Reviewerly/exHarmony huggingface dataset:
Closed-loop randomized kinodynamic path planning for an autonomous underwater vehicle

A randomized kinodynamic path planning algorithm based on the incremental sampling-based method is proposed here as the state-of-the-art in this field applicable in an autonomous underwater vehicle. Designing a feasible path for this vehicle from an initial position and velocity to a target position and velocity in three-dimensional spaces by considering the kinematic constraints such as obstacles avoidance and dynamic constraints such as hard bounds and non-holonomic characteristic of AUV are the main motivation of this research. For this purpose, a closed-loop rapidly-exploring random tree (CL-RRT) algorithm is presented. This CL-RRT consists of three tightly coupled components: a RRT algorithm, three fuzzy proportional-derivative controllers for heading and diving control and a six degree-of-freedom nonlinear AUV model. The branches of CL-RRT are expanded in the configuration space by considering the kinodynamic constraints of AUV. The feasibility of each branch and random offspring vertex in the CL-RRT is checked against the mentioned constraints of AUV. Next, if the planned branch is feasible by the AUV, then the control signals and related vertex are recorded through the path planner to design the final path. This proposed algorithm is implemented on a single board computer (SBC) through the xPC Target and then four test-cases are designed in 3D space. The results of the processor-in-the-loop tests are compared by the conventional RRT and indicate that the proposed CL-RRT not only in a rapid manner plans an initial path, but also the planned path is feasible by the AUV.
Double Relaxed Regression for Image Classification

This paper addresses two fundamental problems: 1) learning discriminative model parameters and 2) avoiding over-fitting, which often occurs in regression-based classification tasks. We formulate these two problems in terms of relaxing both the strict binary label matrix and graph regularization term into more flexible forms so that the margins between different classes are enlarged as much as possible and the problem of over-fitting is avoided to some extent. This task is accomplished by the proposed double relaxed regression (DRR) method. The convex problem of DRR is solved efficiently with an iterative procedure. Extensive experiments on synthetic and real world image data sets demonstrate the effectiveness of the proposed method in terms of both classification accuracy and running time.
A real-time framework for kinodynamic planning in dynamic environments with application to quadrotor obstacle avoidance

The objective of this paper is to present a full-stack, real-time motion planning framework for kinodynamic robots and then show how it is applied and demonstrated on a physical quadrotor system operating in a laboratory environment. The proposed framework utilizes an offline–online computation paradigm, neighborhood classification through machine learning, sampling-based motion planning with an optimal cost distance metric, and trajectory smoothing to achieve real-time planning for aerial vehicles. This framework accounts for dynamic obstacles with an event-based replanning structure and a locally reactive control layer that minimizes replanning events. The approach is demonstrated on a quadrotor navigating moving obstacles in an indoor space and stands as, arguably, one of the first demonstrations of full-online kinodynamic motion planning, with execution cycles of 3 Hz to 5 Hz. For the quadrotor, a simplified dynamics model is used during the planning phase to accelerate online computation. A trajectory smoothing phase, which leverages the differentially flat nature of quadrotor dynamics, is then implemented to guarantee a dynamically feasible trajectory.
CluWords

In this paper, we advance the state-of-the-art in topic modeling by means of a new document representation based on pre-trained word embeddings for non-probabilistic matrix factorization. Specifically, our strategy, called CluWords, exploits the nearest words of a given pre-trained word embedding to generate meta-words capable of enhancing the document representation, in terms of both, syntactic and semantic information. The novel contributions of our solution include: (i)the introduction of a novel data representation for topic modeling based on syntactic and semantic relationships derived from distances calculated within a pre-trained word embedding space and (ii)the proposal of a new TF-IDF-based strategy, particularly developed to weight the CluWords. In our extensive experimentation evaluation, covering 12 datasets and 8 state-of-the-art baselines, we exceed (with a few ties) in almost cases, with gains of more than 50% against the best baselines (achieving up to 80% against some runner-ups). Finally, we show that our method is able to improve document representation for the task of automatic text classification.
Collaborative Additional Variational Autoencoder for Top-N Recommender Systems

