Reviewerly/exHarmony huggingface dataset:
Meteosat image sequence coding in the Radon field

The high demand for Meteosat image sequences for various fields and the high rate of presentation of methods dealing with these latter is facing with the low rate of presentation of remote sensing image sequence compression methods generally for different imagers. To tackle this issue, a novel Meteosat image sequence coding method in the Radon field is presented in this paper. The idea behind this paper is to show the contribution of the coding in Radon field to an efficient lossless compression. The Radon field, represented here by a new fast Mojette transform (FMT), gives a homogeneous field through a Mojette matrix engendered by the intra-projections and inter-projections redundancy. As a consequence, the use of an efficient block-based motion estimation (ME) algorithm such as adaptive rood pattern search (ARPS) becomes possible in a such environment, which can help to avoid many problems encountered when using the same techniques for remote sensing image as a block effect by way of example. In order to achieve a high lossless compression, the Sorted Run Length Coding (SRLC) is applied to encode besides the motion vectors (MVs), the residual error represented by separate long sequences of zeros in addition to other repeated symbols. The test phase showed that our proposed scheme exceeds the performances of the JPEG-LS standard. The proposed scheme can also be used as pre-technique for methods using Meteosat images in general and their sequences in particular after the decompression process such as meteorological phenomena tracking, segmentation, and so on.
Spread binary artificial fish swarm algorithm combined with double-fault measure for ensemble pruning

Ensemble pruning is usually used to improve classification ability of an ensemble using less number of classifiers, and it is an NP-hard problem. Existing ensemble pruning approaches always find the optimal sub-ensemble using diversity of classifiers or running heuristic search algorithms separate ly. Diversity and accuracy of classifiers are widely recognized as two important properties of an ensemble. The increase of the diversity of classifiers must lead to the decrease of the average accuracy of the whole classifiers, and vice versa, so there is a tradeoff between diversity and accuracy of classifiers. Finding the tradeoff is the key to a successful ensemble. Heuristic algorithms have good results when it comes to finding the tradeoff, but it is unfeasible to do an exhaustive search. Hence, we propose a Spread Binary Artificial Fish swarm algorithm combined with a Double-fault measure for Ensemble Pruning (SBAFDEP) using a combination of diversity measures and heuristic algorithms. First, the classifiers in an initial pool are pre-pruned using a double-fault measure, which significantly alleviates the computational complexity of ensemble pruning. Second, the final ensemble is efficiently assembled from the retaining classifiers after pre-pruning using the proposed Spread Binary Artificial Fish Swarm Algorithm (SBAFSA). Simulation and experiment results on 25 UCI datasets show that SBAFDEP performs better than other state-of-the-art pruning approaches. It provides a novel research idea for ensemble pruning.
Collecting Ecologically Valid Data in Location-Aware Augmented Reality Settings

Collecting data in a mobile augmented reality (AR) settings is challenging, as participants are dispersed in the physical space and move often; therefore, it is imperative that new techniques are investigated to facilitate richer and more ecologically-valid data collection. This study examined three in vivo techniques for collecting authentic data in mobile, AR learning situations: (a) tablet-based audio recording, (b) students' researcher-led videotaping, and (c) head-mounted wearable cameras. Participants were eighteen 11th grade students, working in pairs. All students completed individual questionnaires examining their perception of the intrusiveness of the data collection technique and participated in interviews about the intrusiveness of technique. Audio and video from students' work was also collected. Findings are used to discuss the advantages and disadvantages of each data collection technique as a method for collecting data in location-aware augmented reality studies.
Segmentation of Post-operative Glioblastoma in MRI by U-Net with Patient-Specific Interactive Refinement

Accurate volumetric change estimation of glioblastoma is very important for post-surgical treatment follow-up. In this paper, an interactive segmentation method was developed and evaluated with the aim to guide volumetric estimation of glioblastoma. U-Net based fully convolutional network is used for initial segmentation of glioblastoma from post contrast MR images. The max flow algorithm is applied on the probability map of U-Net to update the initial segmentation and the result is displayed to the user for interactive refinement. Network update is performed based on the corrected contour by considering patient specific learning to deal with large context variations among different images. The proposed method is evaluated on a clinical MR image database of 15 glioblastoma patients with longitudinal scan data. The experimental results depict an improvement of segmentation performance due to patient specific fine-tuning. The proposed method is computationally fast and efficient as compared to state-of-the-art interactive segmentation tools. This tool could be useful for post-surgical treatment follow-up with minimal user intervention.
Adaptive deblocking technique based on separate modes for removing compression effects in JPEG coded images

