Reviewerly/exHarmony huggingface dataset:
Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences

Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and combining them using fusion technique. The major focus of the feature descriptor is to exploits the action dissimilarities. The key contribution of the proposed approach is to built robust features descriptor that can work for underlying video sequences and various classification models. To achieve the objective of the proposed work, HAR has been performed in the following manner. First, moving object detection and segmentation are performed from the background. The features are calculated using the histogram of oriented gradient (HOG) from a segmented moving object. To reduce the feature descriptor size, we take an averaging of the HOG features across non-overlapping video frames. For the frequency domain information we have calculated regional features from the Fourier hog. Moreover, we have also included the velocity and displacement of moving object. Finally, we use fusion technique to combine these features in the proposed work. After a feature descriptor is prepared, it is provided to the classifier. Here, we have used well-known classifiers such as artificial neural networks (ANNs), support vector machine (SVM), multiple kernel learning (MKL), Meta-cognitive Neural Network (McNN), and the late fusion methods. The main objective of the proposed approach is to prepare a robust feature descriptor and to show the diversity of our feature descriptor. Though we are using five different classifiers, our feature descriptor performs relatively well across the various classifiers. The proposed approach is performed and compared with the state-of-the-art methods for action recognition on two publicly available benchmark datasets (KTH and Weizmann) and for cross-validation on the UCF11 dataset, HMDB51 dataset, and UCF101 dataset. Results of the control experiments, such as a change in the SVM classifier and the effects of the second hidden layer in ANN, are also reported. The results demonstrate that the proposed method performs reasonably compared with the majority of existing state-of-the-art methods, including the convolutional neural network-based feature extractors.
Towards Topic-Guided Conversational Recommender System

Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations.To develop an effective CRS, the support of high-quality datasets is essential.Existing CRS datasets mainly focus on immediate requests from users, while lack proactive guidance to the recommendation scenario.In this paper, we contribute a new CRS dataset named TG-ReDial (Recommendation through Topic-Guided Dialog).Our dataset has two major features.First, it incorporates topic threads to enforce natural semantic transitions towards the recommendation scenario.Second, it is created in a semi-automatic way, hence human annotation is more reasonable and controllable.Based on TG-ReDial, we present the task of topic-guided conversational recommendation, and propose an effective approach to this task.Extensive experiments have demonstrated the effectiveness of our approach on three sub-tasks, namely topic prediction, item recommendation and response generation.
Corner Proposal Network for Anchor-Free, Two-Stage Object Detection

The goal of object detection is to determine the class and location of objects in an image. This paper proposes a novel anchor-free, two-stage framework which first extracts a number of object proposals by finding potential corner keypoint combinations and then assigns a class label to each proposal by a standalone classification stage. We demonstrate that these two stages are effective solutions for improving recall and precision, respectively, and they can be integrated into an end-to-end network. Our approach, dubbed Corner Proposal Network (CPN), enjoys the ability to detect objects of various scales and also avoids being confused by a large number of false-positive proposals. On the MS-COCO dataset, CPN achieves an AP of $$49.2\%$$ which is competitive among state-of-the-art object detection methods. CPN also fits the scenario of computational efficiency, which achieves an AP of $$41.6\%$$ / $$39.7\%$$ at 26.2/43.3 FPS, surpassing most competitors with the same inference speed. Code is available at https://github.com/Duankaiwen/CPNDet .
Class Anchor Clustering: A Loss for Distance-based Open Set Recognition

In open set recognition, deep neural networks encounter object classes that were unknown during training. Existing open set classifiers distinguish between known and unknown classes by measuring distance in a network's logit space, assuming that known classes cluster closer to the training data than unknown classes. However, this approach is applied post-hoc to networks trained with cross-entropy loss, which does not guarantee this clustering behaviour. To overcome this limitation, we introduce the Class Anchor Clustering (CAC) loss. CAC is a distance-based loss that explicitly trains known classes to form tight clusters around anchored class-dependent centres in the logit space. We show that training with CAC achieves state-of-the-art performance for distance-based open set classifiers on all six standard benchmark datasets, with a 15.2% AUROC increase on the challenging TinyImageNet, without sacrificing classification accuracy. We also show that our anchored class centres achieve higher open set performance than learnt class centres, particularly on object-based datasets and large numbers of training classes.
A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach

