining to accelerate scheduling and exploits recurrent neural networks (RNNs) to predict compression speed. Meanwhile, we build Swallow, an efficient scheduling system that implements our proposed algorithms. It minimizes coflow completion time (CCT) while guaranteeing resource conservation and starvation freedom. The results of both trace-driven simulations and real experiments show the superiority of our algorithm, over existing one. Specifically, Swallow speeds up CCT and job completion time (JCT) by up to 1.47χ and 1.66χ on average, respectively, over the SEBF in Varys, one of the most efficient coflow scheduling algorithms so far. Moreover, with coflow compression, Swallow reduces data traffic by up to 48.41 percent on average.
Practical Evaluation of VMAF Perceptual Video Quality for WebRTC Applications

WebRTC is the umbrella term for several emergent technologies aimed to exchange real-time media in the Web. Like other media-related services, the perceived quality of WebRTC communication can be measured using Quality of Experience (QoE) indicators. QoE assessment methods can be classified as subjective (users’ evaluation scores) or objective (models computed as a function of different parameters). In this paper, we focus on VMAF (Video Multi-method Assessment Fusion), which is an emergent full-reference objective video quality assessment model developed by Netflix. VMAF is typically used to assess video streaming services. This paper evaluates the use of VMAF in a different type of application: WebRTC. To that aim, we present a practical use case built on the top of well-known open source technologies, such as JUnit, Selenium, Docker, and FFmpeg. In addition to VMAF, we also calculate other objective QoE video metrics such as Visual Information Fidelity in the pixel domain (VIFp), Structural Similarity (SSIM), or Peak Signal-to-Noise Ratio (PSNR) applied to a WebRTC communication in different network conditions in terms of packet loss. Finally, we compare these objective results with a subjective evaluation using a Mean Opinion Score (MOS) scale to the same WebRTC streams. As a result, we found a strong correlation of the subjective video quality perceived in WebRTC video calls with the objective results computed with VMAF and VIFp in comparison with SSIM and PSNR and their variants.
A multi-scale strategy for deep semantic segmentation with convolutional neural networks

A novel multi-scale scheme is proposed to improve the performance of deep semantic segmentation based on Convolutional Neural Networks (CNNs). The fundamental idea is to combine the information from different intermediate layers by introducing new multi-scale loss (mLoss) function. We also show that it can be calculated by three different modules. The advantage of mLoss functions is that the loss of all layers could be optimized in one-shot without additional modifications of the training algorithm. The proposed strategy is also applied to improve the performance of Unet and FCN, and the structures of multi-scale loss functions are presented as well. Numerical validations are performed on two datasets, including the benchmark Pascal VOC 2012 dataset and the PICC dataset from medical treatment. It is illustrated that our multi-scale approach yields faster learning convergence rate and better accuracy.
Square-root accurate continuous-discrete extended-unscented Kalman filtering methods with embedded orthogonal and J-orthogonal QR decompositions for estimation of nonlinear continuous-time stochastic models in radar tracking

This paper presents a number of new state estimation algorithms, which unify the best features of the accurate continuous-discrete extended and unscented Kalman filters in treating nonlinear continuous-time stochastic systems with discrete measurements. In particular, our mixed-type algorithms succeed in estimating continuous-discrete stochastic systems with nonlinear and/or nondifferentiable measurements. The main weakness of these methods is the need for the Cholesky decomposition of predicted covariance matrices. Such a factorization is highly sensitive to numerical integration and round-off errors committed, which may result in losing the covariance’s positivity and, hence, failing the Cholesky decomposition. The latter problem is usually solved in the form of square-root filtering implementations, which propagate not the covariance matrix but its square root (Cholesky factor), only. Unfortunately, negative weights arising in applications of our mixed-type methods to high-dimensional stochastic systems preclude from designing conventional square-root filters. We address the mentioned issue with one-rank Cholesky factor updates or with hyperbolic QR transforms used for yielding J-orthogonal square-root filters. These novel algorithms are justified theoretically and examined and compared numerically to the non-square-root one in severe conditions of tackling a seven-dimensional radar tracking problem, where an aircraft executes a coordinated turn, in the presence of Gaussian or glint noise.
Mixture Density Generative Adversarial Networks

