Reviewerly/exHarmony huggingface dataset:
Data Centers Job Scheduling with Deep Reinforcement Learning

Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space. To adapt the complex computing environment in data centers, we proposed an innovative Advantage Actor-Critic (A2C) deep reinforcement learning based approach called A2cScheduler for job scheduling. A2cScheduler consists of two agents, one of which, dubbed the actor, is responsible for learning the scheduling policy automatically and the other one, the critic, reduces the estimation error. Unlike previous policy gradient approaches, A2cScheduler is designed to reduce the gradient estimation variance and to update parameters efficiently. We show that the A2cScheduler can achieve competitive scheduling performance using both simulated workloads and real data collected from an academic data center.
Large Deviations Behavior of the Logarithmic Error Probability of Random Codes

This work studies the deviations of the error exponent of the constant composition code ensemble around its expectation, known as the error exponent of the typical random code (TRC). In particular, it is shown that the probability of randomly drawing a codebook whose error exponent is smaller than the TRC exponent is exponentially small; upper and lower bounds for this exponent are given, which coincide in some cases. In addition, the probability of randomly drawing a codebook whose error exponent is larger than the TRC exponent is shown to be double-exponentially small; upper and lower bounds to the double-exponential exponent are given. The results suggest that codebooks whose error exponent is larger than the error exponent of the TRC are extremely rare. The key ingredient in the proofs is a new large deviations result of type class enumerators with dependent variables.
Dense convolutional feature histograms for robust visual object tracking

Despite recent breakthroughs in the field, Visual Object Tracking remains an open and challenging task in Computer Vision. Modern applications require trackers to not only be accurate but also very fast, even on embedded systems. In this work, we use features from Convolutional Neural Networks to build histograms, which are more adept at handling appearance variations, in an end-to-end trainable architecture. To deal with the internal covariate shift that occurs when extracting histograms from convolutional features as well as to incorporate informations from the multiple levels of the neural hierarchy, we propose and use a novel densely connected architecture where histograms from multiple layers are concatenated to produce the final representation. Experimental results validate our hypotheses on the benefits of using histograms as opposed to standard convolutional features, as the proposed histogram-based tracker surpasses recently proposed sophisticated trackers on multiple benchmarks. Long-term tracking results also reaffirm the usefulness of the proposed tracker in more challenging scenarios, where appearance variations are more severe and traditional trackers fail.
Covariance descriptors on a Gaussian manifold and their application to image set classification

Covariance descriptors (CovDs) for image set classification have been widely studied recently. Different from the conventional CovDs, which describe similarities between pixels at different locations, we focus more on similarities between regions that convey more comprehensive information. In this paper, we extract pixel-wise features of image regions and represent them by Gaussian models. We extend the conventional covariance computation onto a special type of Riemannian manifold, namely a Gaussian manifold, so that it is applicable to our image set data representation provided in terms of Gaussian models. We present two methods to calculate a Riemannian local difference vector on the Gaussian manifold (RieLDV-G) and generate our proposed Riemannian covariance descriptors (RieCovDs) using the resulting RieLDV-G. By measuring the recognition accuracy achieved on benchmarking datasets, we demonstrate experimentally the superior performance of our proposed RieCovDs descriptors, as compared with state-of-the-art methods. (The code is available at: https://github.com/Kai-Xuan/RiemannianCovDs)
Image splicing detection based on convolutional neural network with weight combination strategy

With the rapid development of splicing manipulation, more and more negative effects have been brought. Therefore, the demand for image splicing detection algorithms is growing dramatically. In this paper, a new image splicing detection method is proposed which is based on convolutional neural network (CNN) with weight combination strategy. In the proposed method, three types of features are selected to distinguish splicing manipulation including YCbCr features, edge features and photo response non-uniformity (PRNU) features, which are combined according to weight by the combination strategy. Different from the other methods, these weight parameters are automatically adjusted during the CNN training process, until the best ratio is obtained. Experiments show that the proposed method has higher accuracy than the other methods using CNN, and the depth of the CNN in the method proposed is much less than the compared methods.
WLEC: A Not So Cold Architecture to Mitigate Cold Start Problem in Serverless Computing

As serverless computing gains popularity among developers for its low costing and elasticity, it has emerged as a promising research field in computer science. Despite its popularity, the cold start remains an issue that needs more attention. In this paper, we address the cold start problem of the serverless platform. We propose WLEC, a container management architecture to minimize the cold start time. WLEC uses a modified S2LRU structure, called S2LRU ++ with an additional third queue. We implement WLEC in OpenLambda and evaluate it in both AWS and Local VM environment with six different metrics in addition to one real-time image resizing application. Among improvements in all metrics, 50% less memory consumption compared to the all-warm method and 31% average cold start duration reduction compared to the no-warm method are the most notable ones.
Parallel algorithms for finding connected components using linear algebra