Machine learning algorithms (e.g., random forest (RF)) have recently been performed in data-driven mineral prospectivity mapping. These methods are highly sensitive to hyperparameter values, since the predictive accuracy of them can significantly increase when the optimized hyperparameters are predefined and then adjusted to training procedure. The main goal of this contribution is to propose a hybrid genetic-based RF model, namely GRF, which is able to automatically adjust the optimized hyperparameters of RF with the excellent predictive accuracy. Therefore, three primary parameters of RF comprising NT, NS and d, were well-tuned employing genetic algorithm (GA) in establishing an efficient RF model. The proposed GRF model and also conventional RF were tested on mineralization-related geo-spatial dataset and the predictive models were generated for comparing the accuracy of the proposed GRF model with that of RF. The input dataset (e.g., multi-element geochemical signature, geological-structural layer and hydrothermal alteration evidences) which acquired from Feizabad district, NE Iran, were translated into mappable targeting criteria in the form of four predictor maps. In addition, the locations of 13 known Cu–Au deposits as prospect data and the locations of 13 randomly selected non-prospect data were used as target variables to train the models. Three authentic validation measures, K-fold cross-validation, confusion matrix and success-rate curves, were employed to evaluate the overall performance of two predictive models. Experimental results suggested the superiority of GRF model over the RF, as the favorable areas derived by GRF model occupy only 9% of the study area while predicting 100% of the known deposits.
Fusion-based quantum computation

The standard primitives of quantum computing include deterministic unitary entangling gates, which are not natural operations in many systems including photonics. Here, we present fusion-based quantum computation, a model for fault tolerant quantum computing constructed from physical primitives readily accessible in photonic systems. These are entangling measurements, called fusions, which are performed on the qubits of small constant sized entangled resource states. Probabilistic photonic gates as well as errors are directly dealt with by the quantum error correction protocol. We show that this computational model can achieve a higher threshold than schemes reported in literature. We present a ballistic scheme which can tolerate a 10.4% probability of suffering photon loss in each fusion, which corresponds to a 2.7% probability of loss of each individual photon. The architecture is also highly modular and has reduced classical processing requirements compared to previous photonic quantum computing architectures.
A Tour of Unsupervised Deep Learning for Medical Image Analysis

Interpretation of medical images for the diagnosis and treatment of complex diseases from high-dimensional and heterogeneous data remains a key challenge in transforming healthcare. In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical image analysis. Several reviews on supervised deep learning are published, but hardly any rigorous review on unsupervised deep learning for medical image analysis is available.The objective of this review is to systematically present various unsupervised deep learning models, tools, and benchmark datasets applied to medical image analysis. Some of the discussed models are autoencoders and their variants, Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBN), Deep Boltzmann Machine (DBM), and Generative Adversarial Network (GAN). Future research opportunities and challenges of unsupervised deep learning techniques for medical image analysis are also discussed.Currently, interpretation of medical images for diagnostic purposes is usually performed by human experts that may be replaced by computer-aided diagnosis due to advancement in machine learning techniques, including deep learning, and the availability of cheap computing infrastructure through cloud computing. Both supervised and unsupervised machine learning approaches are widely applied in medical image analysis, each of them having certain pros and cons. Since human supervisions are not always available or are inadequate or biased, therefore, unsupervised learning algorithms give a big hope with lots of advantages for biomedical image analysis.
A Lightweight Blockchain-Based IoT Identity Management Approach

Identity management is a fundamental feature of Internet of Things (IoT) ecosystem, particularly for IoT data access control. However, most of the actual works adopt centralized approaches, which could lead to a single point of failure and privacy issues that are tied to the use of a trusted third parties. A consortium blockchain is an emerging technology that provides a neutral and trustable computation and storage platform that is suitable for building identity management solutions for IoT. This paper proposes a lightweight architecture and the associated protocols for consortium blockchain-based identity management to address privacy, security, and scalability issues in a centralized system for IoT. Besides, we implement a proof-of-concept prototype and evaluate our approach. We evaluate our work by measuring the latency and throughput of the transactions while using different query actions and payload sizes, and we compared it to other similar works. The results show that the approach is suitable for business adoption.
Post-processing in solar forecasting: Ten overarching thinking tools

