Reviewerly/exHarmony huggingface dataset:
High-Quality Color Image Compression by Quantization Crossing Color Spaces

Coding of a color image usually happens in the YCbCr space so that the rate-distortion optimization is conducted in this space. Due to the use of a non-unitary matrix in the RGB-to-YCbCr conversion, an optimal coding performance achieved in the YCbCr space does not guarantee an optimal quality in the RGB space, which would impact most display devices that need RGB signals as the inputs. In this paper, we first study the relationship between the coding distortions of the compressed RGB signals and the quantization errors occurred in the coded YCbCr signals. Then, we design a new quantization scheme crossing the RGB and YCbCr spaces to achieve a high-quality color image compression with the YCbCr 4:4:4 format. Although our proposed quantization takes place in the YCbCr space, it aims at reducing the coding distortion in the RGB space as much as possible. Experimental results demonstrate that our proposed method offers a significant quality gain over the existing block-based coding methods for various images.
LSDSAR, a Markovian a contrario framework for line segment detection in SAR images

In this paper, we propose a generic method for the detection of line segments in SAR images. The approach relies on an a contrario framework and is inspired by the state-of-the art LSD detector. As with all a contrarioapproaches, false detections are controlled through the use of a background model, whose development is especially challenging in the framework of SAR images. Indeed, statistical characteristics of SAR images strongly differ from those of optical images, making the use of existing background models intrinsically inadequate. In order to circumvent this problem, we proceed in two steps. First, the building blocks of the detector, namely the local orientations, are computed carefully to avoid any spatial bias. Second, we propose a new background model, in which the spatial dependency between local orientations are modeled with a Markov chain. This is in strong contrast with most existing a contrario methods who heavily rely on independence assumptions. We provide a complete and detailed algorithm for our line segment detector, and perform experiments on synthetic and real images demonstrating its efficiency.
Causal Discovery in the Presence of Missing Data

Missing data are ubiquitous in many domains such as healthcare. When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be di ...
Monogamy relations for the generalized <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>W</mml:mi></mml:math> -class states beyond qubits

In this article, we consider the monogamy relations for the generalized $W$-class states. Here we first present an analytical formula on Tsallis-$q$ entanglement ($\mathrm{T}q\mathrm{EE}$) and Tsallis-$q$ entanglement of assistance ($\mathrm{T}q\mathrm{EEoA}$) of a reduced density matrix for a generalized $W$-class state. By using the analytical formula, we present a monogamy relation in terms of the squared $\mathrm{T}q\mathrm{EE}$ for a generalized $W$-class state. Then we present generalized monogamy relations in terms of $\mathrm{T}q\mathrm{EE}$ for a generalized $W$-class state. We also present a monogamy relation in terms of an arbitrary power of $\mathrm{T}q\mathrm{EE}$ for a generalized $W$-class state. Last, we apply our results to quantum games and present the bound of the nonclassicality of the quantum games when restricting to the generalized $W$-class states.
Robust procedural model fitting with a new geometric similarity estimator

Procedural model fitting (PMF) is a generalization of classical model fitting and has numerous applications for computer vision and computer graphics. The task of PMF is to search a geometric model set for the model that is most similar to a set of data points. We propose a strict and robust similarity estimator for PMF to handle imperfect data. The proposed estimator is based on the error from model to data, while most other estimators are based on the error from data to model. We then use the proposed estimator to guide the cuckoo search algorithm to search for the most similar model. To accelerate the search process, we also propose a coarse-to-fine model dividing strategy to early reject dissimilar models. In this paper, the proposed PMF method is applied to fit building models on laser scanning data. It is also applied to fit character models on eighteen variants of imperfect MNIST data to achieve few-shot pattern recognition. In the 5-shot recognition, our method outperforms the state-of-the-art method on thirteen variants of the imperfect data. In particular, for one of the data corrupted by grid lines, our method obtains a high accuracy of 65%, whereas the state-of-the-art method only obtains an accuracy of 30%.
Finding a marked node on any graph via continuous-time quantum walks