Finding connected components is one of the most widely used operations on a graph. Optimal serial algorithms for the problem have been known for half a century, and many competing parallel algorithms have been proposed over the last several decades under various different models of parallel computation. This paper presents a class of parallel connected-component algorithms designed using linear-algebraic primitives. These algorithms are based on a PRAM algorithm by Shiloach and Vishkin and can be designed using standard GraphBLAS operations. We demonstrate two algorithms of this class, one named LACC for Linear Algebraic Connected Components, and the other named FastSV which can be regarded as LACC’s simplification. With the support of the highly-scalable Combinatorial BLAS library, LACC and FastSV outperform the previous state-of-the-art algorithm by a factor of up to 12x for small to medium scale graphs. For large graphs with more than 50B edges, LACC and FastSV scale to 4K nodes (262K cores) of a Cray XC40 supercomputer and outperform previous algorithms by a significant margin. This remarkable performance is accomplished by (1) exploiting sparsity that was not present in the original PRAM algorithm formulation, (2) using high-performance primitives of Combinatorial BLAS, and (3) identifying hot spots and optimizing them away by exploiting algorithmic insights.
Image encryption based on Kronecker product over finite fields and DNA operation

This paper reports an image encryption algorithm based on a matrix of Kronecker products and a deoxyribonucleic acid (DNA) operation over finite fields. First, a plaintext image is mapped from pixel gray-levels to the finite field. Subsequently, the image pixels are scrambled and diffused simultaneously by the Kronecker-product matrix based on a permutation polynomial over finite fields. Finally, a DNA operation is carried out to obtain greater confusion and scrambling. A widespread of security analyses and experimental results show that the proposed algorithm is safe against several common attacks. Moreover, there were no rounding errors in computation over finite fields, thereby ensuring a strict lossless image encryption and decryption. Due to the intrinsic nonlinearity of the permutation polynomials in the finite field, the proposed image encryption system is nonlinear and can resist known-plaintext and chosen-plaintext attacks.
Uncertainty-Based Rejection Wrappers for Black-Box Classifiers

Machine Learning as a Service platform is a very sensible choice for practitioners that want to incorporate machine learning to their products while reducing times and costs. However, to benefit their advantages, a method for assessing their performance when applied to a target application is needed. In this work, we present a robust uncertainty-based method for evaluating the performance of both probabilistic and categorical classification black-box models, in particular APIs, that enriches the predictions obtained with an uncertainty score. This uncertainty score enables the detection of inputs with very confident but erroneous predictions while protecting against out of distribution data points when deploying the model in a productive setting. We validate the proposal in different natural language processing and computer vision scenarios. Moreover, taking advantage of the computed uncertainty score, we show that one can significantly increase the robustness and performance of the resulting classification system by rejecting uncertain predictions.
A Multi-Objective Trajectory Planning Method for Collaborative Robot

Aiming at the characteristics of high efficiency and smoothness in the motion process of collaborative robot, a multi-objective trajectory planning method is proposed. Firstly, the kinematics model of the collaborative robot is established, and the trajectory in the workspace is converted into joint space trajectory using inverse kinematics method. Secondly, seven-order B-spline functions are used to construct joint trajectory sequences to ensure the continuous position, velocity, acceleration and jerk of each joint. Then, the trajectory competitive multi-objective particle swarm optimization (TCMOPSO) algorithm is proposed to search the Pareto optimal solutions set of the robot’s time-energy-jerk optimal trajectory. Further, the normalized weight function is proposed to select the appropriate solution. Finally, the algorithm simulation experiment is completed in MATLAB, and the robot control experiment is completed using the Robot Operating System (ROS). The experimental results show that the method can achieve effective multi-objective optimization, the appropriate optimal trajectory can be obtained according to the actual requirements, and the collaborative robot is actually operating well.
A Lightweight Post-Quantum Lattice-Based RSA for Secure Communications

