Reviewerly/exHarmony huggingface dataset:
Multimodal verge for scale and pose variant real time face tracking and recognition

In recent times face tracking and face recognition have turned out to be increasingly dynamic research field in image processing. This work proposed the framework DEtecting Contiguous Outliers in the LOw-rank Representation for face tracking, in this algorithm the background is assessed by a low-rank network and foreground articles can be distinguished as anomalies. This is suitable for non-rigid foreground motion and moving camera. The face of a foreground person is caught from the frame and then it is contrasted and the speculated pictures stored in the dataset. Here we used Viola-Jones algorithm for face recognition. This approach outperforms the traditional algorithms on multimodal video methodologies and it works adequately on extensive variety of security and surveillance purposes. Results on the continuous demonstrate that the proposed calculation can correctly obtain facial features points. The algorithm is relegate on the continuous camera input and under ongoing ecological conditions.
Factors Affecting the Intention to Adopt M-Learning

Over the recent years, emerging technological applications have been used for making student learning more effective and interactive. M-Learning has been one such technological initiative which has shown promising benefits in the higher education context. Even though the importance of mobile learning has been researched by many, the factors influencing mobile learning adoption intention has not been addressed sufficiently, particularly in the Sri Lankan context. Hence, the purpose of this paper was to present a conceptual model to examine the factors influencing the intention to adopt mobile learning by the students engaged in higher education. Based on a comprehensive literature review, this study extended the Technology Acceptance Model (TAM) (Davis, 1989) with mobile self-efficacy, intrinsic motivation to use mobile devices and the system quality of the m-Learning system. The model describes how the aforementioned factors influence the higher education students&amp;rsquo; intention to adopt m-Learning via survey data collected from 151 postgraduate students. The findings suggest that the model explained the factors influencing the intention to adopt m-Learning among students in higher education. In detail, the mobile self -efficacy, system quality and intrinsic motivation significantly influenced the intention to adopt m-Learning. The results could be utilized for increasing the adoption of m-Learning practices and developing mobile applications useful for teaching and learning purposes. This study has incorporated three independent constructs in extending the TAM model; namely, system quality, mobile self-efficacy and intrinsic motivation. These were derived from the IS Success theory, Self-efficacy theory and Self-determination theory respectively. Accordingly, this study intends to address the theoretical gap in the higher education context pertaining to the adoption of mobile learning. Since Mobile Self-Efficacy and System Quality were the most significant factors that affect the perceived ease of use and perceived usefulness, these factors should be given prominence when developing mobile enabled Learning Management Systems within institutions.
An Empirical Study on Writer Identification and Verification From &lt;italic&gt;Intra-Variable&lt;/italic&gt; Individual Handwriting

The handwriting of a person may vary substantially with factors, such as mood, time, space, writing speed, writing medium/tool, writing a topic, and so on.It becomes challenging to perform automated writer verification/identification on a particular set of handwritten patterns (e.g., speedy handwriting) of an individual, especially when the system is trained using a different set of writing patterns (e.g., normal speed) of that same person.However, it would be interesting to experimentally analyze if there exists any implicit characteristic of individuality which is insensitive to high intra-variable handwriting.In this paper, we study some handcrafted features and auto-derived features extracted from intra-variable writing.Here, we work on writer identification/verification from highly intra-variable offline Bengali writing.To this end, we use various models mainly based on handcrafted features with support vector machine and features auto-derived by the convolutional network.For experimentation, we have generated two handwritten databases from two different sets of 100 writers and enlarged the dataset by a data-augmentation technique.We have obtained some interesting results.
Formal constraint-based compilation for noisy intermediate-scale quantum systems

Noisy, intermediate-scale quantum (NISQ) systems are expected to have a few hundred qubits, minimal or no error correction, limited connectivity and limits on the number of gates that can be performed within the short coherence window of the machine. The past decade's research on quantum programming languages and compilers is directed towards large systems with thousands of qubits. For near term quantum systems, it is crucial to design tool flows which make efficient use of the hardware resources without sacrificing the ease and portability of a high-level programming environment. In this paper, we present a compiler for the Scaffold quantum programming language in which aggressive optimization specifically targets NISQ machines with hundreds of qubits. Our compiler extracts gates from a Scaffold program, and formulates a constrained optimization problem which considers both program characteristics and machine constraints. Using the Z3 SMT solver, the compiler maps program qubits to hardware qubits, schedules gates, and inserts CNOT routing operations while optimizing the overall execution time. The output of the optimization is used to produce target code in the OpenQASM language, which can be executed on existing quantum hardware such as the 16-qubit IBM machine. Using real and synthetic benchmarks, we show that it is feasible to synthesize near-optimal compiled code for current and small NISQ systems. For large programs and machine sizes, the SMT optimization approach can be used to synthesize compiled code that is guaranteed to finish within the coherence window of the machine.
Multi-tenant secret-key assignment over quantum key distribution networks

