Reviewerly/exHarmony huggingface dataset:
Image Segmentation Using an Active Contour Model Based on the Difference Between Local Intensity Averages and Actual Image Intensities

The local intensity fitting active contour models can handle inhomogeneous images, but they suffer from the shortcomings of poor performance in segmenting images with severe intensity inhomogeneity and being sensitive to initializations. To overcome these problems, we put forward a robust active contour model by introducing two adjustment coefficient functions. The energy functional of the proposed model is presented by integrating the local fitting term and two adjustment coefficient functions. The local fitting term is defined by introducing two local fitting functions that approximate the image intensities inside and outside of the contour. These two adjustment coefficient functions, which improve the segmentation performance and enhance the robustness to initialization, are constructed by utilizing the Sigmoid function as well as the difference between local intensity averages and image actual intensities. The results of the experiments on synthetic and real images demonstrate that the presented model not only is capable of handling intensity inhomogeneity better under more flexible initializations but also takes less time in comparison with other region-based models. Furthermore, these two adjustment coefficients can be employed to other local intensity fitting models to enhance the robustness to initialization and to decrease the segmentation time.
Feature Selection Based on Twin Support Vector Regression

Twin support vector regression (TSVR) is a regression algorithm based on the support vector regression (SVR) and the spirit of the support vector machine (TWSVM) . However, some feature selection algorithms of support vector regression, such as recursive feature elimination, can’t be applied to TSVR, so a recursive feature selection method based on TSVR is proposed. By analyzing the weights, the ε -insensitive upper and lower bound functions in TSVR are analyzed. The two weight vectors are merged, and the weight vector is sorted and deleted with reference to the recursive feature elimination (RFE). The experimental results on several UCI datasets demonstrate demonstrate the effectiveness of the algorithm on feature selection and improves the regression performance.
The Challenge: From MPEG Intellectual Property Rights Ontologies to Smart Contracts and Blockchains [Standards in a Nutshell]

The Moving Picture Experts Group (MPEG) is an International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) working group that develops media coding standards. These standards include a set of ontologies for the codification of intellectual property rights (IPR) information related to media. The Media Value Chain Ontology (MVCO) facilitates rights tracking for fair, timely, and transparent payment of royalties by capturing user roles and their permissible actions on a particular IP entity. The Audio Value Chain Ontology (AVCO) extends MVCO functionality related to the description of IP entities in the audio domain, e.g., multitrack audio and time segments. The Media Contract Ontology (MCO) facilitates the conversion of narrative contracts to digital ones. Furthermore, the axioms in these ontologies can drive the execution of rights-related workflows in controlled environments, e.g., blockchains, where transparency and interoperability is favored toward fair trade of music and media. Thus, the aim of this article is to create awareness of the MPEG IPR ontologies developed in the last few years and the work currently taking place addressing the challenge identified toward the execution of such ontologies as smart contracts on blockchain environments.
Depth-based human activity recognition via multi-level fused features and fast broad learning system

Human activity recognition using depth videos remains a challenging problem while in some applications the available training samples is limited. In this article, we propose a new method for human activity recognition by crafting an integrated descriptor called multi-level fused features for depth sequences and devising a fast broad learning system based on matrix decomposition for classification. First, the surface normals are computed from original depth maps; the histogram of the surface normal orientations is obtained as a low-level feature by accumulating the contributions from normals, then a high-level feature is acquired by sparse coding and pooling on the aggregation of polynormals. After that, the principal component analysis is applied to the conjunction of the two-level features in order to obtain a low-dimensional and discriminative fused feature. At last, fast broad learning system based on matrix decomposition is proposed to accelerate the training process and enhance the classification results. The recognition results on three benchmark data sets show that our method outperforms the state-of-the-art methods in term of accuracy, especially when the number of training samples is small.
Spectrum-preserving sparsification for visualization of big graphs

We present a novel spectrum-preserving sparsification algorithm for visualizing big graph data. Although spectral methods have many advantages, the high memory and computation costs due to the involved Laplacian eigenvalue problems could immediately hinder their applications in big graph analytics. In this paper, we introduce a practically efficient, nearly-linear time spectral sparsification algorithm for tackling real-world big graph data. Besides spectral sparsification, we further propose a node reduction scheme based on intrinsic spectral graph properties to allow more aggressive, level-of-detail simplification. To enable effective visual exploration of the resulting spectrally sparsified graphs, we implement spectral clustering and edge bundling. Our framework does not depend on a particular graph layout and can be integrated into different graph drawing algorithms. We experiment with publicly available graph data of different sizes and characteristics to demonstrate the efficiency and effectiveness of our approach. To further verify our solution, we quantitatively compare our method against different graph simplification solutions using a proxy quality metric and statistical properties of the graphs.
Factory Maintenance Application Using Augmented Reality