Forecasts are always wrong, otherwise, they are merely deterministic calculations. Besides leveraging advanced forecasting methods, post-processing has become a standard practice for solar forecasters to improve the initial forecasts. In this review, the post-processing task is divided into four categories: (1) deterministic-to-deterministic (D2D) post-processing, (2) probabilistic-to-deterministic (P2D) post-processing, (3) deterministic-to-probabilistic (D2P) post-processing, and (4) probabilistic-to-probabilistic (P2P) post-processing. Additionally, a total of ten overarching thinking tools, namely, (1) regression (D2D), (2) filtering (D2D), (3) resolution change (D2D), (4) summarizing predictive distribution (P2D), (5) combining deterministic forecasts (P2D), (6) analog ensemble (D2P), (7) method of dressing (D2P), (8) probabilistic regression (D2P), (9) calibrating ensemble forecasts (P2P), and (10) combining probabilistic forecasts (P2P), are proposed. These thinking tools can be thought of as the “style” or “mechanism” of post-processing. In that, the utilization of thinking tools circumvents the common pitfalls of classifying the literature by methods (e.g., statistics, machine-learning, or numerical weather prediction), which often leads to a “who used what method” type of roster review that is clearly ineffective, non-exhaustive, and dull. When myriads of post-processing methods are mapped to countable few thinking tools, it allows solar forecasters to enumerate the styles of adjustment that could be performed on a set of initial forecasts, which makes a post-processing task clearly goal-driven. Besides the thinking tools, this paper also emphasizes on the value of post-processing, and provides an outlook for future research. Although this paper is revolved around solar, the materials herein discussed can also be applied to wind and other forecasting areas.
Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.
Multi-view subspace clustering via partition fusion

Multi-view clustering is an important approach for analyzing multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained increasing attention due to its encouraging performance. Essentially, it integrates multi-view information into graphs, which are then fed into spectral clustering algorithm for final results. However, its performance may degrade due to noises existing in each individual view or inconsistencies between heterogeneous features. Orthogonal to current work, we propose to fuse multi-view information in a partition space, which enhances the robustness of Multi-view clustering. Specifically, we generate multiple partitions and integrate them to find a shared partition. The proposed model unifies graph learning, generation of basic partitions, and view weight learning. These three components co-evolve towards better quality outputs. We have conducted comprehensive experiments on benchmark datasets and our empirical results verify the effectiveness and robustness of our approach.
Breast cancer: One-stage automated detection, segmentation, and classification of digital mammograms using UNet model based-semantic segmentation

Breast cancer is one of the most common cancers in women. It is known as asymptomatic cancer that presents no noticeable symptoms in its early stage. Thus, regular mammography screening helps detect breast cancer early before it spreads to nearby normal tissues or to other organs. Hence, an automated system for early detection of breast cancer in mammograms will assist radiologists in their diagnosis. The rapid advance of deep learning models has considerably arisen much interest in their application to medical imaging problems, particularly for breast cancer diagnosis. In this paper, we propose an end-to-end UNet model for the detection, segmentation, and classification of breast masses in one-stage. The proposed model is evaluated in terms of its performance in segmenting and classifying breast masses using the publicly available datasets, DDSM and INbreast. The mass segmentation and classification evaluation results give an intersection over union (IOU) score of 90.50% and an area under the curve of 99.88% for both DDSM and INbreast datasets. The proposed model scores a dice coefficient of 99.20% and 99.56% and a weighted F1-score of 99.19% and 99.65% for the DDSM and INbreast datasets, respectively. The results show that the proposed model outperforms the existing methodologies.
Colorectal Histology Tumor Detection Using Ensemble Deep Neural Network

With a mortality rate of approximately 33.33%, Colorectal cancer serves as the second most prevalent malignant tumor type in the world. AI-guided clinical care/tool can help in reducing health disparities, specifically in resource-constrained regions. In this paper, using multi-class tissue features, we proposed an Ensemble Deep Neural Network to Tumor in Colorectal Histology images. On two different publicly available datasets: NCT-CRC-HE-100K (107,180 images) and Colorectal Histology (5000 images), we achieved accuracies of 96.16% and 92.83%, respectively. When datasets are combined, it provided a benchmark accuracy of 99.13%. We efficiently used resourced data, thereby achieving results that outperformed the state-of-the-art works.
A novel one-dimensional chaotic map generator and its application in a new index representation-based image encryption scheme

