Reviewerly/exHarmony huggingface dataset:
Video summarization via spatio-temporal deep architecture

Video summarization has unprecedented importance to help us overview current ever-growing amount of video collections. In this paper, we propose a novel dynamic video summarization model based on deep learning architecture. We are the first to solve the imbalanced class distribution problem in video summarization. The over-sampling algorithm is used to balance the class distribution on training data. The novel two-stream deep architecture with the cost-sensitive learning is proposed to handle the class imbalance problem in feature learning. In the spatial stream, RGB images are used to represent the appearance of video frames, and in the temporal stream, multi-frame motion vectors with deep learning framework is firstly introduced to represent and extract temporal information of the input video. The proposed method is evaluated on two standard video summarization datasets and a standard emotional dataset. Empirical validations for video summarization demonstrate that our model achieves performance improvement over the existing and state-of-the-art methods. Moreover, the proposed method is able to highlight the video content with the active level of arousal in affective computing task. In addition, the proposed frame-based model has another advantage. It can automatically preserve the connection between consecutive frames. Although the summary is constructed based on the frame level, the final summary is comprised of informative and continuous segments instead of individual separate frames.
A Hybrid convolutional neural network for sketch recognition

With the popularity of touch-screen devices, it is becoming increasingly important to understand users’ free-hand sketches in computer vision and human-computer interaction. Most of existing sketch recognition methods employ the similar strategies used in image recognition, relying on appearance information represented by hand-crafted features or deep features from convolutional neural networks. We believe that sketch recognition can benefit from learning both appearance and shape representation. In this paper, we propose a novel architecture, named Hybrid CNN, which is composed of A-Net and S-Net. They describe appearance information and shape information, respectively. Hybrid CNN is then comprehensively evaluated in the sketch classification and retrieval tasks on different datasets, including TU-Berlin, Sketchy and Flickr15k. Experimental results demonstrate that the Hybrid CNN achieves competitive accuracy compared with the state-of-the-art methods.
The digital culture of students of pedagogy specialising in the humanities in Santiago de Chile

Students on pedagogical degree courses have grown up with technology and are considered millennials, meaning it is pertinent to describe some aspects of their digital culture in order to better orient their initial teacher training and future professional performance, when all teachers will be digital natives. This paper describes some aspects of the current cultural paradigm and reports the results of an exploratory work conducted with students on pedagogical humanities degree. Using a survey, we inquired about the students’ general characteristics, cyberculture, their level of use of software, their use of technological devices, and their digital skills. The results show that there are no significant differences between the groups compared, and that students seem to have homogeneous characteristics when it comes to their approach to technology.
Complex Attack Linkage Decision-Making in Edge Computing Networks

The edge computing network refers to a new paradigm of edge-side big data computing networks, which integrates networks, computing, storage, and business core capabilities. It is close to users, the Internet of Things (IoT), or data source side. The edge computing network is generated by the common development of cloud computing and the IoT. The core is the massive uplink monitoring collection and downlink decision-making control big data generated by intelligent sensing devices, solving the problem of low data computing efficiency and performance under the centralized cloud computing model. Compared with traditional cloud computing networks, the edge computing network has more abundant terminal types, more frequent data real-time interaction, more complex transmission network technology systems, and more intelligent and interconnected business systems. Moreover, this situation is aggravated with the mobile edge computing, e.g., model proximity service increasingly prevalent in daily life. However, the ubiquitous and open features of edge computing networks expose network security risks to all parts of the system, facing severe security protection challenges. To solve the linkage disposal and minimum cost response of complex attacks, we propose an attack linkage disposal decision-making method for edge computing network systems based on attribute attack graphs. A simplified attribute attack graph is constructed through the network security alarm association and false-alarm determination, and formal correlation analysis is performed on the causal relationship of the alarm information. On this basis, the linkage defense strategy decision computing is transformed into the minimum dominance set solution of the attribute attack graph. Finally, a linkage disposal strategy execution point decision algorithm based on the greedy algorithm is designed, which constructs a set of attack linkage disposal decision-making technologies with optimal defense cost. It provides a powerful guarantee for timely and effectively active defense.
A recurrent emotional CMAC neural network controller for vision-based mobile robots

