Reviewerly/exHarmony huggingface dataset:
Data Augmentation in Deep Learning-Based Fusion of Depth and Inertial Sensing for Action Recognition

This article covers a deep learning-based decision fusion approach for action or gesture recognition via simultaneous utilization of a depth camera and a wearable inertial sensor. The deep learning approach involves using a convolutional neural network (CNN) for depth images captured by a depth camera and a combination of CNN and long short–term memory network for inertial signals captured by a wearable inertial sensor, followed by a decision-level fusion. Due to the limited size of the training data, a data augmentation procedure is carried out by generating depth images corresponding to different orientations of the depth camera and by generating inertial signals corresponding to different orientations of the inertial sensor placement on the body. The results obtained indicate the positive impact of the decision-level fusion as well as the data augmentation on the recognition accuracies.
Feature selection based on feature interactions with application to text categorization

Feature selection is an import preprocessing approach for machine learning and text mining. It reduces the dimensions of high-dimensional data. A popular approach is based on information theoretic measures. Most of the existing methods used two- and three-dimensional mutual information terms that are ineffective in detecting higher-order feature interactions. To fill this gap, we employ two- through five-way interactions for feature selection. We first identify a relaxed assumption to decompose the mutual information-based feature selection problem into a sum of low-order interactions. A direct calculation of the decomposed interaction terms is computationally expensive. We employ five-dimensional joint mutual information, a computationally efficient measure, to estimate the interaction terms. We use the ‘maximum of the minimum’ nonlinear approach to avoid the overestimation of the feature significance. We also apply the proposed method to text categorization. To evaluate the performance of the proposed method, we compare it with eleven popular feature selection methods, eighteen benchmark data and seven text categorization data. Experimental results with four different types of classifiers provide concrete evidence that higher-order interactions are effective in improving feature selection methods.
Unsupervised Clustering-Based Short-Term Solar Forecasting

Solar forecasting accuracy is highly affected by weather conditions, therefore, weather awareness forecasting models are expected to improve the forecasting performance. However, it may not be available or reliable to classify different forecasting tasks by only using predefined meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting method is developed for short-term (1-h-ahead) global horizontal irradiance (GHI) forecasting. This UC-based method consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The daily GHI time series is first clustered by an Optimized Cross-validated ClUsteRing (OCCUR) method, which determines the optimal number of clusters and best clustering results. Then, support vector machine pattern recognition is adopted to recognize the category of a certain day using the first four hours' data in the forecasting stage. GHI forecasts are generated by the most suitable models in different clusters, which are built by a two-layer machine learning based multi-model (M3) forecasting framework. The developed UC-M3 method is validated by using 1-year of data with 13 solar features from three information sources. Numerical results show that 1) UC-based models outperform non-UC (all-in-one) models with the same M3 architecture by approximately 20%; and 2) M3-based models also outperform the single-algorithm machine learning models by approximately 20%.
Computationally Eff&lt;roman&gt;i&lt;/roman&gt;cient Multi-Agent Multi-Object Tracking With Labeled Random Finite Sets

This paper addresses multi-agent multi-object tracking with labeled random finite sets via <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Generalized Covariance Intersection</i> (GCI) fusion. While standard GCI fusion of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Labeled Multi-Object</i> (LMO) densities is labelwise and hence fully parallelizable, previous work unfortunately revealed that its fusion performance is highly sensitive to the unavoidable label inconsistencies among different agents. In order to overcome the label inconsistency sensitivity problem, we present a novel approach for the GCI fusion of LMO densities that is both robust to label inconsistencies and computationally efficient. The novel approach consists of, first, finding the best matching between labels of different agents by minimization of a suitable label inconsistency indicator, and, then, performing GCI fusion labelwise according to the obtained label matching. Furthermore, it is shown how the label matching problem, which is at the core of the proposed method, can be formulated as a linear assignment problem of finite length (efficiently solvable in polynomial time by the Hungarian algorithm), exactly for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Labeled Multi-Bernoulli</i> densities and approximately for arbitrary LMO densities. Simulation experiments are carried out to demonstrate the robustness and effectiveness of the proposed approach in challenging tracking scenarios.
Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression

