Reviewerly/exHarmony huggingface dataset:
HSS-GCN: A Hierarchical Spatial Structural Graph Convolutional Network for Vehicle Re-identification

Vehicle re-identification (Re-ID) is the task aiming to identify the same vehicle from images captured by different cameras. Recent years have seen various appearance-based approaches focusing only on global features or exploring local features to obtain more subtle details which can alleviate the subtle inter-instance problem. However, few emphasize the spatial geometrical structure relationship among local regions or between the global region and local regions. To explore above-mentioned spatial structure relationship, this paper proposes a hierarchical spatial structural graph convolutional network (HSS-GCN) for vehicle Re-ID, in which we firstly construct a hierarchical spatial structural graph with the global region and local regions as nodes and a two-hierarchy relationship as edges, and later learning discriminative structure features with a GCN module under the constraints of metric learning. To augment the performance of our proposed network, we jointly combine the classification loss with metric learning loss. Extensive experiments conducted on the public VehicleID and VeRi-776 datasets validate the effectiveness of our approach in comparison with recent works.
Convolutional Neural Network for Blur Images Detection as an Alternative for Laplacian Method

With the prevalence of digital cameras, the number of digital images increases quickly, which raises the demand for non-manual image quality assessment. While there are many methods considered useful for detecting blurriness, in this paper we propose and evaluate a new method that uses a deep convolutional neural network, which can determine whether an image is blurry or not. Experimental results demonstrate the effectiveness of the proposed scheme and are compared to deterministic methods using the confusion matrix.
Preservice music teachers in Korea and their collaborative reflection with peers

Recently, many researchers and scholars have focused on reflective teaching to support good practices in teaching. For effective reflection, it is necessary to encourage teachers’ reflection by promoting collaboration during which they can discuss various teaching issues and problems while supporting each other. The purpose of this study was to investigate Korean preservice music teachers’ experience in collaborative reflection sessions. I used data collected through observations, the participants’ reflective journals, and individual interviews. The results showed that as the participants gained experience while observing and teaching music classes, the contents of their reflections on technical and practical matters became more varied. Additionally, the participants’ reflective thinking, applied critically, tended to focus on the social context of the music classroom. In addition, participants predominantly showed technical level of reflective thinking rather than practical and critical levels. Finally, the results revealed that the collaborative approach enabled prospective music teachers to broaden the scope of their reflective thinking, and a sense of emotional safety in the collaborative group allowed them to honestly confront their own teaching worries and problems.
Multiparameter quantum metrology with postselection measurements

We analyze simultaneous quantum estimations of multiple parameters with postselection measurements in terms of a trade-off relation. The system, or a sensor, is characterized by a set of parameters, interacts with a measurement apparatus (MA), and then is postselected onto a set of orthonormal final states. Measurements of the MA yield an estimation of the parameters. We first derive classical and quantum Cramér–Rao lower bounds and then discuss their archivable condition and the trade-offs in the postselection measurements, in general, including the case when a sensor is in a mixed state. Its whole information can, in principle, be obtained via the MA, which is not possible without postselection. We then apply the framework to simultaneous measurements of phase and its fluctuation as an example.
Facial Expression Recognition Using Kernel Entropy Component Analysis Network and DAGSVM

Facial expression recognition (FER) plays a significant part in artificial intelligence and computer vision. However, most of facial expression recognition methods have not obtained satisfactory results based on low-level features. The existed methods used in facial expression recognition encountered the major issues of linear inseparability, large computational burden, and data redundancy. To obtain satisfactory results, we propose an innovative deep learning (DL) model using the kernel entropy component analysis network (KECANet) and directed acyclic graph support vector machine (DAGSVM). We use the KECANet in the feature extraction stage. In the stage of output, binary hashing and blockwise histograms are adopted. We sent the final output features to the DAGSVM classifier for expression recognition. We test the performance of our proposed method on three databases of CK+, JAFFE, and CMU Multi-PIE. According to the experiment results, the proposed method can learn high-level features and provide more recognition information in the stage of training, obtaining a higher recognition rate.
DeepCADRME: A deep neural model for complex adverse drug reaction mentions extraction

