Reviewerly/exHarmony huggingface dataset:
Perception-aware time optimal path parameterization for quadrotors

The increasing popularity of quadrotors has given rise to a class of predominantly vision-driven vehicles. This paper addresses the problem of perception-aware time optimal path parametrization for quadrotors. Although many different choices of perceptual modalities are available, the low weight and power budgets of quadrotor systems makes a camera ideal for on-board navigation and estimation algorithms. However, this does come with a set of challenges. The limited field of view of the camera can restrict the visibility of salient regions in the environment, which dictates the necessity to consider perception and planning jointly. The main contribution of this paper is an efficient time optimal path parametrization algorithm for quadrotors with limited field of view constraints. We show in a simulation study that a state-of-the-art controller can track planned trajectories, and we validate the proposed algorithm on a quadrotor platform in experiments.
Decentralized Task Allocation in Multi-Agent Systems Using a Decentralized Genetic Algorithm

In multi-agent collaborative search missions, task allocation is required to determine which agents will perform which tasks. We propose a new approach for decentralized task allocation based on a decentralized genetic algorithm (GA). The approach parallelizes a genetic algorithm across the team of agents, making efficient use of their computational resources. In the proposed approach, the agents continuously search for and share better solutions during task execution. We conducted simulation experiments to compare the decentralized GA approach and several existing approaches. Two objectives were considered: a min-sum objective (minimizing the total distance traveled by all agents) and a min-time objective (minimizing the time to visit all locations of interest). The results showed that the decentralized GA approach yielded task allocations that were better on the min-time objective than those created by existing approaches and solutions that were reasonable on the min-sum objective. The decentralized GA improved min-time performance by an average of 5.6% on the larger instances. The results indicate that decentralized evolutionary approaches have a strong potential for solving the decentralized task allocation problem.
A feedback-based combinatorial fair economical double auction resource allocation model for cloud computing

Resource provisioning by cloud computing is one of the emerging fields for providing computing services anywhere at any time without physical barriers. It not only reduces the cost of set-up and maintenance of infrastructure at the on-site level but also helps in optimal resource utilization and reduction of e-waste generation to save the environment. It provides versatile resources to run different types of applications efficiently without worrying about delay or failure. Auction emerges as one of the most promising pricing schemes for the provisioning of resources in cloud computing. Providers and customers have different mind-set. The providers desire to earn more profit and lead the market, while customers prefer economical rates with high quality of service for the resources. In the virtual world, the providers’ genuineness is crucial, and the feedback from the customers plays a vital role that quantifies this genuineness. This paper proposes a Feedback-based Combinatorial Fair Economical Double Auction Resource Allocation Model (FCFEDARA) that investigates the genuineness of providers based on the offered prices for the required resources along with the feedback given by the customers. The proposed framework provides a common platform for the customers to access resources from different providers at optimal prices, as well as prioritizes genuine providers with good feedback over in-genuine providers with bad feedback. In the combinatorial double auction model, customers and providers submit their bundle bid along with a list of resources in the auction. The proposed model takes care of the truthfulness of the providers, penalizes market spoilers, and gives priority to those providers who have good customer feedback. Three different cases with their implications have analyzed, and evaluated based on the bid, configuration of resources, and customer feedback. Case 1 focuses on feedback given by the customers. In Case 2 and Case 3, providers’ genuineness, as well as their feedback from the customers, are key factors to determine winner providers. It is in contrast with earlier models where genuineness is based only on the bid and configuration of resources offered by providers. The proposed model was simulated on the CloudSim simulator, a Java-based simulator that provides a cloud computing environment, and results are evaluated and compared with two popular existing models: CDARA, its variant model, and the author’s previous approach (CFEDARA). The experimental results and the graphs have clearly shown the promotion of genuine providers with good feedback, fair allocation with economical pricing while maintaining the overall profit of the system. The participation ratio of customers and providers is maintained to sustain demand and supply in the virtual market.
An online self-organizing modular neural network for nonlinear system modeling