Collaborative filtering (CF) has been generally used in recommender systems when faced some practical problems.Due to the sparsity of the rating matrix, the traditional CF-based approach has a significant decline in recommendation performance.Gradually, a hybrid method, using side information and rating information, has been widely employed and achieves great performance.Together with side information and rating information, the hybrid method can overcome the data sparsity and cold-start problems.However, they seem to fail to take into consideration the fact that the sparsity of single side information.To solve this problem, we take full advantage of the characteristics of deep learning that can learn effective representation and propose a novel deep learning model named additional variational autoencoder that considers both content and tag information of the item.The model learns effective latent representations from additional side information, including content information and tag information in an unsupervised manner.With the help of graphical models, it can extract the implicit relationships between users and items effectively.A large number of experimental results on two actual datasets show that our proposed model is superior to other methods, and the performance improvement is achieved.
High-Performance Visual Tracking With Extreme Learning Machine Framework

In real-time applications, a fast and robust visual tracker should generally have the following important properties: 1) feature representation of an object that is not only efficient but also has a good discriminative capability and 2) appearance modeling which can quickly adapt to the variations of foreground and backgrounds. However, most of the existing tracking algorithms cannot achieve satisfactory performance in both of the two aspects. To address this issue, in this paper, we advocate a novel and efficient visual tracker by exploiting the excellent feature learning and classification capabilities of an emerging learning technique, that is, extreme learning machine (ELM). The contributions of the proposed work are as follows: 1) motivated by the simplicity and learning ability of the ELM autoencoder (ELM-AE), an ELM-AE-based feature extraction model is presented, and this model can provide a compact and discriminative representation of the inputs efficiently and 2) due to the fast learning speed of an ELM classifier, an ELM-based appearance model is developed for feature classification, and is able to rapidly distinguish the object of interest from its surroundings. In addition, in order to cope with the visual changes of the target and its backgrounds, the online sequential ELM is used to incrementally update the appearance model. Plenty of experiments on challenging image sequences demonstrate the effectiveness and robustness of the proposed tracker.
Quaternion-based weighted nuclear norm minimization for color image denoising

The quaternion method plays an important role in color image processing, because it represents the color image as a whole rather than as a separate color space component, thus naturally handling the coupling among color channels. The weighted nuclear norm minimization (WNNM) scheme assigns different weights to different singular values, leading to more reasonable image representation method. In this paper, we propose a novel quaternion weighted nuclear norm minimization (QWNNM) model and algorithm under the low rank sparse framework. The proposed model represents the color image as a low rank quaternion matrix, where quaternion singular value decomposition can be calculated by its equivalent complex matrix. We solve the QWNNM by adaptively assigning different singular values with different weights. Color image denoising is implemented by QWNNM based on non-local similarity priors. In this new color space, the inherent color structure can be well preserved during image reconstruction. For high noise levels, we apply a Gaussian low pass filter (LPF) to the noisy image as a preprocessing before QWNNM, which reduces the iteration numbers and improves the denoised results. The experimental results clearly show that the proposed method outperforms K-SVD, QKSVD and WNNM in terms of both quantitative criteria and visual perceptual.
SketchHelper: Real-Time Stroke Guidance for Freehand Sketch Retrieval

Text-based retrieval systems have been popular, but content-based retrieval systems have gained widespread acceptance in recent years to directly retrieve diverse media based on their visual content, such as color, texture, and shape. Among many content-based retrieval systems, sketch-based media retrieval systems have attracted attention recently with the proliferation of tablet PCs and smart mobile devices. Sketch-based retrieval requires the user to draw a freehand sketch query, but freehand drawing can be challenging for those with limited drawing skills. This degrades retrieval performance, since successful retrieval depends on the quality of the sketch query image drawn by the user. To address this issue, we propose a real-time stroke guidance for freehand sketch retrieval that continuously displays next-stroke shadow sketches on the canvas based on the user's step-by-step partial strokes. We train a stroke guidance network that learns the mapping between the step-wise stroke relations to predict the user's next stroke. The proposed stroke guidance for freehand sketch retrieval system runs on a five step next-stroke prediction model that identifies candidate next-stroke sketches from a database of millions of sketches. The system retrieves variable number of sketch object classes at different drawing stages. During the initial sketching stage, diverse drawing possibilities are covered by retrieving multiple sketch classes; as the sketching progresses, the intended sketch class is narrowed down to one. Deep binary hashing is employed for efficient similarity matching of relevant next-stroke sketches. We extend the Google QuickDraw dataset to create a five step sketch stroke database. Qualitative and quantitative experiments are conducted to verify the effectiveness of the proposed system, which can be utilized for drawing guidance, tracing, and sketch retrieval. Tracing refers to the act of copying the shadowed line of a guiding image by drawing over its lines.
Fine-Grained Visual-Textual Representation Learning