Conventional RSA algorithm, being a basis for several proposed cryptosystems, has remarkable security laps with respect to confidentiality and integrity over the internet which can be compromised by state-of-the-art attacks, especially, for different types of data generation, transmission, and analysis by IoT applications.This security threat hindrance is considered to be a hard problem to solve on classical computers.However, bringing quantum mechanics into account, the concept no longer holds true.So, this calls out for the modification of the conventional pre-quantum RSA algorithm into a secure post-quantum cryptographic-based RSA technique.In this research, we propose a post-quantum lattice-based RSA (LB-RSA) for IoT-based cloud applications to secure the shared data and information.The proposed work is validated by implementing it in 60-dimensions.The key size is about 1.152 × 10 5 -bits and generation time is 0.8 hours.Furthermore, it has been tested with AVISPA, which confirms security in the presence of an intruder.Moreover, the proposed LB-RSA technique is compared with the existing state-of-the-art techniques.The empirical results advocate that the proposed lattice-based variant is not only safe but beats counterparts in terms of secured data sharing.
Plastic Packaging, Food Supply, and Everyday Life

This article presents practice-theoretical conceptions of societal relations to nature as a fruitful alternative to common system approaches in social-ecological research. Via the example of plastic food packaging, two different practice-theoretical approaches to food supply are discussed regarding their suitability for relating the material properties of packaging to their everyday use by producers, retailers, and consumers: (1) the network approach (portraying food supply as a network of practices; these practices include material elements that interrelate with other elements like competence or meaning) and (2) the nexus approach (investigating the interrelation between social practices and material arrangements in which they take place). Depending on the given research interest, both perspectives have their pros and cons: the network approach is stronger in understanding the everyday use of technologies, while the nexus approach encourages the integration of infrastructures and environmental contexts that are not directly observable within the practice.
Summary-aware attention for social media short text abstractive summarization

Neural sequence-to-sequence (Seq2Seq) models are currently dominant approaches in social media short text abstractive summarization task. However, the computation of source attention weight in Seq2Seq models does not fully consider the information of the previously generated summary, which harms the coherence of the summary. In addition, given the noisy source text, it is difficult for Seq2Seq models to learn an accurate source context. To address these two shortcomings, we propose summary-aware attention for social media short text abstractive summarization. In this work, we use the source hidden states and attended summary vectors to compute summary-aware attention weight. The attended summary vectors contribute to generate a coherent summary. Moreover, under the guidance of the attended summary vectors, the attention mechanism increases the weight of the related content and reduces the weight of noise. Following the previous work, we evaluate our model on a popular Chinese social media dataset. Both automatic and human evaluation results demonstrate that our model achieves significant improvements compared with strong baselines.
Stop and Smell the Chalk Flowers

HCI researchers have begun to more systematically study non-digital transient approaches for displaying information in public space, for example, in the form of chalk infographics. These approaches provide several benefits compared to digital displays, such as: ad-hoc deployment, barrier-free interaction, and being more sustainable. However, one limitation is their hyperlocal scale and impact. Speculating on urban robots as agents for scaling up physicalised displays, we describe the exploratory design and deployment of Woodie, a slow-moving robot that draws on the ground using conventional chalk sticks. We deployed Woodie for three weeks in a quiet laneway situated within a highly urbanised area. Data collected from observations, video logs and interviews revealed that Woodie successfully attracted people's attention and acted as a facilitator for collaborative, creative placemaking. Furthermore, Woodie provoked emotional responses and was perceived as a living being. Findings are interpreted to describe opportunities urban robots provide for the design of future pervasive urban displays.
3DSymm: Robust and Accurate 3D Reflection Symmetry Detection

Reflection symmetry is a very commonly occurring feature in both natural and man-made objects, which helps in understanding objects better and makes them visually pleasing. Detection of reflection symmetry is a fundamental problem in the field of computer vision and computer graphics which aids in understanding and representing reflective symmetric objects. In this work, we attempt the problem of detecting the 3D global reflection symmetry of a 3D object represented as a point cloud. The main challenge is to handle outliers, missing parts, and perturbations from the perfect reflection symmetry. We propose a descriptor-free approach, in which, we pose the problem of reflection symmetry detection as an optimization problem and provide a closed-form solution. We show that the proposed method achieves state-of-the-art performance on the standard dataset.
Composing Flexibly-Organized Step-by-Step Tutorials from Linked Source Code, Snippets, and Outputs

Programming tutorials are a pervasive, versatile medium for teaching programming. In this paper, we report on the content and structure of programming tutorials, the pain points authors experience in writing them, and a design for a tool to help improve this process. An interview study with 12 experienced tutorial authors found that they construct documents by interleaving code snippets with text and illustrative outputs. It also revealed that authors must often keep related artifacts of source programs, snippets, and outputs consistent as a program evolves. A content analysis of 200 frequently-referenced tutorials on the web also found that most tutorials contain related artifacts—duplicate code and outputs generated from snippets—that an author would need to keep consistent with each other. To address these needs, we designed a tool called Torii with novel authoring capabilities. An in-lab study showed that tutorial authors can successfully use the tool for the unique affordances identified, and provides guidance for designing future tools for tutorial authoring.
A Novel UAV Sensing Image Defogging Method