Extracting mentions of Adverse Drug Reaction (ADR) from biomedical texts, aiming to support pharmacovigilance and drug safety surveillance, remains a challenging task as many ADR mentions are nested, discontinuous and overlapping. To solve these issues, in this paper, we propose a deep neural model for Complex Adverse Drug Reaction Mentions Extraction, called DeepCADRME. It first transforms the ADR mentions extraction problem as an N-level tagging sequence. Then, it feeds the sequences to an N-level model based on contextual embeddings where the output of the pre-trained model of the current level is used to build a new deep contextualized representation for the next level. This allows the DeepCADRME system to transfer knowledge between levels. Experimental results performed on the TAC 2017 ADR dataset, show the effectiveness of DeepCADRME which leads to a new state-of-the-art performance by reaching a F1 of 85.35% and 85.41% with and without mention types, respectively. The evaluation results also highlight the benefits of exploring language model to effectively extract different types of ADR mentions.
Reinforcement Learning in Linear Quadratic Deep Structured Teams: Global Convergence of Policy Gradient Methods

In this paper, we study the global convergence of model-based and model-free policy gradient descent and natural policy gradient descent algorithms for linear quadratic deep structured teams. In such systems, agents are partitioned into a few sub-populations wherein the agents in each subpopulation are coupled in the dynamics and cost function through a set of linear regressions of the states and actions of all agents. Every agent observes its local state and the linear regressions of states, called deep states. For a sufficiently small risk factor and/or sufficiently large population, we prove that model-based policy gradient methods globally converge to the optimal solution. Given an arbitrary number of agents, we develop model-free policy gradient and natural policy gradient algorithms for the special case of risk-neutral cost function. The proposed algorithms are scalable with respect to the number of agents due to the fact that the dimension of their policy space is independent of the number of agents in each sub-population. Simulations are provided to verify the theoretical results.
Value probability analysis for linear phase estimation in sinusoidal structured-light range imaging

Linear phase estimation is crucial for accuracy of sinusoidal structured-light range imaging. This Letter presents value probability analysis for a continuously differentiable function and shows that the probability of a value of the function is closely related to the derivative of the function. We apply the value probability analysis to the intensity, phase, and intermediate values in sinusoidal structured light, and we show that the probability adjustment of phase is more effective than those of other values for linear phase estimation. The phase probability adjustment is applied to synthetic and real scenarios of sinusoidal structured-light range imaging to show the effectiveness of the presented analysis and adjustment.
Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor

Abstract This study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Using suitable databases, it is possible to validate and assess available methods in different research fields. This database has application in different fields such as face recognition, age estimation and Facial Expression Recognition and Facial Micro Expressions Recognition. Image databases based on their size and resolution are mostly large. Color images usually consist of three channels namely Red, Green and Blue. But in the last decade, another aspect of image type has emerged, named “depth image”. Depth images are used in calculating range and distance between objects and the sensor. Depending on the depth sensor technology, it is possible to acquire range data differently. Kinect sensor version 2 is capable of acquiring color and depth data simultaneously. Facial expression recognition is an important field in image processing, which has multiple uses from animation to psychology. Currently, there is a few numbers of color-depth (RGB-D) facial micro expressions recognition databases existing. With adding depth data to color data, the accuracy of final recognition will be increased. Due to the shortage of color-depth based facial expression databases and some weakness in available ones, a new and almost perfect RGB-D face database is presented in this paper, covering Middle-Eastern face type. In the validation section, the database will be compared with some famous benchmark face databases. For evaluation, Histogram Oriented Gradients features are extracted, and classification algorithms such as Support Vector Machine, Multi-Layer Neural Network and a deep learning method, called Convolutional Neural Network or are employed. The results are so promising.
A Receding Horizon Multi-Objective Planner for Autonomous Surface Vehicles in Urban Waterways

We propose a novel receding horizon planner for an autonomous surface vehicle (ASV) performing path planning in urban waterways. Feasible paths are found by repeatedly generating and searching a graph reflecting the obstacles observed in the sensor field-of-view. We also propose a novel method for multi-objective motion planning over the graph by leveraging the paradigm of lexicographic optimization and applying it to graph search within our receding horizon planner. The competing resources of interest are penalized hierarchically during the search. Higher-ranked resources cause a robot to incur non-negative costs over the paths traveled, which are occasionally zero-valued. The framework is intended to capture problems in which a robot must manage resources such as risk of collision. This leaves freedom for tie-breaking with respect to lower-priority resources; at the bottom of the hierarchy is a strictly positive quantity consumed by the robot, such as distance traveled, energy expended or time elapsed. We conduct experiments in both simulated and real-world environments to validate the proposed planner and demonstrate its capability for enabling ASV navigation in complex environments.
Efficient scale estimation methods using lightweight deep convolutional neural networks for visual tracking