Modular neural network (MNN) has distinct advantage in many fields such as pattern recognition and pattern recognition. However it is still a challenge to dynamically adjust the MNN structure for dynamic nonlinear system modeling. This paper proposes a novel online self-organizing MNN (OSOMNN) for nonlinear system modeling. In OSOMNN, an online task decomposition algorithm and a self-organizing algorithm for subnetwork are introduced. Firstly, the task decomposition algorithm is implemented by the online clustering method based on distance and local density, which can online divide the original task into several simpler subtasks. Then subnetworks with single-layer feedforward neural network are built to learn the divided subtasks. Moreover, this paper develops a self-organizing algorithm for subnetwork, which can dynamically adjust its structure and is trained by the improved online gradient method with fixed memory mechanism (FMOGM). To demonstrate the effectiveness of OSOMNN for nonlinear system modeling, experimental investigations using four benchmark nonlinear systems and the monthly sunspots time series show that OSOMNN can automatically add or merge the subnetwork modules and optimize the structure of subnetworks for nonlinear system modeling with a better generalization performance than the established alternatives.
Review on Word2Vec Word Embedding Neural Net

The word2vec model consists of more useful applications in different NLP tasks. The semantic meaning given by word2vec for each word in vector representations has served useful task in machine learning text classification. They are employed in finding analogy, syntactic, and semantic analysis of words. Word2vec falls in two flavors CBOW and Skip-Gram. Given a context, they used to predict a word and vice versa are also true. In order to optimize the efficiency of word2vec, they have introduced two computational techniques namely hierarchical softmax and negative sampling. The proposed research work is more focused on introducing the models, computational technique, and various fields of word2vec applications. Word2vec is compared based on the metrics and their performance is evaluated by comparing with other existing models.
Distribution Cognisant Loss for Cross-Database Facial Age Estimation With Sensitivity Analysis

Existing facial age estimation studies have mostly focused on intra-database protocols that assume training and test images are captured under similar conditions. This is rarely valid in practical applications, where we typically encounter training and test sets with different characteristics. In this article, we deal with such situations, namely subjective-exclusive cross-database age estimation. We formulate the age estimation problem as the distribution learning framework, where the age labels are encoded as a probability distribution. To improve the cross-database age estimation performance, we propose a new loss function which provides a more robust measure of the difference between ground-truth and predicted distributions. The desirable properties of the proposed loss function are theoretically analysed and compared with the state-of-the-art approaches. In addition, we compile a new balanced large-scale age estimation database. Last, we introduce a novel evaluation protocol, called subject-exclusive cross-database age estimation protocol, which provides meaningful information of a method in terms of the generalisation capability. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art age estimation methods under both intra-database and subject-exclusive cross-database evaluation protocols. In addition, in this article, we provide a comparative sensitivity analysis of various algorithms to identify trends and issues inherent to their performance. This analysis introduces some open problems to the community which might be considered when designing a robust age estimation system.
Atrous convolutional feature network for weakly supervised semantic segmentation

Weakly supervised semantic segmentation has been attracting increasing attention as it can alleviate the need for expensive pixel-level annotations through the use of image-level labels. Relevant methods mainly rely on the implicit object localization ability of convolutional neural networks (CNNs). However, generated object attention maps remain mostly small and incomplete. In this paper, we propose an Atrous Convolutional Feature Network (ACFN) to generate dense object attention maps. This is achieved by enhancing the context representation of image classification CNNs. More specifically, cascaded atrous convolutions are used in the middle layers to retain sufficient spatial details, and pyramidal atrous convolutions are used in the last convolutional layers to provide multi-scale context information for the extraction of object attention maps. Moreover, we propose an attentive fusion strategy to adaptively fuse the multi-scale features. Our method shows improvements over existing methods on both the PASCAL VOC 2012 and MS COCO datasets, achieving state-of-the-art performance.
Cartographic Scale in Immersive Virtual Environments

Abstract Scale has been a defining criterion of mapmaking for centuries. However, this criterion is fundamentally questioned by highly immersive virtual reality (VR) systems able to represent geographic environments at a high level of detail and, thus, providing the user with a feeling of being present in VR space. In this paper, we will use the concept of scale as a vehicle for discussing some of the main differences between immersive VR and non-immersive geovisualization products. Based on a short review of diverging meanings of scale we will propose possible approaches to the issue of both spatial and temporal scale in immersive VR. Our considerations shall encourage a more detailed treatment of the specific characteristics of immersive geovisualization to facilitate deeper conceptual integration of immersive and non-immersive visualization in the realm of cartography.
Aesthetic-Aware Image Style Transfer

