Reviewerly/exHarmony huggingface dataset:
Markov random field-based image inpainting with direction structure distribution analysis for maintaining structure coherence

Image inpainting provides a means for restoration of an image with some damaged portions and has been widely applied to many fields. However, many existing inpainting methods still suffer the difficulties of effectively and efficiently maintaining structure coherence. To tackle the problems, this paper proposes a direction structure distribution analysis strategy for Markov Random Field (MRF) -based image inpainting algorithms. In the strategy, the desired direction structures (DDS) are selected and applied to guide the inpainting process. Given a degraded image, four kinds of direction edge information are extracted by super-wavelet transform, canny and dilation operators. Then local direction edge gradient magnitude is applied for direction structure distribution analysis and DDS are selected according to gradient variance. Afterwards, offsets of similar patches are calculated and only a few dominated offsets are chosen as candidate offsets. Finally, graph-cut optimization technology is utilized to solve energy function. Experimental results show that the proposed method achieves generally better performance than nine state-of-the-art methods in terms of the abilities of maintaining structure coherence and the computational cost on inpainting different kinds of degraded images.
DEICTIC: A compositional and declarative gesture description based on hidden markov models

The consumer-level devices that track the user's gestures eased the design and the implementation of interactive applications relying on body movements as input. Gesture recognition based on computer vision and machine-learning focus mainly on accuracy and robustness. The resulting classifiers label precisely gestures after their performance, but they do not provide intermediate information during the execution. Human-Computer Interaction research focused instead on providing an easy and effective guidance for performing and discovering interactive gestures. The compositional approaches developed for solving such problem provide information on both the whole gesture and on its sub-parts, but they exploit heuristic techniques that have a low recognition accuracy. In this paper, we introduce DEICTIC, a compositional and declarative description for stroke gestures, which uses basic Hidden Markov Models (HMMs) to recognise meaningful predefined primitives (gesture sub-parts) and it composes them to recognise complex gestures. It provides information for supporting gesture guidance and it reaches an accuracy comparable with state-of-the-art approaches, evaluated on two datasets from the literature. Through a developer evaluation, we show that the implementation of a guidance system with DEICTIC requires an effort comparable to compositional approaches, while the definition procedure and the perceived recognition accuracy is comparable to machine learning.
GiantClient: Video HotSpot for Multi-User Streaming

In this paper, we propose a cooperative multi-user video streaming system, termed GiantClient, for videos encoded using scalable video coding (SVC). The proposed system allows a group of users to watch a video on a single screen. The users, who may have different data plans from different carriers or different levels of energy, can collaborate to fetch the SVC-encoded video at high quality and avoid running into re-buffering. Using SVC, each layer of every chunk of the video can be fetched by only one of the cooperating users. Therefore, we formulate the streaming problem that obtains the quality and the fetching policy decisions as an optimization problem. The objective is to optimize a novel quality-of-experience metric that maintains a tradeoff between maximizing the quality of every chunk and ensuring fairness among all video chunks for the minimum re-buffering time. The problem is constrained with the available bandwidth, the chunk deadlines, and the imposed maximum contribution constraints by users. Moreover, we propose a low-complexity algorithm to solve the proposed optimization problem. A real implementation of the system with real SVC-encoded videos and real bandwidth traces reveal the robustness and performance of the proposed algorithm.
CAM-based HDR image reproduction using CA–TC decoupled JCh decomposition

High dynamic range (HDR) imaging has been developed for acquiring scene with a high dynamic range. Tone-mapping operators are used to reproduce the dynamic range of HDR content on a low dynamic range (LDR) devices. iCAM06 is a tone-mapping algorithm based on the color appearance model. iCAM06 contains chromatic adaptation and tone compression; however, the chrominance component of the chromatic adaptation is not preserved after tone compression. This paper describes a novel decoupled processing for the chromatic adaptation and tone compression to preserve the chrominance component and reduce the color distortion of iCAM06. Further, a color appearance model is used to separate and combine the chrominance and luminance components of the decoupled processes. The rendered results confirm that the proposed algorithm is superior to the conventional algorithm.
A Study on Effective Measurement of Search Results from Search Engines

This article describes how as internet technology continues to change and improve lives and societies worldwide, effective global information management becomes increasingly critical, and effective Internet information retrieval systems become more and more significant in providing Internet users worldwide with accurate and complete information. Search engine evaluation is an important research field as search engines directly determine the quality of information users' Internet searches. Relevance-decrease pattern/model plays an important role in search engine result evaluation. This research studies effective measurement of search results through investigating relevance-decrease patterns of search results from two popular search engines: Google and Bing. The findings can be applied to relevance-evaluation of search results from other information retrieval systems such as OPAC, can help make search engine evaluations more accurate and sound, and can provide global information management personnel with valuable insights.
Student body gesture recognition based on Fisher broad learning system