In recent years, visual tracking methods that are based on discriminative correlation filters (DCFs) have been very promising. However, most of these methods suffer from a lack of robust scale estimation skills. Although a wide range of recent DCF-based methods exploit the features that are extracted from deep convolutional neural networks (CNNs) in their translation model, the scale of the visual target is still estimated by hand-crafted features. Whereas the exploitation of CNNs imposes a high computational burden, this paper exploits pre-trained lightweight CNNs models to propose two efficient scale estimation methods, which not only improve the visual tracking performance but also provide acceptable tracking speeds. The proposed methods are formulated based on either holistic or region representation of convolutional feature maps to efficiently integrate into DCF formulations to learn a robust scale model in the frequency domain. Moreover, against the conventional scale estimation methods with iterative feature extraction of different target regions, the proposed methods exploit proposed one-pass feature extraction processes that significantly improve the computational efficiency. Comprehensive experimental results on the OTB-50, OTB-100, TC-128 and VOT-2018 visual tracking datasets demonstrate that the proposed visual tracking methods outperform the state-of-the-art methods, effectively.
Exploiting structured high-level knowledge for domain-specific visual classification

In the last decade, deep learning models have yielded impressive performance on visual object recognition and image classification. However these methods still rely on learning visual data distributions and show difficulties in dealing with complex scenarios where visual appearance only is not enough to effectively tackle them. This is the case, for instance, of fine-grained image classification in domain-specific applications for which it is very complex to employ data-driven models because of the lack of large amounts of samples and that, instead, can be solved by resorting to specialized human knowledge. However, encoding this specialized knowledge and injecting it into deep models is not trivial. In this paper, we address this problem by: a) employing computational ontologies to model specialized knowledge in a structured representation and, b) building a hybrid visual-semantic classification framework. The classification method performs inference over a Bayesian Network graph, whose structure depends on the knowledge encoded in an ontology and evidences are built using the outputs of deep networks. We test our approach on a fine-grained classification task, employing an extremely complex dataset containing images from several fruit varieties as well as visual and semantic annotations. Since the classification is done at the variety level (e.g., discriminating between different cherry varieties), appearance changes slightly and expert domain knowledge — making using of contextual information — is required to perform classification accurately. Experimental results show that our approach significantly outperforms standard deep learning–based classification methods over the considered scenario as well as existing methods leveraging semantic information for classification. These results demonstrate, on one hand, the difficulty of purely-visual deep methods in tackling small and highly-specialized datasets and, on the other hard, the capabilities of our approach to effectively encode and use semantic knowledge for enhanced accuracy.
Physical-Layer Authentication Using Channel State Information and Machine Learning

Strong authentication in an interconnected wireless environment continues to be an important, but sometimes elusive goal. Research in physical-layer authentication using channel features holds promise as a technique to improve network security for a variety of devices. We propose the use of machine learning and measured multiple-input multiple-output communications channel information to make a decision on whether or not to authenticate a particular device. This work analyzes the use of received channel state information from the wireless environment and demonstrates the employment of a generative adversarial neural network (GAN) trained with received channel data to authenticate a transmitting device. We compared a variety of machine learning techniques and found that the local outlier factor (LOF) algorithm reached 100% accuracy at lower signal to noise ratios (SNR) than other algorithms. However, before LOF reached 100%, we also show that the GAN was more accurate at lower SNR levels.
Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation

We humans can impeccably search for a target object, given its name only, even in an unseen environment. We argue that this ability is largely due to three main reasons: the incorporation of prior knowledge (or experience), the adaptation of it to the new environment using the observed visual cues and most importantly optimistically searching without giving up early. This is currently missing in the state-of-the-art visual navigation methods based on Reinforcement Learning (RL). In this paper, we propose to use externally learned prior knowledge of the relative object locations and integrate it into our model by constructing a neural graph. In order to efficiently incorporate the graph without increasing the state-space complexity, we propose Graph-based Value Estimation (GVE) module. GVE provides a more accurate baseline for estimating the Advantage function in actor-critic RL algorithm. This results in reduced value estimation error and, consequently, convergence to a more optimal policy. Through empirical studies, we show that our agent, dubbed as the optimistic agent, has a more realistic estimate of the state value during a navigation episode which leads to a higher success rate. Our extensive ablation studies show the efficacy of our simple method which achieves the state-of-the-art results measured by the conventional visual navigation metrics, e.g. Success Rate (SR) and Success weighted by Path Length (SPL), in AI2THOR environment.
Federated Geo-Distributed Clouds: Optimizing Resource Allocation Based on Request Type Using Autonomous and Multi-objective Resource Sharing Model

