Reviewerly/exHarmony huggingface dataset:
Iterative Dynamic Generic Learning for Face Recognition From a Contaminated Single-Sample Per Person

This article focuses on a new and practical problem in single-sample per person face recognition (SSPP FR), i.e., SSPP FR with a contaminated biometric enrolment database (SSPP-ce FR), where the SSPP-based enrolment database is contaminated by nuisance facial variations in the wild, such as poor lightings, expression change, and disguises (e.g., wearing sunglasses, hat, and scarf). In SSPP-ce FR, the most popular generic learning methods will suffer serious performance degradation because the prototype plus variation (P+V) model used in these methods is no longer suitable in such scenarios. The reasons are twofold. First, the contaminated enrolment samples could yield bad prototypes to represent the persons. Second, the generated variation dictionary is simply based on the subtraction of the average face from generic samples of the same person and cannot well depict the intrapersonal variations. To address the SSPP-ce FR problem, we propose a novel iterative dynamic generic learning (IDGL) method, where the labeled enrolment database and the unlabeled query set are fed into a dynamic label feedback network for learning. Specifically, IDGL first recovers the prototypes for the contaminated enrolment samples via a semisupervised low-rank representation (SSLRR) framework and learns a representative variation dictionary by extracting the "sample-specific" corruptions from an auxiliary generic set. Then, it puts them into the P+V model to estimate labels for query samples. Subsequently, the estimated labels will be used as feedback to modify the SSLRR, thus updating new prototypes for the next round of P+V-based label estimation. With the dynamic learning network, the accuracy of the estimated labels is improved iteratively by virtue of the steadily enhanced prototypes. Experiments on various benchmark face data sets have demonstrated the superiority of IDGL over state-of-the-art counterparts.
Video question answering via grounded cross-attention network learning

Video Question Answering is a burgeoning and challenging task in visual information retrieval (VIR), which automatically generates the answer to a question based on referenced video content. Different from the existing visual question answering methods which mainly focus on static image content, video question answering takes temporal dimension into account because of the essential difference in the structure between image and video. In this paper, we study the problem of video question answering from the viewpoint of grounded cross-attention network learning. Specifically, we propose a novel hierarchical cross-attention mechanism of mutual attention learning for video question answering, named as GCANet. We first obtain the multi-level rough video representation from frame-level video features and clip-level video features. Then, we utilize region proposal network to generate object-level grounded video features as grounded video representations. Next, the grounded question-video representation is learned by the first layer of the GCANet framework, named as Q−O cross-attention layer. The second Q−V−H cross-attention layer of the GCANet framework helps to learn the joint question-video representation based on both rough representation and grounded representation of video for video question answering. We construct two large-scale video question answering datasets. The experimental results on the proposed datasets demonstrate the effectiveness of our model.
Attention, please: A Spatio-temporal Transformer for 3D Human Motion Prediction

In this paper, we propose a novel architecture for the task of 3D humanmotion modelling. We argue that the problem can be interpreted as a generativemodelling task: A network learns the conditional synthesis of human poses wherethe model is conditioned on a seed sequence. Our focus lies on the generationof plausible future developments over longer time horizons, whereas previouswork considered shorter time frames of up to 1 second. To mitigate the issue ofconvergence to a static pose, we propose a novel architecture that leveragesthe recently proposed self-attention concept. The task of 3D motion predictionis inherently spatio-temporal and thus the proposed model learns highdimensional joint embeddings followed by a decoupled temporal and spatialself-attention mechanism. The two attention blocks operate in parallel toaggregate the most informative components of the sequence to update the jointrepresentation. This allows the model to access past information directly andto capture spatio-temporal dependencies explicitly. We show empirically thatthis reduces error accumulation over time and allows for the generation ofperceptually plausible motion sequences over long time horizons as well asaccurate short-term predictions. Accompanying video available athttps://youtu.be/yF0cdt2yCNE .
Detection of Item Preknowledge Using Response Times

Benefiting from item preknowledge is a major type of fraudulent behavior during educational assessments. This article suggests a new statistic that can be used for detecting the examinees who may have benefited from item preknowledge using their response times. The statistic quantifies the difference in speed between the compromised items and the non-compromised items of the examinees. The distribution of the statistic under the null hypothesis of no preknowledge is proved to be the standard normal distribution. A simulation study is used to evaluate the Type I error rate and power of the suggested statistic. A real data example demonstrates the usefulness of the new statistic that is found to provide information that is not provided by statistics based only on item scores.
Autonomic resource provisioning for multilayer cloud applications with K‐nearest neighbor resource scaling and priority‐based resource allocation