Current local density-based anomaly detection methods are limited in that the local density estimation and the neighborhood density estimation are not accurate enough for complex and large databases, and the detection performance depends on the size parameter of the neighborhood. In this paper, we propose a new kernel function to estimate samples' local densities and propose a weighted neighborhood density estimation to increase the robustness to changes in the neighborhood size. We further propose a local kernel regression estimator and a hierarchical strategy for combining information from the multiple scale neighborhoods to refine anomaly factors of samples. We apply our general anomaly detection method to image saliency detection by regarding salient pixels in objects as anomalies to the background regions. Local density estimation in the visual feature space and kernel-based saliency score propagation in the image enable the assignment of similar saliency values to homogenous object regions. Experimental results on several benchmark datasets demonstrate that our anomaly detection methods overall outperform several state-of-art anomaly detection methods. The effectiveness of our image saliency detection method is validated by comparison with several state-of-art saliency detection methods.
Abnormal Event Detection From Videos Using a Two-Stream Recurrent Variational Autoencoder

With the massive deployment of distributed video surveillance systems, the automatic detection of abnormal events in video streams has become an urgent need. An abnormal event can be considered as a deviation from the regular scene; however, the distribution of normal and abnormal events is severely imbalanced, since the abnormal events do not frequently occur. To make use of a large number of video surveillance videos of regular scenes, we propose a semi-supervised learning scheme, which only uses the data that contains the ordinary scenes. The proposed model has a two-stream structure that is composed of the appearance and motion streams. For each stream, a recurrent variational autoencoder can model the probabilistic distribution of the normal data in a semi-supervised learning scheme. The appearance and motion features from the two streams can provide complementary information to describe this probabilistic distribution. Comprehensive experiments validate the effectiveness of our proposed scheme on several public benchmark data sets which include the Avenue, the Ped1, the Ped2, the Subway-entry, and the Subway-exit.
What are the Biases in My Word Embedding?

This paper presents an algorithm for enumerating biases in word embeddings. The algorithm exposes a large number of offensive associations related to sensitive features such as race and gender on publicly available embeddings, including a supposedly "debiased" embedding. These biases are concerning in light of the widespread use of word embeddings. The associations are identified by geometric patterns in word embeddings that run parallel between people's names and common lower-case tokens. The algorithm is highly unsupervised: it does not even require the sensitive features to be pre-specified. This is desirable because: (a) many forms of discrimination?such as racial discrimination-are linked to social constructs that may vary depending on the context, rather than to categories with fixed definitions; and (b) it makes it easier to identify biases against intersectional groups, which depend on combinations of sensitive features. The inputs to our algorithm are a list of target tokens, e.g. names, and a word embedding. It outputs a number of Word Embedding Association Tests (WEATs) that capture various biases present in the data. We illustrate the utility of our approach on publicly available word embeddings and lists of names, and evaluate its output using crowdsourcing. We also show how removing names may not remove potential proxy bias.
A utility-based news recommendation system

News platforms exhibit both the challenges as well as opportunities for enhancing the functionalities of recommendation systems in today's big data environment. Novel use of big data storage and programming models can improve news recommendation systems through efficient handling and analysis of clickstream data and a better understanding of users' interests. Most existing approaches to news recommendation consider users' clicks as the implicit feedback to understand user behaviors. However, “clicks” may not be an effective indicator of real user interests. We address this problem by developing a novel news recommendation system based on a news utility model. Given the new utility model, we propose a two stage news recommendation framework. The framework first generates article-level recommendation rules based on the utility model, then integrates the notion of utility and probabilistic topic models and generates topic-level recommendation rules. We argue that the proposed utility-based news recommendation system also addresses the news cold start problem which is one of the most challenging obstacles for news agencies. We evaluate the framework on a massive real dataset (two billion records) obtained from a major newspaper (i.e., The Globe and Mail) in Canada and show that it outperforms the existing methods.
Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller

Abstract Weed is a phenomenon which is looks for optimality and finds the best environment for life and quickly adapts itself to environmental conditions and resists changes. Considering these features, a powerful optimization algorithm is developed in this study. The invasive weed optimization algorithm (IWO) is a population-based evolutionary optimization method inspired by the behavior of weed colonies. In this paper, the IWO algorithm is based on chaos theory. Among parameters of weed optimization algorithm, standard deviation affects the performance of the algorithm significantly. Therefore, chaotic maps are used in the standard deviation parameter. Performance of the chaotic invasive weed development method is investigated on five benchmark functions, using logistic chaotic mapping. Additionally, the problem of setting the PID controller parameters for a DC motor using the proposed method is discussed. The statistical results on optimization problems show that the improved chaotic weed algorithm has gained fast convergence rate and high accuracy. Highlights Improved Invasive weed optimization Algorithm (IWO) based on Chaos theory. Improved setting the parameters of PID controller uses Chaotic IWO Algorithm.
Adaptive color deconvolution for histological WSI normalization

