Reviewerly/exHarmony huggingface dataset:
Towards Green Service Composition Approach in the Cloud

With the increasing popularity of cloud computing, many notable quality of service (QoS)-aware service composition approaches have been incorporated in service-oriented cloud computing systems. However, these approaches are implemented without considering the energy and network resource consumption of the composite services. The increases in energy and network resource consumption resulting from these compositions can incur a high cost in data centers. In this paper, the trade-off among QoS performance, energy consumption, and network resource consumption in a service composition process is first analyzed. Then, a green service composition approach is proposed. It gives priority to those composite services that are hosted on the same virtual machine, physical server, or edge switch with end-to-end QoS guarantee. It fulfills the green service composition optimization by minimizing the energy and network resource consumption on physical servers and switches in cloud data centers. Experimental results indicate that, with comparisons to other approaches, our approach saves 20-50 percent of energy consumption and 10-50 percent of network resource consumption.
Perceptual image quality assessment through spectral analysis of error representations

In this paper, we analyze the statistics of error signals to assess the perceived quality of images. Specifically, we focus on the magnitude spectrum of error images obtained from the difference of reference and distorted images. Analyzing spectral statistics over grayscale images partially models interference in spatial harmonic distortion exhibited by the visual system but it overlooks color information, selective and hierarchical nature of visual system. To overcome these shortcomings, we introduce an image quality assessment algorithm based on the Spectral Understanding of Multi-scale and Multi-channel Error Representations, denoted as SUMMER. We validate the quality assessment performance over 3 databases with around 30 distortion types. These distortion types are grouped into 7 main categories as compression artifact, image noise, color artifact, communication error, blur, global and local distortions. In total, we benchmark the performance of 17 algorithms along with the proposed algorithm using 5 performance metrics that measure linearity, monotonicity, accuracy, and consistency. In addition to experiments with standard performance metrics, we analyze the distribution of objective and subjective scores with histogram difference metrics and scatter plots. Moreover, we analyze the classification performance of quality assessment algorithms along with their statistical significance tests. Based on our experiments, SUMMER significantly outperforms majority of the compared methods in all benchmark categories
GAME-SCORE: Game-based energy-aware cloud scheduler and simulator for computational clouds

Energy-awareness remains one of the main concerns for today's cloud computing (CC) operators. The optimisation of energy consumption in both cloud computational clusters and computing servers is usually related to scheduling problems. The definition of an optimal scheduling policy which does not negatively impact to system performance and task completion time is still challenging. In this work, we present a new simulation tool for cloud computing, GAME-SCORE, which implements a scheduling model based on the Stackelberg game. This game presents two main players: a) the scheduler and b) the energy-efficiency agent. We used the GAME-SCORE simulator to analyse the efficiency of the proposed game-based scheduling model. The obtained results show that the Stackelberg cloud scheduler performs better than static energy-optimisation strategies and can achieve a fair balance between low energy consumption and short makespan in a very short time.
Approximate Unitary t-Designs by Short Random Quantum Circuits Using Nearest-Neighbor and Long-Range Gates

We prove that $poly(t) \cdot n^{1/D}$-depth local random quantum circuits with two qudit nearest-neighbor gates on a $D$-dimensional lattice with n qudits are approximate $t$-designs in various measures. These include the "monomial" measure, meaning that the monomials of a random circuit from this family have expectation close to the value that would result from the Haar measure. Previously, the best bound was $poly(t)\cdot n$ due to Brandao-Harrow-Horodecki (BHH) for $D=1$. We also improve the "scrambling" and "decoupling" bounds for spatially local random circuits due to Brown and Fawzi. One consequence of our result is that assuming the polynomial hierarchy (PH) is infinite and that certain counting problems are $\#P$-hard on average, sampling within total variation distance from these circuits is hard for classical computers. Previously, exact sampling from the outputs of even constant-depth quantum circuits was known to be hard for classical computers under the assumption that PH is infinite. However, to show the hardness of approximate sampling using this strategy requires that the quantum circuits have a property called "anti-concentration", meaning roughly that the output has near-maximal entropy. Unitary 2-designs have the desired anti-concentration property. Thus our result improves the required depth for this level of anti-concentration from linear depth to a sub-linear value, depending on the geometry of the interactions. This is relevant to a recent proposal by the Google Quantum AI group to perform such a sampling task with 49 qubits on a two-dimensional lattice and confirms their conjecture that $O(\sqrt n)$ depth suffices for anti-concentration. We also prove that anti-concentration is possible in depth O(log(n) loglog(n)) using a different model.
Blind assessment for stereo images considering binocular characteristics and deep perception map based on deep belief network

