Reviewerly/exHarmony huggingface dataset:
Game-Theoretic Resource Pricing and Provisioning Strategies in Cloud Systems

We consider several Software as a Service (SaaS) providers that offer services using the Cloud resources provided by an Infrastructure as a Service (IaaS) provider which adopts a pay-per-use scheme similar to the Amazon EC2 service, comprising flat, on demand, and spot virtual machine instances.For this scenario, we study the virtual machine provisioning and spot pricing strategies.We consider a two-stage provisioning scheme.In the first stage, the SaaS providers determine the optimal number of required flat and on demand instances.Then, in the second stage, the IaaS provider sells its unused capacity as spot instances for which the SaaS providers compete by submitting a bid.We study two different IaaS provider pricing strategies: the first assumes the IaaS provider sets a unique price; in the second, instead, the IaaS provider can set different prices for different customers.We model the resulting problem as a Stackelberg game.For each pricing scheme, we show the existence of the game equilibrium and provide the solution algorithms.Through numerical evaluation we compare the provisioning and spot price under the two different pricing strategies as function of the system parameters.
A Machine Learning Based Framework for Verification and Validation of Massive Scale Image Data

Big data validation and system verification are crucial for ensuring the quality of big data applications. However, a rigorous technique for such tasks is yet to emerge. During the past decade, we have developed a big data system called CMA for investigating the classification of biological cells based on cell morphology which is captured in diffraction images. CMA includes a collection of scientific software tools, machine learning algorithms, and a large-scale cell image repository. In order to ensure the quality of big data system CMA, we developed a framework for rigorously validating the massive scale image data as well as adequately verifying both the software tools and machine learning algorithms. The validation of big data is conducted by iteratively selecting the data using a machine learning approach. An experimental approach guided by a feature selection algorithm is introduced in the framework to select an optimal feature set for improving the machine learning performance. The verification of software and algorithms is developed on the iterative metamorphic testing approach due to the non-testable property of the software and algorithms. A machine learning approach is introduced for developing test oracles iteratively to ensure the adequacy of the test coverage criteria. Performance of the machine learning algorithm is evaluated with a stratified N-fold cross validation and confusion matrix. We describe the design of the proposed big data verification and validation framework with CMA as the case study, and demonstrate its effectiveness through verifying and validating the dataset, the software and the algorithms in CMA.
Modular and Holomorphic Graph Functions from Superstring Amplitudes

We compare two classes of functions arising from genus-one superstring amplitudes: modular and holomorphic graph functions. We focus on their analytic properties, we recall the known asymptotic behaviour of modular graph functions and we refine the formula for the asymptotic behaviour of holomorphic graph functions. Moreover, we give new evidence of a conjecture which relates these two asymptotic expansions.
Patterns and Pace: Quantifying Diverse Exploration Behavior with Visualizations on the Web

The diverse and vibrant ecosystem of interactive visualizations on the web presents an opportunity for researchers and practitioners to observe and analyze how everyday people interact with data visualizations. However, existing metrics of visualization interaction behavior used in research do not fully reveal the breadth of peoples' open-ended explorations with visualizations. One possible way to address this challenge is to determine high-level goals for visualization interaction metrics, and infer corresponding features from user interaction data that characterize different aspects of peoples' explorations of visualizations. In this paper, we identify needs for visualization behavior measurement, and develop corresponding candidate features that can be inferred from users' interaction data. We then propose metrics that capture novel aspects of peoples' open-ended explorations, including exploration uniqueness and exploration pacing. We evaluate these metrics along with four other metrics recently proposed in visualization literature by applying them to interaction data from prior visualization studies. The results of these evaluations suggest that these new metrics 1) reveal new characteristics of peoples' use of visualizations, 2) can be used to evaluate statistical differences between visualization designs, and 3) are statistically independent of prior metrics used in visualization research. We discuss implications of these results for future studies, including the potential for applying these metrics in visualization interaction analysis, as well as emerging challenges in developing and selecting metrics depicting visualization explorations.
Coherent Optical Control of a Quantum-Dot Spin-Qubit in a Waveguide-Based Spin-Photon Interface

Spin-photon interfaces based on an (In,Ga)As quantum dot coupled to a waveguide are a promising avenue toward scalable quantum information processing, but coherent control of the spin state is challenging, because of the complicated near-to-far-field polarization transformation induced by the waveguide. The authors search for a particular polarization of light that excites a circular dipole in the quantum dot, and use this polarization for the coherent control of an electron's spin. They obtain a coherence time of 2.2 ns, comparable to the typical value in bulk media. The authors' method for polarization-controlled excitation can be readily applied to other nanophotonic structures, too.
Copy-move forgery detection using binary discriminant features