Generative Adversarial Networks have a surprising ability to generate sharp and realistic images, but they are known to suffer from the so-called mode collapse problem. In this paper, we propose a new GAN variant called Mixture Density GAN that overcomes this problem by encouraging the Discriminator to form clusters in its embedding space, which in turn leads the Generator to exploit these and discover different modes in the data. This is achieved by positioning Gaussian density functions in the corners of a simplex, using the resulting Gaussian mixture as a likelihood function over discriminator embeddings, and formulating an objective function for GAN training that is based on these likelihoods. We show how formation of these clusters changes the probability landscape of the discriminator and improves the mode discovery of the GAN. We also show that the optimum of our training objective is attained if and only if the generated and the real distribution match exactly. We support our theoretical results with empirical evaluations on three mode discovery benchmark datasets (Stacked-MNIST, Ring of Gaussians and Grid of Gaussians), and four image datasets (CIFAR-10, CelebA, MNIST, and Fashion-MNIST). Furthermore, we demonstrate (1) the ability to avoid mode collapse and discover all the modes and (2) superior quality of the generated images (as measured by the Fréchet Inception Distance (FID)), achieving the lowest FID compared to all baselines.
A fog-based hybrid intelligent system for energy saving in smart buildings

In recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper proposes a hybrid intelligent system which exploits a fog-based architecture to achieve energy efficiency in smart buildings. Our proposal combines reactive intelligence, for quick adaptation to the ever-changing environment, and deliberative intelligence, for performing complex learning and optimization. Such hybrid nature allows our system to be adaptive, by reacting in real time to relevant events occurring in the environment and, at the same time, to constantly improve its performance by learning users' needs. The effectiveness of our approach is validated in the application scenario of a smart home by extensive experiments on real sensor traces. Experimental results show that our system achieves substantial energy savings in the management of a smart environment, whilst satisfying users' needs and preferences.
Secure Distributed On-Device Learning Networks with Byzantine Adversaries

Privacy concerns exist when the central server has copies of datasets. Hence, there is a paradigm shift for learning networks to change from centralized in-cloud learning to distributed on-device learning. Benefitting from parallel computing, on-device learning networks have a lower bandwidth requirement than in-cloud learning networks. Moreover, on-device learning networks also have several desirable characteristics such as privacy preserving and flexibility. However, on-device learning networks are vulnerable to the malfunctioning terminals across the networks. The worst-case malfunctioning terminals are the Byzantine adversaries, that can perform arbitrary harmful operations to compromise the learned model based on the full knowledge of the networks. Hence, the design of secure learning algorithms becomes an emerging topic in the on-device learning networks with Byzantine adversaries. In this article, we present a comprehensive overview of the prevalent secure learning algorithms for the two promising on-device learning networks: Federated-Learning networks and decentralized-learning networks. We also review several future research directions in the Federated- Learning and decentralized-learning networks.
Sparse Representation-Based Video Quality Assessment for Synthesized 3D Videos

The temporal flicker distortion is one of the most annoying noises in synthesized virtual view videos when they are rendered by compressed multi-view video plus depth in Three Dimensional (3D) video system. To assess the synthesized view video quality and further optimize the compression techniques in 3D video system, objective video quality assessment which can accurately measure the flicker distortion is highly needed. In this paper, we propose a full reference sparse representation based video quality assessment method towards synthesized 3D videos. Firstly, a synthesized video, treated as a 3D volume data with spatial (X-Y) and temporal (T) domains, is reformed and decomposed as a number of spatially neighboring temporal layers, i.e., X-T or Y-T planes. Gradient features in temporal layers of the synthesized video and strong edges of depth maps are used as key features in detecting the location of flicker distortions. Secondly, dictionary learning and sparse representation for the temporal layers are then derived and applied to effectively represent the temporal flicker distortion. Thirdly, a rank pooling method is used to pool all the temporal layer scores and obtain the score for the flicker distortion. Finally, the temporal flicker distortion measurement is combined with the conventional spatial distortion measurement to assess the quality of synthesized 3D videos. Experimental results on synthesized video quality database demonstrate our proposed method is significantly superior to other state-of-the-art methods, especially on the view synthesis distortions induced from depth videos.
Cooperative strategy for constructing interval type-2 fuzzy neural network