Spatial search by a discrete-time quantum walk can find a marked node on any ergodic, reversible Markov chain $P$ quadratically faster than its classical counterpart, i.e., in a time that is in the square root of the hitting time of $P$. However, in the framework of continuous-time quantum walks, it was previously unknown whether such general speedup is possible. In fact, in this framework, the widely used quantum algorithm by Childs and Goldstone fails to achieve such a speedup. Furthermore, it is not clear how to apply this algorithm for searching any Markov chain $P$. In this article we aim to reconcile the apparent differences between the running times of spatial search algorithms in these two frameworks. We first present a modified version of the Childs and Goldstone algorithm which can search for a marked element for any ergodic, reversible $P$ by performing a quantum walk on its edges. Although this approach improves the algorithmic running time for several instances, it cannot provide a generic quadratic speedup for any $P$. Second, using the framework of interpolated Markov chains, we provide a spatial search algorithm by a continuous-time quantum walk which can find a marked node on any $P$ in the square root of the classical hitting time. In the scenario where multiple nodes are marked, the algorithmic running time scales as the square root of a quantity known as the extended hitting time. Our results establish a connection between discrete-time and continuous-time quantum walks and can be used to develop a number of Markov chain-based quantum algorithms.
Escaping the curse of dimensionality in similarity learning: Efficient Frank-Wolfe algorithm and generalization bounds

Similarity and metric learning provides a principled approach to construct a task-specific similarity from weakly supervised data. However, these methods are subject to the curse of dimensionality: as the number of features grows large, poor generalization is to be expected and training becomes intractable due to high computational and memory costs. In this paper, we propose a similarity learning method that can efficiently deal with high-dimensional sparse data. This is achieved through a parameterization of similarity functions by convex combinations of sparse rank-one matrices, together with the use of a greedy approximate Frank-Wolfe algorithm which provides an efficient way to control the number of active features. We show that the convergence rate of the algorithm, as well as its time and memory complexity, are independent of the data dimension. We further provide a theoretical justification of our modeling choices through an analysis of the generalization error, which depends logarithmically on the sparsity of the solution rather than on the number of features. Our experiments on datasets with up to one million features demonstrate the ability of our approach to generalize well despite the high dimensionality as well as its superiority compared to several competing methods.
Monitoring questionnaires to ensure positive interdependence and individual accountability in a chemical process synthesis following collaborative PBL approach

Teammates in a Project Based Learning (PBL) methodology do not all start with the same motivation, expectations or self-commitment, which can lead to disappointing learning experiences. Thus, the main difficulties in cooperative learning are promoting positive interdependence and individual accountability. This work aims to analyze whether monitoring questionnaires (MQs) allow rating each teammate’s individual accountability in a chemical process design project. Students took MQs just after finishing each deliverable, in order to verify each one’s knowledge of the essential aspects of the team project. We assume that, if all the teammates work responsibly and cooperatively, they will be able to answer the MQs easily and correctly. The MQs were designed to be: 1) generic; 2) succinct; and, 3) quickly answered in the classroom. In addition, different rating methods were analyzed to incorporate MQs scores into each individual’s project grade. The best results were obtained when student’s individual grade was computed as the product of the team assignment grade and the weighting factor WF3, calculated as the individual average MQs rating divided by the highest MQs rating in the team. A compensation factor (0.75–0.85) was included to correct the excessive downgrading for students with intermediate project grade.
Improving sentiment analysis for twitter data by handling negation rules in the Serbian language

The importance of determining sentiment for short text increases with the rise in the number of comments on social networks. The presence of negation in these texts affects their sentiment, because it has a greater range of action in proportion to the length of the text. In this paper, we examine how the treatment of negation impacts the sentiment of tweets in the Serbian language. The grammatical rules that influence the change of polarity are processed. We performed an analysis of the effect of the negation treatment on the overall process of sentiment analysis. A statistically significant relative improvement was obtained (up to 31.16% or up to 2.65%) when the negation was processed using our rules with the lexicon-based approach or machine learning methods. By applying machine learning methods, an accuracy of 68.84% was achieved on a set of positive, negative and neutral tweets, and an accuracy of as much as 91.13% when applied to the set of positive and negative tweets.
Low-resolution image categorization via heterogeneous domain adaptation

Most of existing image categorizations assume that the given datasets have a good resolution and quality. However, the assumption is often violated in real applications. In this paper, we study the low-resolution (LR) image categorization. By utilizing labeled high-resolution (HR) images as auxiliary information, we formulate the problem as a heterogeneous domain adaptation problem and propose a Discriminative Joint Distribution Adaptation (DJDA) model to solve it. The DJDA model projects both LR and HR images into an intermediate subspace with a well-designed objective function, where the distance between classes is expected to be enlarged and the distribution divergence to be reduced. As a result, the discriminative knowledge for HR images can be transferred effectively to LR images. Experimental results demonstrate the proposed DJDA method produces significantly superior categorization accuracies against state-of-the-art competitors.
Optimal usage of quantum random access memory in quantum machine learning