In recent years, blind image quality assessment in the field of 2D image/video has gained the popularity, but its applications in 3D image/video are to be generalized. In this paper, we propose an effective blind metric evaluating stereo images via deep belief network (DBN). This method is based on wavelet transform with both 2D features from monocular images respectively as image content description and 3D features from a novel depth perception map (DPM) as depth perception description. In particular, the DPM is introduced to quantify longitudinal depth information to align with human stereo visual perception. More specifically, the 2D features are local histogram of oriented gradient (HoG) features from high frequency wavelet coefficients and global statistical features including magnitude, variance and entropy. Meanwhile, the global statistical features from the DPM are characterized as 3D features. Subsequently, considering binocular characteristics, an effective binocular weight model based on multiscale energy estimation of the left and right images is adopted to obtain the content quality. In the training and testing stages, three DBN models for the three types features separately are used to get the final score. Experimental results demonstrate that the proposed stereo image quality evaluation model has high superiority over existing methods and achieve higher consistency with subjective quality assessments.
Recurrent Flow-Guided Semantic Forecasting

Understanding the world around us and making decisions about the future is a critical component to human intelligence. As autonomous systems continue to develop, their ability to reason about the future will be the key to their success. Semantic anticipation is a relatively under-explored area for which autonomous vehicles could take advantage of (e.g., forecasting pedestrian trajectories). Motivated by the need for real-time prediction in autonomous systems, we propose to decompose the challenging semantic forecasting task into two subtasks: current frame segmentation and future optical flow prediction. Through this decomposition, we built an efficient, effective, low overhead model with three main components: flow prediction network, feature-flow aggregation LSTM, and end-to-end learnable warp layer. Our proposed method achieves state-of-the-art accuracy on short-term and moving objects semantic forecasting while simultaneously reducing model parameters by up to 95% and increasing efficiency by greater than 40x.
Tracking a Golf Ball With High-Speed Stereo Vision System

Professional long drivers can hit golf balls to a speed of up to 350 km/h. Extremely short exposure time is required to capture the objects moving that fast without blur. The very high frame rate is also required to capture the sufficient sequence of object movements within the field of view in order to predict trajectory, which means a sacrifice in frame image resolution at fixed data throughput. This paper presents a high-speed stereo vision system that is able to track the golf ball motion at the speed of up to 360 km/h under normal indoor lighting conditions. Binocular camera operated at 810 frames/s with 35-μs exposure time, requiring no additional strobe lights in order not to compromise user experiences. Ball markers were designed for spin measurement. To detect balls and identify markers from highly underexposed low resolution images, new algorithms were developed and implemented on field-programmable gate array board with an advanced RISC machines CPU. The experimental results showed high detection rate and high accuracy with overall processing time less than 300 ms.
MSFD: Multi-Scale Segmentation-Based Feature Detection for Wide-Baseline Scene Reconstruction

A common problem in wide-baseline matching is the sparse and non-uniform distribution of correspondences when using conventional detectors, such as SIFT, SURF, FAST, A-KAZE, and MSER. In this paper, we introduce a novel segmentation-based feature detector (SFD) that produces an increased number of accurate features for wide-baseline matching. A multi-scale SFD is proposed using bilateral image decomposition to produce a large number of scale-invariant features for wide-baseline reconstruction. All input images are over-segmented into regions using any existing segmentation technique, such as Watershed, Mean-shift, and simple linear iterative clustering. Feature points are then detected at the intersection of the boundaries of three or more regions. The detected feature points are local maxima of the image function. The key advantage of feature detection based on segmentation is that it does not require global threshold setting and can, therefore, detect features throughout the image. A comprehensive evaluation demonstrates that SFD gives an increased number of features that are accurately localized and matched between wide-baseline camera views; the number of features for a given matching error increases by a factor of 3-5 compared with SIFT; feature detection and matching performance are maintained with increasing baseline between views; multi-scale SFD improves matching performance at varying scales. Application of SFD to sparse multi-view wide-baseline reconstruction demonstrates a factor of 10 increases in the number of reconstructed points with improved scene coverage compared with SIFT/MSER/A-KAZE. Evaluation against ground-truth shows that SFD produces an increased number of wide-baseline matches with a reduced error.
Full-Reference Image Quality Assessment by Combining Features in Spatial and Frequency Domains