Fine-grained visual categorization is to recognize hundreds of subcategories belonging to the same basic-level category, which is a highly challenging task due to the quite subtle and local visual distinctions among similar subcategories. Most existing methods generally learn part detectors to discover discriminative regions for better categorization performance. However, not all parts are beneficial and indispensable for visual categorization, and the setting of part detector number heavily relies on prior knowledge as well as experimental validation. As is known to all, when we describe the object of an image via textual descriptions, we mainly focus on the pivotal characteristics, and rarely pay attention to common characteristics as well as the background areas. This is an involuntary transfer from human visual attention to textual attention, which leads to the fact that textual attention tells us how many and which parts are discriminative and significant to categorization. So textual attention could help us to discover visual attention in image. Inspired by this, we propose a fine-grained visual-textual representation learning (VTRL) approach, and its main contributions are: (1) Fine-grained visual-textual pattern mining devotes to discovering discriminative visual-textual pairwise information for boosting categorization performance through jointly modeling vision and text with generative adversarial networks (GANs), which automatically and adaptively discovers discriminative parts. (2) Visual-textual representation learning jointly combines visual and textual information, which preserves the intra-modality and inter-modality information to generate complementary fine-grained representation, as well as further improves categorization performance.
Security in hardware assisted virtualization for cloud computing—State of the art issues and challenges

The advantages of virtualization technology have resulted in its wide spread adoption in cloud computing infrastructures. However it has also introduced a new set of security threats that are serious in nature. Many of these threats are unique in virtualized environments and not pertinent in the traditional computing scenarios. Hence these threats have been less studied and thus less addressed by most of the security application vendors. For this reason, it becomes important to carefully analyze the various threats arising at different components of virtualization and thus effectively create solutions to defend the systems against them. This survey attempts to highlight the significant vulnerabilities and expose the readers to the various existing attacks related to Hardware assisted virtualization, as it has become the most widely used form of virtualization in building modern day massive data centers and cloud infrastructures. A Bayesian attack graph model is presented for evaluating the risks associated with the identified threats. A detailed discussion of various countermeasures proposed against the identified threats is presented along with the enumeration of challenges in adopting them.
Is deep learning better than traditional approaches in tag recommendation for software information sites?

Inspired by the success of deep learning in other domains, this new technique been gaining widespread recent interest in being applied to diverse data analysis problems in software engineering. Many deep learning models, such as CNN, DBN, RNN, LSTM and GAN, have been proposed and recently applied to software engineering tasks including effort estimation, vulnerability analysis, code clone detection, test case selection, requirements analysis and many others. However, there is a perception that applying deep learning is a "silver bullet" if it can be applied to a software engineering data analysis problem. This motivated us to ask the question as to whether deep learning is better than traditional approaches in tag recommendation task for software information sites. In this paper we test this question by applying both the latest deep learning approaches and some traditional approaches on tag recommendation task for software information sites. This is a typical Software Engineering automation problem where intensive data processing is required to link disparate information to assist developers. Four different deep learning approaches – TagCNN, TagRNN, TagHAN and TagRCNN – are implemented and compared with three advanced traditional approaches – EnTagRec, TagMulRec, and FastTagRec. Our comprehensive experimental results show that the performance of these different deep learning approaches varies significantly. The performance of TagRNN and TagHAN approaches are worse than traditional approaches in tag recommendation tasks. The performance of TagCNN and TagRCNN approaches are better than traditional approaches in tag recommendation tasks. Therefore, using appropriate deep learning approaches can indeed achieve better performance than traditional approaches in tag recommendation tasks for software information sites.
Using K-core Decomposition on Class Dependency Networks to Improve Bug Prediction Model's Practical Performance

In recent years, Complex Network theory and graph algorithms have been proved to be effective in predicting software bugs. On the other hand, as a widely-used algorithm in Complex Network theory, k-core decomposition has been used in software engineering domain to identify key classes. Intuitively, key classes are more likely to be buggy since they participate in more functions or have more interactions and dependencies. However, there is no existing research uses k-core decomposition to analyze software bugs. To fill this gap, we first use k-core decomposition on Class Dependency Networks to analyze software bug distribution from a new perspective. An interesting and widely existed tendency is observed: for classes in k-cores with larger k values, there is a stronger possibility for them to be buggy. Based on this observation, we then propose a simple but effective equation named as top-core which improves the order of classes in the suspicious class list produced by effort-aware bug prediction models. Based on an empirical study on 18 open-source Java systems, we show that the bug prediction models' performances are significantly improved in 85.2 percent experiments in the cross-validation scenario and in 80.95 percent experiments in the forward-release scenario, after using top-core. The models' average performances are improved by 11.5 and 12.6 percent, respectively. It is concluded that the proposed top-core equation can help the testers or code reviewers locate the real bugs more quickly and easily in software bug prediction practices.
Blind Robust 3D Mesh Watermarking Based on Mesh Saliency and Wavelet Transform for Copyright Protection