By considering an unreliable oracle in a query-based model of quantum learning, we present a tradeoff relation between the oracle's reliability and the reusability of the quantum state of the input data. The tradeoff relation manifests as the fundamental upper bound on the reusability. This limitation on the reusability would increase the quantum access to the input data, i.e., the usage of quantum random access memory (qRAM), repeating the preparation of a superposition of large (or big) input data on the query failure. However, it is found that a learner can obtain a correct answer even from an unreliable oracle without any additional usage of qRAM; i.e., the complexity of the qRAM query does not increase even with an unreliable oracle. This is enabled by repeatedly cycling the quantum state of the input data to the upper bound on the reusability.
Campus Data Analysis Based on Positive and Negative Sequential Patterns

Campus data analysis is becoming increasingly important in mining students’ behavior. The consumption data of college students is an important part of the campus data, which can reflect the students’ behavior to a great degree. A few methods have been used to analyze students’ consumption data, such as classification, association rules, clustering, decision trees, time series, etc. However, they do not use the method of sequential patterns mining, which results in some important information missing. Moreover, they only consider the occurring (positive) events but do not consider the nonoccurring (negative) events, which may lead to some important information missing. So this paper uses a positive and negative sequential patterns mining algorithm, called NegI-NSP, to analyze the consumption data of students. Moreover, we associate students’ consumption data with their academic grades by adding the students’ academic grades into sequences to analyze the relationship between the students’ academic grades and their consumptions. The experimental results show that the students’ academic performance has significant correlation with the habits of having breakfast regularly.
A Distributed Flexible Delay-Tolerant Proximal Gradient Algorithm

We develop and analyze an asynchronous algorithm for distributed convex optimization when the objective can be written as a sum of smooth functions, local to each worker, and a non-smooth function.Unlike many existing methods, our distributed algorithm is adjustable to various levels of communication cost, delays, machines' computational power, and functions' smoothness.A unique feature is that the stepsizes do not depend on communication delays nor number of machines, which is highly desirable for scalability.We prove that the algorithm converges linearly in the strongly convex case, and provide guarantees of convergence for the non-strongly convex case.The obtained rates are the same as the vanilla proximal gradient algorithm over some introduced epoch sequence that subsumes the delays of the system.We provide numerical results on large-scale machine learning problems to demonstrate the merits of the proposed method.
Hierarchical clustering that takes advantage of both density-peak and density-connectivity

This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm. Our investigation begins with formally defining the types of clusters DP and DBSCAN are designed to detect; and then identifies the kinds of distributions that DP and DBSCAN individually fail to detect all clusters in a dataset. These identified weaknesses inspire us to formally define a new kind of clusters and propose a new method called DC-HDP to overcome these weaknesses to identify clusters with arbitrary shapes and varied densities. In addition, the new method produces a richer clustering result in terms of hierarchy or dendrogram for better cluster structures understanding. Our empirical evaluation results show that DC-HDP produces the best clustering results on 14 datasets in comparison with 7 state-of-the-art clustering algorithms.
Coherence and quantum correlations measure sensitivity to dephasing channels

We introduce measures of quantum coherence as the speed of evolution of a system under decoherence. That is, coherence is the ability to estimate a dephasing channel, quantified by the quantum Fisher information. We extend the analysis to interferometric noise estimation, proving that quantum discord is the minimum sensitivity to local dephasing. A physically motivated set of free operations for discord is proposed. The amount of discord created by strictly incoherent operations is upper-bounded by the initial coherence.
Leveraging semantic segmentation with learning-based confidence measure

The standard paradigm of semantic segmentation is first training a multi-class classifier to estimate the probability distributions of the pixels’ possible labels and then employing a graphical model to encode contextual information to obtain a globally optimal solution. However, evaluating the quality of the trained classifier is a rarely investigated problem so far and its consequential influence on post-processing is also not yet analyzed. In this paper, we focus on the two problems and propose to estimate the confidence of the classifier by additionally learning a binary classifier via ensemble learning and convolutional neural network (CNN) respectively. Compared with the hand-crafted confidence measures, our learning-based methods are proved to be more effective. Moreover, the estimated confidences are employed to modulate the probability distributions to leverage the post-processing step and high-quality label maps can be established finally. We evaluate our methods on two public available datasets Sift Flow and Pascal-Context, and the results demonstrate that learning to predict confidences is an effective and promising strategy for improving semantic segmentation frameworks.
Real‐time face recognition based on pre‐identification and multi‐scale classification

