Reviewerly/exHarmony huggingface dataset:
Review of dual axis solar tracking and development of its functional model

The use of solar energy is in the upswing due to its environmental friendliness and abundance. That notwithstanding, efficiency remains a major problem in many of the applications. Mitigation is normally in the form of tracking systems. This paper therefore investigates dual axis solar tracking systems from two dimensions. Firstly, a review of extant literature was conducted to draw up a trajectory of where we are in the efficiency map, Therefore it was found that the current efficiency of dual axis tracking configuration is about 35-43%. Secondly, from the above review, a generic functional model of how an efficient and effective tracking system should be is presented. The two components, coevolving, shall be used to inform the design and development of an efficient solar tracker.
Efficient Ranked Multi-Keyword Retrieval With Privacy Protection for Multiple Data Owners in Cloud Computing

With the significant rise in the demand for infinite storage resource and high-quality retrieval service, cloud computing is deemed to be a highly remarkable technology. For privacy concerns, several research works on ranked multi-keyword search over the encrypted cloud data under multiple data owner model have been developed. However, most of these schemes are vulnerable to keyword guessing attacks and equivalence test attack. Besides, they cannot accurately return the top-k ranked search results from the encrypted data of different data owners to the data users. Obviously, these drawbacks may incur leakage of privacy-sensitive keyword information and the inaccuracy of returned search results. Therefore, in this article, we first propose a novel efficient ranked multi-keyword retrieval scheme with keyword privacy for multiple data owners, which empowers the cloud server to perform multi-keyword search over the cloud data and then, return the top-k ranked search results to data users without leaking any keyword and trapdoor information. Additionally, we show through rigorous security analysis that our scheme is secure against the attacks launched by inside attackers and outside attackers. Finally, the performance evaluation indicates that our scheme has more satisfactory features than the existing ranked multi-keyword search schemes.
Impact of machine learning techniques on hand gesture recognition

In this study, we propose a vision-based mouse controller capable of controlling objects from a distant location via hand gestures. The proposed hybrid model constitutes hand detection, prediction of hand states and direction and finally, with the aid of deep learning algorithm, we systematically c ontrol hand gestures to reposition objects on computer screen. This hybrid system is explicitly designed to control mouse on computer screen during formal presentation. Random movement of hand from up to down and right to left move the mouse pointer and sends signal to the system utilizing states of the hand. Here, close hand places the mouse button on active mode while open hand releases the button. The proposed hybrid model is made up of two modules: Single Shot Multi Box Detection (SSD) structure utilized to detect hand while Convolutional Neural Network (CNN) is utilized for prediction. For comparative purposes, we performed similar experiment where SSD is used for hand detection while Radial Basis Function Network (RBFN) is used for hand states prediction. In the comparative results of hand states prediction, SSD+CNN greatly outperformed SSD+RBFN. The proposed hybrid model is vision-based hence, it does not require additional hardware to perform its task. Overall performance of the framework depicts that the system is accurate and robust.
Sampling Techniques for Large-Scale Object Detection From Sparsely Annotated Objects

Efficient and reliable methods for training of object detectors are in higher demand than ever, and more and more data relevant to the field is becoming available. However, large datasets like Open Images Dataset v4 (OID) are sparsely annotated, and some measure must be taken in order to ensure the training of a reliable detector. In order to take the incompleteness of these datasets into account, one possibility is to use pretrained models to detect the presence of the unverified objects. However, the performance of such a strategy depends largely on the power of the pretrained model. In this study, we propose part-aware sampling, a method that uses human intuition for the hierarchical relation between objects. In terse terms, our method works by making assumptions like “a bounding box for a car should contain a bounding box for a tire”. We demonstrate the power of our method on OID and compare the performance against a method based on a pretrained model. Our method also won the first and second place on the public and private test sets of the Google AI Open Images Competition 2018.
Predicting Graph Signals Using Kernel Regression Where the Input Signal is Agnostic to a Graph

We propose a kernel regression method to predict a target signal lying over a graph when an input observation is given. The input and the output could be two different physical quantities. In particular, the input may not be a graph signal at all or it could be agnostic to an underlying graph. We use a training dataset to learn the proposed regression model by formulating it as a convex optimization problem, where we use a graph-Laplacian based regularization to enforce that the predicted target is a graph signal. Once the model is learnt, it can be directly used on a large number of test data points one-by-one independently to predict the corresponding targets. Our approach employs kernels between the various input observations, and as a result the kernels are not restricted to be functions of the graph adjacency/Laplacian matrix. We show that the proposed kernel regression exhibits a smoothing effect, while simultaneously achieving noise-reduction and graph-smoothness. We then extend our method to the case when the underlying graph may not be known apriori, by simultaneously learning an underlying graph and the regression coefficients. Using extensive experiments, we show that our method provides a good prediction performance in adverse conditions, particularly when the training data is limited in size and is noisy. In graph signal reconstruction experiments, our method is shown to provide a good performance even for a highly under-determined subsampling.
Multi-view diffusion maps