Three-dimensional models have been extensively used in several applications including computer-aided design (CAD), video games, medical imaging due to the processing capability improvement of computers, and the development of network bandwidth. Therefore, the necessity of implementing 3D mesh watermarking schemes aiming to protect copyright has increased considerably. In this paper, a blind robust 3D mesh watermarking method based on mesh saliency and wavelet transform for copyright protection is proposed. The watermark is inserted by quantifying the wavelet coefficients using quantization index modulation (QIM) according to the mesh saliency of the 3D semiregular mesh. The synchronizing primitive is the distance between the mesh center and salient points in the descending order. The experimental results show the high imperceptibility of the proposed scheme while ensuring a good robustness against a wide range of attacks including smoothing, additive noise, element reordering, similarity transformations, etc.
An Efficient Edge Artificial Intelligence MultiPedestrian Tracking Method With Rank Constraint

Characterized by the ability to handle varying number of objects, tracking by detection framework becomes increasingly popular in multiobject tracking (MOT) problem. However, the tracking performance heavily depends on the object detector. Considering that data association optimization and association affinity model are two key parts in MOT, an online multipedestrian tracking method is proposed to formulate a more effective association affinity model. It includes a two-step data association taking advantage of rank-based dynamic motion affinity model. The rank-based dynamic motion affinity model is used to estimate the object state and refine the trajectory for each of target to achieve the noiseless trajectory. Both strategies are beneficial to eliminate ambiguous detection responses during association. To fairly verify the proposed method, three public datasets are adopted. Both qualitative and quantitative experiment results demonstrate the superiorities of the proposed tracking algorithm in comparison with its counterparts.
Improving Medical Code Prediction from Clinical Text via Incorporating Online Knowledge Sources

Clinical notes contain detailed information about health status of patients for each of their encounters with a health system. Developing effective models to automatically assign medical codes to clinical notes has been a long-standing active research area. Despite a great recent progress in medical informatics fueled by deep learning, it is still a challenge to find the specific piece of evidence in a clinical note which justifies a particular medical code out of all possible codes. Considering the large amount of online disease knowledge sources, which contain detailed information about signs and symptoms of different diseases, their risk factors, and epidemiology, there is an opportunity to exploit such sources. In this paper we consider Wikipedia as an external knowledge source and propose Knowledge Source Integration (KSI), a novel end-to-end code assignment framework, which can integrate external knowledge during training of any baseline deep learning model. The main idea of KSI is to calculate matching scores between a clinical note and disease related Wikipedia documents, and combine the scores with output of the baseline model. To evaluate KSI, we experimented with automatic assignment of ICD-9 diagnosis codes to the emergency department clinical notes from MIMIC-III data set, aided by Wikipedia documents corresponding to the ICD-9 codes. We evaluated several baseline models, ranging from logistic regression to recently proposed deep learning models known to achieve the state-of-the-art accuracy on clinical notes. The results show that KSI consistently improves the baseline models and that it is particularly successful in assignment of rare codes. In addition, by analyzing weights of KSI models, we can gain understanding about which words in Wikipedia documents provide useful information for predictions.
Study of Digital Competence of the Students and Teachers in Ukraine

Professional fulfillment of the personality at the conditions of the digital economy requires the high level of digital competency. One of the ways to develop these competencies is education. However, to provide the implementation of digital education at a high level, the digital competency of the teachers and students is a must. This paper presents explanations on the level determination of the digital competencies for teachers and students in Ukraine according to the DigComp recommendations. We tried to identify the main factors that reflect the degree of readiness teachers and students for digital education based on their self-evaluation. We also attempted to estimate the level of digital competencies based on the analysis of Case-Studies execution results. The complex analysis let us assess the connection between respondents’ self-evaluation and their real competencies. Here we provide a methodology and a model of level competencies determination by means of a survey, expert case rating and the results of the statistical analysis. On the basis of the obtained results, this paper suggests further research prospects and recommendations on the digital competency development in educational institutions in Ukraine.