Adaptive post-processing filtering techniques significantly improve the perceptual quality of decoded images by alleviating compression artifacts. However, there are still some noticeable compression artifacts in the block-based coded images especially at a low bit rate. In this paper, we proposed a three-step deblocking filter technique that removes the compression artifacts, undesired noise, and corner outliers. The proposed deblocking technique has been applied to the variety of JPEG compressed images and results are compared with existing post-processing techniques on the basis of standard metrics such as PSNR, PSNR-B. Experimental simulation results illustrate that the proposed technique could effectively improve the perceptual quality of various images and outperform existing deblocking technique.
Automated Chinese Essay Scoring Based on Multilevel Linguistic Features

Writing assessments make up an important part of the learning process as one masters the important linguistic skill of writing. However, this process has not been implemented effectively or on a large scale because the task of essay scoring is very time-consuming. The solution to this problem is AES, where machines are used to automatically score essays. In fact, the application of AES to English learning has been successful. Due to differences in linguistic characteristics, a redesign is needed before AES can be applied to Chinese learning. The purpose of this chapter is to introduce ACES, an automated system for scoring Chinese essays, and explain the basic framework, design principles, and scoring accuracy of the system. Unlike some end-to-end AES systems, ACES’ basic framework is designed to provide more interpretative features. The experimental results show that the performance of the ACES system is stable and reliable, and on par with other commercial English AES systems.
Environment-Aware Multi-Target Tracking of Pedestrians

When navigating mobile robotic systems in dynamic environments, the ability to predict where pedestrians will move in the next few seconds is crucial. To tackle this problem, many solutions have been developed which take the environment's influence on human navigation behavior into account. However, few do this in a way which seamlessly generalizes to new environments where no prior observations of pedestrians are available. In this letter, we propose a novel method that uses convolutional neural networks (CNNs) to predict local statistics about the direction of likely human motion. Specifically, the network takes crops of a floor plan as an input and computes a cell-wise probability distributions over directions conditioned on the direction in the last time step. These environment-aware motion models can be used to improve the performance of existing tracking algorithms. We evaluated our approach with two different existing trackers, a discrete, grid-based filter as well as a particle filter and show that using our proposed method as motion model considerably improves prediction quality in previously unseen test environments. Even though we train the CNN entirely in simulation, our experiments suggest that the learned models generalize to real pedestrian data.
Extracting Temporal Patterns from Large-Scale Text Corpus

Knowledge, in practice, is time-variant and many relations are only valid for a certain period of time. This phenomenon highlights the importance of designing temporal patterns, i.e., indicating phrases and their temporal meanings, for temporal knowledge harvesting. However, pattern design is extremely laborious and time consuming even for a single relation. Therefore, in this work, we study the problem of temporal pattern extraction by automatically analysing a large-scale text corpus with a small number of seed temporal facts. The problem is challenging considering the ambiguous nature of natural language and the huge amount of documents we need to analyse in order to obtain highly representative temporal patterns. To this end, we introduce various techniques, including corpus annotation, pattern generation, scoring and clustering, to reduce ambiguity in the text corpus and improve both accuracy and coverage of the extracted patterns. We conduct extensive experiments on real world datasets and the experimental results verify the effectiveness of our proposals.
Pedagogical Applications of Chinese Parallel Corpora

Parallel corpora are a unique resource in language acquisition that enables learners to conceptualize a target language through the established schemas of their first language by providing parallel representations of text in two or more languages. Parallel corpora are defined as specialized translation corpora that consist of source texts in one language that are aligned with translation texts in one or more additional languages. The following chapter thoroughly explores the pedagogical application of parallel corpora in general, before taking an in-depth look at how English L1 beginning-level learners of Mandarin Chinese applied a Chinese–English parallel corpus. In addition to elucidating the specific observed outcomes of parallel corpora in this unique learning context, numerous parallel corpus resources are detailed with suggestions for pedagogical application, and an extensive review of potential further applications based on continued research in the field is enumerated and analyzed.
A Brief History of Search Results Ranking

The theory and practice of search results ranking, as currently offered by most web search engines, is older than one might think. The first proposal for a system of ranking was in a JACM paper in 1960. Through the remainder of the twentieth century, extensive research work was done on ranking systems—on devising methods of ranking, on the use of learning methods in association with ranking, and on the comparative evaluation of alternative methods. The web search engines of the tail end of the century made much use of this paper.
Generalizing expectation propagation with mixtures of exponential family distributions and an application to Bayesian logistic regression