The fast growth in digital image transmission technologies requires more secure and effective image encryption schemes to provide essential security. In this paper, we present a novel one-dimensional chaotic map amplifier (1-DCMA). The evaluation of the proposed chaotic system shows that the 1-DCMA improve the chaotic behavior, control parameters’ structure, and sensitivity of the 1-D chaotic maps used as input. We further implement a chaotic map generated by the 1-DCMA in a new asymmetric image encryption scheme (Amp-Lg-IE). Using the secret key, the proposed encryption algorithm adds rows and implement a new index representation (IR) concept with shifting sequences to manipulate the pixels’ positions and values synchronously. Finally, we execute bit-level operations to obtain the ciphered image. The simulation and security analysis prove that the Amp-Lg-IE, in a satisfying time, can encrypt a plain image into an unidentified random-like one with high resistance to different types of threats and attacks.
Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

Anomaly detection in energy consumption is a crucial step towards developing efficient energy saving systems, diminishing overall energy expenditure and reducing carbon emissions. Therefore, implementing powerful techniques to identify anomalous consumption in buildings and providing this information to end-users and managers is of significant importance. Accordingly, two novel schemes are proposed in this paper; the first one is an unsupervised abnormality detection based on one-class support vector machine, namely UAD-OCSVM, in which abnormalities are extracted without the need of annotated data; the second is a supervised abnormality detection based on micromoments (SAD-M2), which is implemented in the following steps: (i) normal and abnormal power consumptions are defined and assigned; (ii) a rule-based algorithm is introduced to extract the micromoments representing the intent-rich moments, in which the end-users make decisions to consume energy; and (iii) an improved K-nearest neighbors model is introduced to automatically classify consumption footprints as normal or abnormal. Empirical evaluation conducted in this framework under three different data sets demonstrates that SAD-M2 achieves both a highest abnormality detection performance and real-time processing capability with considerably lower computational cost in comparison with other machine learning methods. For instance, up to 99.71% accuracy and 99.77% F1 score have been achieved using a real-world data set collected at the Qatar University energy lab.
A novel multi population based particle swarm optimization for feature selection

Feature selection is an integral part of any machine learning system and the success of such systems highly depends on the relevance of features with the target domain. Feature selection can be classified as NP-Hard problem since a large number of possible solutions exists especially when the feature space is high dimensional. In addition to standard feature selection algorithms, evolutionary algorithms have also yielded promising results. In this paper, a novel multi population based particle swarm optimization (MPPSO) is proposed for feature selection. In this method, multi population start with initial solutions generated by random and Relieff based initialization and searches solution space simultaneously using both populations. 26 UCI and 3 ASU datasets are used to evaluate the performance of the method. The results show that MPPSO generally achieves better average classification accuracies than the other algorithms. Specifically, for the datasets with a large number of features, MPPSO achieves the smallest number of selected features with highest classification accuracies compared to other algorithms.
PRRNet: Pixel-Region relation network for face forgery detection

As advanced facial manipulation technologies develop rapidly, one can easily modify an image by changing the identity or the facial expression of the target person, which threatens social security. To address this problem, face forgery detection becomes an important and challenging task. In this paper, we propose a novel network, called Pixel-Region Relation Network (PRRNet), to capture pixel-wise and region-wise relations respectively for face forgery detection. The main motivation is that a facial manipulated image is composed of two parts from different sources, and the inconsistencies between the two parts is a significant kind of evidence for manipulation detection. Specifically, PRRNet contains two serial relation modules, i.e. the Pixel-Wise Relation (PR) module and the Region-Wise Relation (RR) module. For each pixel in the feature map, the PR module captures its similarities with other pixels to exploit the local relations information. Then, the PR module employs a spatial attention mechanism to represent the manipulated region and the original region separately. With the representations of the two regions, the RR module compares them with multiple metrics to measure the inconsistency between these two regions. In particular, the final predictions are obtained totally based on whether the inconsistencies exist. PRRNet achieves the state-of-the-art detection performance on three recent proposed face forgery detection datasets. Besides, our PRRNet shows the robustness when trained and tested on different image qualities.