Vision-based mobile robots often suffer from the difficulties of high nonlinear dynamics and precise positioning requirements, which leads to the development demand of more powerful nonlinear approximation in controlling and monitoring of mobile robots. This paper proposes a recurrent emotional cerebellar model articulation controller (RECMAC) neural network in meeting such demand. In particular, the proposed network integrates a recurrent loop and an emotional learning mechanism into a cerebellar model articulation controller (CMAC), which is implemented as the main component of the controller module of a vision-based mobile robot. Briefly, the controller module consists of a sliding surface, the RECMAC, and a compensator controller. The incorporation of the recurrent structure in a slide model neural network controller ensures the retaining of the previous states of the robot to improve its dynamic mapping ability. The convergence of the proposed system is guaranteed by applying the Lyapunov stability analysis theory. The proposed system was validated and evaluated by both simulation and a practical moving-target tracking task. The experimentation demonstrated that the proposed system outperforms other popular neural network-based control systems, and thus it is superior in approximating highly nonlinear dynamics in controlling vision-based mobile robots.
Generalization of feature embeddings transferred from different video anomaly detection domains

Detecting anomalous activity in video surveillance often suffers from limited availability of training data. Transfer learning may close this gap, allowing to use existing annotated data from some source domain. However, analyzing the source feature space in terms of its potential for transfer of learning to another context is still to be investigated. This paper reports a study on video anomaly detection, focusing on the analysis of feature embeddings of pre-trained CNNs with the use of novel cross-domain generalization measures that allow to study how source features generalize for different target video domains. This generalization analysis represents not only a theoretical approach, can be useful in practice as a path to understand which datasets allow better transfer of knowledge. Our results confirm this, achieving better anomaly detectors for video frames and allowing analysis of transfer learning’s positive and negative aspects.
A Lightweight Three-Factor User Authentication Protocol for the Information Perception of IoT

With the development of computer hardware technology and network technology, the Internet of Things as the extension and expansion of traditional computing network has played an increasingly important role in all professions and trades and has had a tremendous impact on people lifestyle. The information perception of the Internet of Things plays a key role as a link between the computer world and the real world. However, there are potential security threats in the Perceptual Layer Network applied for information perception because Perceptual Layer Network consists of a large number of sensor nodes with weak computing power, limited power supply, and open communication links. We proposed a novel lightweight authentication protocol based on password, smart card and biometric identification that achieves mutual authentication among User, GWN and sensor node. Biometric identification can increase the non-repudiation feature that increases security. After security analysis and logical proof, the proposed protocol is proven to have a higher reliability and practicality.
Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components

Different types of outliers have existed in the monitoring data of wind turbines, which are not conducive to the follow-up data mining.However, the complex inner characteristics of the monitoring data pose major challenges to detect the outliers.To address this problem, an unsupervised outlier detection approach combining stacked denoising autoencoder (SDAE) and density-grid-based clustering method is proposed.First, the characteristics of the outliers in supervisory control and data acquisition data caused by different reasons are analyzed.Then, the SDAE is utilized to extract features by training the original data.Furthermore, the density-grid-based clustering method is applied to achieve the clustering results.Window width is added to classify the outliers as isolated outliers, missing data, and fault data according to the duration of abnormal data.The monitoring data of four wind turbines are sampled as the training data to demonstrate the effectiveness of the proposed method.The results show that the proposed model can effectively identify the isolated outliers, missing data, and fault information in the high dimensional data set by unsupervised learning.
GAMA: Genetic Automated Machine learning Assistant

Successful machine learning applications hinge on a plethora of design decisions, which require extensive experience and relentless empirical evaluation.To train a successful model, one has to decide which algorithms to use, how to preprocess the data, and how to tune any hyperparameters that influence the final model.Automating this process of algorithm selection and hyperparameter optimization in the context of machine learning is often called AutoML (Automated Machine Learning).
Range Scaling Global U-Net for Perceptual Image Enhancement on Mobile Devices