Expectation propagation (EP) is a widely used deterministic approximate inference algorithm in Bayesian machine learning. Traditional EP approximates an intractable posterior distribution through a set of local approximations which are updated iteratively. In this paper, we propose a generalized version of EP called generalized EP (GEP), which is a new method based on the minimization of KL divergence for approximate inference. However, when the variance of the gradient is large, the algorithm may need a long time to converge. We use control variates and develop a variance reduced version of this method called GEP-CV. We evaluate our approach on Bayesian logistic regression, which provides faster convergence and better performance than other state-of-the-art approaches.
A reliable reputation computation framework for online items in E-commerce

Most of online trading platforms allow consumers to give personal ratings to online items. By computing the weighted mean of the ratings, the reputation values of online items can be derived to assist consumers to make purchasing decisions. However, it is never a simple task to derive a reliable reputation value of any given item and existing works fail to achieve this. Thus, in this paper, we propose a reliable reputation computation framework for online items which can be adopted by online trading platforms or run by a third party to provide reputation computation as a service. At first, a fine-grained two-phase detection method is proposed to detect malicious ratings. After filtering out the ratings detected as malicious, the weights of the remaining ratings are determined by computing the degrees to which the users giving these ratings are interested in a target item. Extensive experiments verify that the proposed reliable reputation computation framework is effective to detect different kinds of malicious ratings and determine the interest degrees of users.
Texture-preserving denoising method for the removal of random-valued impulse noise in gray-scale images

Image denoising has become one of the fundamental problems in the field of image processing. We present an iterative image denoising method named as multitexton noise identification and local dissimilarity-based noise removal that incorporates a noise restoration phase followed by a noise identification phase for gray-scale images corrupted by random-valued impulse noise (RVIN). Multiple textons of distinct orientations arranged on the basis of radial symmetry are proposed that give sharp edges of restored images. The noise identification phase works on an adaptive threshold range by employing the local statistics of textons. Moreover, the proposed method can better be utilized to deal with the uncertainty observed in identification of noisy and edge pixels. The dissimilarity of the identified corrupted pixel with its four-connected neighboring pixels is computed by considering the similarity among these four-connected pixels, to restore the gray-level intensities of identified corrupted pixels at the second phase. Standard gray-scale benchmark test images are used to assess the quantitative and visual performance of the proposed method by comparing it with state-of-the-art denoising methods. The extensive experimental results reveal that the presented method outperforms in terms of peak-signal-to-noise ratio, structural similarity index measurement, miss detection, false detection, and visual results for both lower and higher intensities of RVIN as compared to other existing techniques.
Measurement Extraction for Two Closely-Spaced Objects Using an Imaging Sensor

This paper considers measurement extraction for two closely-spaced objects with unknown equal intensities in an imaging sensor's focal plane array (FPA). Given a screen of FPA data, the first part of the measurement extractor, target location estimator, can extract the location estimates for two targets or one, with the corresponding accuracy given by the Cramér Rao lower bound (CRLB). The second part of the measurement extractor, target detector, selects among the hypotheses of two resolved targets and a single one using information-theoretic criteria and hypothesis tests. Simulation results have been conducted to evaluate the measurement extraction performance including the probability of resolving the two hypotheses, and the efficiency and unbiasedness of the target location estimates for the selected hypothesis using different hypothesis detection schemes. The generalized likelihood ratio test (GLRT) based on linearized observation model using second order Taylor series expansion is most appealing as it provides an explicit expression of the probability of detecting two targets as a function of the target separations, the signal-to-noise ratio at a given false resolution probability. It is shown that the simulation-based resolution performance for the GLRT using the estimated center location of the two targets matches well with the analytic performance assuming known center.
CNN-Based Ringing Effect Attenuation of Vibroseis Data for First-Break Picking

In the field of exploration geophysics, vibroseis system is one of the widely used seismic sources to acquire seismic data. “Ringing effect” is a common phenomenon in vibroseis data due to the limited frequency bandwidth of the vibroseis system, which degrades the performance of automatic first-break picking. In this letter, we proposed a deringing method for vibroseis data using a deep convolutional neural network (CNN). In this method, we use end-to-end network structure to obtain the deringed data directly and skip connections to improve model training performance and preserve the details of vibroseis data. For real vibroseis data processing, train data set is first generated from the data to be processed. We extract seismic wavelet and pseudoreflectivity series from real vibroseis data and use them to synthesize training data, which resembles real data. Pseudoreflectivity series with a broader frequency range is used as a training label. Experiments are conducted both on synthetic and real vibroseis data. The experiment results show that deep CNN-based method can attenuate the ringing effect effectively and expand the bandwidth of vibroseis data. The short-time average/long-time average ratio method for first-break picking also shows improvement on deringed vibroseis data.
Effects of Communication Restriction on Online Multi-robot Exploration in Bounded Environments