Quantum key distribution (QKD) networks are promising to progress towards widespread practical deployment over existing fiber infrastructures in the near future. Given the high cost and difficulty of deploying QKD networks, multi-tenancy becomes promising to improve cost efficiency for future QKD networks. In a multi-tenant QKD network, multiple QKD tenants can share the same QKD network infrastructure to obtain secret keys for securing their data transfer. Since the secret-key resources are finite and precious in QKD networks, how to achieve efficient multi-tenant secret-key assignment (MTKA) to satisfy the secret-key demands of multiple QKD tenants over QKD networks becomes a significant problem. In this regard, this study addresses the MTKA problem over QKD networks. A new multi-tenant QKD network architecture is proposed based on software defined networking (SDN) and quantum key pool (QKP) techniques. A secret-key rate sharing scheme is presented and a heuristic algorithm is designed to implement efficient MTKA over QKD networks. A new performance metric, namely matching degree (MD) that reflects the balance between QKD network secret-key resources and QKD tenant requests, is defined and evaluated. Simulation studies indicate that high QKD tenant requests accommodation and efficient secret-key resource usage can be achieved via maximizing the value of MD.
An Online and Scalable Model for Generalized Sparse Nonnegative Matrix Factorization in Industrial Applications on Multi-GPU

Generalized sparse nonnegative matrix factorization (SNMF) has been proven useful in extracting information and representing sparse data with various types of probabilistic distributions from industrial applications, e.g., recommender systems and social networks.However, current solution approaches for generalized SNMF are based on the manipulation of whole sparse matrices and factor matrices, which will result in large-scale intermediate data.Thus, these approaches cannot describe the high-dimensional and sparse matrices in mainstream industrial and big data platforms, e.g., graphics processing unit (GPU) and multi-GPU, in an online and scalable manner. To overcome these issues, an online, scalable, and single-thread-based SNMF for CUDA parallelization on GPU (CUSNMF) and multi-GPU (MCUSNMF) is proposed in this article. First, theoretical derivation is conducted, which demonstrates that the CUSNMF depends only on the products and sums of the involved feature tuples. Next, the compactness, which can follow the sparsity pattern of sparse matrices, endows the CUSNMF with online learning capability and the fine granularity gives it high parallelization potential on GPU and multi-GPU. Finally, the performance results on several real industrial datasets demonstrate the linear scalability of the time overhead and the space requirement and the validity of the extension to online learning. Moreover, CUSNMF obtains speedup of 7X on a P100 GPU compared to that of the state-of-the-art parallel approaches on a shared memory platform.
Robust Median Filtering Forensics Using Image Deblocking and Filtered Residual Fusion

Median filtering (MF) is frequently applied to conceal the traces of forgery and therefore can provide indirect forensic evidence of tampering when investigating composite images.The existing MF forensic methods, however, ignore how JPEG compression affects median filtered images, resulting in heavy performance degradation when detecting filtered images stored in the JPEG format.In this paper, we propose a new robust MF forensic method based on a modified convolutional neural network (CNN).First, relying on the analysis of the influence on median filtered images caused by JPEG compression, we effectively suppress the interference using image deblocking.Second, the fingerprints left by MF are highlighted via filtered residual fusion.These two functions are fulfilled with a deblocking layer and a fused filtered residual (FFR) layer.Finally, the output of the FFR layer becomes input when extracting multiple features for further classification using a tailor-made CNN.The extensive experimental results show that the proposed method outperforms the state-of-the-art methods in both JPEG compressed and small-sized MF image detection.
Fuzzy C-Means Clustering Interval Type-2 Cerebellar Model Articulation Neural Network for Medical Data Classification

This paper presents a fuzzy c-means clustering interval type-2 cerebellar model articulation neural network (FCM-IT2CMANN) method to help physicians improve diagnostic accuracy.The proposed method combines two classifiers, in which the IT2CMANN is the primary classifier and the fuzzy c-means algorithm is the pre-classifier.First, the data are divided into n c groups using the pre-classifier, and then, the main classifier is applied to determine whether the sample is in a healthy or diseased state.Implementing the gradient descent method, the adaptive laws for updating the FCM-IT2CMANN parameters are derived.Furthermore, the system convergence is proven by the Lyapunov stability theory.Finally, the classification of breast cancer and liver disease datasets from the University of California at Irvine is conducted to illustrate the effectiveness of the proposed classifier.
Analytics of Heterogeneous Breast Cancer Data Using Neuroevolution