Observing student body gesture has been widely used to assess teaching effectiveness over the past few decades. However, manual observation is not suitable for the automatic data analysis in the field of learning analytics. Consequently, a student body gesture recognition method based on Fisher Broad Learning System (FBLS) and Local Log-Euclidean Multivariate Gaussian (L 2 EMG) is proposed in this paper. FBLS is designed by introducing the discriminative information into the hidden layer of Broad Learning System (BLS) and reducing the dimensionality of hidden-layer representations. FBLS has superiorities in accuracy and speed. In addition, L 2 EMG, which is a highly distinctive descriptor, characterizes the local image with a multivariate Gaussian distribution. So L 2 EMG features are fed into the FBLS for recognition in this paper. Extensive experimental results on self-built dataset show that the proposed student body gesture recognition method obtains better results than other benchmarking methods.
Ensemble Decision for Spam Detection Using Term Space Partition Approach

This paper proposes an ensemble decision approach which combines global and local features of e-mails together to detect spam effectively. In the proposed method, a special feature construction method named term space partition (TSP) is utilized to divide the whole term space into several subspaces and adopt different feature construction strategies on each of them, respectively. This method can make each term play a distinct and important role when conducting detection. This method is utilized and extended by introducing the sliding window technique to extract local features from e-mails. The global classifier and local classifiers are constructed on a global feature vector set and local feature vector sets, respectively, and together make the ensemble decision by adopting the voting technique. The principles of the TSP-based approach and mechanism of the ensemble decision method are presented in detail. Five different and standard benchmark corpora are applied to experiments for performance evaluation of this proposed method. Comprehensive experimental results show that the proposed method brings significant performance improvement and better robustness on the basis of the TSP-based approach. In addition, the proposed method outperforms the current prevalent and state-of-the-art approaches, especially when a comprehensive consideration of performance, efficiency, and robustness is taken. This endows it with flexible capability and adaptivity in the real-world applications.
Adding Neural Network Controllers to Behavior Trees without Destroying Performance Guarantees

In this paper, we show how Behavior Trees that have performance guarantees, in terms of safety and goal convergence, can be extended with components that were designed using machine learning, without destroying those performance guarantees.Machine learning approaches such as reinforcement learning or learning from demonstration can be very appealing to AI designers that want efficient and realistic behaviors in their agents. However, those algorithms seldom provide guarantees for solving the given task in all different situations while keeping the agent safe. Instead, such guarantees are often easier to find for manually designed model-based approaches. In this paper we exploit the modularity of behavior trees to extend a given design with an efficient, but possibly unreliable, machine learning component in a way that preserves the guarantees. The approach is illustrated with an inverted pendulum example.
Low Information Omnibus (LIO) Priors for Dirichlet Process Mixture Models

Dirichlet process mixture (DPM) models provide flexible modeling for distributions of data as an infinite mixture of distributions from a chosen collection. Specifying priors for these models in individual data contexts can be challenging. In this paper, we introduce a scheme which requires the investigator to specify only simple scaling information. This is used to transform the data to a fixed scale on which a low information prior is constructed. Samples from the posterior with the rescaled data are transformed back for inference on the original scale. The low information prior is selected to provide a wide variety of components for the DPM to generate flexible distributions for the data on the fixed scale. The method can be applied to all DPM models with kernel functions closed under a suitable scaling transformation. Construction of the low information prior, however, is kernel dependent. Using DPM-of-Gaussians and DPM-of-Weibulls models as examples, we show that the method provides accurate estimates of a diverse collection of distributions that includes skewed, multimodal, and highly dispersed members. With the recommended priors, repeated data simulations show performance comparable to that of standard empirical estimates. Finally, we show weak convergence of posteriors with the proposed priors for both kernels considered.
Anomaly detection via adaptive greedy model

Anomaly detection is one of the fundamental problems within diverse research areas and application domains. In comparison with most sparse representation based anomaly detection methods adopting a relaxation term of sparsity via ℓ1 norm, we propose an unsupervised anomaly detection method optimized via an adaptive greedy model based on ℓ0 norm constraint, which is more accurate, robust and sparse in theory. Firstly for feature representation, a concise feature space is learned in an unsupervised way via stacked autoencoder network. We propose a dictionary selection model based on ℓ2, 0 norm constraint to select an optimal small subset of the training data to construct a condense dictionary, which can improve accuracy and reduce computational burden simultaneously. Finally, each testing sample is reconstructed by ℓ0 norm constraint based sparse representation, and anomalies are determined depending on the sparse reconstruction scores accordingly. For model optimization, an adaptive forward-backward greedy model is utilized to optimize this nonconvex problem with the theoretical guarantee. Our proposed method is evaluated with our real industrial dataset and benchmark datasets, and various experimental results demonstrate that our proposed method is comparable with conventional supervised methods and performs better than most comparative unsupervised methods.
ADMM for image restoration based on nonlocal simultaneous sparse Bayesian coding

