Reviewerly/exHarmony huggingface dataset:
Orientation Independent Activity/Gesture Recognition Using Wearable Motion Sensors

Activity/gesture recognition using wearable motion sensors, also known as inertial measurement units (IMUs), provides an important context for many ubiquitous sensing applications. The design of the activity/gesture recognition algorithms typically requires information about the placement and orientation of the IMUs on the body, and the signal processing is often designed to work with a known orientation and placement of the sensors. However, sensors could be worn or held differently. Therefore, signal processing algorithms may not perform as well as expected. In this paper, we present an orientation independent activity/gesture recognition approach by exploring a novel feature set that functions irrespective of how the sensors are oriented. We also propose a template refinement technique to determine the best representative segment of each gesture thus improving the recognition accuracy. We evaluated our approach in the context of two applications: 1) activity of daily living recognition and 2) hand gesture recognition. The experimental results show that our approach achieves 98.2% and 95.6% average accuracies for subject dependent testing of activities of daily living and gestures, respectively.
A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar

A phased array radar (PAR) is used to detect new targets and update the information of those detected targets. Generally, a large number of tasks need to be performed by a single PAR in a finite time horizon. In order to utilize the limited time and the energy resources, it is necessary to provide an efficient task scheduling algorithm. However, the existing radar task scheduling algorithms can't be utilized to release the full potential of the PAR, because of those disadvantages such as full PAR task structure ignored, only good performance in one aspect considered and just heuristic or the meta-heuristic method utilized. Aiming at above issues, an optimization model for the PAR task scheduling and a hybrid adaptively genetic (HAGA) algorithm are proposed. The model considers the full PAR task structure and integrates multiple principles of task scheduling, so that multi-aspect performance can be guaranteed. The HAGA incorporates the improved GA to explore better solutions while using the heuristic task interleaving algorithm to utilize wait intervals to interleave subtasks and calculate fitness values of individuals in efficient manners. Furthermore, the efficiency and the effectiveness of the HAGA are both improved by adopting chaotic sequences for the population initialization, the elite reservation and the mixed ranking selection, as well as designing the adaptive crossover and the adaptive mutation operators. The simulation results demonstrate that the HAGA possesses merits of global exploration, faster convergence, and robustness compared with three state-of-art algorithms—adaptive GA, hybrid GA and highest priority and earliest deadline first heuristic (HPEDF) algorithm.
Quantum firefly swarms for multimodal dynamic optimization problems

Optimization problems have attracted attention of researchers for decades. Commonly, problem related data and problem domain are assumed to be exactly known beforehand and to remain stationary. However, numerous real life optimization problems are dynamic. In practice, unpredictable events like due date changes, arrivals of new jobs or cancellations yield to changes in parameters, constraints or variables. In addition to the challenges of traditional stationary optimization problems, diverse parts of the problem space should also be monitored to keep track of moving optima in dynamic problems. Therefore, dividing a population (swarm) into smaller sized sub-swarms is a promising strategy particularly for multi-modal problems. In this context, the present work extends Firefly Algorithm (FA) as a multi-population based algorithm to solve multi-modal dynamic optimization problems due to its popularity and demonstrated competitive performance. Quantum particles are employed to monitor the neighborhoods of the best solutions of each sub-swarm in order to overcome the loss of diversity problem. Quantum strategy is also used to respond to dynamic events. Moreover, economical FA along with a simpler move function is introduced in order to consume fitness evaluations more efficiently. Most of the previous approaches ignore prioritizing sub-swarms which can be advantageous. For example, sub-swarms can either be evolved sequentially, randomly or they can be prioritized via some learning-based techniques. Thus, more promising regions might be discovered at earlier evaluations. In this context, the proposed FA extension is further enhanced with such prioritizing strategies, which are based on the feedback from sub-swarms. The experiments are conducted on the well-known Moving Peaks Benchmark along with comparisons with well-known methods. The proposed FA is found as promising and competitive according to the outcomes of the comprehensive experimental study.
An Incremental Deep Convolutional Computation Model for Feature Learning on Industrial Big Data

The deep convolutional computation model (DCCM) enabled remarkable progress in feature learning of industrial big data in Internet of Things. However, as a typical static deep learning model, it is difficult to learn features for incremental industrial big data. To solve this problem, we propose an incremental DCCM by developing two incremental algorithms, i.e., parameter-incremental algorithm and structure-incremental algorithm. The parameter-incremental algorithm aims to incrementally train the fully connected layers together with fine tuning for incorporating the new knowledge into the prior one. Then, the structure-incremental algorithm is used to transfer the previous knowledge by introducing an updating rule of the tensor convolutional, pooling, and fully connected layers. Furthermore, the dropout strategy is extended into the tensor fully connected layer to improve the robustness of the proposed model. Finally, extensive experiments are carried out on the representative datasets including CIFRA and CUAVE to justify the proposed model in terms of adaption, preservation, and convergence efficiency.
Image Co-Saliency Detection and Co-Segmentation via Progressive Joint Optimization