In multi-robot exploration for real-world applications, the amount of communication available in a given environment can vary greatly, and has significant effects on the performance of the robot team. We compare algorithms which use three communication models, from unlimited global communication to unplanned, semi-random encounters between robots; and we evaluate each algorithms' performance with different sized robot teams in three environments. We provide simulation results showing how the algorithms perform under optimal conditions, and discuss how real-world situations impact performance.
MCVAE: Margin-based Conditional Variational Autoencoder for Relation Classification and Pattern Generation

Relation classification is a basic yet important task in natural language processing. Existing relation classification approaches mainly rely on distant supervision, which assumes that a bag of sentences mentioning a pair of entities and extracted from a given corpus should express the same relation type of this entity pair. The training of these models needs a lot of high-quality bag-level data. However, in some specific domains, such as medical domain, it is difficult to obtain sufficient and high-quality sentences in a text corpus that mention two entities with a certain medical relation between them. In such a case, it is hard for existing discriminative models to capture the representative features (i.e., common patterns) from diversely expressed entity pairs with a given relation. Thus, the classification performance cannot be guaranteed when limited features are obtained from the corpus. To address this challenge, in this paper, we propose to employ a generative model, called conditional variational autoencoder (CVAE), to handle the pattern sparsity. We define that each relation has an individually learned latent distribution from all possible sentences expressing this relation. As these distributions are learned based on the purpose of input reconstruction, the model's classification ability may not be strong enough and should be improved. By distinguishing the differences among different relation distributions, a margin-based regularizer is designed, which leads to a margin-based CVAE (MCVAE) that can significantly enhance the classification ability. Besides, MCVAE can automatically generate semantically meaningful patterns that describe the given relations. Experiments on two real-world datasets validate the effectiveness of the proposed MCVAE on the tasks of relation classification and relation-specific pattern generation.
Using Well-Founded Relations for Proving Operational Termination

In this paper, we study operational termination, a proof theoretical notion for capturing the termination behavior of computational systems. We prove that operational termination can be characterized at different levels by means of well-founded relations on specific formulas which can be obtained from the considered system. We show how to obtain such well-founded relations from logical models which can be automatically generated using existing tools.
Make Evolutionary Multiobjective Algorithms Scale Better with Advanced Data Structures: Van Emde Boas Tree for Non-dominated Sorting

We improve the worst-case time complexity of non-dominated sorting, an operation frequently used in evolutionary multiobjective algorithms, to $$O(n \cdot (\log n)^{k-2} \log \log n)$$ , where n is the number of solutions, k is the number of objectives, and the random-access memory computation model is assumed. This improvement was possible thanks to the van Emde Boas tree, an "advanced" data structure which stores a set of non-negative integers less than n and supports many queries in $$O(\log \log n)$$ . This is not only a theoretical improvement, as we also provide an efficient implementation of the van Emde Boas tree, which resulted in a competitive algorithm that scales better than other algorithms when n grows, at least for small numbers of objectives greater than two.
Opening and Reusing Transparent Peer Reviews with Automatic Article Annotation

An increasing number of scientific publications are created in open and transparent peer review models: a submission is published first, and then reviewers are invited, or a submission is reviewed in a closed environment but then these reviews are published with the final article, or combinations of these. Reasons for open peer review include giving better credit to reviewers, and enabling readers to better appraise the quality of a publication. In most cases, the full, unstructured text of an open review is published next to the full, unstructured text of the article reviewed. This approach prevents human readers from getting a quick impression of the quality of parts of an article, and it does not easily support secondary exploitation, e.g., for scientometrics on reviews. While document formats have been proposed for publishing structured articles including reviews, integrated tool support for entire open peer review workflows resulting in such documents is still scarce. We present AR-Annotator, the Automatic Article and Review Annotator which employs a semantic information model of an article and its reviews, using semantic markup and unique identifiers for all entities of interest. The fine-grained article structure is not only exposed to authors and reviewers but also preserved in the published version. We publish articles and their reviews in a Linked Data representation and thus maximise their reusability by third party applications. We demonstrate this reusability by running quality-related queries against the structured representation of articles and their reviews.