Interval type-2 fuzzy neural network (IT2FNN) has attracted considerable interest for modeling nonlinear dynamic systems in recent years. However, this promising technique is confronting the problem that constructing a suitable IT2FNN is a potential challenge ignored by most researchers. To solve this problem, a self-constructing interval type-2 fuzzy neural network (SC-IT2FNN), based on the cooperative strategies, is proposed in this paper. The main contributions of this paper are: First, a comprehensive evaluation algorithm (CEA), cooperating with the parameter optimization, is developed to design the structure of SC-IT2FNN to enhance its generalization performance. Second, a hierarchical optimization mechanism, cooperating with the nonlinear and linear parameters of SC-IT2FNN, is proposed to accelerate its learning speed. Third, the convergence of SC-IT2FNN is theoretically analyzed in detail to ensure its successful applications. Finally, several benchmark nonlinear systems and a real application are utilized to evaluate the effectiveness of SC-IT2FNN. The results demonstrate that our proposed SC-IT2FNN significantly improve the modeling performance in terms of high accuracy and compact structure.
High-performance Implementation of Elliptic Curve Cryptography Using Vector Instructions

Elliptic curve cryptosystems are considered an efficient alternative to conventional systems such as DSA and RSA. Recently, Montgomery and Edwards elliptic curves have been used to implement cryptosystems. In particular, the elliptic curves Curve25519 and Curve448 were used for instantiating Diffie-Hellman protocols named X25519 and X448. Mapping these curves to twisted Edwards curves allowed deriving two new signature instances, called Ed25519 and Ed448, of the Edwards Digital Signature Algorithm. In this work, we focus on the secure and efficient software implementation of these algorithms using SIMD parallel processing. We present software techniques that target the Intel AVX2 vector instruction set for accelerating prime field arithmetic and elliptic curve operations. Our contributions result in a high-performance software library for AVX2-ready processors. For example, our library computes digital signatures 19% (for Ed25519) and 29% (for Ed448) faster than previous optimized implementations. Also, our library improves by 10% and 20% the execution time of X25519 and X448, respectively.
360SRL: A Sequential Reinforcement Learning Approach for ABR Tile-Based 360 Video Streaming

Tile-based 360-degree video (360 video) streaming, employed with adaptive bitrate (ABR) algorithms, is a promising approach to offer high video quality of experience (QoE) within limited network bandwidth. Existing ABR algorithms, however, fail to achieve optimal performance in real-world fluctuated network conditions as they heavily rely on unbiased bandwidth predictions. Recently, reinforcement learning (RL) has shown promising potential in generating better ABR algorithms in 2D video streaming. However, unlike existed work in 2D video streaming, directly applying RL in the tile-based 360 video streaming is infeasible due to the resulting exponential decision space. To overcome these limitations, we propose in this paper 360SRL, an improved ABR algorithm employing Sequential RL (360SRL). Firstly, we reduce the decision space of 360SRL from exponential to linear by introducing a sequential ABR decision structure, thus making it feasible to be employed with RL. Secondly, instead of relying on accurate bandwidth predictions, 360SRL learns to make ABR decisions solely through observations of the resulting QoE performance of past decisions. Finally, we compare 360SRL to state-of-the-art ABR algorithms using trace-driven experiments. The experiment results demonstrate that 360SRL outperforms state-of-the-art algorithms with around 12% improvement in average QoE.
On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem

Background. Companies commonly invest major effort into removing, respectively not introducing, technical debt issues detected by static analysis tools such as SonarQube, Cast, or Coverity. These tools classify technical debt issues into categories according to severity, and developers commonly pay attention to not introducing issues with a high level of severity that could generate bugs or make software maintenance more difficult. Objective. In this work, we aim to understand the diffuseness of Technical Debt (TD) issues and the speed with which developers remove them from the code if they introduced such an issue. The goal is to understand which type of TD is more diffused and how much attention is paid by the developers, as well as to investigate whether TD issues with a higher level of severity are resolved faster than those with a lower level of severity. We conducted a case study across 78K commits of 33 Java projects from the Apache Software Foundation Ecosystem to investigate the distribution of 1.4M TD items. Results. TD items introduced into the code are mostly related to code smells (issues that can increase the maintenance effort). Moreover, developers commonly remove the most severe issues faster than less severe ones. However, the time needed to resolve issues increases when the level of severity increases (minor issues are removed faster that blocker ones). Conclusion. One possible answer to the unexpected issue of resolution time might be that severity is not correctly defined by the tools. Another possible answer is that the rules at an intermediate severity level could be the ones that technically require more time to be removed. The classification of TD items, including their severity and type, require thorough investigation from a research point of view.
A Real-Time Interactive Augmented Reality Depth Estimation Technique for Surgical Robotics