We present a novel computational model for simultaneous image co-saliency detection and co-segmentation that concurrently explores the concepts of saliency and objectness in multiple images. It has been shown that the co-saliency detection via aggregating multiple saliency proposals by diverse visual cues can better highlight the salient objects; however, the optimal proposals are typically region-dependent and the fusion process often leads to blurred results. Co-segmentation can help preserve object boundaries, but it may suffer from complex scenes. To address these issues, we develop a unified method that addresses co-saliency detection and co-segmentation jointly via solving an energy minimization problem over a graph. Our method iteratively carries out the region-wise adaptive saliency map fusion and object segmentation to transfer useful information between the two complementary tasks. Through the optimization iterations, sharp saliency maps are gradually obtained to recover entire salient objects by referring to object segmentation, while these segmentations are progressively improved owing to the better saliency prior. We evaluate our method on four public benchmark data sets while comparing it to the state-of-the-art methods. Extensive experiments demonstrate that our method can provide consistently higher-quality results on both co-saliency detection and co-segmentation.
Mining top-k high utility itemsets with effective threshold raising strategies

Top-K High Utility Itemset (HUI) mining problem offers greater flexibility to a decision maker in specifying her/his notion of item utility and the desired number of patterns. It obviates the need for a decision maker to determine an appropriate minimum utility threshold value using a trial-and-error process. The top-k HUI mining problem, however, is more challenging and requires use of effective threshold raising strategies. Several threshold raising strategies have been proposed in the literature to improve the overall efficiency of mining top-k HUIs. This paper advances the state-of-the-art and presents a new Top-K HUI method (THUI). A novel Leaf Itemset Utility (LIU) structure and a threshold raising strategy is proposed to significantly improve the efficiency of mining top-k HUIs. A new utility lower bound estimation method is also introduced to quickly raise the minimum utility threshold value. The proposed THUI method is experimentally evaluated on several benchmark datasets and compared against two state-of-the-art methods. Our experimental results reveal that the proposed THUI method offers one to three orders of magnitude runtime performance improvement over other related methods in the literature, especially on large, dense and long average transaction length datasets. In addition, the memory requirements of the proposed method are found to be lower.
High-Fidelity Monocular Face Reconstruction Based on an Unsupervised Model-Based Face Autoencoder

In this work, we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network with an expert-designed generative model that serves as decoder. The core innovation is the differentiable parametric decoder that encapsulates image formation analytically based on a generative model. Our decoder takes as input a code vector with exactly defined semantic meaning that encodes detailed face pose, shape, expression, skin reflectance, and scene illumination. Due to this new way of combining CNN-based with model-based face reconstruction, the CNN-based encoder learns to extract semantically meaningful parameters from a single monocular input image. For the first time, a CNN encoder and an expert-designed generative model can be trained end-to-end in an unsupervised manner, which renders training on very large (unlabeled) real world datasets feasible. The obtained reconstructions compare favorably to current state-of-the-art approaches in terms of quality and richness of representation. This work is an extended version of [1] , where we additionally present a stochastic vertex sampling technique for faster training of our networks, and moreover, we propose and evaluate analysis-by-synthesis and shape-from-shading refinement approaches to achieve a high-fidelity reconstruction.
Geometry Meets Semantics for Semi-supervised Monocular Depth Estimation

Depth estimation from a single image represents a very exciting challenge in computer vision. While other image-based depth sensing techniques leverage on the geometry between different viewpoints (e.g., stereo or structure from motion), the lack of these cues within a single image renders ill-posed the monocular depth estimation task. For inference, state-of-the-art encoder-decoder architectures for monocular depth estimation rely on effective feature representations learned at training time. For unsupervised training of these models, geometry has been effectively exploited by suitable images warping losses computed from views acquired by a stereo rig or a moving camera. In this paper, we make a further step forward showing that learning semantic information from images enables to improve effectively monocular depth estimation as well. In particular, by leveraging on semantically labeled images together with unsupervised signals gained by geometry through an image warping loss, we propose a deep learning approach aimed at joint semantic segmentation and depth estimation. Our overall learning framework is semi-supervised, as we deploy groundtruth data only in the semantic domain. At training time, our network learns a common feature representation for both tasks and a novel cross-task loss function is proposed. The experimental findings show how, jointly tackling depth prediction and semantic segmentation, allows to improve depth estimation accuracy. In particular, on the KITTI dataset our network outperforms state-of-the-art methods for monocular depth estimation.
Intraoperative augmented reality with heads-up displays in maxillofacial surgery: a systematic review of the literature and a classification of relevant technologies