Tissue converting lines represent one of the key plant in the paper production field: with them, paper tissue is converted into its final form for domestic and sanitary usage. One of the key points of the tissue converting lines is the productivity and the possibility to follow conversion process at relativity low cost. Despite the actual lines have yet an high productivity, the study of the state of the art has shown that choke points still exist, caused by inadequate automation. In this paper, we present the preliminary results of a project which aims at removing such obstacle towards complete automation, by introducing a set of innovations based on ICT solutions applied to advanced automation. In detail, advanced computer vision and video analytics methods will be applied to pervasively monitor converting lines and to automatically extract process information in order to self-regulate specific machine and global parameters. Big data analysis methodologies will be also integrated to obtain new knowledge and infer optimal management models which could be used for the predictive maintenance. Augmented reality interfaces are being designed and developed to support converting line monitoring and maintenance, both ordinary and extraordinary. An Artificial Intelligence module provides suggestions and instructions to the operators in order to guarantee production level even in case of unskilled staff. The automation of such processes will improve factory safety, decrease manual interventions and, thus, will increase production line up-time and efficiency.
Cold Start Problem Alleviation in a Research Paper Recommendation System Using the Random Walk Approach on a Heterogeneous User-Paper Graph

Recommendation approaches generally fail to recommend newly-published papers as relevant, owing to the lack of prior information about the said papers and, more particularly, problems associated with cold starts. It would appear, to all intents and purposes, that researchers currently interact more on social networks than they normally would in academic circles, and relationships of a purely academic nature have witnessed a paradigm shift, in keeping with this new trend. In existing paper recommendation methods, the social interaction factor has yet to play a pivotal role. The authors propose a social network-based research paper recommendation method, that alleviates cold start problems by incorporating users' social interaction, as well as topical relevancy, among assorted papers in the Mendeley academic social network using a novel approach, random walk Ergodic Markov Chain. The system yields improved results after cold start alleviation, compared with the existing system.
Effectively Testing System Configurations of Critical IoT Analytics Pipelines

The emergence of the Internet of Things has seen the introduction of numerous connected devices used for the monitoring and control of even Critical Infrastructures. Distributed stream processing has become key to analyzing data generated by these connected devices and improving our ability to make decisions. However, optimizing these systems towards specific Quality of Service targets is a difficult and time-consuming task, due to the large-scale distributed systems involved, the existence of so many configuration parameters, and the inability to easily determine the impact of tuning these parameters.In this paper we present an approach for the effective testing of system configurations for critical IoT analytics pipelines. We demonstrate our approach with a prototype that we called Timon which is integrated with Kubernetes. This tool allows pipelines to be easily replicated in parallel and evaluated to determine the optimal configuration for specific applications. We demonstrate the usefulness of our approach by investigating different configurations of an exemplary geographically-based traffic monitoring application implemented in Apache Flink.
Virtual Adversarial Training for DS-CNN Based Small-Footprint Keyword Spotting

Serving as the tigger of a voice-enabled user interface, on-device keyword spotting model has to be extremely compact, efficient and accurate. In this paper, we adopt a depth-wise separable convolutional neural network (DS-CNN) as our small-footprint KWS model, which is highly competitive to these ends. However, recent study has shown that a compact KWS system is very vulnerable to small adversarial perturbations while augmenting the training data with specifically-generated adversarial examples can improve performance. In this paper, we further improve KWS performance through a virtual adversarial training (VAT) solution. Instead of using adversarial examples for data augmentation, we propose to train a DS-CNN KWS model using adversarial regularization, which aims to smooth model's distribution and thus to improve robustness, by explicitly introducing a distribution smoothness measure into the loss function. Experiments on a collected KWS corpus using a circular microphone array in far-field scenario show that the VAT approach brings 31.9% relative false rejection rate (FRR) reduction compared to the normal training approach with cross entropy loss, and it also surpasses the adversarial example based data augmentation approach with 10.3% relative FRR reduction.
Infrastructure Aware Heterogeneous-Workloads Scheduling for Data Center Energy Cost Minimization