Digital images have numerous applications in various fields such as cyber forensics, courts, newspapers, magazines, medical field and many more. Therefore, the trustfulness of the contents of an image is very essential. Image manipulation has become very much simple and common due to the development of high resolution digital cameras, powerful computers and numerous image editing software. Hence, falsification of visual content of images is not at all limited to specialists and detection of forged images have become a highly challenging task. Of the different kinds of image forgery, the proposed method deals with one particular kind known as copy-move forgery. The method is an integration of the conventional block-based and keypoint based methods. The method first identifies the forged keypoint locations and then localizes the forged region using a region extraction technique. To improve the accuracy of detection, a Binary Discriminative Feature descriptor (BDF) is used for feature detection and matching. The suspected regions are identified by replacing the matching feature points with corresponding superpixel blocks. The neighbouring blocks are then merged with the suspected regions based on color similarity, using Color Histogram Matching and a final morphological close operation extracts the detected forged regions. Experimental results show that the proposed method has better detection accuracy, in terms of precision, recall and F1 Score, when compared with the state-of-the-art methods of copy-move forgery detection, under conditions of plain copy-move forgery as well as post-processing conditions like brightness changes, contrast adjustments, color reduction and blurring.
Preliminary assessment of two spatio-temporal forecasting technics for hourly satellite-derived irradiance in a complex meteorological context

This paper examines two spatio-temporal approaches for short-term forecasting of global horizontal irradiance using gridded satellite-derived irradiances as experimental support. The first approach is a spatio-temporal vector autoregressive (STVAR) model combined with a statistical process for optimum selection of input variables. The second is an existing operational cloud motion vector (CMV) model. An evaluation of the predictive performance of these models is presented for a case study area in the Caribbean Islands. This region is characterized by a large diversity of microclimates and land/sea contrasts, creating a challenging solar forecasting context. Using scaled persistence as a reference, we benchmark the performance of the two spatio-temporal models over an extended 220 × 220 km domain, and for three specific, climatically distinct locations within this domain. We also assess the influence of intra-day solar resource variability on model performance. Finally, we present preliminary evidence that a blend of CMV and STVAR forecasts leads to improved accuracy under all conditions.
Quantum experiments and graphs. III. High-dimensional and multiparticle entanglement

Quantum entanglement plays an important role in quantum information processes, such as quantum computation and quantum communication. Experiments in laboratories are unquestionably crucial to increase our understanding of quantum systems and inspire new insights into future applications. However, there are no general recipes for the creation of arbitrary quantum states with many particles entangled in high dimensions. Here we exploit a recent connection between quantum experiments and graph theory and answer this question for a plethora of classes of entangled states. We find experimental setups for Greenberger-Horne-Zeilinger states, $W$ states, general Dicke states, and asymmetrically high-dimensional multipartite entangled states. This result sheds light on the producibility of arbitrary quantum states using photonic technology with probabilistic pair sources and allows us to understand the underlying technological and fundamental properties of entanglement.
How to Calibrate Your Adversary's Capabilities? Inverse Filtering for Counter-Autonomous Systems

We consider an adversarial Bayesian signal processing problem involving "us" and an "adversary". The adversary observes our state in noise; updates its posterior distribution of the state and then chooses an action based on this posterior. Given knowledge of "our" state and sequence of adversary's actions observed in noise, we consider three problems: (i) How can the adversary's posterior distribution be estimated? Estimating the posterior is an inverse filtering problem involving a random measure - we formulate and solve several versions of this problem in a Bayesian setting. (ii) How can the adversary's observation likelihood be estimated? This tells us how accurate the adversary's sensors are. We compute the maximum likelihood estimator for the adversary's observation likelihood given our measurements of the adversary's actions where the adversary's actions are in response to estimating our state. (iii) How can the state be chosen by us to minimize the covariance of the estimate of the adversary's observation likelihood? "Our" state can be viewed as a probe signal which causes the adversary to act; so choosing the optimal state sequence is an input design problem. The above questions are motivated by the design of counter-autonomous systems: given measurements of the actions of a sophisticated autonomous adversary, how can our counter-autonomous system estimate the underlying belief of the adversary, predict future actions and therefore guard against these actions.
Moment-rotation prediction of precast beam-to-column connections using extreme learning machine