Due to the problems exist in non-geographic federated clouds, the geographic ones are considered. Nevertheless, the approaches that have already been proposed to allocate resources across the geographical federated clouds have two basic problems that we will address in this article: (1) Lack of proper distribution of user requests leading to increases file transfer volume and cost, as well as response time to user requests, (2) Lack of appropriate resource sharing among requests due to: (1) the use of a centralized DC and (2) considering the satisfaction of single objective which case (1) suffers the problem of single-point of failure and case (2) raises an obstacle for the situations need considering multi conflicting objectives. Concerning the problem of one, it should be said that as federal DCs are distributed globally in the geographic clouds, the cost of file transfer between DCs in these clouds is more focused than the concentrated ones. Since there has been no work in this field in the geo-distributed federated clouds, we have presented a new scheduling mechanism based on hypervolume for the distribution of applications that leads to increasing service quality and reducing file transfer cost. Concerning the problem of two, the previous solutions in the geographic federated clouds have focused on a centralized resource sharing with single objective (increase of the cloud service provider (CSP) profit). These solutions not only just consider the CSP profitability, but, because of the possibility of failure of central broker of resource-sharing, suffer the single-point of failure. In this paper, we propose a new, autonomic and peer-to-peer multi-objective resource sharing approach that considers objectives: (1) enhancing the CSP's profit, (2) decreasing the network latency and (3) decreasing file transfer traffic and (3) increasing fairness in CSPs' profit. The techniques presented in this paper are evaluated by extensive experiments using real workloads. To validate the proposed method, we have extended the CloudSim tool. The results of our experiments show the increase of performance in the scheduling and resource-sharing objectives among which the main objectives of average rate of success, profit and execution time were enhanced 8.5%, 15.47% and 25.84%, respectively compared with previous studies.
Multilateral Personal Portfolio Authentication System Based on Hyperledger Fabric

Korean education-related evaluation agencies utilize a centralized system that directly manages learner data. This leaves the intellectual property of the organizations and the personal information of the students vulnerable to leakage should the central server be attacked. In this study, the researchers propose a multilateral personal portfolio authentication system that guarantees the reliability, integrity, and transparency of the data such as learner's schooling history. The system uses the features of blockchain in a distributed network wherein a learner submits schooling data to a peer in the network. The data is then verified through an agreement among the peers and recorded in a chronologically encrypted ledger. The proposed system is implemented based on Hyperledger Fabric and the analysis thereof conducted by evaluating its processing speed, capacity, and security. The analysis indicates that the system is able to successfully intercept the transactions of unvalidated users, thereby preventing the recording of incorrect data by an unauthorized user. Furthermore, it provides a record of previously made changes to a learner's profile, thus improving the integrity and reliability of the data. This system provides a platform to share learner information safely and promptly among schools, certification authorities, and higher learning institutions.
A Novel Semi-quantum Private Comparison Scheme Using Bell Entangle States

Private comparison is the basis of many encryption technologies, and several related Quantum Private Comparison (QPC) protocols have been published in recent years.In these existing protocols, secret information is encoded by using conjugate coding or orthogonal states, and all users are quantum participants.In this paper, a novel semi-quantum private comparison scheme is proposed, which employs Bell entangled states as quantum resources.Two semi-quantum participants compare the equivalence of their private information with the help of a semi-honest third party (TP).Compared with the previous classical protocols, these two semi-quantum users can only make some particular action, such as to measure, prepare and reflect quantum qubits only in the classical basis f 0 j i; 1 j ig, and TP needs to perform Bell basis measurement on reflecting qubits to obtain the results of the comparison.Further, analysis results show that this scheme can avoid outside and participant attacks and its' qubit efficiency is better than the other two protocols mentioned in the paper.