Breast cancer prognostic modeling is difficult since it is governed by many diverse factors. Given the low median survival and large scale breast cancer data, which comes from high throughput technology, the accurate and reliable prognosis of breast cancer is becoming increasingly difficult. While accurate and timely prognosis may save many patients from going through painful and expensive treatments, it may also help oncologists in managing the disease more efficiently and effectively. Data analytics augmented by machine-learning algorithms have been proposed in past for breast cancer prognosis; and however, most of these could not perform well owing to the heterogeneous nature of available data and model interpretability related issues. A robust prognostic modeling approach is proposed here whereby a Pareto optimal set of deep neural networks (DNNs) exhibiting equally good performance metrics is obtained. The set of DNNs is initialized and their hyperparameters are optimized using the evolutionary algorithm, NSGAIII. The final DNN model is selected from the Pareto optimal set of many DNNs using a fuzzy inferencing approach. Contrary to using DNNs as the black box, the proposed scheme allows understanding how various performance metrics (such as accuracy, sensitivity, F1, and so on) change with changes in hyper-parameters. This enhanced interpretability can be further used to improve or modify the behavior of DNNs. The heterogeneous breast cancer database requires preprocessing for better interpretation of categorical variables in order to improve prognosis from classifiers. Furthermore, we propose to use a neural network-based entity-embedding method for categorical features with high cardinality. This approach can provide a vector representation of categorical features in multidimensional space with enhanced interpretability. It is shown with evidence that DNNs optimized using evolutionary algorithms exhibit improved performance over other classifiers mentioned in this paper.
Robust thermal infrared object tracking with continuous correlation filters and adaptive feature fusion

Thermal infrared (TIR) object tracking is one of the most challenging tasks in computer vision. This paper proposes a robust TIR tracker based on the continuous correlation filters and adaptive feature fusion (RCCF-TIR). Firstly, the Efficient Convolution Operators (ECO) framework is selected to build the new tracker. Secondly, an optimized feature set for TIR tracking is adopted in the framework. Finally, a new strategy of feature fusion based on average peak-to-correlation energy (APCE) is employed. Experiments on the VOT-TIR2016 (Visual Object Tracking-TIR2016) and PTB-TIR (A Thermal Infrared Pedestrian Tracking Benchmark) dataset are carried out and the results indicate that the proposed RCCF-TIR tracker combines good accuracy and robustness, performs better than the state-of-the-art trackers and has the ability to handle various challenges.
Zernike Moment-Based Spatial Image Steganography Resisting Scaling Attack and Statistic Detection

The mainstream adaptive steganography algorithm is difficult to resist scaling attack, and the anti-detection performance of the algorithm based on quantization index modulation is not high enough. To solve the above problems, this paper proposes a spatial image adaptive steganography algorithm based on Zernike moment to resist scaling attack and statistic detection. First, the Zernike moment of the normalized cover image is extracted and the new cover is obtained by the dither modulation algorithm. Second, the minimized distortion embedding is realized by Spatial UNIversal WAvelet Relative Distortion (S-UNIWARD) and syndrome-trellis codes coded. And finally, the stego image is generated by changing the original Zernike moment according to the modified amplitude of the new cover. The experimental results show that the proposed algorithm is robust to the three common scaling attacks, and the message extraction error rate is significantly lower than the S-UNIWARD steganography after the scaling attacks; moreover, compared with the steganography algorithm based on quantization index modulation, the proposed algorithm has higher detection resistance.
THE CONTRIBUTION OF SENTINEL-2 SATELLITE IMAGES FOR GEOLOGICAL MAPPING IN THE SOUTH OF TAFILALET BASIN (EASTERN ANTI-ATLAS, MOROCCO)

Abstract. Geological mapping in desert, mountainous or densely vegetated areas are sometimes faced with many constraints. Recently several remote sensing methods are used on ASTER or LANDSAT imagery for making that task easier. The aim of this paper is to evaluate the applicability of some of these methods on Sentinel-2A images. The study, therefore, focuses on a lithological classification using these multispectral images in the south of the Tafilalet basin. To achieve this goal, two L1C level images were used. Decorelation stretch combined with the optimal index factor (OIF) and Minimum Noise Fraction (MNF) were the main improvements used for RGB combination images. The classifiers Spectral Angle Mapper (SAM) and the Maximum Likelihood classifier (MLC) have been evaluated for a most accurate classification to be used for our lithofacies mapping. The latest drawn geological maps and RGB images of false colour combinations were used to select regions of interest (ROI) as the endmembers to use for these classifiers. Obtained results showed a clear discrimination of the different lithological units of the study area. Classifications evaluation showed that the Maximum likelihood classifier is more accurate with an overall accuracy of 76% and a Kappa coefficient is 0.74. Finally, this study has shown the importance of the use of sentinel-2 multispectral images in geological mapping and has shown that the high spectral resolution of the VNIR and SWIR bands creates a synergy with the high spatial resolution for optimal lithological mapping.
Detecting Fraudulent Financial Statements for the Sustainable Development of the Socio-Economy in China: A Multi-Analytic Approach

