Reviewerly/exHarmony huggingface dataset:
Can a Quantum Walk Tell Which Is Which?A Study of Quantum Walk-Based Graph Similarity

We consider the problem of measuring the similarity between two graphs using continuous-time quantum walks and comparing their time-evolution by means of the quantum Jensen-Shannon divergence. Contrary to previous works that focused solely on undirected graphs, here we consider the case of both directed and undirected graphs. We also consider the use of alternative Hamiltonians as well as the possibility of integrating additional node-level topological information into the proposed framework. We set up a graph classification task and we provide empirical evidence that: (1) our similarity measure can effectively incorporate the edge directionality information, leading to a significant improvement in classification accuracy; (2) the choice of the quantum walk Hamiltonian does not have a significant effect on the classification accuracy; (3) the addition of node-level topological information improves the classification accuracy in some but not all cases. We also theoretically prove that under certain constraints, the proposed similarity measure is positive definite and thus a valid kernel measure. Finally, we describe a fully quantum procedure to compute the kernel.
Initial Matting-Guided Visual Tracking With Siamese Network

Fully-convolutional Siamese networks for visual tracking have drawn great attention in balancing tracking accuracy and speed.However, there is still some inherent inaccuracy with advanced trackers, since they only learn a general matching model from large scale datasets by off-line training.This generates the target template without sufficient discriminant information and does not adapt well to the current tracking sequence.In this paper, we introduce the channel attention mechanism into the network to better learn the matching model and, during the online tracking phase, we design an initial matting guidance strategy in which: 1) the superpixel matting algorithm is applied to extract the target foreground in the initial frame, and 2) the matted image with foreground only is fed into the network and fused with the original image feature.Under matting guidance, the fused target template has more details for representation of target appearance and more structural information from superpixels for robust tracking.The experimental results on object tracking benchmark (OTB) show that our approach achieves excellent performance while it also provides real-time tracking speed.
Motion and appearance based background subtraction for freely moving cameras

With increasing popularity of smart phone cameras and wearable cameras, it is imperative to develop robust vision systems in analyzing videos captured by these freely moving cameras. In this paper, we propose a novel motion and appearance based algorithm for foreground/background segmentation of these videos. Unlike existing methods, it does not require any prior information nor does it restrict camera motion or scene geometry. The proposed algorithm first estimates a dense motion field between two consecutive frames, and obtains a motion-based foreground probability estimate for each pixel by comparing the motion field with its low-rank approximation. In parallel, color features are extracted by sliding a fixed-size neighborhood window over the entire image. Using the motion-based probability estimates, highly probable foreground and background color features are identified and used to learn foreground and background appearance models. These models then generate appearance-based probability estimate for each pixel. To overcome the inaccuracies in appearance modeling and background motion approximation, we incorporate an innovative Mega-pixel denoising process that uses color segmentation to smooth out the probability estimates. Finally, the denoised probability estimates are combined with the image gradient map to produce the output foreground mask under the Graph-Cut optimization framework. To cope with non-stationary dynamic scenes, the foreground and background appearance models are continuously updated with highly probable foreground and background color features. Extensive evaluations on publicly available test sequences show that the proposed technique outperforms six state-of-the-art algorithms.
Usability Evaluation Model for Biometric System considering Privacy Concern Based on MCDM Model

Biometric devices play an integral role in consumer’s daily life, providing a seamless environment. However, it is essential to measure the usability of biometrics, owing to the elements of biometrics satisfying both usability and security. This study redefines the elements of biometrics pertaining to usability determined in previous studies and adds elements of psychological relevance, such as privacy concerns. To organize the interrelated usability structure systemically, this paper applies the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) to derive the usability structure. Thereupon, the established structure is applied in the clustered weighted Analytical Network Processes (ANP) to generate the proposed usability evaluation model. By these methods, the pertinent relationships between the factors are clarified and the weight of each element is determined. In the empirical study, 106 students measured usability of the fingerprint recognition system, iris recognition system, and facial recognition system employing our usability evaluation model. The results of this model generate the quantitative score of usability for biometric systems and suggest strategies to increase the score. The proposed usability evaluation model can comprehensively assist usability practitioners to evaluate biometric systems.
Constraint learning based gradient boosting trees