In this paper, we address the challenging task of achieving multi-view dimensionality reduction. The goal is to effectively use the availability of multiple views for extracting a coherent low-dimensional representation of the data. The proposed method exploits the intrinsic relation within each view, as well as the mutual relations between views. The multi-view dimensionality reduction is achieved by defining a cross-view model in which an implied random walk process is restrained to hop between objects in the different views. The method is robust to scaling and insensitive to small structural changes in the data. We define new diffusion distances and analyze the spectra of the proposed kernel. We show that the proposed framework is useful for various machine learning applications such as clustering, classification, and manifold learning. Finally, by fusing multi-sensor seismic data we present a method for automatic identification of seismic events.
Multilabel Classification With Label-Specific Features and Classifiers: A Coarse- and Fine-Tuned Framework

Multilabel classification deals with instances assigned with multiple labels simultaneously. It focuses on learning a mapping from feature space to label a space for out-of-sample extrapolation. The mapping can be seen as a feature selection process in the feature domain or as a classifier training process in the classifier domain. The existing methods do not effectively learn the mapping when combining these two domains together. In this article, we derive a mechanism to extract label-specific features in local and global levels. We also derive a mechanism to train label-specific classifiers in individual and joint levels. Extracting features globally and training classifiers jointly can be seen as a dual process of learning the mapping function on two domains in a coarse-tuned way, while extracting features locally and training classifiers individually can be seen as a dual process of learning the mapping function on two domains in a fine-tuned way. The two-level feature selection and the two-level classifier training are derived to make the entire mapping learning process robust. Finally, extensive experimental results on several benchmarks under four domains are presented to demonstrate the effectiveness of the proposed approach.
Capturing Object Detection Uncertainty in Multi-Layer Grid Maps

We propose a deep convolutional object detector for automated driving applications that also estimates classification, pose and shape uncertainty of each detected object. The input consists of a multi-layer grid map which is well-suited for sensor fusion, free-space estimation and machine learning. Based on the estimated pose and shape uncertainty we approximate object hulls with bounded collision probability which we find helpful for subsequent trajectory planning tasks. We train our models based on the KITTI object detection data set. In a quantitative and qualitative evaluation some models show a similar performance and superior robustness compared to previously developed object detectors. However, our evaluation also points to undesired data set properties which should be addressed when training data-driven models or creating new data sets.
Variational Regularized Transmission Refinement for Image Dehazing

High-quality dehazing performance is highly dependent upon the accurate estimation of transmission map. In this work, the coarse estimation version is first obtained by weightedly fusing two different transmission maps, which are generated from foreground and sky regions, respectively. A hybrid variational model with promoted regularization terms is then proposed to assisting in refining transmission map. The resulting complicated optimization problem is effectively solved via an alternating direction algorithm. The final haze-free image can be effectively obtained according to the refined transmission map and atmospheric scattering model. Our dehazing framework has the capacity of preserving important image details while suppressing undesirable artifacts, even for hazy images with large sky regions. Experiments on both synthetic and realistic images have illustrated that the proposed method is competitive with or even outperforms the state-of-the-art dehazing techniques under different imaging conditions.
Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images

In the semantic segmentation of street scenes the reliability of the prediction and therefore uncertainty measures are of highest interest. We present a method that generates for each input image a hierarchy of nested crops around the image center and presents these, all re-scaled to the same size, to a neural network for semantic segmentation. The resulting softmax outputs are then post processed such that we can investigate mean and variance over all image crops as well as mean and variance of uncertainty heat maps obtained from pixel-wise uncertainty measures, like the entropy, applied to each crop's softmax output. In our tests, we use the publicly available DeepLabv3+ MobilenetV2 network (trained on the Cityscapes dataset) and demonstrate that the incorporation of crops improves the quality of the prediction and that we obtain more reliable uncertainty measures. These are then aggregated over predicted segments for either classifying between IoU = 0 and IoU > 0 (meta classification) or predicting the IoU via linear regression (meta regression). The latter yields reliable performance estimates for segmentation networks, in particular useful in the absence of ground truth. For the task of meta classification we obtain a classification accuracy of 81.93% and an AUROC of 89.89%. For meta regression we obtain an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 84.77%. These results yield significant improvements compared to other approaches.
An efficient hybrid similarity measure based on user interests for recommender systems

