Reviewerly/exHarmony huggingface dataset:
Dynamic Facial Features for Inherently Safer Face Recognition

Among the many known type of intra-class variations, facial expressions are considered particularly challenging, as witnessed by the large number of methods that have been proposed to cope with them. The idea inspiring this work is that dynamic facial features (DFF) extracted from facial expressions while a sentence is pronounced, could possibly represent a salient and inherently safer biometric identifier, due to the greater difficulty in forging a time variable descriptor instead of a static one. We therefore investigated on how a set of geometrical features, defined as distances between landmarks located in the lower half of face, changes across time while a sentence is uttered to find the most effective yet compact representation. The features vectors built upon these time-series were used to train a deep feed-forward neural network on the OuluVS visual-speech database. Testing in identification modality resulted in 98.2% of average recognition accuracy, 0.64% of equal error rate and a remarkable robustness to how the sentence is pronounced.
Forecasting Soil Moisture Based on Evaluation of Time Series Analysis

Precision agriculture is a technique that is incorporated to produce high crop yield with the best utilization of available resources. Traditional farming is adversely affected due to improper resource management. In order to overcome the efforts of a farmer, a model for the soil moisture forecasting has been proposed in this manuscript to deliver better after-effects of farming. The proposed model uses long short-term memory (LSTM) to predict soil moisture. The model is trained on a dataset acquired from IIT Kanpur agricultural site. For analyzing the performance of the model mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE) has been used as performance metrics. This paper is paving way for the early prediction of the soil moisture that can be used with other advanced innovative irrigation techniques.
A Brief Review of Image Restoration Techniques Based on Generative Adversarial Models

Images are possibly degraded by various reasons, the typical forms of degradation are: blur, noise, low resolution, and etc. Image restoration techniques try to recover the degraded images to the original images with maximum fidelity. Image restoration is a challenging task and also an import research area in image processing. During the decades, researchers have proposed many restoration methods such as inverse filter, Weiner filter, wavelet analysis, support vector machine, and etc. Recently, deep learning has been increasingly popular among researchers and has obtained remarkable results. In this paper, we briefly review the approaches based on generative adversarial networks (GANs) for image restoration. The typical GANs based restoration methods for image super-resolution, image denoising, image inpainting and image deblurring are introduced and discussed.
Why Beta Priors: Invariance-Based Explanation

In the Bayesian approach, to describe a prior distribution on the set [0, 1] of all possible probability values, typically, a Beta distribution is used. The fact that there have been many successful applications of this idea seems to indicate that there must be a fundamental reason for selecting this particular family of distributions. In this paper, we show that the selection of this family can indeed be explained if we make reasonable invariance requirements.
Machine Learning Image Analysis for Asset Inspection

Abstract Total E&amp;P UK Ltd and Merkle have undertaken a proof of concept project to investigate machine learning image analysis applied to image and video data from onshore and offshore sites, in preparation for the deployment of an autonomous asset inspection ground robot in 2019. The aim is to better understand the feasibility of these methods, and to demonstrate the benefits of robotic inspection with regard to improving safety and efficiency, enhancing data capture and reducing operational costs. An object detection model was developed based on high-performance open source algorithms. Transfer learning was applied using a custom-built image library and the result is a model able to detect a range of different types of items in the industrial environment including mobile equipment, process equipment, infrastructure and personnel (with and without PPE). The object detection model is used to feed into object classification anomaly detection models to look at the state of selected pieces of identified equipment, such as whether a valve is open or closed, which can be placed in the context of the expected state of the process equipment by relating it to the digital twin for the asset. An additional object detection model was developed to operate as a gas leak detection system for infrared cameras. The object detection model achieved good results and model performance was driven by the number and quality of images used for the training. An anomaly detection model designed to detect whether ball valves were open or closed delivered good results, with high accuracy and balanced false positive and false negative detection rates. The overall performance of the infrared gas detection model was restricted by the limited volume and variability of the training data, although the false positive detection rate was very low. A significant part of the machine learning was devoted to the development of a consistent labelled image library for oil and gas equipment, infrastructure and gas leaks. Image transformations were tested but boosting the number of images using transforms gave variable results. Additional training and testing data is needed to ensure that the models are as robust as possible, especially for the gas leak detection model. Once the models are productionised and in use, additional data can be used to periodically retrain the models for improved performance. In addition to the machine learning algorithms, a fundamental aspect of the project is the development of the overall technical architecture, supporting the data science. This includes enabling the transfer of data from inspection robots or other connected camera devices into a data store in a cloud computing environment and returning the results to dashboard systems with different levels of detail depending on user requirements.
Joint Object Detection and Depth Estimation in Multiplexed Image