Predictive regression models aim to find the most accurate solution to a given problem, often without any constraints related to the model’s predicted values. Such constraints have been used in prior research where they have been applied to a subpopulation within the training dataset which is of greater interest and importance. In this research we introduce a new setting of regression problems, in which each instance can be assigned a different constraint, defined based on the value of the target (predicted) attribute. The new use of constraints is taken into account and incorporated into the learning process, and is also considered when evaluating the induced model. We propose two algorithms which are modifications to the regression boosting method. There are two advantages of the proposed algorithms: they are not dependent on the base learner used during the learning process, and they can be adopted by any boosting technique. We implemented the algorithms by modifying the gradient boosting trees (GBT) model, and we also introduced two measures for evaluating the models that were trained to solve the constraint problems. We compared the proposed algorithms to three baseline algorithms using four real-life datasets. Due to the algorithms’ focus on satisfying the constraints, in most cases the results showed significant improvement in the constraint-related measures, with just a minimal effect on the general prediction error. The main impact of the proposed approach is in its ability to derive a model with a higher level of assurance for specific cases of interest (i.e., the constrained cases). This is extremely important and has great significance in various use cases and expert and intelligent systems, particularly critical systems, such as critical healthcare systems (e.g., when predicting blood pressure or blood sugar level), safety systems (e.g., when aiming to estimate the distance of cars or airplanes from other objects), or critical industrial systems (e.g., require to estimate their usability along time). In each of these cases, there is a subpopulation of all instances that is of greater interest to the expert or system, and the sensitivity of the model’s error changes according to the real value of the predicted feature. For example, for a subpopulation of patients (e.g., patients under the age of eight, or patients known to be at risk), physicians often require a sensitive model that accurately predicts blood pressure values.
Author profiling for age and gender using combinations of features of various types

The process of automatic identification of an author's demographic traits like gender, age, native language, geographical location, personality type and others from his/her written text is termed as author profiling (AP). Currently, it has engaged the research community due to its promising uses in security, marketing, forensic, bogus account identification on public networks. A variety of benchmark corpora (English text) released by PAN shared task is used to perform our experiments. This study presents a Content-based approach for detection of author's traits (age group and gender) for same-genre author profiles. In our proposed method, we used a different set of features including syntactic n-grams of part-of-speech tags, traditional n-grams of part-of-speech tags, the combination of word n-grams and combination of character n-grams. We tried a range of classifier for several profile sizes. We used the word uni-grams and character tri-grams as our baseline approaches. We achieved best accuracy of 0.496 and 0.734 for both traits, i.e., age group and gender respectively, by applying the combination of word n-grams of various sizes. Experimental results signify that the combination of word n-grams can produce good results on benchmark corpora.
Nonadiabatic storage of short light pulses in an atom-cavity system

We demonstrate the storage of 5-ns light pulses in a single rubidium atom coupled to a fiber-based optical resonator. Our storage protocol addresses a regime beyond the conventional adiabatic limit and approaches the theoretical bandwidth limit. We extract the optimal control laser pulse properties from a numerical simulation of our system and measure storage efficiencies of $(8.1\ifmmode\pm\else\textpm\fi{}1.1)%$, in close agreement with the maximum expected efficiency. Such well-controlled and high-bandwidth atom-photon interfaces are key components for future hybrid quantum networks.
Frozen entanglement and quantum correlations of one-parameter qubit−qutrit states under classical noise effects

We provide a detailed analysis of the dynamics of entanglement and quantum correlations for one-parameter qubit-qutrit states under independent or common classical noises influence. Namely the static noise, the Ornstein-Uhlenbeck (OU) noise and the random telegraph noise. Independently of the intrinsic features of the noises, entanglement measured by negativity and quantum correlations measured by measured-induced disturbance (MID) vanish after a finite time under the effects of independent noise environments. In a common environment setup, we show the existence of specific and very important features of perfect insulation of the systems quantum properties from noise effects, for suitable range of the entanglement parameter. We refer these phenomena to as frozen entanglement and frozen quantum correlations. The dichotomy between entanglement (separability) and quantum correlations is strengthened by our results, with the robustness of MID over entanglement and existence of separable qubit-qutrit states with non-zero quantum correlations.
On Unsupervised Deep Learning Solutions for Coherent MU-SIMO Detection in Fading Channels

In this paper, unsupervised deep learning solutions for multiuser single-input multiple-output (MU-SIMO) coherent detection are extensively investigated. According to the ways of utilizing the channel state information at the receiver side (CSIR), deep learning solutions are divided into two groups. One group is called equalization and learning, which utilizes the CSIR for channel equalization and then employ deep learning for multiuser detection (MUD). The other is called direct learning, which directly feeds the CSIR, together with the received signal, into deep neural networks (DNN) to conduct the MUD. It is found that the direct learning solutions outperform the equalization-and-learning solutions due to their better exploitation of the sequence detection gain. On the other hand, the direct learning solutions are not scalable to the size of SIMO networks, as current DNN architectures cannot efficiently handle many co-channel interferences. Motivated by this observation, we propose a novel direct learning approach, which can combine the merits of feedforward DNN and parallel interference cancellation. It is shown that the proposed approach trades off the complexity for the learning scalability, and the complexity can be managed due to the parallel network architecture.
Fine-Grained Congestion Control for Multipath TCP in Data Center Networks