Group-based sparse representation (GSR) models of natural images decompose each patch in group as a sparse linear combination from an over-complete dictionary and assume the sparse coefficients of each patch have a common set of nonzero support. Although the GSR models have shown great success in image restoration (IR) applications, however, current models are simple extension of traditional L0 or L1 sparse models and lack spatial adaption and principled fashion. In this paper, we propose a novel GSR model calling simultaneous sparse Bayesian coding (SSBC) model. In this model, the shared scaling variables in patch group are first learned by the empirical Bayesian strategy. Based on the learned scaling variables, the sparse coefficients can be efficiently solved by the posterior means of the coefficients. We further generalize this model to process general IR tasks with the alternating direction method of multipliers (ADMM) techniques. Extensive experiments on image denoising, inpainting, deblurring and single image super-resolution demonstrate that the proposed method achieves notable objective and subjective improvements over many state-of-the-art restored methods.
A comprehensive multi-objective approach of service selection for service processes with twofold restrictions

To build complex business processes with QoS constraints by services with inter-service correlations is a challenge. Meeting this challenge involves the QoS-aware service selection problem for service processes with twofold restrictions (QoS constraints and inter-service correlations). Recently, QoS-aware service selection has been addressed from a multi-objective perspective. However, existing research fails to comprehensively consider both QoS constraints and inter-service correlations that widely exist in a real business world. Besides, none of them comprehensively considers both service selection at build time and run time, which may lead to high economic compensation caused by breach penalties at run time. In this paper, we present a comprehensive multi-objective optimization model for service selection with twofold restrictions. Furthermore, an improved multi-objective evolutionary algorithm framework for this model (MOEAQI) is proposed, which can provide a systematic solution for service selection at build time and run time respectively. In this framework, a constrained-domination principle is designed for the specific objectives and constraints in different phases, and several improved strategies are proposed to increase the searching effectiveness and efficiency. Finally, three sets of experiments with different problem sizes are constructed, and our approach is tested in comparison to the state-of-the-art algorithms for service selection problems including GDE3, E3MOGA and DASC. The performance of each algorithm is evaluated in the aspects of set coverage, spacing and computation time. Experimental results demonstrate that our approach is more effective than existing methods for service selection with twofold restrictions.
Mapping of hydrothermal alteration in the upper mantle-lower crust transition zone of the Tayin Massif, Sultanate of Oman using remote sensing technique

This study maps hydrothermal alteration of Wadi Tayin Massif of the Sultanate of Oman using the spectral bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the image processing methods namely band ratios, false color composite (FCC), spectral angle mapper (SAM), mixture tuned matched filtering (MTMF) and linear spectral unmixing (LSU) to understand alteration of the upper mantle-lower crust transition zone. The study of FCC image of the spectral bands 8, 4, and 1 in Red (R), Green (G), and Blue (B), respectively, showed the presence of the hydrothermal alteration in shades of purple and brown over the mantle peridotites, dunites, and crustal gabbros. The ASTER band ratios R:9/8, G:4/3, B:2/1 used in this study able to show the occurrence and distribution of alteration in orange distinctly. The SAM, LSU and MTMF image processing methods applied over the region confirmed the presence of alteration and altered minerals in the rocks. The assessment of accuracy for the methods provided the high accuracy of 91.86% and Kappa Coefficient = 0.89 in the LSU method. The image interpretations are studied in the field and the occurrence of alterations are validated by presence of strong serpentinization in the peridotites and dunites, and secondary veins in the micro cracks of gabbros. Laboratory studies showed presence of the serpentinization, iddingsitization, chloritization and development of pargasite in the altered rocks. This study shows application of the ASTER sensor to map hydrothermal alteration found near the fast-spreading oceanic ridge.
Hands-Free one-Time and continuous authentication using glass wearable devices