The performance of precast concrete structures is greatly influenced by the behaviour of beam-to-columnconnections. A single connection may be required to transfer several loads simultaneously so each one of those loads must beconsidered in the design. A good connection combines practicality and economy, which requires an understanding of severalfactors; including strength, serviceability, erection and economics. This research work focuses on the performance aspect of aspecific type of beam-to-column connection using partly hidden corbel in precast concrete structures. In this study, the results ofexperimental assessment of the proposed beam-to-column connection in precast concrete frames was used. The purpose of thisresearch is to develop and apply the Extreme Learning Machine (ELM) for moment-rotation prediction of precast beam-tocolumnconnections. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). Thereliability of the computational models was accessed based on simulation results and using several statistical indicators.
A Novel Key-Point Detector Based on Sparse Coding

Most popular hand-crafted key-point detectors such as Harris corner, MSER, SIFT, SURF rely on some specific pre-designed structures for detection of corners, blobs, or junctions in an image. The very nature of pre-designed structures can be considered a source of inflexibility for these detectors in different contexts. Additionally, the performance of these detectors is also highly affected by non-uniform change in illumination. To the best of our knowledge, while there are some previous works addressing one of the two aforementioned problems, there currently lacks an efficient method to solve both simultaneously. In this paper, we propose a novel Sparse Coding based Key-point detector (SCK) which is fully invariant to affine intensity change and independent of any particular structure. The proposed detector locates a key-point in an image, based on a complexity measure calculated from the block surrounding its position. A strength measure is also proposed for comparing and selecting the detected key-points when the maximum number of key-points is limited. In this paper, the desirable characteristics of the proposed detector are theoretically confirmed. Experimental results on three public datasets also show that the proposed detector achieves significantly high performance in terms of repeatability and matching score.
Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS

The end-to-end TTS, which can predict speech directly from a given sequence of graphemes or phonemes, has shown improved performance over the conventional TTS. However, its predicting capability is still limited by the acoustic/phonetic coverage of the training data, usually constrained by the training set size. To further improve the TTS quality in pronunciation, prosody and perceived naturalness, we propose to exploit the information embedded in a syntactically parsed tree where the inter-phrase/word information of a sentence is organized in a multilevel tree structure. Specifically, two key features: phrase structure and relations between adjacent words are investigated. Experimental results in subjective listening, measured on three test sets, show that the proposed approach is effective to improve the pronunciation clarity, prosody and naturalness of the synthesized speech of the baseline system.
Collision-Free Video Synopsis Incorporating Object Speed and Size Changes

This paper presents a new surveillance video synopsis method which performs much better than previous approaches in terms of both compression ratio and artifact. Previously, a surveillance video was usually compressed by shifting the moving objects of that video forward along the time axis, which inevitably yielded serious collision and chronological disorder artifacts between the shifted objects. The main observation of this paper is that these artifacts can be alleviated by changing the speed or size of the objects, since with varied speed and size the objects can move more flexibly to avoid collision points or to keep chronological relationships. Based on this observation, we propose a video synopsis method that performs object shifting, speed changing, and size scaling simultaneously. We show how to integrate the three heterogeneous operations into a single optimization framework and achieve high-quality synopsis results. Unlike previous approaches that usually use alternative optimization strategies to solve synopsis optimizations, we develop a Metropolis sampling algorithm to find the solution for our three-variable optimization problem. A variety of experiments demonstrate the effectiveness of our method.
An ontology constructing technology oriented on massive social security policy documents

To solve the problem brought from enormous policy documents and complex management in the social security domain, the article uses ontology as the way of representing and storing knowledge. The article first constructs the framework of ontology through manual work so that it can ensure the relative accuracy of the ontology structure. Then it achieves the automatic ontology expansion based on the inclusion relationship of property sets or operational object sets. The article uses a semi-automatic method that extracts hierarchical concepts and non-hierarchical concepts from domain thesaurus by using the method combining statistics with rules to construct the ontology. Besides constructing the ontology, the article proposes the concepts of concept phrase vector model and high frequency characteristics phrase vector model. The experiment result indicates that ontology semi-automatic construction process can help experts to construct the social security ontology effectively oriented on massive policy documents and is a considerable reference for the construction of ontology in other domains.
Large-Scale Text Classification Using Scope-Based Convolutional Neural Network: A Deep Learning Approach