In data center networks, MultiPath TCP (MPTCP) obtains both higher network utilization and fairer allocation of capacity by exploring multiple paths simultaneously. However, MPTCP experiences more queue oscillation in switch buffer with the increasing of the number of subflows. When using explicit congestion notification as the congestion indication to track the queue length in switch buffer, it is difficult for MPTCP to capture the accurate congestion state, resulting in wrong behavior in congestion control. Therefore, we propose an enhanced MPTCP protocol, namely, advanced MPTCP (AMP), which adjusts the time granularity of the congestion detection and control under a different number of subflows. The test results show that compared with Linked Increases Algorithm and eXplicit MultiPath, AMP achieves lower latency for small flows and higher throughput for large flows.
Fuzzy logic based associative classifier for slow learners prediction

Education is a collective intelligence system where a group of persons ranging from students to management thinks and work together to achieve institutions’ goals. The primary goal of every institution is to accomplish excellent end-semester examination results. A good result is achieved through pr oper training given by the educators and in response to the performance of students in the examination. Training is cost accounting, whereas students’ performance is unpredictable. Outlier analysis in the education system has been stipulated in recent decades to predict the students’ uncertain behavior in learning activities which are utilized to alert the education systems. Fuzzy Logic System can handle such uncertainties in learning activities. The major issues that affect the accuracy of fuzzy based outlier detection methods are fixing appropriate membership function and validating the fuzzy rules before extracting outliers. To remedy these issues the proposed Fuzzy Temporal Outlier Detection (FTOD) method detects outliers from mid-semester examination results using fuzzy logic based associative classifier with optimal membership functions. The resultant outliers distinguish the slow learners from spurious-slow learners with high accuracy than the existing FARIM and modified-FARIM algorithms. Thus, educators can provide cost-effective training to enrich the slow learners’ cognition to score high in end-semester examinations.
An effective hybrid harmony search for the asymmetric travelling salesman problem

This article is part of a study aiming to adapt the Harmony Search (HS) algorithm to the asymmetric travelling salesman problem. The population-based approach of the HS algorithm and its weak exploitation mechanism constituted a good reason for starting the research on improving the method, by applying the approach found in other population-based metaheuristics characterized by the same imperfection. The proposed HS is supplemented with the mechanism allowing the efficiency of the exploitation process to be increased by combining the algorithm with hill climbing. The proposed approach assumes modifying the solutions at three different places, affecting the HS exploration and exploitation mechanism in different ways. As a result, the appropriate approach to the effective design of hybrid harmony search is recommended. In addition, by measuring the actual time of the method's execution, the recommended ratio of time devoted to local search and the creation of harmonies was determined.
Intelligent system for products personalization and design using genetic algorithm

In the early manufacturing industry, characterized by mass production, the design was mainly concentrated on one or a type of products, less innovation was required, longer, and more substantial investment was allowed. To adapt to the user’s requirements for product personalization and diversifica tion, and continuously expand the space for product innovation, this paper proposes an intelligent product design method. Traditional design theory is creatively combined with the proposed model to achieve the intended goal. The application models of two interactive genetic algorithms based on the compound fitness allocation strategy (HFAS) and the graded fitness allocation strategy are proposed, and the parameters of the compound fitness allocation strategy are analyzed. A case study of the proposed model is implemented, and two methods of Full Permutation and fuzzy evaluation are applied to evaluate the design results.
Word-Level Emotion Embedding Based on Semi-Supervised Learning for Emotional Classification in Dialogue

Emotion classification has been remarkable studies in recent years. However, most of works do not consider the context information such as a flow of emotions. In this paper, we propose the emotion classification in dialogue based on the semi-supervised word-level emotion embedding. For the word-level emotion embedding, we use the NRC Emotion Lexicon which is a list of English words and their associations with eight basic emotions. By adding word-level emotion vectors, we obtain an utterance-level emotion vector. We train a single layer LSTM-based classification network in dialogue. Also, we will evaluate our model on the EmotionLines which is dataset with emotions labeling on all utterances in each dialogue. The experiment plan is described in this paper.
Single image blind deblurring based on the fractional-order differential