Summary Providing a pool of various resources and services to customers on the Internet in exchanging money has made cloud computing as one of the most popular technologies. Management of the provided resources and services at the lowest cost and maximum profit is a crucial issue for cloud providers. Thus, cloud providers proceed to auto‐scale the computing resources according to the users' requests in order to minimize the operational costs. Therefore, the required time and costs to scale‐up and down computing resources are considered as one of the major limits of scaling which has made this issue an important challenge in cloud computing. In this paper, a new approach is proposed based on MAPE‐K loop to auto‐scale the resources for multilayered cloud applications. K‐nearest neighbor (K‐NN) algorithm is used to analyze and label virtual machines and statistical methods are used to make scaling decision. In addition, a resource allocation algorithm is proposed to allocate requests on the resources. Results of the simulation revealed that the proposed approach results in operational costs reduction, as well as improving the resource utilization, response time, and profit.
Shape and Reflectance Reconstruction Using Concentric Multi-Spectral Light Field

Recovering the shape and reflectance of non-Lambertian surfaces remains a challenging problem in computer vision since the view-dependent appearance invalidates traditional photo-consistency constraint. In this paper, we introduce a novel concentric multi-spectral light field (CMSLF) design that is able to recover the shape and reflectance of surfaces of various materials in one shot. Our CMSLF system consists of an array of cameras arranged on concentric circles where each ring captures a specific spectrum. Coupled with a multi-spectral ring light, we are able to sample viewpoint and lighting variations in a single shot via spectral multiplexing. We further show that our concentric camera and light source setting results in a unique single-peak pattern in specularity variations across viewpoints. This property enables robust depth estimation for specular points. To estimate depth and multi-spectral reflectance map, we formulate a physics-based reflectance model for the CMSLF under the surface camera (S-Cam) representation. Extensive synthetic and real experiments show that our method outperforms the state-of-the-art shape reconstruction methods, especially for non-Lambertian surfaces.
A Task Allocation In IoT Using Ant Colony Optimization

Internet of Things (IoT) is a paradigm provides a possibility to connect several heterogeneous Things/Devices into internet. The heterogeneity of those devices is a big challenge in terms of traffic heterogeneity and services requirements. Furthermore, for the network stability, several considerations must be taken into account in order to execute any task by any IoT node. More precisely, the nodes random tasks distribution leads to fail some nodes and also to reduce the network lifetime. A task can be composed at least of one subtask, which holds a collection of capabilities that must be verified to obtain a relevant service. In this contribution, an algorithm based on the Ant Colony Optimization (ACO) to address the IoT task allocation issues. We assume that each subtask has only one capability, and the goal is to distribute the task capabilities to the most competent nodes for reducing resource consumption of the network. The simulation result shows that our proposed algorithm is more efficient also adaptable in terms of the desired capabilities and the shortest path length.
v‐plots: Designing Hybrid Charts for the Comparative Analysis of Data Distributions

Abstract Comparing data distributions is a core focus in descriptive statistics, and part of most data analysis processes across disciplines. In particular, comparing distributions entails numerous tasks, ranging from identifying global distribution properties, comparing aggregated statistics (e.g., mean values), to the local inspection of single cases. While various specialized visualizations have been proposed (e.g., box plots, histograms, or violin plots), they are not usually designed to support more than a few tasks, unless they are combined. In this paper, we present the v‐plot designer; a technique for authoring custom hybrid charts, combining mirrored bar charts, difference encodings, and violin‐style plots. v‐plots are customizable and enable the simultaneous comparison of data distributions on global, local, and aggregation levels. Our system design is grounded in an expert survey that compares and evaluates 20 common visualization techniques to derive guidelines for the task‐driven selection of appropriate visualizations. This knowledge externalization step allowed us to develop a guiding wizard that can tailor v‐plots to individual tasks and particular distribution properties. Finally, we confirm the usefulness of our system design and the user‐guiding process by measuring the fitness for purpose and applicability in a second study with four domain and statistic experts.
A Rectified RRT* with Efficient Obstacles Avoidance Method for UAV in 3D Environment

Our paper presents a static obstacles avoidance and path planning method for Unmanned Aerial Vehicle (UAV) in outdoor three-dimension (3D) environment. We propose a rectified rapidly exploring random tree (RRT*) algorithm where a method is proposed for smoothing and rectifying the final path generated by RRT* in order to reduce the energy consumption during the flight. We also introduce an obstacle avoidance method which allows an efficient free-path collision generation in a complex 3D operating state space. This method ensures that the environment modeling is very close to the reality which allows a high accuracy obstacle avoidance with a secure distance between the UAV and the obstacles edges. Our algorithm was validated through the experiments including the Hardware-in-the-loop simulation (HIL) and real outdoor flights.
Seismic Traffic Noise Attenuation Using $l_{p}$ -Norm Robust PCA