Style transfer aims to synthesize an image which inherits the content of one image while preserving a similar style of the other one. The "style'' of an image usually refers to its unique feeling conveyed from visual features, which is highly related to the aesthetic effect of the image. Aesthetic effect can be mainly decomposed as two factors: colour and texture. Previous methods like Neural Style Transfer and Colour Transfer have shown strong abilities in transferring colour and texture features. However, such approaches neglect to further disentangle colour and texture, which makes some of unique aesthetic effects designed by human artists hard to express. In this paper, we propose a novel problem called Aesthetic-Aware Image Style Transfer task, which aims to transfer colour and texture separately and independently to manipulate the aesthetic effect of an image. We propose a novel Aesthetic-Aware Model-Optimisation-Based Style Transfer (AAMOBST) model to solve this problem. Specifically, AAMOBST is a multi-reference, two-path model. It uses different reference images to decide desired colour and texture features. It can segregate colour and texture into two distinct paths and transfer them independently. Qualitative and quantitative experiments show that our model can decide colour and texture features separately and is able to keep one of them fixed while changing the other one, which is not applicable for previous methods. Furthermore, on tasks that are applicable for previous methods (such as style transfer, colour-preserved transfer and colour-only transfer), our model shows comparable abilities with other baseline methods.
Improving student teachers’ TPACK self-efficacy through lesson planning practice in the virtual platform

How teachers use technology efficiently in their classes is an important issue because learning environments are also affected by rapid developments in instructional technology. In the current study, we investigated how designing an inquiry-based technology-enhanced lesson plan on the virtual platform influences the TPACK (Technological Pedagogical Content Knowledge) self-efficacy of pre-service teachers and also how they integrated educational technology into their lesson plans. A total of 38 undergraduate students at a teacher education programme participated in the study. Data were gathered by the self-efficacy scale and the lesson plans developed by the pre-service teachers. The findings revealed a significant increase in the TPACK self-efficacy of the pre-service teachers after they designed an inquiry-based technology-enhanced lesson plan on the virtual platform. Besides, lesson plan analysis showed that pre-service teachers generally followed the phases of inquiry-based learning and integrated online scaffolding tools and virtual laboratories into their plans at a moderate level.
Learning Scales from Points

Counting people automatically through computer vision technology is a challenging task. Recently, convolution neural network (CNN) based methods have made significant progress. Nonetheless, large scale variations of instances caused by, for example, perspective effects remain unsolved. Moreover, it is problematic to estimate scales with only point annotations. In this paper, we propose a scale-aware probabilistic model to handle this problem. Unlike previous methods that generate a single density map where instances of various scales are processed indiscriminately, we propose a density pyramid network (DPN), where each pyramid level handles instances within a particular scale range. Furthermore, we propose a scale distribution estimator (SDE) to learn scales of people from input data, under the weak supervision of point annotations. Finally, we adopt an instance-level probabilistic scale-aware model (IPSM) to guide the multi-scale training of DPN explicitly. Qualitative and quantitative experimental results demonstrate the effectiveness of the proposed method, which achieves competitive results on four widely used benchmarks.
Visual-Semantic Graph Matching for Visual Grounding

Visual Grounding is the task of associating entities in a natural language sentence with objects in an image. In this paper, we formulate visual grounding as a graph matching problem to find node correspondences between a visual scene graph and a language scene graph. These two graphs are heterogeneous, representing structure layouts of the sentence and image, respectively. We learn unified contextual node representations of the two graphs by using a cross-modal graph convolutional network to reduce their discrepancy. The graph matching is thus relaxed as a linear assignment problem because the learned node representations characterize both node information and structure information. A permutation loss and a semantic cycle-consistency loss are further introduced to solve the linear assignment problem with or without ground-truth correspondences. Experimental results on two visual grounding tasks, i.e., referring expression comprehension and phrase localization, demonstrate the effectiveness of our method.
ShapeCaptioner: Generative Caption Network for 3D Shapes by Learning a Mapping from Parts Detected in Multiple Views to Sentences

3D shape captioning is a challenging application in 3D shape understanding. Captions from recent multi-view based methods reveal that they cannot capture part-level characteristics of 3D shapes. This leads to a lack of detailed part-level description in captions, which human tend to focus on. To resolve this issue, we propose ShapeCaptioner, a generative caption network, to perform 3D shape captioning from semantic parts detected in multiple views. Our novelty lies in learning the knowledge of part detection in multiple views from 3D shape segmentations and transferring this knowledge to facilitate learning the mapping from 3D shapes to sentences. Specifically, ShapeCaptioner aggregates the parts detected in multiple colored views using our novel part class specific aggregation to represent a 3D shape, and then, employs a sequence to sequence model to generate the caption. Our outperforming results show that ShapeCaptioner can learn 3D shape features with more detailed part characteristics to facilitate better 3D shape captioning than previous work.
MMNet