Image deblurring is a fundamental problem in image processing. When the blur type is unknown, the problem becomes more challenging, which is called blind deblurring. In this paper, we propose a novel variational method for single image blind deblurring based on the fractional-order differential, which can overcome the staircase effect produced by the total variation regularization and alleviate the ringing artifact in deblurring. The fractional-order gradient fidelity term is added in the cost functional to improve the restoration. Moreover, we make use of the edge detect function with the fractional-order gradient to preserve sharp edges, and the Bregman iteration to reconstruct more structures. The primal–dual algorithm is developed to solve the proposed model. Numerical experiments show that our method is able to get the sharp image without the staircase effect and correctly estimate the unknown blur kernel.
Vocabulary in Traditional Chinese Medicine

Abstract This article reports on a corpus-based study of Traditional Chinese Medicine (TCM) vocabulary. It first provides a vocabulary profile of English-medium Traditional Chinese Medicine textbooks and journal articles using Nation’s (2012) British National Corpus/Corpus of Contemporary American English (BNC/COCA 25,000) frequency word lists and supplementary word lists of proper nouns, abbreviations, and compounds. Then, it categorizes items outside Nation’s BNC/COCA into Chinese loan words (e.g., qi, yang ) and medical lexis (e.g., cinnamomi , rehmanniae ), which cover 5.93% of the TCM Corpora in total. The next analysis focuses on Schmitt and Schmitt’s (2014) high, mid, low-frequency vocabulary framework and how it differs from Western medicine. Finally, a vocabulary load analysis shows that to reach 98%, 13,000 word families plus four supplementary lists and two TCM-specific lists are needed. Together, these analyses provide us with a rounded picture of TCM vocabulary. Implications for pedagogy and suggestions for future research follow.
Behavior Monitoring Using Learning Techniques and Regular-Expressions-Based Pattern Matching

This paper addresses the problem of maneuver recognition and behavior anomaly detection for generic targets by means of pattern matching techniques. The problem analysis is performed making specific reference to moving vehicles in a multi-lane road scenario, but the proposed technique can be easily extended to significantly different monitoring contexts. The potential extensions include, but are not limited to, public surveillance in train station or airport, road incidents and relative precursors detection, and vehicle trajectories monitoring. The overall proposed solution consists of a trajectory analysis tool and a string-matching method. This allows the integration of two different approaches, to detect both a priori defined patterns of interest and generic maneuver/behavior standing out from those regularly exhibited. The proposed string matching algorithm is newly developed in this paper, based on Regular Expressions. For generating reference patterns, a technique for the automatic definition of a dictionary of regular expressions matching the commonly observed target maneuvers is developed. The advantages of the proposed approach are extensively analyzed and tested by means of numerical simulations and experiments.
Event2Vec: Learning Event Representations Using Spatial-Temporal Information for Recommendation

Event-based social networks (EBSN), such as meetup.com and plancast.com , have witnessed increased popularity and rapid growth in recent years. In EBSN, a user can choose to join any events such as a conference, house party, or drinking event. In this paper, we present a novel model—Event2Vec, which explores how representation learning for events incorporating spatial-temporal information can help event recommendation in EBSN. The spatial-temporal information represents the physical location and the time where and when an event will take place. It typically has been modeled as a bias in conventional recommendation models. However, such an approach ignores the rich semantics associated with the spatial-temporal information. In Event2Vec, the spatial-temporal influences are naturally incorporated into the learning of latent representations for events, so that Event2Vec predicts user's preference on events more accurately. We evaluate the effectiveness of the proposed model on three real datasets; our experiments show that with a proper modeling of the spatial-temporal information, we can significantly improve event recommendation performance.
Fractal and multifractal analysis of atherosclerotic plaque in ultrasound images of the carotid artery

Stroke is the cause of death following ischemic heart disease in 87% of cases. The chance of stroke rises with the severity of carotid stenosis and the thickening of the carotid artery due to the deposition of plaque. This study analyses the non-linear parameters of ultrasound images of the plaque in the carotid artery and classifies the images based on the textural features. The non-linear analysis is implemented via fractal and multifractal methods. The fractal dimensions and the lacunarity differ significantly for symptomatic and asymptomatic plaques. The occurrence of multifractal spectra and the scaling exponent function, i.e., interleaving sets of singularity strength, proves the multi-scaling property. The multifractal characteristics quantify the heterogeneity in the textural features, and this could be used for improving the classification of symptomatic and asymptomatic plaque. The results show the significance of fractal parameters of plaque in deciding the severity of plaque and hence, aiding the diagnostic process.