Traffic noise is often coupled with seismic signals when seismic data are acquired close to the road. The traffic noise usually exhibits high amplitudes in the recorded seismic profile, and it is a challenging task to remove it. In this article, we propose a workflow to attenuate seismic traffic noise using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${l}_{p}$ </tex-math></inline-formula> -norm robust principal component analysis (RPCA). This method is implemented in the frequency domain, where the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${l}_{p}$ </tex-math></inline-formula> -norm RPCA is applied to each frequency slice and results in a low-rank approximation of seismic reflections. The alternating direction method of multipliers (ADMM) algorithm is included in the implementation for efficiency. The proposed workflow is demonstrated on a 3-D field shot gather contaminated by complex traffic noise. The performance indicates that our method can remove strong traffic noise effectively and, in turn, accentuate the seismic reflections with balanced amplitudes.
Performance Analysis of Segmentation Algorithms for the Detection of Breast Cancer

One of the main reasons for cancer death globally is breast cancer. The detection of breast cancer from Hematoxylin (H) and & Eosin (E) stained pathology image is significant and digital pathologists are struggling to get accurate final decision. The objective of this study was to carry out the performance analysis of segmentation algorithms for the detection of breast cancer. This study proposed a method for computer assisted diagnosis and classification of breast cancer from microscopic slide images by means of biologically explicable features. The suggested methodology consists of different stages involving image enhancement, nuclei segmentation, extraction of features, and to end with the classification. A comparative analysis was done on various segmentation algorithms and finally, a suitable and effective method was utilized in the proposed approach. The contrast-limited adaptive histogram equalization is performed on the input images for contrast enhancement. Similarly, k-means clustering segmentation algorithm is used for segmenting the nuclei in the proposed work because it outperforms the other commonly used algorithms during comparative analysis. Gray level texture features were extracted in the feature extraction step. Finally, support vector machine classifier was employed for classification of breast cancer histopathology images into benign and malignant classes because it is the most efficient classifier comparing to the other usual classifiers for this purpose. The performance analysis of the suggested design was estimated using familiar parameters such as accuracy, sensitivity, specificity, etc. and achieved an accuracy of 91.1 %.
Confidence guided anomaly detection model for anti-concept drift in dynamic logs

Log data records system state and runtime behaviors, and is usually used to diagnose system failures and detect anomalies. However, the accuracy of log-based anomaly detection algorithms will reduce dramatically in dynamic logs since the system more complex than ever before, a phenomenon known as concept drift. In this paper, we design a confidence-guide anomaly detection model that combines multiple algorithms, called Multi-CAD. We first propose a statistical value p_value to measure the non-conformity between logs and establish a link in the new log and previous logs, and can also choose multiple suitable algorithms as the non-conformity measure to calculate scores for combined detection instead of to make a decision. And then, we design a confidence-guided parameter adjustment method to anti-concept drift in dynamic logs and update the score set with the corresponding label from a trusted result that contains a label, non-conformity score, and confidence by a feedback mechanism as the previous experience for the following-up detection. Finally, we demonstrate that Multi-CAD will make a balance performance in precision rate, recall rate, and F_measure, and detect actual anomalies on multiple datasets. An extensive set of experiment results highlight that Multi-CAD will increase almost 20% on average in recall rate and F_measure compared with four typical algorithms on the HDFS benchmark dataset, where it achieves 98.2% in precision rate, 95.2% in recall rate, and 96.7% in F_measure.
CloudFNF: An ontology structure for functional and non-functional features of cloud services

Recently, cloud computing becomes one of the main orientations of many researchers and companies in the IT area. Therefore, a huge number of cloud services have been developed. Because of the diversity and heterogeneity in providing these services, it is urgently needed to develop a unified cloud ontology. Such ontology can classify these services appropriately and participate as a mapping layer to present such services in a unified description format. Although many studies have been conducted to build cloud ontologies, they have adopted the cloud service layers-based structure. On the other hand, the existing cloud services may involve functionalities from different layers due to the continual increase in the complexity of customer demands. Unfortunately, there are no clear relations to organize the interventions among these layers. Therefore, such services may be difficult to be classified into a specific cloud service layer. Additionally, the layers-based structure of ontologies represents an obstacle to address important issues, such as cloud service recommendation. Despite there are few cloud service functionality-based cloud ontologies, they suffer from many overlaps, lack of semantic relations, or poor granularity of concepts. Also, all the existing cloud ontologies (i.e., layers-based and functionality-based) lack important criteria, such as completeness, consistency, conciseness, clarity, preciseness, and granularity. In this paper, a comprehensive cloud ontology called CloudFNF has been proposed to overcome such drawbacks. According to the structure of the proposed ontology, cloud services are classified as functionality-based instead of layers-based. Also, non-functional features of cloud services (i.e., configuration and QoS features) are considered to enable services of the same functionalities to be ranked efficiently. Based on our previously suggested cloud ontology evaluation framework, our proposed cloud ontology has been evaluated compared to the most related cloud ontologies. The evaluation results show that the proposed CloudFNF ontology outperforms the other ontologies.
Digital Image Forgery Detection based on Expectation Maximization Algorithm