Color consistency of histological images is significant for developing reliable computer-aided diagnosis (CAD) systems. However, the color appearance of digital histological images varies across different specimen preparations, staining, and scanning situations. This variability affects the diagnosis and decreases the accuracy of CAD approaches. It is important and challenging to develop effective color normalization methods for digital histological images. We proposed a novel adaptive color deconvolution (ACD) algorithm for stain separation and color normalization of hematoxylin-eosin-stained whole slide images (WSIs). To avoid artifacts and reduce the failure rate of normalization, multiple prior knowledges of staining are considered and embedded in the ACD model. To improve the capacity of color normalization for various WSIs, an integrated optimization is designed to simultaneously estimate the parameters of the stain separation and color normalization. The solving of ACD model and application of the proposed method involves only pixel-wise operation, which makes it very efficient and applicable to WSIs. The proposed method was evaluated on four WSI-datasets including breast, lung and cervix cancers and was compared with 6 state-of-the-art methods. The proposed method achieved the most consistent performance in color normalization according to the quantitative metrics. Through a qualitative assessment for 500 WSIs, the failure rate of normalization was 0.4% and the structure and color artifacts were effectively avoided. Applied to CAD methods, the area under receiver operating characteristic curve for cancer image classification was improved from 0.842 to 0.914. The average time of solving the ACD model is 2.97 s. The proposed ACD model has prone effective for color normalization of hematoxylin-eosin-stained WSIs in various color appearances. The model is robust and can be applied to WSIs containing different lesions. The proposed model can be efficiently solved and is effective to improve the performance of cancer image recognition, which is adequate for developing automatic CAD programs and systems based on WSIs.
Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems

Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms. However, existing approaches often use out-of-the-box sequence models which are limited by speed and memory consumption, are often infeasible for production environments, and usually do not incorporate cross-session information, which is crucial for effective recommendations. Here we propose Hierarchical Temporal Convolutional Networks (HierTCN), a hierarchical deep learning architecture that makes dynamic recommendations based on users' sequential multi-session interactions with items. HierTCN is designed for web-scale systems with billions of items and hundreds of millions of users. It consists of two levels of models: The high-level model uses Recurrent Neural Networks (RNN) to aggregate users' evolving long-term interests across different sessions, while the low-level model is implemented with Temporal Convolutional Networks (TCN), utilizing both the long-term interests and the short-term interactions within sessions to predict the next interaction. We conduct extensive experiments on a public XING dataset and a large-scale Pinterest dataset that contains 6 million users with 1.6 billion interactions. We show that HierTCN is 2.5x faster than RNN-based models and uses 90% less data memory compared to TCN-based models. We further develop an effective data caching scheme and a queue-based mini-batch generator, enabling our model to be trained within 24 hours on a single GPU. Our model consistently outperforms state-of-the-art dynamic recommendation methods, with up to 18% improvement in recall and 10% in mean reciprocal rank.
A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for global optimization problems

This study proposes an improved version of the Symbiotic Organisms Search (SOS) algorithm called Quasi-Oppositional Chaotic Symbiotic Organisms Search (QOCSOS). This improved algorithm integrated Quasi-Opposition-Based Learning (QOBL) and Chaotic Local Search (CLS) strategies with SOS for a better quality solution and faster convergence. To demonstrate and validate the new algorithm’s effectiveness, the authors tested QOCSOS with twenty-six mathematical benchmark functions of different types and dimensions. In addition, QOCSOS optimized placements for distributed generation (DG) units in radial distribution networks and solved five structural design optimization problems, as practical optimization problems challenges. Comparative results showed that QOCSOS provided more accurate solutions than SOS and other methods, suggesting viability in dealing with global optimization problems.
Standardization Status of Immersive Video Coding