Imaging of unmanned aerial vehicle easily suffer from haze, resulting in decline in the quality of required remote sensing images. The influence brings great challenges in later analysis and process. Although dark channel prior has acquired substantial achievements, some limitations, including imprecise estimation of atmospheric light, color distortion, and lower brightness of defogging image, still exist. In this article, to target these drawbacks, a novel defogging method for single image is proposed. First, a novel atmospheric scattering model is proposed to define the more accurate atmospheric light by introducing an adaptive variable strategy. Next, unlike traditional dark channel prior, a novel estimation method is presented by fusing dark and light channels to estimate more precise atmospheric light and transmittance. Then, we adopt the gray image corresponding to color image as a guidance image to refine the transmittance to further decrease the time complexity. Aiming at the region of low transmittance, a novel compensation function is created to improve the region of low transmittance and avoid color distortion. Moreover, a simple and effective calculation method is proposed to determine parameters in compensation function. Finally, the clear remote sensing image is established by an improved atmospheric scattering model. Extensive experiments on real-world datasets demonstrate that the proposed method outperforms several other state-of-the-art approaches both on subjective and objective quality evaluations.
A first-person promise? A content-analysis of immersive journalistic productions

Major news outlets increasingly use immersive techniques in their journalistic productions. The idea is that, through the application of immersive technologies, the news consumer can engage with and be part of the story. However, we do not know, to what extent this promise is actually fulfilled in productions currently accessible to news audiences. This study uses a multi-step approach to fill this knowledge gap. First, based on an interdisciplinary literature review, we propose a conceptual model of immersive journalism. We then use the elements of this model to content analyze 189 journalistic productions that are labeled as immersive by the producers, including 360 degree videos, computer-generated VR, and interactive, 360 web productions, stemming from a variety of outlets and countries. Results show that the level of user immersion in these productions is actually limited, with little interaction and technical inclusion possibilities. Our results contribute to an emerging field of journalism studies, in which the user plays a new role when interacting with advancing technologies.
Training feedforward neural network via multiobjective optimization model using non-smooth <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si9.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> regularization

The paper presents a new approach to optimize the Multilayer Perceptron Neural Network (MLPNN), to deal with the generalization problem. As known, most supervised learning algorithms aim to minimize the training error. However, the mentioned methods, based only on error minimizing, may generate a solution with an insufficient generalization performance. This present work proposes a multiobjective modelling problem involving two objectives: accuracy and complexity since the learning problem is multiobjective by nature. The learning task is carried on by minimizing both objectives simultaneously, according to Pareto domination concept, using NSGAII (Non-dominated Sorting Genetic Algorithm II) as a solver. This method leads us to a set of solutions called Pareto front, being the optimal solutions set, the adequate MLPNN need to be extracted. We show empirically that the proposed method is capable of reducing the neural networks topology and improved generalization performance, in addition to a good classification rate compared to different methods.
SCODED: Statistical Constraint Oriented Data Error Detection

Statistical Constraints (SCs) play an important role in statistical modeling and analysis. This paper brings the concept to data cleaning and studies how to leverage SCs for error detection. SCs provide a novel approach that has various application scenarios and works harmoniously with downstream statistical modeling. Entailment relationships between SCs and integrity constraints provide analytical insight into SCs. We develop SCODED, an SC-Oriented Data Error Detection system, comprising two key components: (1) SC Violation Detection : checks whether an SC is violated on a given dataset, and (2) Error Drill Down : identifies the top-k records that contribute most to the violation of an SC. Experiments on synthetic and real-world data show that SCs are effective in detecting data errors that violate them, compared to state-of-the-art approaches.
Using the Bees Algorithm to solve combinatorial optimisation problems for TSPLIB

Abstract The Bees Algorithm (BA) is a metaheuristic algorithm to find good solutions to optimisation problems in reasonable computing times. This paper is the first to report on the use of the BA to solve 9 combinatorial optimisation problems (COP) with more than 100 cities from TSPLIB rigorously to test the performance of the algorithm. The work employed a basic version of the BA for COP and TSPLIB datasets involving between 100 and 200 cities. The results obtained show that deviations from the best-found tour lengths for the datasets with 100 cities and 200 cities were approximately 2.5% and 7.5%. The reason for this jump in deviations was that the number of iterations was kept constant for all experiments while the solution space increased factorially with the number of cities. This research can be replicated and modified through Google Colab.