Abstract Recommender systems are used to suggest items to users based on their interests. They have been used widely in various domains, including online stores, web advertisements, and social networks. As part of their process, recommender systems use a set of similarity measurements that would assist in finding interesting items. Although many similarity measurements have been proposed in the literature, they have not concentrated on actual user interests. This paper proposes a new efficient hybrid similarity measure for recommender systems based on user interests. This similarity measure is a combination of two novel base similarity measurements: the user interest–user interest similarity measure and the user interest–item similarity measure. This hybrid similarity measure improves the existing work in three aspects. First, it improves the current recommender systems by using actual user interests. Second, it provides a comprehensive evaluation of an efficient solution to the cold start problem. Third, this similarity measure works well even when no corated items exist between two users. Our experiments show that our proposed similarity measure is efficient in terms of accuracy, execution time, and applicability. Specifically, our proposed similarity measure achieves a mean absolute error (MAE) as low as 0.42, with 64% applicability and an execution time as low as 0.03 s, whereas the existing similarity measures from the literature achieve an MAE of 0.88 at their best; these results demonstrate the superiority of our proposed similarity measure in terms of accuracy, as well as having a high applicability percentage and a very short execution time.
Implicit Semantic Data Augmentation for Deep Networks

In this paper, we propose a novel implicit semantic data augmentation (ISDA) approach to complement traditional augmentation techniques like flipping, translation or rotation. Our work is motivated by the intriguing property that deep networks are surprisingly good at linearizing features, such that certain directions in the deep feature space correspond to meaningful semantic transformations, e.g., adding sunglasses or changing backgrounds. As a consequence, translating training samples along many semantic directions in the feature space can effectively augment the dataset to improve generalization. To implement this idea effectively and efficiently, we first perform an online estimate of the covariance matrix of deep features for each class, which captures the intra-class semantic variations. Then random vectors are drawn from a zero-mean normal distribution with the estimated covariance to augment the training data in that class. Importantly, instead of augmenting the samples explicitly, we can directly minimize an upper bound of the expected cross-entropy (CE) loss on the augmented training set, leading to a highly efficient algorithm. In fact, we show that the proposed ISDA amounts to minimizing a novel robust CE loss, which adds negligible extra computational cost to a normal training procedure. Although being simple, ISDA consistently improves the generalization performance of popular deep models (ResNets and DenseNets) on a variety of datasets, e.g., CIFAR-10, CIFAR-100 and ImageNet. Code for reproducing our results are available at https://github.com/blackfeather-wang/ISDA-for-Deep-Networks.
Benchmarking Gaze Prediction for Categorical Visual Search

The prediction of human shifts of attention is a widelystudied question in both behavioral and computer vision, especially in the context of a free viewing task. However, search behavior, where the fixation scanpaths are highly dependent on the viewer's goals, has received far less attention, even though visual search constitutes much of a person's everyday behavior. One reason for this is the absence of real-world image datasets on which search models can be trained. In this paper we present a carefully created dataset for two target categories, microwaves and clocks, curated from the COCO2014 dataset. A total of 2183 images were presented to multiple participants, who were tasked to search for one of the two categories. This yields a total of 16184 validated fixations used for training, making our microwave-clock dataset currently one of the largest datasets of eye fixations in categorical search. We also present a 40-image testing dataset, where images depict both a microwave and a clock target. Distinct fixation patterns emerged depending on whether participants searched for a microwave (n=30) or a clock (n=30) in the same images, meaning that models need to predict different search scanpaths from the same pixel inputs. We report the results of several state-of-the-art deep network models that were trained and evaluated on these datasets. Collectively, these datasets and our protocol for evaluation provide what we hope will be a useful test-bed for the development of new methods for predicting category-specific visual search behavior.
Top-Push Constrained Modality-Adaptive Dictionary Learning for Cross-Modality Person Re-Identification