Perceptual image enhancement on mobile devices—smart phones in particular—has drawn increasing industrial efforts and academic interests recently. Compared to digital single-lens reflex (DSLR) cameras, cameras on smart phones typically capture lower-quality images due to various hardware constraints. Without additional information, it is a challenging task to enhance the perceptual quality of a single image especially when the computation has to be done on mobile devices. In this paper we present a novel deep learning based approach—the Range Scaling Global U-Net (RSGUNet)—for perceptual image enhancement on mobile devices. Besides the U-Net structure that exploits image features at different resolutions, proposed RSGUNet learns a global feature vector as well as a novel range scaling layer that alleviate artifacts in the enhanced images. Extensive experiments show that the RSGUNet not only outputs enhanced images with higher subjective and objective quality, but also takes less inference time. Our proposal wins the 1st place by a great margin in track B of the Perceptual Image Enhancement on Smartphones Challenge (PRIM2018). Code is available at https://github.com/MTlab/ECCV-PIRM2018 .
Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization

Most real-world optimization problems tackle a large number of decision variables, known as Large-Scale Global Optimization (LSGO) problems. In general, the metaheuristic algorithms for solving such problems often suffer from the “curse of dimensionality.” In order to improve the disadvantage of Grey Wolf Optimizer when solving the LSGO problems, three genetic operators are embedded into the standard GWO and a Hybrid Genetic Grey Wolf Algorithm (HGGWA) is proposed. Firstly, the whole population using Opposition-Based Learning strategy is initialized. Secondly, the selection operation is performed by combining elite reservation strategy. Then, the whole population is divided into several subpopulations for cross-operation based on dimensionality reduction and population partition in order to increase the diversity of the population. Finally, the elite individuals in the population are mutated to prevent the algorithm from falling into local optimum. The performance of HGGWA is verified by ten benchmark functions, and the optimization results are compared with WOA, SSA, and ALO. On CEC’2008 LSGO problems, the performance of HGGWA is compared against several state-of-the-art algorithms, CCPSO2, DEwSAcc, MLCC, and EPUS-PSO. Simulation results show that the HGGWA has been greatly improved in convergence accuracy, which proves the effectiveness of HGGWA in solving LSGO problems.
Inductive conformal predictor for convolutional neural networks: Applications to active learning for image classification

Conformal prediction uses the degree of strangeness (nonconformity) of data instances to determine the confidence values of new predictions. We propose an inductive conformal predictor for convolutional neural networks (CNNs), referring to it as ICP-CNN, which uses a novel nonconformity measure that produces reliable confidence values. Furthermore, ICP-CNN is used to improve classification performance through active learning, selecting instances from an unlabeled pool based on the evaluation of three criteria: informativeness, diversity, and information density. Distance metric learning is employed to measure diversity, using a similarity measure that adapts to the database being used. Moreover, information density is considered to filter outliers. Experiments conducted on face and object recognition databases demonstrate that ICP-CNN improves the classification performance of CNNs, outperforming previously proposed active learning techniques, while producing reliable confidence values.
Camera Identification Based on Domain Knowledge-Driven Deep Multi-Task Learning

Camera identification has recently attracted considerable attention in the image forensic field of research. Several algorithms have been established based on the hand-crafted features and deep learning, through analysis of the traces achieved by the digital image acquisition process. Although these approaches have led to a breakthrough in the image forensics, some important problems still remain unsolved. For instance, extracting the hand-crafted features with human efforts is a difficult and time-consuming process, while data-driven deep learning methods tend to learn features that represent image contents rather than cameras' characteristics. To fully take advantages of both hand-crafted and data-driven technologies, we propose a domain knowledge-driven method, which consists of one pre-processing module, one feature extractor, and one hierarchical multi-task learning procedure. The pre-processing module can introduce the domain knowledge to the subsequent deep learning network. Moreover, for device-level identification, hierarchical multi-task learning can provide more supervise information from the brand and model. The proposed framework is evaluated on three different tasks, i.e., the brand, model, and device-level identification using original and manipulated images. Our classification results demonstrate that the proposed method is effective and robust. To evaluate the robustness of the proposed method, we create a new database for the cell-phone identification and evaluate the proposed method. It is found that the accuracy of the cell-phone device identification can reach 84.3%, which is much higher than that of the camera identification. Moreover, the t-distributed stochastic neighbor embedding visualization results confirm that the features of different cell-phone devices are visually more separable than cameras.
Fast 3D-HEVC Depth Map Encoding Using Machine Learning