Text classification is one of the most important and typical tasks in Natural Language Processing (NLP) which can be applied for many applications.Recently, deep learning approaches has shown their advantages in solving text classification problem, in which Convolutional Neural Network (CNN) is one of the most successful model in the field.In this paper, we propose a novel deep learning approach for categorizing text documents by using scope-based convolutional neural network.Different from windowbased CNN, scope does not require the words that construct a local feature have to be contiguous.It can represent deeper local information of text data.We propose a large-scale scope-based convolutional neural network (LSS-CNN), which is based on scope convolution, aggregation optimization, and max pooling operation.Based on these techniques, we can gradually extract the most valuable local information of the text document.This paper also discusses how to effectively calculate the scope-based information and parallel training for large-scale datasets.Extensive experiments have been conducted on real datasets to compare our model with several state-of-the-art approaches.The experimental results show that LSS-CNN can achieve both effectiveness and good scalability on big text data.
Fast and Accurate Depth Estimation From Sparse Light Fields

We present a fast and accurate method for dense depth reconstruction, which is specifically tailored to process sparse, wide-baseline light field data captured with camera arrays. In our method, the source images are over-segmented into non-overlapping compact superpixels. We model superpixel as planar patches in the image space and use them as basic primitives for depth estimation. Such superpixel-based representation yields desired reduction in both memory and computation requirements while preserving image geometry with respect to the object contours. The initial depth maps, obtained by plane-sweeping independently for each view, are jointly refined via iterative belief-propagation-like optimization in superpixel domain. During the optimization, smoothness between the neighboring superpixels and geometric consistency between the views are enforced. To ensure rapid information propagation into textureless and occluded regions, together with the immediate superpixel neighbors, candidates from larger neighborhoods are sampled. Additionally, in order to make full use of the parallel graphics hardware a synchronous message update schedule is employed allowing to process all the superpixels of all the images at once. This way, the distribution of the scene geometry becomes distinctive already after the first iterations, facilitating stability and fast convergence of the refinement procedure. We demonstrate that a few refinement iterations result in globally consistent dense depth maps even in the presence of wide textureless regions and occlusions. The experiments show that while the depth reconstruction takes about a second per full high-definition view, the accuracy of the obtained depth maps is comparable with the state-of-the-art results, which otherwise require much longer processing time.
Hybrid channel based pedestrian detection

Pedestrian detection has achieved great improvements with the help of Convolutional Neural Networks (CNNs). CNN can learn high-level features from input images, but the insufficient spatial resolution of CNN feature channels (feature maps) may cause a loss of information, which is harmful especially to small instances. In this paper, we propose a new pedestrian detection framework, which extends the successful RPN+BF framework to combine handcrafted features and CNN features. RoI-pooling is used to extract features from both handcrafted channels (e.g. HOG+LUV, CheckerBoards or RotatedFilters) and CNN channels. Since handcrafted channels always have higher spatial resolution than CNN channels, we apply RoI-pooling with larger output resolution to handcrafted channels to keep more detailed information. Our ablation experiments show that the developed handcrafted features can reach better detection accuracy than the CNN features extracted from the VGG-16 net, and a performance gain can be achieved by combining them. Experimental results on Caltech pedestrian dataset with the original annotations and the improved annotations demonstrate the effectiveness of the proposed approach. When using a more advanced RPN in our framework, our approach can be further improved and get competitive results on both benchmarks.
Real-time Malaria Parasite Screening in Thick Blood Smears for Low-Resource Setting

Malaria is a serious public health problem in many parts of the world. Early diagnosis and prompt effective treatment are required to avoid anemia, organ failure, and malaria-associated deaths. Microscopic analysis of blood samples is the preferred method for diagnosis. However, manual microscopic examination is very laborious and requires skilled health personnel of which there is a critical shortage in the developing world such as in sub-Saharan Africa. Critical shortages of trained health personnel and the inability to cope with the workload to examine malaria slides are among the main limitations of malaria microscopy especially in low-resource and high disease burden areas. We present a low-cost alternative and complementary solution for rapid malaria screening for low resource settings to potentially reduce the dependence on manual microscopic examination. We develop an image processing pipeline using a modified YOLOv3 detection algorithm to run in real time on low-cost devices. We test the performance of our solution on two datasets. In the dataset collected using a microscope camera, our model achieved 99.07% accuracy and 97.46% accuracy on the dataset collected using a mobile phone camera. While the mean average precision of our model is on par with human experts at an object level, we are several orders of magnitude faster than human experts as we can detect parasites in images as well as videos in real time.