Objective image quality assessment employs mathematical and computational theory to objectively assess the quality of output images based on the human visual system (HVS). In this paper, a novel approach based on multifeature extraction in the spatial and frequency domains is proposed. We combine the gradient magnitude and phase congruency maps to generate a local structure (LS) map, which can perceive local structural distortions. The LS matches well with HVS and highlights differences with details. For complex visual information, such as texture and contrast sensitivity, we deploy the log-Gabor filter, and spatial frequency, respectively, to effectively capture their variations. Moreover, we employ the random forest (RF) to overcome the limitations of existing pooling methods. Compared with support vector regression, RF can obtain better prediction results. Extensive experimental results on the five benchmark databases indicate that the proposed method precedes all the state-of-the-art image quality assessment metrics in terms of prediction accuracy. In addition, the proposed method is in compliance with the subjective evaluations.
Vection does not necessitate visually induced motion sickness

Vection, i.e. a visually induced illusory sense of self-motion, is assumed to play an essential role in visually induced motion sickness (VIMS). However, its precise role is unknown. Following the sensory conflict theory, a constant state of vection is not expected to lead to a visual-vestibular conflict whereas variability in vection, i.e. change in vection strength over time, would. In this study we investigated whether variability in vection rather than vection caused VIMS in participants exposed to constant optic flow using a head-mounted display. Strongest possible vection (i.e. 100% on a 0–100% scale) was reported by 16 out of 18 participants at some point during the experiment, with a total average vection score over the experiment of 58.6%. Initial motion sickness symptoms were reported by 15 out of 18 participants, although only averaging 1.78 on an 11-point scale. Neither average vection strength nor variability in vection were significantly correlated with motion sickness. Relating our findings to the literature, we argue that vection should be understood not as a direct cause of VIMS, but as a perceptual state still depending on other visual factors before VIMS occurs. Vection by itself, even if it is experienced strongly, does not necessitate VIMS.
Multi-modal matrix factorization with side information for recommending massive open online courses

Massive open online courses (MOOCs) have recently gained a huge users' attention on the Web. They are considered as a highly promising form of teaching from leading universities such as Stanford and Berkeley. However, users confront the problem of choosing among thousands of offered MOOCs. In such a scenario of severe "information overload", recommender systems can be very useful to recommend the right course to a user, since they base their operation on past user's log history. For example, Coursera recommends courses to users so that, they can acquire those skills, that are expected from their ideal job. These user's preferences are not expected to be independent from others choices, as users follow trends of similar behaviour. In this paper, we propose, xSVD++, where the "x" means that it is a multi-dimensional Matrix Factorization (MMF) model combined with Collaborative Filtering (CF) algorithm, which exploits information from external resources (i.e., users' skills, courses' characteristics, etc.) to predict course trends and to perform rating predictions according to them. Our experimental results indicate that xSVD++ is superior over classic and non-negative matrix factorization algorithms and the state-of-the-art CMF, and SVD++ algorithms.
Joint Feature and Texture Coding: Toward Smart Video Representation via Front-End Intelligence

In this paper, we provide a systematical overview and analysis on the joint feature and texture representation framework, which aims to smartly and coherently represent the visual information with the front-end intelligence in the scenario of video big data applications. In particular, we first demonstrate the advantages of the joint compression framework in terms of both reconstruction quality and analysis accuracy. Subsequently, the interactions between visual feature and texture in the compression process are further illustrated. Finally, the future joint coding scheme by incorporating the deep learning features is envisioned, and future challenges toward seamless and unified joint compression are discussed. The joint compression framework, which bridges the gap between visual analysis and signal-level representation, is expected to contribute to a series of applications, such as video surveillance and autonomous driving.
Deadline-Aware MapReduce Job Scheduling with Dynamic Resource Availability

As MapReduce is becoming ubiquitous in large-scale data analysis, many recent studies have shown that the performance of MapReduce could be improved by different job scheduling approaches, e.g., Fair Scheduler and Capacity Scheduler. However, most exiting MapReduce job schedulers focus on the scenario that MapReduce cluster is stable and pay little attention to the MapReduce cluster with dynamic resource availability. In fact, MapReduce cluster resources may fluctuate as there is a growing number of Hadoop clusters deployed on hybrid systems, e.g., infrastructure powered by mix of traditional and renewable energy, and cloud platforms hosting heterogeneous workloads. Thus, there is a growing need for providing predictable services to users who have strict requirements on job completion times in such dynamic environments. In this paper, we propose, RDS, a Resource and Deadline-aware Hadoop job Scheduler that takes future resource availability into consideration when minimizing job deadline misses. We formulate the job scheduling problem as an online optimization problem and solve it using an efficient receding horizon control algorithm. To aid the control, we design a self-learning model to estimate job completion times. We further extend the design of RDS scheduler to support flexible performance goals in various dynamic clusters. In particular, we use flexible deadline time bounds instead of the single fixed job completion deadline. We have implemented RDS in the open-source Hadoop implementation and performed evaluations with various benchmark workloads. Experimental results show that RDS substantially reduces the penalty of deadline misses by at least 36 and 10 percent compared with Fair Scheduler and Earliest Deadline First (EDF) scheduler, respectively. In a Hadoop cluster running partially on renewable energy, the experimental result shows the green power based resource prediction approach can further reduce the penalty of deadline misses by 16 percent compared to Auto-Regressive Integrated Moving Average (ARIMA) prediction approach.
A pair-based task scheduling algorithm for cloud computing environment