Based on increasing availability of capture and display devices dedicated to immersive media, coding, and transmission of these media has recently become a highest-priority subject of standardization. Different levels of immersiveness are defined with respect to an increasing degree of freedom in terms of movements of the observer within the immersive media scene. The level ranges from three degrees of freedom allowing the user to look around in all directions from a fixed point of view to six degrees of freedom, where the user can freely alter the viewpoint within the immersive media scene. The moving pictures experts group (MPEG) of ISO/IEC is developing a standards suite on “Coded Representation of Immersive Media,” called MPEG-I, to provide technical solutions for building blocks of the media transmission chain, ranging from architecture, systems tools, coding of video and audio signals, to point clouds and timed text. In this paper, an overview on recent and ongoing standardization efforts in this area is presented. While some specifications, such as high efficiency video coding or version 1 of the omnidirectional media format, are already available, other activities are under development or in the exploration phase. This paper addresses the status of these efforts with a focus on video signals, indicates the development timelines, summarizes the main technical details, and provides pointers to further points of reference.
A Human–Machine Interface Based on Eye Tracking for Controlling and Monitoring a Smart Home Using the Internet of Things

People with severe disabilities may have difficulties when interacting with their home devices due to the limitations inherent to their disability. Simple home activities may even be impossible for this group of people. Although much work has been devoted to proposing new assistive technologies to improve the lives of people with disabilities, some studies have found that the abandonment of such technologies is quite high. This work presents a new assistive system based on eye tracking for controlling and monitoring a smart home, based on the Internet of Things, which was developed following concepts of user-centered design and usability. With this system, a person with severe disabilities was able to control everyday equipment in her residence, such as lamps, television, fan, and radio. In addition, her caregiver was able to monitor remotely, by Internet, her use of the system in real time. Additionally, the user interface developed here has some functionalities that allowed improving the usability of the system as a whole. The experiments were divided into two steps. In the first step, the assistive system was assembled in an actual home where tests were conducted with 29 participants without disabilities. In the second step, the system was tested with online monitoring for seven days by a person with severe disability (end-user), in her own home, not only to increase convenience and comfort, but also so that the system could be tested where it would in fact be used. At the end of both steps, all the participants answered the System Usability Scale (SUS) questionnaire, which showed that both the group of participants without disabilities and the person with severe disabilities evaluated the assistive system with mean scores of 89.9 and 92.5, respectively.
Simultaneously Detecting and Counting Dense Vehicles From Drone Images

Unmanned aerial vehicles are an essential component in the realization of Industry 4.0. With drones helping to improve industrial safety and efficiency in utilities, construction, and communication, there is an urgent need for drone-based intelligent applications. In this paper, we develop a unified framework to simultaneously detect and count vehicles from drone images. We first explore why the state-of-the-art detectors fail in highly dense drone scenes, which provides more appropriate insights. Then, we propose an effective loss to push the anchors toward matching the ground-truth boxes as much as possible, specifically designed for scale-adaptive anchor generation. Inspired by attention mechanisms in the human visual system, we maximize the mutual information between object classes and features by combining bottom-up cues with top-down attention mechanisms specifically designed for feature extraction. Finally, we build a counting layer with a regularized constraint related to the number of vehicles. Extensive experiments demonstrate the effectiveness of our approach. For both tasks, our proposed method achieves state-of-the-art results on all four challenging datasets. In particular, our results reduce error by a larger factor than previous methods.
Local Electricity Storage for Blockchain-Based Energy Trading in Industrial Internet of Things

The peer-to-peer energy trading has been achieved among nodes in industrial Internet of Things. To establish a secure private market, some meaningful works propose the concept of the energy chain, where one block is added in a linear and chronological order once the trading pair of nodes (buyer and seller) has a valid transaction verified by data audit (e.g., a hash value). Since the buyer applies virtual coins from the credit bank to buy others' surplus energy, a considerable credit utility is obtained if all nodes are encouraged to meet local power loads out of self-interest. However, such frequent transactions have huge operational overhead, including a long chain maintaining many blocks and an expensive energy transportation cost between trading pairs. To solve these challenging issues, our method enables nodes to satisfy their power loads through local stored energy (self-sufficiency), before participating as sellers if they still have considerable surplus electricity. Without transactions made by some self-sufficient nodes, the operational overhead can be mitigated in a more secure environment. Taking the classic Internet of energy as a case study, we demonstrate the effectiveness of our solutions, and it can achieve a good tradeoff between credit utility and operational overhead.