With the development of high-resolution digital cameras and photographs the probabilities of images and pictures being forged is high as the photographers have the ability to manipulate and forge the images as they desire. This can lead to a misuse of technology as forgery can lead to a maj or crime or difference in important and critical situation or places such as court, hospital and also education system. This project planned to analyses a digital image by mistreatment EM rule. The rule was enforced at intervals MATLAB and run on a sample library of cast and unmodified pictures, including a variety of pictures from Associate in Nursing Multi meddling dataset.
Weapon Classification using Deep Convolutional Neural Network

Increasing crimes in public nowadays pose a serious need of active surveillance systems to overcome such happenings. Type of weapon used in the crime determines its seriousness and nature of crime. An active surveillance with weapon classification can help deciding the course of action while identifying the possibilities of any crime happening. This paper presents a novel approach for weapon classification using Deep Convolutional Neural Networks (DCNN). That is based on the VGGNet architecture. VGGNet is the most recognized CNN architecture which got its place in ImageNet competition 2014, organized for image classification problems. Thus, weights of pre-trained VGG 16 model are taken as the initial weights of convolutional layers for the proposed architecture, where three classes: knife, gun and no-weapon are used to train the classifier. To fine tune the weights of the proposed DCNN, it is trained on the images of these classes downloaded from internet and other captured in the lab. Experiments are performed on Nvidia GeForce GTX1050 Ti G PU to achieve faster and exhaustive training on a large image set. A higher accuracy level of 98.41 % is achieved for weapon classification.
Layers Modification of Convolutional Neural Network for Pneumonia Detection

Abstract Pneumonia is a bacterial, virus and fungi infection that attacks respiratory function. The disease causes air sacs in the lungs inflamed and swollen. It conditions produce lungs filled with fluid and mucus. Generally, the detection of pneumonia was done by chest x-ray images. This study discusses the detection of pneumonia through x-ray images using Convolutional Neural Network. The CNN model was Visual Group Geometry VGG16 and VGG19. As a comparison, we used the modified CNN 35 layer. The experiment using public data from Chest X-Ray Images - Kaggle. Data consist of 2 classes: normal and pneumonia with a total of 624 images. The results using VGG16 show a performance measure of sensitivity 92.75%, specificity 96.8%, and accuracy 94.1%. The result of VGG19 has sensitivity 96.6%, specificity 94.3%, and accuracy 95.7%. For CNN 35 layer has sensitivity 95.1%, specificity 98.5%, and accuracy 96.3%.
Detection and visualization of encoded local features as anatomical predictors in cross-sectional images of Lauraceae

Abstract This paper describes computer vision-based quantitative microscopy and its application toward better understanding species specificity. An image dataset of the Lauraceae family that consists of nine species across six genera was investigated, and structural features were quantified using encoded local features implemented in a bag-of-features framework. Of the algorithms used for feature detection, the scale-invariant feature transform (SIFT) achieved the best performance in species discrimination. In the bag-of-features framework with the SIFT features, each image is represented by a histogram of codewords. The codewords were further analyzed by mapping them to each image to visualize the corresponding anatomical elements. From this analysis, we were able to classify and quantify the modes of aggregation of different combinations of cell elements based on clustered codewords. An analysis of the term frequency–inverse document frequency weights revealed that blob-based codewords are generally shared by all species, whereas corner-based codewords are more species specific.
Deep-Learning Ensemble for Offline Arabic Handwritten Words Recognition

In recent years, ensemble learning methods show great effectiveness in improving model performance in several applications. Ensemble techniques rely on the incorporation of multiple different models together to get one optimal model. The primary assumption of ensemble techniques is that the cooperation among various classifiers will probably compensate for the mistakes of a single classifier and consequently, the ensemble's general output prediction would be better than the prediction of a single classifier. A key issue in the combination of classifiers is the diversity among its members. In this paper, we utilized model averaging as an ensemble learning technique for offline Arabic handwritten word recognition to train three residual networks (ResNet18) models. We demonstrate improvements by incorporating diversity in output prediction by using distinct techniques of optimization. To validate the proposed method, experiments have been carried on the IFN/ENIT (v2.0ple) database which contains 32,492 handwritten Arabic words of 937 unique Arabic words.