A huge amount of energy consumption, the cost of this usage and environmental effects have become serious issues for commercial cloud providers. Solar energy is a promising clean energy source, to provide some portion of the Internet data center's (IDC's) energy usage which can reduce environmental effects and total energy costs. Moreover, due to the high energy consumption of the cooling system, considering cooling power in job scheduling can provide efficient solutions to reduce total energy consumption. In this article, we investigate the problem of minimizing the energy cost of an IDC and propose an algorithm which schedules heterogeneous IDC workloads, by considering available renewable energy, cooling subsystem, and electricity rate structure. We evaluate the effectiveness and feasibility of our algorithm using real and synthetic workload traces. The simulation results illustrate how our proposed solution reduces the data center's energy cost by up to 46 percent compared to previous solutions. Moreover, results show that our solution is capable of reducing energy cost of data centers under different weather conditions, and rate structures.
Uniform Access to Multiform Data Lakes using Semantic Technologies

Increasing data volumes have extensively increased application possibilities. However, accessing this data in an ad hoc manner remains an unsolved problem due to the diversity of data management approaches, formats and storage frameworks, resulting in the need to effectively access and process distributed heterogeneous data at scale. For years, Semantic Web techniques have addressed data integration challenges with practical knowledge representation models and ontology-based mappings. Leveraging these techniques, we provide a solution enabling uniform access to large, heterogeneous data sources, without enforcing centralization; thus realizing the vision of a Semantic Data Lake. In this paper, we define the core concepts underlying this vision and the architectural requirements that systems implementing it need to fulfill. Squerall, an example of such a system, is an extensible framework built on top of state-of-the-art Big Data technologies. We focus on Squerall's distributed query execution techniques and strategies, empirically evaluating its performance throughout its various sub-phases.
Statistical evaluation of stream sediment geochemical data from Tchangue-Bikoui drainage system, Southern Cameroon: a regional perspective

Stream sediments play a significant role in geochemical exploration by identifying possible sources of anomalous element concentration. In this study, we use a multi-element association analysis to investigate the concentration of gold and associated elements in stream sediment samples from the Tchangue-Bikoui drainage area, Southern Cameroon. The main river and its tributaries were sampled, panned for gold grain recovery and the 100 μm size fractions analyzed for Au and associated elements by inductively coupled plasma atomic emission spectrometry (ICP-AES). A total of 50 samples were collected defining an area of ~145 km2 and 20 samples selected for geochemical analysis for a suite of 20 elements. Gold grades estimated by the panning and weighing technique rarely exceed 0.45 g/t and generally erratic while the Au concentrations from chemical analysis range from 0.01ppb to 4.00ppb. R-mode factor analysis produced a five-factor model accounting for 62.4% of the total variance. The Ni-Au-V-Sr association indicated gold mineralization while the others reflect lithologic control. Mo-Cr-Hg-Ag-As-Zr and Pb-Cu show no spatial relationship to Au and point to a typical primary mesothermal sulphide mineralization in the area which can be further investigated. Anomalous gold concentrations clustered mostly in the southeastern part downstream of River Tchangue defining a potential primary gold target.
MTSAD: Multivariate Time Series Abnormality Detection and Visualization

Detecting outliers is one of the fundamental tasks in visual analytics and valuable in many application domains, such as suspicious network cyberattack recognition. This paper introduces an approach to analyzing and visualizing high-dimensional time series, focusing on identifying multivariate observations that are significantly different from the others. We also propose a prototype, called MTSAD, to guide users when interactively exploring abnormalities in large time series. The prototype contains two views: the main window provides an overview of identified outliers overtime, the detail window investigates and explores the ranked temporal data entries based on their outlying contributions to the overall plots. The visual interface supports a full range of interactions, such as lensing, brushing and linking, ranking, and filtering. To validate the benefits and usefulness of our approach, we demonstrate MTSAD on real-world datasets of different numbers of attributes.
Comparative Performance Characterization of Mobile AR Frameworks in the Context of AR-Based Grocery Shopping Applications

A number of Augmented Reality (AR) frameworks are now available and used to support the development of mobile AR applications. In this paper, we measure and compare the recognition performance of the commercial AR frameworks and identify potential issues that can occur in the real application environment. For experiments, we assume a situation in which a consumer purchases food products in a grocery store and consider an application scenario in which AR content related to the products is displayed on a smartphone screen by recognizing such products. We use four performance metrics to compare the performance of the selected AR frameworks, Vuforia, ARCore, and MAXST. Experimental results show that Vuforia is relatively superior to the others. The limitation of the AR frameworks is also identified when they are used in a real grocery store environment.
A Stochastic Framework for Keyframe Extraction