Most existing RGB-D salient object detection (SOD) methods directly extract and fuse raw features from RGB and depth backbones. Such methods can be easily restricted by low-quality depth maps and redundant cross-modal features. To effectively capture multi-scale cross-modal fusion features, this paper proposes a novel Multi-stage and Multi-Scale Fusion Network (MMNet), which consists of a cross-modal multi-stage fusion module (CMFM) and a bi-directional multi-scale decoder (BMD). Similar to the mechanism of visual color stage doctrine in human visual system, the proposed CMFM aims to explore the useful and important feature representations in feature response stage, and effectively integrate them into available cross-modal fusion features in adversarial combination stage. Moreover, the proposed BMD learns the combination of cross-modal fusion features from multiple levels to capture both local and global information of salient objects and further reasonably boost the performance of the proposed method. Comprehensive experiments demonstrate that the proposed method can achieve consistently superior performance over the other 14 state-of-the-art methods on six popular RGB-D datasets when evaluated by 8 different metrics.
Robust multi-view k-means clustering with outlier removal

Contemporary datasets are often comprised of multiple views of data, which provide complete and complementary information in different views, and multi-view clustering is one of the most crucial techniques in multi-view data analysis. However, traditional multi-view clustering methods are sensitive to noises and outliers, suffering from severe performance degradation when the dataset contains many outliers. Moreover, the commonly used multi-view clustering methods are restricted by high time complexity. To address these problems, we propose a robust multi-view k-means algorithm with outlier detection, i.e., Multi-View Clustering with Outlier Removal (MVCOR). This method is designed to remove the outliers and thus boosts the clustering performance on multi-view data with low time complexity. By defining two types of outliers, MVCOR uses the well-defined outlier removal strategy to categorize all the outliers into two specific clusters and performs robust clustering on the clean data at the same time. This strategy significantly improves the clustering performance as well as the model robustness, making MVCOR a more practical approach for real-world scenarios. Besides, the proposed model is efficiently optimized by a well-designed alternating minimization algorithm which is strictly proved to be convergent. Extensive experiments on both synthetic and real-world datasets demonstrate that MVCOR consistently outperforms the related clustering methods on clustering performance as well as robustness to outliers, and achieves comparable performance to the state-of-the-art multi-view outlier detection methods.
Domain Adversarial Reinforcement Learning for Partial Domain Adaptation

Partial domain adaptation aims to transfer knowledge from a label-rich source domain to a label-scarce target domain (i.e., the target categories are a subset of the source ones), which relaxes the common assumption in traditional domain adaptation that the label space is fully shared across different domains. In this more general and practical scenario on partial domain adaptation, a major challenge is how to select source instances from the shared categories to ensure positive transfer for the target domain. To address this problem, we propose a domain adversarial reinforcement learning (DARL) framework to progressively select source instances to learn transferable features between domains by reducing the domain shift. Specifically, we employ a deep Q-learning to learn policies for an agent to make selection decisions by approximating the action-value function. Moreover, domain adversarial learning is introduced to learn a common feature subspace for the selected source instances and the target instances, and also to contribute to the reward calculation for the agent that is based on the relevance of the selected source instances with respect to the target domain. Extensive experiments on several benchmark data sets clearly demonstrate the superior performance of our proposed DARL over existing state-of-the-art methods for partial domain adaptation.
Learning recommendation with formal concept analysis for intelligent tutoring system

The aim of this research was to develop a learning recommendation component in an intelligent tutoring system (ITS) that dynamically predicts and adapts to a learner's style. In order to develop a proper ITS, we present an improved knowledge base supporting adaptive learning, which can be achieved by a suitable knowledge construction. This process is illustrated by implementing a web-based online tutor system. In addition, our knowledge structure provides adaptive presentation and personalized learning with the proposed adaptive algorithm, to retrieve content according to individual learner characteristics. To demonstrate the proposed adaptive algorithm, pre-test and post-test were used to evaluate suggestion accuracy of the course in a class for adapting to a learner's style. In addition, pre- and post-testing were also used with students in a real teaching/learning environment to evaluate the performance of the proposed model. The results show that the proposed system can be used to help students or learners achieve improved learning.