Although the term augmented reality appears increasingly in published studies, the real-time, image-guided (so-called 'hands-free' and 'heads-up') surgery techniques are often confused with other virtual imaging procedures. A systematic review of the literature was conducted to classify augmented reality applications in the fields of maxillofacial surgery. Publications containing the terms 'augmented reality', 'hybrid reality', and 'surgery' were sought through a search of three medical databases, covering the years 1995-2018. Thirteen publications containing enough usable data to perform a comparative analysis of methods used and results obtained were identified. Five out of 13 described a method based on a hands-free and heads-up augmented reality approach using smart glasses or a headset combined with tracking. Most of the publications reported a minimum error of less than 1mm between the virtual model and the patient. Augmented reality during surgery may be classified into four categories: heads-up guided surgery (type I) with tracking (Ia) or without tracking (Ib); guided surgery using a semi-transparent screen (type II); guided surgery based on the digital projection of images onto the patient (type III); and guided surgery based on the transfer of digital data to a monitor display (type IV).
Multi-camera calibration for accurate geometric measurements in industrial environments

Accurate camera calibration is a challenging task required for 3D reconstruction sensors. In structured light sensors based on laser line projection, it is necessary to determine the position and orientation of the camera relative to the laser plane. The standard approach based on laser plane fitting requires a difficult setup, particularly in multi-camera configurations. This work proposes a calibration method based on a calibration plate with protruding cylinders. The projection of the laser line on the calibration plate produces a set of partial contours of ellipses, resulting from the projection of the cross section of the cylinders. These contours are used to calculate the position and orientation of the camera accurately. The proposed procedure is divided in two steps: a coarse calibration based on a robust estimator, which provides a rough approximation, and a fine calibration that iteratively optimizes the solution. The result is an accurate and robust procedure that can be applied to multiple cameras simultaneously on devices with limited computational power. Extended tests are used to validate the procedure with test pieces. Results indicate the calibration can be performed in 30 ms, producing a calibration error of only 0.027 mm.
Examining the impact of deterrence factors and norms on resistance to Information Systems Security

Numerous studies have found that employees are the principal source of adverse Information Systems Security (ISS) incidents in organizational settings. Consequently, the ISS research focuses on examining factors that affect employees' behaviour towards complying with ISS policy. Most of this research, based on the theory of reasoned action, considers that employees' intention to comply with ISS policies is a good predictor of their behaviour. This paper argues that the employees' compliance with ISS policies within organizations is usually enforced, and that the non-compliance is mainly due to the resistance towards these policies. This research examines the role of organizational punishment and organizational norms in impacting employees' resistance towards the ISS policies. The data were collected from 133 employees of 10 organizations spanning four industries and the hypotheses were tested and validated using PLS-SEM analytical procedures. The results show that moral and descriptive norms are useful in reducing the resistance.
Selective Pressure Strategy in differential evolution: Exploitation improvement in solving global optimization problems

The paper proposes a modification of Differential Evolution mutation strategies with the introduction of selective pressure, which is implemented by applying proportional, rank-based and tournament selection. Based on the new mutation strategies, a new algorithm called LSHADE-SP is proposed, which is a modification of the LSHADE algorithm, with various types of selective pressure implementation. The algorithm is tested against the Congress on Evolutionary Computation (CEC) 2017 competition on real-parameter optimization benchmark functions to demonstrate the advantage of using selective pressure. The comparison shows that applying linear rank, exponential rank and tournament selection deliver faster convergence, if a proper selective pressure is applied. The experiments were conducted for both classical mutation strategies, like rand/1 and best/1, and the best state-of-the art strategies, with various parameter adaptations. The results demonstrate that the algorithm with selective pressure is superior to the best state-of-the-art non-hybrid DE algorithms. The resulting algorithm, LSHADE-SP, obtained one of the best results among the algorithms that were winners of the CEC 2017 competition on real-parameter bound-constrained optimization.
Graph Multiview Canonical Correlation Analysis