Identifying financial statement fraud activities is very important for the sustainable development of a socio-economy, especially in China’s emerging capital market. Although many scholars have paid attention to fraud detection in recent years, they have rarely focused on both financial and non-financial predictors by using a multi-analytic approach. The present study detected financial statement fraud activities based on 17 financial and 7 non-financial variables by using six data mining techniques including support vector machine (SVM), classification and regression tree (CART), back propagation neural network (BP-NN), logistic regression (LR), Bayes classifier (Bayes) and K-nearest neighbor (KNN). Specifically, the research period was from 2008 to 2017 and the sample is companies listed on the Shanghai stock exchange and Shenzhen stock exchange, with a total of 536 companies of which 134 companies were allegedly involved in fraud. The stepwise regression and principal component analysis (PCA) were also adopted for reducing variable dimensionality. The experimental results show that the SVM data mining technique has the highest accuracy across all conditions, and after using stepwise regression, 13 significant variables were screened and the classification accuracy of almost all data mining techniques was improved. However, the first 16 principal components transformed by PCA did not yield better classification results. Therefore, the combination of SVM and the stepwise regression dimensionality reduction method was found to be a good model for detecting fraudulent financial statements.
A Latent State-Based Multimodal Execution Monitor with Anomaly Detection and Classification for Robot Introspection

Robot introspection is expected to greatly aid longer-term autonomy of autonomous manipulation systems. By equipping robots with abilities that allow them to assess the quality of their sensory data, robots can detect and classify anomalies and recover appropriately from common anomalies. This work builds on our previous Sense-Plan-Act-Introspect-Recover (SPAIR) system. We introduce an improved anomaly detector that exploits latent states to monitor anomaly occurrence when robots collaborate with humans in shared workspaces, but also present a multiclass classifier that is activated with anomaly detection. Both implementations are derived from Bayesian non-parametric methods with strong modeling capabilities for learning and inference of multivariate time series with complex and uncertain behavior patterns. In particular, we explore the use of a hierarchical Dirichlet stochastic process prior to learning a Hidden Markov Model (HMM) with a switching vector auto-regressive observation model (sHDP-VAR-HMM). The detector uses a dynamic log-likelihood threshold that varies by latent state for anomaly detection and the anomaly classifier is implemented by calculating the cumulative log-likelihood of testing observation based on trained models. The purpose of our work is to equip the robot with anomaly detection and anomaly classification for the full set of skills associated with a given manipulation task. We consider a human–robot cooperation task to verify our work and measure the robustness and accuracy of each skill. Our improved detector succeeded in detecting 136 common anomalies and 368 nominal executions with a total accuracy of 91.0%. An overall anomaly classification accuracy of 97.1% is derived by performing the anomaly classification on an anomaly dataset that consists of 7 kinds of detected anomalies from a total of 136 anomalies samples.
Infrared LEDs-Based Pose Estimation With Underground Camera Model for Boom-Type Roadheader in Coal Mining

Accurate and reliable pose estimation of a boom-type roadheader is of great importance in order to maintain the efficiency of automatic coal mining. The stability and accuracy of conventional measurement methods are difficult to be guaranteed on account of vibration noise, magnetic disturbance, electrostatic interference, and other factors in an underground environment. In this paper, a vision-based non-contact measurement method for cutting-head pose estimation is presented, which deploy a 16-point infrared LED target on the boom-type roadheader to tackle the low-illumination, high-dust, and complicated background. By establishing a monocular vision measurement system, the cutting-head pose is estimated through processing the LED target images obtained from an explosion-proof industrial camera mounted on the roadheader. After analyzing the measurement mechanism, an underground camera model based on the equivalent focal length is built to eliminate refraction errors caused by the two-layer glasses for explosion-proof and dust removal. Then, the pose estimation processes, including infrared LEDs feature point extraction, spot center location, and an improved P4P method based on dual quaternions, are carried out. The influence factors of cutting-head pose estimation accuracy are further studied by modeling, and the error distribution of the main parameters is investigated and evaluated. The numerical simulation and experimental evaluation are designed to verify the performance of the proposed method. The results show that the pose estimation error is in line with the numerical prediction, achieving the requirements of cutting-head pose estimation in underground roadway construction in the coal mine.