This paper presents a fast depth map encoding for 3D-High Efficiency Video Coding (3D-HEVC) based on static decision trees.We used data mining and machine learning to correlate the encoder context attributes, building the static decision trees.Each decision tree defines that a depth map Coding Unit (CU) must be or not be split into smaller blocks, considering the encoding context through the evaluation of the encoder attributes.Specialized decision trees for I-frames, P-frames and B-frames define the partitioning of 64 × 64, 32 × 32, and 16 × 16 CUs.We trained the decision trees using data extracted from the 3D-HEVC Test Model considering all-intra and random-access configurations, and we evaluated the proposed approach considering the common test conditions.The experimental results demonstrated that this approach can halve the 3D-HEVC encoder computational effort with less than 0.24% of BD-rate increase on the average for all-intra configuration.When running on random-access configuration, our solution is able to reduce up to 58% the complete 3D-HEVC encoder computational effort with a BD-rate drop of only 0.13%.These results surpass all related works regarding computational effort reduction and BD-rate.
A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and featu res. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model.
Semi-Supervised Deep Fuzzy C-Mean Clustering for Imbalanced Multi-Class Classification

Semi-supervised learning has been successfully connected in the research fields of machine learning such as data mining and dynamic data analysis.Imbalance class learning is one of the most challenging issues for classification.In recent years, the core focal point of numerous researchers has been on data classification of multi-class imbalanced datasets.In this paper, we proposed semi-supervised deep Fuzzy C-mean clustering for imbalanced multi-class classification (DFCM-MC).In our paper, the word ''Deep'' is used to show how decomposition strategy is applied deeply, first, decomposes the original semi-supervised data into supervised (labeled) and unsupervised (unlabeled) data.For training the model, we used unlabeled data along with labeled data to extract discriminative information, which is useful for classification.Second, it further decomposes the supervised and unsupervised data into multi intra-cluster that to address the problem of multi-class imbalance data, which tends to maximize intra-cluster classes and intra-cluster features.We propose a novel approach DFCM-MC by utilizing multi-intra clusters to extract new features to control redundancy for multi-class imbalance classification, which associates the maximum similarity of features between multi-intra clusters.Furthermore, we improve the classification performance of the DFCM-MC, apply the re-sampling technique to handle the imbalance data for classification.We conduct our experiments on 18 benchmark multi-class imbalanced datasets to demonstrate the performance of our proposed approach with the four state-of-the-art learning algorithms for multi-class imbalance data with three performance measures (mean of accuracy, mean of f-measure, and mean of area under the curve).The experiment results demonstrate that our proposed approach performs better due to their capacity to recognize and consolidate fundamental information from unsupervised data.
deepBioWSD: effective deep neural word sense disambiguation of biomedical text data

Abstract Objective In biomedicine, there is a wealth of information hidden in unstructured narratives such as research articles and clinical reports. To exploit these data properly, a word sense disambiguation (WSD) algorithm prevents downstream difficulties in the natural language processing applications pipeline. Supervised WSD algorithms largely outperform un- or semisupervised and knowledge-based methods; however, they train 1 separate classifier for each ambiguous term, necessitating a large number of expert-labeled training data, an unattainable goal in medical informatics. To alleviate this need, a single model that shares statistical strength across all instances and scales well with the vocabulary size is desirable. Materials and Methods Built on recent advances in deep learning, our deepBioWSD model leverages 1 single bidirectional long short-term memory network that makes sense prediction for any ambiguous term. In the model, first, the Unified Medical Language System sense embeddings will be computed using their text definitions; and then, after initializing the network with these embeddings, it will be trained on all (available) training data collectively. This method also considers a novel technique for automatic collection of training data from PubMed to (pre)train the network in an unsupervised manner. Results We use the MSH WSD dataset to compare WSD algorithms, with macro and micro accuracies employed as evaluation metrics. deepBioWSD outperforms existing models in biomedical text WSD by achieving the state-of-the-art performance of 96.82% for macro accuracy. Conclusions Apart from the disambiguation improvement and unsupervised training, deepBioWSD depends on considerably less number of expert-labeled data as it learns the target and the context terms jointly. These merit deepBioWSD to be conveniently deployable in real-time biomedical applications.