The sudden growth in the Closed Circuit TeleVision (CCTV) installations has paved the way for intensive video analytics. Video summarization, being a method of representing keyframes of a voluminous video, plays a major role in the video processing. Several researchers have focused on the key frame extraction since late 90’s. However, several challenges still exist in keyframe extraction. The main challenge in keyframe extraction is to identify the representative frames based on the contents. Most of the existing methods adapt deterministic approaches, which involves more computational complexity and result in poor accuracy. This work aims to improve the accuracy rate by introducing a stochastic framework that uses the techniques such as binning, Markov chain, Transition Probability Matrix (TPM), and Permutation computation. The experimental results demonstrate that the proposed framework outperforms the existing methods VSUMM and VSUKFE in terms of both computational efficiency and accuracy.
When Massive GPU Parallelism Ain't Enough

Multidimensional Long Short-Term Memory (MD-LSTM) neural network is an extension of one-dimensional LSTM for data with more than one dimension that allows MD-LSTM to show state-of-the-art results in various applications including handwritten text recognition, medical imaging, and many more. However, efficient implementation suffers from very sequential execution that tremendously slows down both training and inference compared to other neural networks. This is the primary reason that prevents intensive research involving MD-LSTM in the recent years, despite large progress in microelectronics and architectures. The main goal of the current research is to provide acceleration for inference of MD-LSTM, so to open a door for efficient training that can boost application of MD-LSTM. By this research we advocate that FPGA is an alternative platform for deep learning that can offer a solution in cases when a massive parallelism of GPUs does not provide the necessary performance required by the application. In this paper, we present the first hardware architecture for MD-LSTM. We conduct a systematic exploration of precision vs. accuracy trade-off using challenging dataset for historical document image binarization from DIBCO 2017 contest, and well known MNIST dataset for handwritten digits recognition. Based on our new architecture we implement FPGA-based accelerator that outperforms NVIDIA K80 GPU implementation in terms of runtime by up to 50x and energy efficiency by up to 746x. At the same time, our accelerator demonstrates higher accuracy and comparable throughput in comparison with state-of-the-art FPGA-based implementations of multilayer perceptron for MNIST dataset.
On the hardnesses of several quantum decoding problems

We classify the time complexities of three decoding problems for quantum stabilizer codes: quantum bounded distance decoding (QBDD), quantum maximum likelihood decoding (QMLD), and quantum minimum error probability decoding (QMEPD). For QBDD, we show that it is NP-hard based on Fujita’s result, and cover the gap of full row rank of check matrices, like what Berlekamp, McEliece, and Tilborg suggested in 1978. Then, we give some insight into the quantum decoding problems to clarify that the degeneracy property is implicitly embedded in any decoding algorithm, independent of the typical definition of degenerate codes. Then, over the depolarizing channel model, we show that QMLD and QMEPD are NP-hard. The NP-hardnesses of these decoding problems indicate that decoding general stabilizer codes is extremely difficult, strengthening the foundation of quantum code-based cryptography.
The CAMS eSense Framework

Earable computing devices can be an important platform for mobile health (mHealth) applications and digital phenotyping, since they allow for collection of detailed sensory data while also providing a platform for contextual delivery of interventions. In this paper we describe how the eSense earable computing platform has been integrated with a programming framework and runtime platform for the design of mHealth applications. The paper details how this programming framework can be used in the design of custom mHealth technologies. It also provide data and insight from an initial study in which this framework was used to collect real-life contextual data, including sensory data from the eSense device.
Story Point-Based Effort Estimation Model with Machine Learning Techniques

Until now, numerous effort estimation models for software projects have been developed, most of them producing accurate results but not providing the flexibility to decision makers during the software development process. The main objective of this study is to objectively and accurately estimate the effort when using the Scrum methodology. A dynamic effort estimation model is developed by using regression-based machine learning algorithms. Story point as a unit of measure is used for estimating the effort involved in an issue. Projects are divided into phases and the phases are respectively divided into iterations and issues. Effort estimation is performed for each issue, then the total effort is calculated with aggregate functions respectively for iteration, phase and project. This architecture of our model provides flexibility to decision makers in any case of deviation from the project plan. An empirical evaluation demonstrates that the error rate of our story point-based estimation model is better than others.