Reviewerly/exHarmony huggingface dataset:
Visual Tracking Control of Humanoid Robot

This paper realizes the independent complex route tracking based on the NAO robot hardware platform and an image processing technology. The camera captures an image and extracts a path through a series of image processes, such as threshold, filtering, and edge detection. The path recognition algorithms and its edge array were used to calculate these relative parameters that drive the anthropomorphic robot for complex route walking for autonomous tracking. These paths include the straight-route curves and the cross routes, which were identified by the different features accurately. Particularly, the slope-matching method is proposed to enable it to track in accordance with the established rules when meeting the cross routes. These methods separately calculate the slope from the intersection to paths in all directions, and then, the matching rate can be obtained by a predefined matching formula in terms of choosing the best path forward. All complex route tracking experiments were conducted by an anthropomorphic robot. The experimental results show that the robot imposed a strong anti-interference ability to filter out the noise and have accurate complex route tracking, which is significant for the anthropomorphic robot visual guide.
Founded World Views with Autoepistemic Equilibrium Logic

Defined by Gelfond in 1991 (G91), epistemic specifications (or programs) are an extension of logic programming under stable models semantics that introduces subjective literals. A subjective literal allows checking whether some regular literal is true in all (or in some of) the stable models of the program, being those models collected in a set called world view. One epistemic program may yield several world views but, under the original G91 semantics, some of them resulted from self-supported derivations. During the last eight years, several alternative approaches have been proposed to get rid of these self-supported world views. Unfortunately, their success could only be measured by studying their behaviour on a set of common examples in the literature, since no formal property of “self-supportedness” had been defined. To fill this gap, we extend in this paper the idea of unfounded set from standard logic programming to the epistemic case. We define when a world view is founded with respect to some program and propose the foundedness property for any semantics whose world views are always founded. Using counterexamples, we explain that the previous approaches violate foundedness, and proceed to propose a new semantics based on a combination of Moore’s Autoepistemic Logic and Pearce’s Equilibrium Logic. The main result proves that this new semantics precisely captures the set of founded G91 world views.
Towards rapid interactive machine learning

Our contribution is the design and evaluation of an interactive machine learning interface that rapidly provides the user with model feedback after every interaction. To address visual scalability, this interface communicates with the user via a "tip of the iceberg" approach, where the user interacts with a small set of recommended instances for each class. To address computational scalability, we developed an O(n) classification algorithm that incorporates user feedback incrementally, and without consulting the data's underlying representation matrix. Our computational evaluation showed that this algorithm has similar accuracy to several off-the-shelf classification algorithms with small amounts of labeled data. Empirical evaluation revealed that users performed better using our design compared to an equivalent active learning setup.
Subjective Saliency Model Driven by Multi-Cues Stimulus for Airport Detection

Traditional saliency models designed for natural scene images usually use human visual characteristics to detect the target, but those salient areas in the remote sensing images (RSIs) may not be the targets we are really interested in. Taking both remote sensing image attributes and airport characteristics into account, we put forward a subjective saliency model driven by multi-cues stimulus for the airport’s detection (MCS-SSM). Different from traditional saliency models, this model mainly relies on the subjective target detection task to find specific target area eliminating disturbance from other salient targets. Based on the low-level features, we train an LDA classifier by only small target samples and then build an object feature map. In the meantime, the shape information based on line density is extracted to get a shape map. Depending on the fusion result of two saliency maps, we optimize the subjective saliency map with SVM classifier. Moreover, MST density map is generated to suppress background and highlight interesting airport regions in the subjective saliency map. Consequently, MCS-SSM can respectively take the target, the background, and the detection task as multiple cues to quickly locate interest airport targets in RSI with a large cover area. MCS-SSM breaks through the limitations on color, texture, and other low-level characteristics compared with traditional saliency models, which are more targeted to detect the specific targets. The extensive experimental results demonstrate that the proposed MCS-SSM outperforms nine state-of-the-art saliency models. Besides, it has a higher detection rate and better effective performance than other three airport detection approaches.
Visual Semantic Image Recommendation