In the cloud computing environment, scheduling algorithms show the vital role of finding a possible schedule of the tasks. Extant literatures have shown that the task scheduling problem is NP-Complete as the objective is to obtain the minimum overall execution time. In this paper, we address the problem of scheduling a set of l tasks with a set of |G| groups to a set of m clouds, such that the overall layover time is minimized. Note that overall layover time is the sum of the timing gaps between paired tasks. Here, we present a pair-based task scheduling algorithm for cloud computing environment, which is based on the well-known optimization algorithm, called Hungarian algorithm. The proposed algorithm considers an unequal number of tasks and clouds, and pairs the tasks to make the scheduling decision. We simulate the proposed algorithm and compare it with three existing algorithms, first-come-first-served, Hungarian algorithm with lease time and Hungarian algorithm with converse lease time in twenty-two different datasets. The performance evaluation shows that the proposed algorithm produces better layover time in comparison to existing algorithms. The proposed algorithm is analyzed theoretically and shown to require O (kpl2) time for k iterations, p repetitions and l tasks.
ITEMa: A methodological approach for cognitive edge computing IoT ecosystems

The ever-increasing spread of Internet of Things (IoT)-based technologies paired with the diffusion of the edge-based computing boosts the development of pervasive cyber ecosystems having the goal of improving the life quality of people and assisting them in daily activities. In this context, cognitive behaviors are purposely required to make such ecosystems able to adapt to people needs and to envisage their behaviors. Despite the growing interest in cognitive ecosystems, still there is a lack of methodological approaches devoted to supporting the design and implementation of such complex systems. This paper proposes ITEMa, an Iot-based smarT Ecosystem Modeling Approach based on a three-layered architecture offering some well-suited abstractions tailored to the development of IoT-based ecosystems which exhibit cognitive behaviors and are able to exploit computational resources located either at the edge of the network or in the Cloud. The effectiveness of the approach is demonstrated through a case study concerning the development of a Smart Office devoted to forecast some usual office activities and to properly adapt the office environmental conditions to them.
Prediction and classification performance of reservoir computing system using mutually delay-coupled semiconductor lasers

A reservoir computing (RC) system based on two mutually delay coupled semiconductor lasers (MDC-SLs) subject to optical injection is proposed, and the chaotic time series prediction and signal classification performance for such a system is numerically investigated via the Santa-Fe time-series prediction task and the signal classification task, respectively. The simulation results indicate that, when adopting this system to process ultra-fast data with rate up to 1 Gbps, the best prediction error 5.1×10−5±5.2×10−6 and signal recognition error 5.5×10−4±8.8×10−5 can be achieved under optimized operation parameters.
APL: Adversarial Pairwise Learning for Recommender Systems

The main objective of recommender systems is to help users select their desired items, where a major challenge is modeling users' preferences based on their historical feedback (e.g., clicks, purchases or check-ins). Recently, several recommendation models have utilized the adversarial technique, which has been successfully used to capture real data distributions in various domains (e.g., computer vision). Nevertheless, the training process of the original adversarial technique is very slow and unstable in the domain of recommender systems. First, the sparsity of the implicit feedback dataset aggravates the inherently intractable adversarial training process. Second, since the original adversarial model is designed for differentiable values (e.g., images), the discrete items also increase the training difficulty. To cope with these issues, we propose a novel method named Adversarial Pairwise Learning (APL), which unifies generative and discriminative models via adversarial learning. Specifically, based on the weaker assumption that the user prefers observed items over generated items, APL exploits pairwise ranking to accelerate the convergence and enhance the stability of adversarial learning. Additionally, a differentiable procedure is adopted to replace the discrete item sampling to optimize APL via backpropagation and stabilize the training process. Extensive experiments under multiple recommendation scenarios demonstrate APL's effectiveness, fast convergence and stability. Our implementation of APL is available at: https://github.com/ZhongchuanSun/APL.