Augmented reality (AR) is a promising technology where the surgeon can see the medical abnormality in the context of the patient. It makes the anatomy of interest visible to the surgeon which otherwise is not visible. It can result in better surgical precision and therefore, potentially better surgical outcomes and faster recovery times. Despite these benefits, the current AR systems suffer from two major challenges; first, incorrect depth perception and, second, the lack of suitable evaluation systems. Therefore, in the current paper we addressed both of these problems. We proposed a color depth encoding (CDE) technique to estimate the distance between the tumor and the tissue surface using a surgical instrument. We mapped the distance between the tumor and the tissue surface to the blue-red color spectrum. For evaluation and interaction with our AR technique, we propose to use a virtual surgical instrument method using the CAD model of the instrument. The users were asked to reach the judged distance in the surgical field using the virtual tool. Realistic tool movement was simulated by collecting the forward kinematics joint encoder data. The results showed significant improvement in depth estimation, time for task completion and confidence, using our CDE technique with and without stereo versus other two cases, that are, Stereo-No CDE and No Stereo-No CDE.
Applications of genetic programming to finance and economics: past, present, future

While the origins of genetic programming (GP) stretch back over 50 years, the field of GP was invigorated by John Koza's popularisation of the methodology in the 1990s. A particular feature of the GP literature since then has been a strong interest in the application of GP to real-world problem domains. One application domain which has attracted significant attention is that of finance and economics, with several hundred papers from this subfield being listed in the GP bibliography. In this article we outline why finance and economics has been a popular application area for GP and briefly indicate the wide span of this work. However, despite this research effort there is relatively scant evidence of the usage of GP by the mainstream finance community in academia or industry. We speculate why this may be the case, describe what is needed to make this research more relevant from a finance perspective, and suggest some future directions for the application of GP in finance and economics.
Kinematic Constraints Based Bi-directional RRT (KB-RRT) with Parameterized Trajectories for Robot Path Planning in Cluttered Environment

Optimal path planning and smooth trajectory planning are critical for effective navigation of mobile robots working towards accomplishing complex missions. For autonomous, real time and extended operations of mobile robots, the navigation capability needs to be executed at the edge. Thus, efficient compute, minimum memory utilization and smooth trajectory are the key parameters that drive the successful operation of autonomous mobile robots. Traditionally, navigation solutions focus on developing robust path planning algorithms which are complex and compute/memory intensive. Bidirectional-RRT(Bi-RRT) based path planning algorithms have gained increased attention due to their effectiveness and computational efficiency in generating feasible paths. However, these algorithms neither optimize memory nor guarantee smooth trajectories. To this end, we propose a kinematically constrained Bi-RRT (KB-RRT) algorithm, which restricts the number of nodes generated without compromising on the accuracy and incorporates kinodynamic constraints for generating smooth trajectories, together resulting in efficient navigation of autonomous mobile robots. The proposed algorithm is tested in a highly cluttered environment on an Ackermannsteering vehicle model with severe kinematic constraints. The experimental results demonstrate that KB-RRT achieves three times (3 X) better performance in terms of convergence rate and memory utilization compared to a standard Bi-RRT algorithm.
Evaluating Practice of Smartphone Use Among University Students in Undergraduate Nursing Education

Smartphones are the most popular and intriguing technological gadgets that have changed the face of communication and laid an irreversible impact on psychosocial behavior of consumers. A large number of undergraduate nursing students own smartphones and use it effectively for means of communication and acquiring information. This study was designed to evaluate the practice of smartphone use in undergraduate nursing students for educational purposes. A cross-sectional survey was conducted in college of nursing (N = 135) in King Saud bin Abdul Aziz University for Health Sciences following IRB approval using a self administered questionnaire, piloted and pre-tested in 30 participants. The study showed that 94.8% students carry their phones consciously at all times whereas 92.6% check their phones as soon as they arise in the morning. A large number of students (77.8%) reported that they document information on their smartphones in the class whereas 24.4% always use them in clinical settings. The most prevalent use of smartphones was to access information on the website (93.3%) which was more frequently reported in group study compared to individual study. A large number of students reported that they participate in WhatsApp study group (89.6%). Accessing social media platforms for academic reasons was reported by 85.2% respondents. Undergraduate nursing students rely heavily on their smartphones for acquiring information and communication. They bear tendency of smartphone addiction and appear to be potential recipients for active learning techniques that may fit their educational needs.
Cracking Open the Black Box

Machine learning (ML) solutions to challenging networking problems, while promising, are hard to interpret; the uncertainty about how they would behave in untested scenarios has hindered adoption. Using a case study of an ML-based video rate adaptation model, we show that carefully applying interpretability tools and systematically exploring the model inputs can identify unwanted or anomalous behaviors of the model; hinting at a potential path towards increasing trust in ML-based solutions.