Image recommendation is an essential component of the modern online image sharing applications (e.g., Flickr), aiming to provide users with interesting images for further exploration. However, most existing approaches tend to treat the image in question as a single object, ignoring the important semantics of the sub-objects within the image. The loss of these semantic objects may lead to the misunderstanding of the user preference toward an image. In this paper, we propose a novel pairwise preference model, called Visual Semantic Model (VSM), to address this issue for a better recommendation. Specifically, we model the image representation by combining the feature embeddings of the fine-grained image objects, the weights of which may be distinct for different users. Then, we enhance the user modeling by taking into account the interacted images along with their relative importance. Two attention networks on both object and image levels are adapted to compute the weights of objects and images, respectively. The experimental results on the Flickr dataset show that our VSM model achieves significant improvements (around 9.18% on average in terms of Precision@5) over the state-of-the-art approaches in terms of the recommendation accuracy.
Computational Light Field Generation Using Deep Nonparametric Bayesian Learning

In this paper, we present a deep nonparametric Bayesian method to synthesize a light field from a single image. Conventionally, light-field capture requires special optical architecture, and the gain in angular resolution often comes at the expense of a reduction in spatial resolution. Techniques for computationally generating the light field from a single image can be expanded further to a variety of applications, ranging from microscopy and materials analysis to vision-based robotic control and autonomous vehicles. We treat the light field as multiple sub-aperture views, and to compute the novel viewpoints, our model contains three major components. First, a convolutional neural network is used for predicting the depth probability map from the image. Second, a multi-scale feature dictionary is constructed within a multi-layer dictionary learning network. Third, the novel views are synthesized taking into account both the probabilistic depth map and the multi-scale feature dictionary. The experiments show that our method outperforms several state-of-the-art novel view synthesis methods in delivering good image resolution.
Classification of breast mass in mammography using anisotropic diffusion filter by selecting and aggregating morphological and textural features

In this paper, we describe a novel mammogram classification framework for classifying breast tissues as normal, benign or malignant. First, we establish a set of symbolic rules for mammograms pre-classification from the particularities of a tissue (the background, class and severity of abnormality) taking into account the different forms of breast according to their abnormality. A decision tree reflecting a preliminary attribution from the region of interest (ROI) of a mammogram represents this pre-classification. The images obtained by this technique are processed with an anisotropic diffusion filter to reduce the noise and preserve edges. Then, a feature matrix is generated using: on the one hand, textural features from both a Gray Level Co-occurrence Matrix (GLCM) and a Gray Level Run Lengths Matrix (GLRLM) applied to all the filtered regions of interest (ROI) of a mammogram; and on the other, after mass region is segmented out, the features extracted from the mass boundary and the margin region between the mass and background for classification. To derive the relevant features from the feature matrix, we resort to RELIEF and MRMR feature selection method separately. The key point of our proposal is the modeling of Back Propagation Neural Network (BPNN) by a network of queues for representing the textural and morphological properties of the masses. The relevant and ordered features are injected in BPNN classifier using queuing network. The standard database MIAS is used for validation of the proposed scheme. In any case, we observed that the opening of closed neural network improves the performance. However, the MRMR feature selection method outperforms RELIEF one the point of view of precision and area under curve of receiver operating characteristic. These measures are respectively 98.1% and0.9650 for normal vs. abnormal classification, whereas, they are respectively 95.2% and 0.9200 for benign vs. malignant classification.
A novel circular points-based self-calibration method for a camera’s intrinsic parameters using RANSAC

Intrinsic parameters in camera calibration are commonly solved with high precision using the homography matrix of space coordinates and image coordinates of a template. However, in practice the accuracy in the extraction of corner points of the calibration plate is usually affected by the lighting environment, leading to obvious fluctuation in the calibration results. This study proposes a novel self-calibration method to improve the calibration accuracy and reduce the instability of calibration results by using circular points and the RANdom SAmple consensus (RANSAC) method. The circular points for the intrinsic parameters are calculated using the cross-ratio method with a calibration plate containing nine corner points. The distance between the circular points and image of the absolute conic is defined. The threshold value of the RANSAC model is simulated by a computer. The intrinsic parameters are initially estimated using the unreliable calibrating images excluded by the RANSAC method. The definition of the threshold is based on the Sampson estimation. The maximum likelihood estimation method is performed to reestimate the intrinsic parameters and optimize the calibration result. The findings of the numerical simulations and experiments on wing-fuselage docking based on monocular vision demonstrate that the proposed method is more robust and efficient at improving the calibration accuracy than the traditional methods. The measurement error is reduced to less than 0.013 mm when the calibration algorithm is applied to actual applications such as wing-fuselage docking.
A Dynamic Local–Global Trust-aware Recommendation approach