In face recognition, searching a person's face in the whole picture is generally too time‐consuming to ensure high‐detection accuracy. Objects similar to the human face or multi‐view faces in low‐resolution images may result in the failure of face recognition. To alleviate the above problems, a real‐time face recognition method based on pre‐identification and multi‐scale classification is proposed in this study. The face area is segmented based on the proportion of human faces in the pedestrian area to reduce the search range, and faces can be robustly detected in complicated scenarios such as heads moving frequently or with large angles. To accurately recognise small‐scale faces, the authors propose the multi‐scale and multi‐channel shallow convolution network, which combines a multi‐scale mechanism on the feature map with a multi‐channel convolution network for real‐time face recognition. It performs face matching only in the pre‐identified face areas instead of the whole image, therefore it is more efficient. Experimental results showed that the proposed real‐time face recognition method detects and recognises faces correctly, and outperforms the existing methods in terms of effectiveness and efficiency.
Feature fusion based automatic aesthetics evaluation of robotic dance poses

Inspired by human dancers who make a comprehensive aesthetic judgement of their own dance poses by using both visual and non-visual information, this paper presents a novel feature fusion based approach to automatic aesthetics evaluation of robotic dance poses in order to improve the performance of robotic choreography creation. Four kinds of features are extracted, namely kinematic feature, region feature, contour feature, and spatial distribution feature of colour block. Based on different feature combinations, machine learning is deployed to train aesthetics models for the automatic judgement on robotic dance poses. The proposed approach has been implemented on a simulated robot environment, and experimental results are presented to verify its feasibility and good performance.
A Distributed Trust Evaluation Protocol with Privacy Protection for Intercloud

Intercloud seeks to facilitate resource sharing among clouds. To support Intercloud, a trust evaluation framework among clouds and users is required. For trust evaluation, conventional protocols are typically based on a centralized architecture focusing on a one-way relationship. For Intercloud, the environment is highly dynamic and distributed, and relationships can be one-way or two-way (i.e., clouds provide services to each other). This paper presents a distributed trust evaluation protocol with privacy protection for Intercloud. The new contributions and innovative features are summarized below. First, feedback is protected by homomorphic encryption with verifiable secret sharing. Second, to cater to the dynamic nature of Intercloud, trust evaluation can be conducted in a distributed manner and is functional even when some of the parties are offline. Third, to facilitate customized trust evaluation, an innovative mechanism is used to store feedback, such that it can be processed flexibly while protecting feedback privacy. The protocol has been proved based on a formal security model. Simulations have been performed to demonstrate the effectiveness of the protocol. The results show that even when half of the clouds are malicious or offline, by choosing suitable operational parameters the protocol can still support effective trust evaluation with privacy protection.
Multi-sequence myocardium segmentation with cross-constrained shape and neural network-based initialization

For myocardial infarction (MI) patients, delayed enhancement (DE) and T2-weighted cardiovascular magnetic resonance imaging (CMR) can play significant roles in diagnosis, prognosis and therapeutic strategy evaluation. However, the non-rigid registration between different CMR sequences is particularly challenging and prevents the use of multi-sequence image analysis. In this article, we propose an approach for segmenting T2 and DE CMR simultaneously with cross-constrained shape and shape discrepancy compensation. A framework for the unified segmentation of multi-sequence images is built based on the coupled level set method. Additionally, a sparse representation-based shape model is optimized under the constraints from both sequences for complementary information sharing. Considering the myocardium shape discrepancy between the two sequences due to non-perfect registration, an error term is added to explicitly model this difference. The intensity feature is extracted with a Gaussian mixture model from each sequence. To obtain a fully automatic approach, the conditional generative adversarial network is adopted for initialization. The results are evaluated with T2 and DE images from 32 MI patients. A promising Dice similarity coefficient of the myocardium is achieved (84.97±4.15% for T2 and 78.13±6.22% for DE CMR). This approach is a pilot work toward automatic, multi-sequence CMR image analysis.
A scoring rubric for automatic short answer grading system

During the past decades, researches about automatic grading have become an interesting issue. These studies focuses on how to make machines are able to help human on assessing students’ learning outcomes. Automatic grading enables teachers to assess student's answers with more objective, consistent, and faster. Especially for essay model, it has two different types, i.e. long essay and short answer. Almost of the previous researches merely developed automatic essay grading (AEG) instead of automatic short answer grading (ASAG). This study aims to assess the sentence similarity of short answer to the questions and answers in Indonesian without any language semantic's tool. This research uses pre-processing steps consisting of case folding, tokenization, stemming, and stopword removal. The proposed approach is a scoring rubric obtained by measuring the similarity of sentences using the string-based similarity methods and the keyword matching process. The dataset used in this study consists of 7 questions, 34 alternative reference answers and 224 student’s answers. The experiment results show that the proposed approach is able to achieve a correlation value between 0.65419 up to 0.66383 at Pearson's correlation, with Mean Absolute Error (𝑀𝐴𝐸) value about 0.94994 until 1.24295. The proposed approach also leverages the correlation value and decreases the error value in each method.