This paper investigates whether glass wearable devices can be used to authenticate end-users, both to grant access (one-time) and to maintain access (continuous), in a hands-free way. We do so by designing and implementing Gauth, a system that enables users to authenticate with a service simply by issuing a voice command, while facing the computer terminal they are going to use to access the service. To achieve this goal, we create a physical communication channel from the terminal to the glass device using machine readable visual codes, say, QR codes, and utilize the device's network adapter to communicate directly with a service. More importantly, we continuously authenticate the user accessing the terminal, exploiting the fact that a user operating a terminal is most likely facing it most of the time. We periodically issue authentication challenges, which are displayed as a QR code on the terminal. This causes the glass device to re-authenticate the user with an appropriate response. We thoroughly evaluate Gauth to determine the technical limits of our approach. We show that even with the relatively low-resolution camera of the Google Glass prototype, QR codes can be consistently processed correctly with an average accuracy of ≈ 88%, and continuous authentication, while strenuous to the battery, is feasible. Finally, we perform a small-scale user study involving students to demonstrate the benefits of our approach. We found that authentication using Gauth takes on average 1.63 s, while using username/password credentials takes 3.85 s and varies greatly depending on the computer-literacy level of the user.
A Constrained Representation Theorem for Well-Shaped Interval Type-2 Fuzzy Sets, and the Corresponding Constrained Uncertainty Measures

The representation theorem for interval type-2 fuzzy sets (IT2 FSs), proposed by Mendel and John, states that an IT2 FS is a combination of all its embedded type-1 (T1) FSs, which can be nonconvex and/or subnormal. These nonconvex and/or subnormal embedded T1 FSs are included in developing many theoretical results for IT2 FSs, including uncertainty measures, the linguistic weighted averages (LWAs), the ordered LWAs (OLWAs), the linguistic weighted power means (LWPMs), etc. However, convex and normal T1 FSs are used in most fuzzy logic applications, particularly computing with words. In this paper, we propose a constrained representation theorem (CRT) for well-shaped IT2 FSs using only its convex and normal embedded T1 FSs, and show that IT2 FSs generated from three word encoding approaches and four computing with words engines (LWAs, OLWAs, LWPMs, and perceptual reasoning) are all well-shaped IT2 FSs. We also compute five constrained uncertainty measures (centroid, cardinality, fuzziness, variance, and skewness) for well-shaped IT2 FSs using the CRT. The CRT and the associated constrained uncertainty measures can be useful in computing with words, IT2 fuzzy logic system design using the principles of uncertainty, and measuring the similarity between two well-shaped IT2 FSs.
Low-Cost Four-Dimensional Experience Theater Using Home Appliances

Multi-sensorial effect or four-dimensional (4-D) effect is considered in the niche arena of film playing but has become a growing business quickly. Installation of 4-D effects are typically expensive, and 4-D films are most often presented in custom-built theaters at special venues. This paper proposes a novel integration of existing video service platform and commercial smart home solution to generate 4-D effects for any non-4-D film (i.e., a 3-D or a 2-D film). Specifically, we describe how the designer can use home appliances to create the effects for a 4-D experience theater at home. Instead of using an expensive and special 4-D movie effect system, we use a low-cost off-the-shelf IoT platform for a smart home to create the 4-D movie effects for any non-4-D film. Therefore, the viewer can enjoy 4-D movies at home without special equipment other than home appliances. In our solution, an IoT device-management system called IoTtalk is utilized to integrate the video service system and the smart home system. Since these systems may be located in distant places, the delays for video streaming and triggering of 4-D effects must be synchronized. We have conducted measurements to indicate that in our design, the synchronization issue can be ignored without affecting the user experience.
Visual saliency detection via integrating bottom-up and top-down information

Selective attention is a process that enables biological and artificial systems to remove the redundant information and highlight the valuable regions in an image. The relevant information is determined by task driven (Top-Down (TD)) or task-independent (Bottom-Up (BU)) factors. In this paper, we present a new computational visual saliency model which uses the combination of BU and TD mechanism for extracting the relevant regions of images with man-made objects. The prior knowledge about man-made objects is the compactness and higher values of different orientations. So, by using maximum and minimum moments of phase congruency covariance and different orientations from Gabor filters, we obtain different feature maps from two mentioned attention mechanisms. Finally these maps are linearly combined which their coefficients are obtained by using the entropy of each feature map. Three region-based databases were used to examine the performance of the proposed method. The experimental results demonstrated the efficiency and effectiveness of this new visual saliency model.
Expectation propagation for Poisson data

The Poisson distribution arises naturally when dealing with data involving counts, and it has found many applications in inverse problems and imaging. In this work, we develop an approximate Bayesian inference technique based on expectation propagation for approximating the posterior distribution formed from the Poisson likelihood function and a Laplace type prior distribution, e.g., the anisotropic total variation prior. The approach iteratively yields a Gaussian approximation, and at each iteration, it updates the Gaussian approximation to one factor of the posterior distribution by moment matching. We derive explicit update formulas in terms of one-dimensional integrals, and also discuss stable and efficient quadrature rules for evaluating these integrals. The method is showcased on two-dimensional PET images.