Trust-aware recommender systems have attracted much attention in recent years because of the popularity of social networks. Some researchers have proposed local trust models to measure trust between two users based on their interactions. However, in most cases, there are few (if any) direct interactions between two users. In such cases, it is useful to associate a user with a global reputation value that reflects the experience of the whole community with that user. In this paper, we propose a Dynamic Local–Global Trust-aware Recommendation (DLGTR) approach, which uses a new hybrid model of local and global trust. In this hybrid model, the relative importance of local trust versus global trust is dynamically determined based on the reliability of information from the two trust models. Moreover, DLGTR uses an incremental method to efficiently update a trust network as new data become available. This allows DLGTR to constantly improve the recommendation quality without loss of efficiency. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art recommendation approaches, especially in terms of prediction accuracy and computational time.
Improved TOPSIS: A multi-criteria decision making for research productivity in cloud security

Cloud Computing (CC) paradigm has become a mainstream solution for deployment of business processes and applications. It provides an ability to utilize geographically distributed resources a pay-as-you-go model. According to industry reports, 76% of prospective cloud clients viewed security as the major barrier in the adaption of CC. During the last decades, the research community has immensely focused on developing and optimizing security techniques to overcome this hurdle in computing. Due to a huge amount of literature, it becomes difficult to comprehend the overall structure and advancements. This domain needs an analytic approach to completely understand the problem domain. Therefore, in accordance to architecture layer of CC is the need of hour. This article represents the same in context to security in CC. Further, a notion on Security Issues (SI's) in CC with respect to vulnerabilities, threats, and attacks are discussed. Paper also proposed two research evaluation parameters that are Effective Citation (EC) and Effective Impact Factor (EIF) for better appreciate and analyse the patterns, trends and other important factors as a basis for directing research activities. A case study is demonstrated based on proposed parameters and generic parameters to rank effective journals by using improved TOPSIS method. Finally, global research trends on security domain are evaluated and analysed.
Investigating the Performance of C and C++ Cryptographic Libraries

This paper presents a study on performance comparison of several cryptographic primitives from six C/C++ open source libraries with the aim to understand the performance cost of the primitives and to find the most efficient primitive and library. The paper will also study the effect of the key length on the performance of variable key-length primitives. The results show that AES is the most efficient block cipher tested. The size of the key has some effect on the performance of the primitives. Smaller key sizes show better performance. However, this is not true for all the primitives with variable key length.
A Study of Use Cases for Smart Contracts Using Blockchain Technology

Blockchain technology is an emerging and rapidly growing technology in the current world scenario. It is a collection of records connected through cryptography. They play a vital role in smart contracts. Smart contracts are present in blockchains which are self-controlled and trustable. It can be integrated across various domains like healthcare, finance, self-sovereign identity, governance, logistics management and home care, etc. The purpose of this article is to analyze the various use cases of smart contracts in different domains and come up with a model which may be used in the future. Subsequently, a detailed description of a smart contract and blockchain is provided. Next, different case-studies related to five different domains is discussed with the help of use case diagrams. Finally, a solution for natural disaster management has been proposed by integrating smart contract, digital identity, policies and blockchain technologies, which can be used effectively for providing relief to victims during times of natural disaster.
Sigma-point multiple particle filtering

In this paper, we introduce two new particle filtering algorithms for high-dimensional state spaces in the multiple particle filtering approach. In multiple particle filtering, the state space is partitioned and a different particle filter is used for each component of the partition. At each time step, all particle filters share information about their marginal densities so that they can adequately approximate the filtering recursion. In this paper, we propose a second order approximation to the involved densities based on sigma-point integration methods. We then introduce two different particle filters that make use of this strategy. Finally, we demonstrate their remarkable performance through simulations of a multiple target tracking scenario with a sensor network.
Feature selection using a set based discrete particle swarm optimization and a novel feature subset evaluation criterion

In many machine learning or patter recognition tasks such as classification, datasets with a large number of features are involved. Feature selection aims at eliminating the redundant and irrelevant features which would bring computational burden and degrade the performance of learning algorithms. Particle swarm optimization (PSO) has been widely used in feature selection due to its global search ability and computational efficiency. However, PSO was originally designed for continuous optimization problems and the discretization of PSO in feature selection is still a problem which needs further investigation. This paper develops a novel feature selection algorithm based on a set based discrete PSO (SPSO). SPSO employs a set based encoding scheme which makes it able to characterize the discrete search space in feature selection problem. It also redefines the velocity term and the corresponding arithmetic operators which enables it to search for the optimal feature subset in the discrete space. In addition, a novel feature subset evaluation criterion based on contribution rate is proposed as the fitness function in SPSO. The proposed criterion does not need any pre-determined parameter to keep the balance between relevance and redundancy of the feature subset. The proposed method is compared with six filter approaches and four wrapper approaches on ten well known UCI dataset and the experimental results demonstrate the proposed method is promising.
An efficient density-based clustering with side information and active learning: A case study for facial expression recognition task