Person re-identification aims to match person captured by multiple non-overlapping cameras that mainly mean standard RGB cameras. In contemporary surveillance, cameras of different modalities such as infrared cameras and depth cameras are introduced because of their unique advantages in poor illumination scenarios. However, re-identifying the persons across such cameras of different modalities is extremely difficult and, unfortunately, seldom discussed. It is mainly caused by extremely different appearances of the person shown under such different camera modalities. In this paper, we tackle this challenging cross-modality people re-identification through a top-push constrained modality-adaptive dictionary learning. The proposed model asymmetrically projects the heterogeneous features from dissimilar modalities onto a common space. In this way, the modality-specific bias is mitigated. Thus, the heterogeneous data can be simultaneously enforced by a shared dictionary in a canonical space. Moreover, a top-push ranking graph regularization is embedded in the proposed model to improve the discriminability, which efficiently further boosts the matching accuracy. In order to implement the proposed model, an iterative process is developed in this paper to optimize these two processes jointly. Extensive experiments on the benchmark SYSU-MM01 and BIWI RGBD-ID person re-identification datasets show promising results which outperform state-of-the-art methods.
Large Margin Partial Label Machine

Partial label learning (PLL) is a multi-class weakly supervised learning problem where each training instance is associated with a set of candidate labels but only one label is the ground truth. The main challenge of PLL is how to deal with the label ambiguities. Among various disambiguation techniques, large margin (LM)-based algorithms attract much attention due to their powerful discriminative performance. However, existing LM-based algorithms either neglect some potential candidate labels in constructing the margin or introduce auxiliary estimation of class capacities which is generally inaccurate. As a result, their generalization performances are deteriorated. To address the above-mentioned drawbacks, motivated by the optimistic superset loss, we propose an LM Partial LAbel machiNE (LM-PLANE) by extending multi-class support vector machines (SVM) to PLL. Compared with existing LM-based disambiguation algorithms, LM-PLANE considers the margin of all potential candidate labels without auxiliary estimation of class capacities. Furthermore, an efficient cutting plane (CP) method is developed to train LM-PLANE in the dual space. Theoretical insights into the effectiveness and convergence of our CP method are also presented. Extensive experiments on various PLL tasks demonstrate the superiority of LM-PLANE over existing LM based and other representative PLL algorithms in terms of classification accuracy.
Development of a Reminiscence Therapy System for the Elderly Using the Integration of Virtual Reality and Augmented Reality

Through virtual interaction and multivariate communication, the social interaction and life satisfaction of elderly people can be improved. This study integrated virtual reality (VR) and augmented reality (AR) to construct a visualized reminiscence therapy system, which provides not only reminiscence entertainment but may also aid in dementia prevention for healthy and sub-healthy (a therapeutic working concept which defines an intermediate stage between health and disease) elderly people. Images taken by an LG 360 CAM are primarily introduced into Power Director 360 as the guide interface. The special effects are joined using After Effects. With the model constructed under 3ds Max, the reminiscence therapy system integrated by virtual reality and augmented reality is then established by the Unity engine. It is shown that the developed 3D ancient building model can be used by Samsung Note 5 smartphones as well as VR CASE glasses to increase the immersive experience for the elderly. Through nostalgic elements triggered by the AR/VR three-dimensional model and video/audio interaction, the feasibility of our integrated system for reminiscence therapy is thus verified. Through reminiscence therapy, memories and thoughts can fully be activated as a therapy for elderly people. The visualized reality system developed in this study can further promote the social interactional satisfaction of elderly people.
Learning the optimal state-feedback via supervised imitation learning

Imitation learning is a control design paradigm that seeks to learn a control policy reproducing demonstrations from expert agents. By substituting expert demonstrations for optimal behaviours, the same paradigm leads to the design of control policies closely approximating the optimal state-feedback. This approach requires training a machine learning algorithm (in our case deep neural networks) directly on state-control pairs originating from optimal trajectories. We have shown in previous work that, when restricted to low-dimensional state and control spaces, this approach is very successful in several deterministic, non-linear problems in continuous-time. In this work, we refine our previous studies using as a test case a simple quadcopter model with quadratic and time-optimal objective functions. We describe in detail the best learning pipeline we have developed, that is able to approximate via deep neural networks the state-feedback map to a very high accuracy. We introduce the use of the softplus activation function in the hidden units of neural networks showing that it results in a smoother control profile whilst retaining the benefits of rectifiers. We show how to evaluate the optimality of the trained state-feedback, and find that already with two layers the objective function reached and its optimal value differ by less than one percent. We later consider also an additional metric linked to the system asymptotic behaviour-time taken to converge to the policy’s fixed point. With respect to these metrics, we show that improvements in the mean absolute error do not necessarily correspond to better policies.