Multiview canonical correlation analysis (MCCA) seeks latent low-dimensional representations encountered with multiview data of shared entities (a.k.a. common sources). However, existing MCCA approaches do not exploit the geometry of the common sources, which may be available \emph{a priori}, or can be constructed using certain domain knowledge. This prior information about the common sources can be encoded by a graph, and be invoked as a regularizer to enrich the maximum variance MCCA framework. In this context, the present paper's novel graph-regularized (G) MCCA approach minimizes the distance between the wanted canonical variables and the common low-dimensional representations, while accounting for graph-induced knowledge of the common sources. Relying on a function capturing the extent low-dimensional representations of the multiple views are similar, a generalization bound of GMCCA is established based on Rademacher's complexity. Tailored for setups where the number of data pairs is smaller than the data vector dimensions, a graph-regularized dual MCCA approach is also developed. To further deal with nonlinearities present in the data, graph-regularized kernel MCCA variants are put forward too. Interestingly, solutions of the graph-regularized linear, dual, and kernel MCCA, are all provided in terms of generalized eigenvalue decomposition. Several corroborating numerical tests using real datasets are provided to showcase the merits of the graph-regularized MCCA variants relative to several competing alternatives including MCCA, Laplacian-regularized MCCA, and (graph-regularized) PCA.
Investigation of a single-axis discrete solar tracking system for reduced actuations and maximum energy collection

This paper proposes a discrete single-axis solar tracking system that only actuates three times a day in the azimuthal plane to follow the sun. The preset tracking angles for the system are based on simulation runs for the site with incorporation of weather data to find optimum angles of discrete tracking. Simulation results indicate that there are no significant differences in total solar energy generation when using tracking angles based on either the isotropic or anisotropic models. Moreover, experimental results in the summer season showed that tracking the sun in only three optimized angle movements yielded around 91–94% of solar energy collection as a continuous single-axis solar tracker that moves three times an hour based on solar calculations. Experimental results agree within 11–17% with simulation data.
Convolutional Neural Network-Based Block Up-Sampling for HEVC

Recently, convolutional neural network (CNN)-based methods have achieved remarkable progress in image and video super-resolution, which inspires research on down-/up-sampling-based image and video coding using CNN. Instead of hand-crafted filters for up-sampling, trained CNN models are believed to be more capable of improving image quality, thus leading to coding gain. However, previous studies either concentrated on intra-frame coding or performed down- and up-sampling of entire frame. In this paper, we introduce block-level down- and up-sampling into inter-frame coding with the help of CNN. Specifically, each block in the P or B frame can either be compressed at the original resolution or down-sampled and compressed at low resolution and then, up-sampled by the trained CNN models. Such block-level adaptivity is flexible to cope with the spatially variant texture and motion characteristics. We further investigate how to enhance the capability of CNN-based up-sampling by utilizing reference frames and study how to train the CNN models by using encoded video sequences. We implement the proposed scheme onto the high efficiency video coding (HEVC) reference software and perform a comprehensive set of experiments to evaluate our methods. The experimental results show that our scheme achieves superior performance to the HEVC anchor, especially at low bit rates, leading to an average 3.8%, 2.6%, and 3.5% BD-rate reduction on the HEVC common test sequences under random-access, low-delay B, and low-delay P configurations, respectively. When tested on high-definition and ultrahigh-definition sequences, the average BD-rate exceeds 5%.
Environmental significance and geochemical speciation of trace elements in Lower Baram River sediments

The geochemistry and distribution of major, trace and rare earth elements (REE's) was studied in the surface sediments of the Lower Baram River during two seasons: the Monsoon (MON) and Post – monsoon (POM). The major geochemical processes controlling the distribution and mobility of major, trace and REE's in the Lower Baram River surface sediments was revealed through factor analysis. The risk assessment of major and trace element levels was studied at three specific levels; i.e. the enrichment level [Contamination Factor (Cf), with the geo-accumulation index (Igeo)], the availability level [metals bound to different fractions, risk assessment code (RAC)], and the biological toxicity level [effect range low (ERL) and effect range medium (ERM)]. The results of all the indices indicate that Cu is the element of concern in the Lower Baram River sediments. The geochemical fractionation of major and trace elements were studied through sequential extraction and the results indicated a higher concentration of Mn in the exchangeable fraction. The element of concern, Cu, was found to be highly associated in the organic bound (F4) fraction during both seasons and a change in the redox, possibly due to storms or dredging activities may stimulate the release of Cu into the overlying waters of the Lower Baram River.
Sampling approaches for applying DBSCAN to large datasets

DBSCAN is a classic clustering method for identifying clusters of different shapes and isolate noisy patterns. Despite these qualities, many articles in the literature address the scalability problem of DBSCAN. This work presents two methods to generate a good sample for the DBSCAN algorithm. The execution time decreases due to the reduction in the number of patterns presented to DBSCAN. One method is an improvement of the Rough-DBSCAN and presented consistently better results. The second is a new heuristic called I-DBSCAN capable of adapting and generating good results for all datasets without the need of any additional parameter.