Data clustering is one of the most important tasks in machine learning and data mining, which aims to discover natural structure of the data, identify relationships between observations inside data sets, or detect outliers. Clustering is traditionally seen as part of unsupervised learning, but in m any situations, side information about the clusters may be available in addition to the values of the features. For example, the cluster labels of some observations may be known (called seeds) or certain observations may be known to belong (or not) to the same cluster (pairwise constraints). Clustering algorithms using such information are called semi-supervised algorithms. A problem is that although many semi-supervised clustering algorithms have been presented in literature over the last decades, each of them usually uses one kind of side information. In this work, we aim to propose a new semi-supervised density based clustering which integrates effectively both kinds of side information, and embeds an active learning strategy in the process of finding clusters, named MCSSDBS. In order to evaluate our proposed method and demonstrate its effectiveness compared with a state-of-the-art semi-supervised density-based clustering (SSDBSCAN), a series of experiments is carried out on both synthetic and real world data sets. First is experiments primarily conducted on 6 data sets from UCI repository. Then, especially for the facial expression recognition task, our tests are performed on two facial data sets: A popular one in literature – the extended Cohn Kanade Data set (CK+), and our own new facial data set collected from volunteers in Vietnam – named ITI facial expression data set. Comparative results conducted show that our method can boost the performance of clustering process.
GPU-based swarm intelligence for Association Rule Mining in big databases

Association Rule Mining (ARM) is a fundamental data mining task that is time-consuming on big datasets. Thus, developing new scalable algorithms for this problem is desirable. Recently, Bee Swarm Optimization (BSO)-based meta-heuristics were shown effective to reduce the time required for ARM. But these approaches were applied only on small or medium scale databases. To perform ARM on big databases, a promising approach is to design parallel algorithms using the massively parallel threads of a GPU processor. While some GPU-based ARM algorithms have been developed, they only benefit from GPU parallelism during the evaluation step of solutions obtained by the BSO-metaheuristics. This paper improves this approach by parallelizing the other steps of the BSO process (diversification and intensification). Based on these novel ideas, three novel algorithms are presented, i) DRGPU (Determination of Regions on GPU), ii) SAGPU (Search Area on GPU, and, iii) ALLGPU (All steps on GPU). These solutions are analyzed and empirically compared on benchmark datasets. Experimental results show that ALLGPU outperforms the three other approaches in terms of speed up. Moreover, results confirm that ALLGPU outperforms the state-of-the-art GPU-based ARM approaches on big ARM databases such as the Webdocs dataset. Furthermore, ALLGPU is extended to mine big frequent graphs and results demonstrate its superiority over the state-of-the-art D-Mine algorithm for frequent graph mining on the large Pokec social network dataset.
Really Small Shoe Boxes: On Realistic Quantum Resource Estimation

The reliable resource estimation and benchmarking of quantum algorithms is a critical component of the development cycle of viable quantum applications for quantum computers of all sizes. Determining resource bottlenecks in algorithms, especially when resource intensive error correction protocols are required, is crucial to reduce the cost of implementing viable algorithms on actual quantum hardware.
Learning Manifold Structures With Subspace Segmentations

Manifold learning has been widely used for dimensionality reduction and feature extraction of data recently. However, in the application of the related algorithms, it often suffers from noisy or unreliable data problems. For example, when the sample data have complex background, occlusions, and/or illuminations, the clustering of data is still a challenging task. To address these issues, we propose a family of novel algorithms for manifold regularized non-negative matrix factorization in this paper. In the algorithms, based on the alpha-beta-divergences, graph regularization with multiple segments is utilized to constrain the data transitivity in data decomposition. By adjusting two tuning parameters, we show that the proposed algorithms can significantly improve the robustness with respect to the images with complex background. The efficiency of the proposed algorithms is confirmed by the experiments on four different datasets. For different initializations and datasets, variations of cost functions and decomposition data elements in the learning are presented to show the convergent properties of the algorithms.