This paper presents an object detection method that can simultaneously estimate the positions and depth of the objects from multiplexed images. Multiplexed image is produced by a new type of imaging device that collects the light from different fields of view using a single image sensor, which is originally designed for stereo, 3D reconstruction and broad view generation using computational imaging. Intuitively, multiplexed image is a blended result of the images of multiple views and both of the appearance and disparities of objects are encoded in a single image implicitly, which provides the possibility for reliable object detection and depth/disparity estimation. Motivated by the recent success of CNN based detector, a multi-anchor detector method is proposed, which detects all the views of the same object as a clique and uses the disparity of different views to estimate the depth of the object. The proposed method is interesting in the following aspects: firstly, both locations and depth of the objects can be simultaneously estimated from a single multiplexed image; secondly, there is almost no computation load increase comparing with the popular object detectors; thirdly, even in the blended multiplexed images, the detection and depth estimation results are very competitive. There is no public multiplexed image dataset yet, therefore the evaluation is based on the simulated multiplexed image using the stereo images from KITTI, and very encouraging results have been obtained.
POLaR: Per-Allocation Object Layout Randomization

Object Layout Randomization (OLR) is a memory randomization approach that makes unpredictable in-object memory layout by shuffling and relocating each member fields of the object. This defense approach has significant security effect for mitigating various types of memory error attacks. However, the current state-of-the-art enforces OLR while compile time. It makes diversified object layout for each binary, but the layout remains equal across the execution. This approach can be effective in case the program binary is hidden from attackers. However, there are several limitations: (i) the security efficacy is built with the premise that the binary is safely undisclosed from adversaries, (ii) the randomized object layout is identical across multiple executions, and (iii) the programmer should manually specify which objects should be affected by OLR. In this paper, we introduce Per-allocation Object Layout Randomization(POLaR): the first dynamic approach of OLR suited for public binaries. The randomization mechanism of POLaR is applied at runtime, and the randomization makes unique object layout even for the same type of instances. As a result, POLaR achieves two previously unmet security primitives. (i) The randomization does not break upon the exposure of the binary. (ii) Repeating the same attack does not result in deterministic behavior. In addition, we also implemented the TaintClass framework based on DFSan project to optimize/automate the target object selection process. To show the efficacy of POLaR, we use several public open-source software and SPEC2006 benchmark suites.
Research on Human-Computer Interaction Mode of Speech Recognition Based on Environment Elements of Command and Control System

Speech interaction belongs to the category of human-computer interaction, which is one of the new interactive methods developed rapidly in recent years. Since the 1990s, speech recognition and interactive systems such as IBM ViaVoice, Apple Siri, Apple Watch and Google Glass have appeared successively. Speech recognition is characterized by flexibility, multi-channel coordination, sensitivity and temporality. There are many factors that affect the accuracy of speech interaction recognition. In the command and control system, environmental noise (dB), human noise (dB), speech speed (word/minute) and frequency (word/minute) are the four key factors that affect the accuracy of speech recognition, but few people carry out relevant researches. This experiment mainly studies the influence of the above four factors on speech recognition and sets several experimental levels. The experimental results show that both human noise and environmental noise have significant effects on speech recognition control. Within the range of normal speed, speed has little effect on speech recognition. Under the condition of complex environmental noise, the recognition rate of the high-frequency speech signal is higher than that of the low-frequency speech signal. Human noise has a great influence on text information input. The experimental results can provide data support for the design of the command and control system.
Performance assessment of the BTDF data compression based on wavelet transforms in daylighting simulation

The bidirectional transmittance distribution function (BTDF), which is used to characterize the light transmission of a complex fenestration system (CFS), commonly involves bulky volume of data that can be a challenge to data storage and transmission in lighting simulation tools and on compact platforms. This paper introduces a compression scheme that is based on planar wavelet transforms with three levels of decomposition to efficiently compress the BTDF data of CFSs and maintain fidelity in daylighting simulation. The root-mean-square error (RMSE) and transmittance error (TE) of compressed medium-resolution BTDF data using three different wavelet bases were evaluated for five different CFS samples at various compression ratio. Based on the results, the generic error from compression did not exceed 20% with CDF9.7 basis for compression ratio up to 200. In the case of rendering an image of a scene in which a CFS was installed at the upper daylighting section of an unilateral façade, errors began to be noticeable at a compression ratio above 70. The uniformity factor g1 was discovered to be relatively sensitive to compression (below 15% error), while the average horizontal illuminance and daylight glare probability (below 10% error) were immune to compression ratio below 100. Compression was also evaluated for a high-resolution BTDF data of external Venetian blinds in the simulation of work-plane illuminance (WPI). With a HDR imaging technique based sky luminance map, the WPI simulation had unnoticeable errors for compressed BTDF data within a compression ratio of 100.
Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation

Smart video sensors for applications related to surveillance and security are IOT-based as they use Internet for various purposes. Such applications include crowd behaviour monitoring and advanced decision support systems operating and transmitting information over internet. The analysis of crowd and pedestrian behaviour is an important task for smart IoT cameras and in particular video processing. In order to provide related behavioural models, simulation and tracking approaches have been considered in the literature. In both cases ground truth is essential to train deep models and provide a meaningful quantitative evaluation. We propose a framework for crowd simulation and automatic data generation and annotation that supports multiple cameras and multiple targets. The proposed approach is based on synthetically generated human agents, augmented frames and compositing techniques combined with path finding and planning methods. A number of popular crowd and pedestrian data sets were used to validate the model, and scenarios related to annotation and simulation were considered.
Research on the Accurate Recommendation Management System for Employment of College Graduates on Hadoop

This paper presents a new System designing method for job recommendation of College Graduates based on hadoop in China. With the enlargement of the scale of Chinese graduates and the improvement of the employing demand of enterprises, the employment situation of college graduates is becoming more and more serious. College graduates can't get satisfactory job, while enterprises can't recruit suitable graduates. In order to improve the satisfaction and efficiency of college graduates employment and enterprise recruitment, an intelligent employment recommendation system was designed in this paper which was based on collaborative filtering recommendation framework Mahout. With the system, the intelligent recommendation mechanism of schools, enterprises and graduates could be realized, and the employment efficiency, the satisfaction of college graduates and the efficiency of recruiting enterprises could be effectively improved. It is beneifiet to the employment department of colleges and universities in predicting employment trends and cultivating the students Abstract.
Clustering Approaches for Top-<i>k</i> Recommender Systems

Clustering-based recommender systems bound the seek of similar users within small user clusters providing fast recommendations in large-scale datasets. Then groups can naturally be distributed into different data partitions scaling up in the number of users the recommender system can handle. Unfortunately, while the number of users and items included in a cluster solution increases, the performance in terms of precision of a clustering-based recommender system decreases. We present a novel approach that introduces a cluster-based distance function used for neighborhood computation. In our approach, clusters generated from the training data provide the basis for neighborhood selection. Then, to expand the search of relevant users, we use a novel measure that can exploit the global cluster structure to infer cluster-outside user’s distances. Empirical studies on five widely known benchmark datasets show that our proposal is very competitive in terms of precision, recall, and NDCG. However, the strongest point of our method relies on scalability, reaching speedups of 20× in a sequential computing evaluation framework and up to 100× in a parallel architecture. These results show that an efficient implementation of our cluster-based CF method can handle very large datasets providing also good results in terms of precision, avoiding the high computational costs involved in the application of more sophisticated techniques.
Novel Context-aware Classification for Highly Accurate Automatic Toll Collection

Toll Vehicle Classification is an important task. Indeed, it has many uses in traffic management and toll collection systems. In this paper, Vinci Autoroutes group Networks (the biggest French Highways concession) are considered, where every year, millions of vehicles are classified in realtime. Then, a small decrease in classification performance can have serious economic losses. Therefore, the accuracy and the time complexity become critical for the toll collection system. The current classification algorithm uses the scene features' to detect vehicles classes. However, it requires a large labeled datasets, and has a limitations when multiple vehicles are in the scene. Herein, we propose a novel context-aware vehicle classification method that takes profit from the semantic spatial relationship of the objects. The experiments show that our method is performing as accurately as the existing model with significantly lower labeled datasets (74 times smaller). Moreover, the obtained accuracy of the proposed method is 99.97% compared to 99.79% achieved by the current method when using the same training set.
Design of rotation, illumination, and scale invariant Gabor texture descriptor for image texture analysis and retrieval

Automatic classification in medical image processing with new problems to computer-aided detection methods is attributed to the dynamics of the image acquisition conditions. Such problems request t...
Fast Adaptive Bilateral Filtering of Color Images

The bilateral filter is popularly used for image enhancement. By using a range kernel along with a spatial kernel, the filter is able to smooth images without excessive blurring of edges. It has been shown that the enhancement capacity of the filter can be boosted by adapting the width of the range kernel at each pixel. A fast algorithm for grayscale images was recently proposed for this so-called adaptive bilateral filter, which is otherwise computationally expensive. This can trivially be extended for color filtering using channelwise processing. However, developing an efficient algorithm that can exploit correlations between color channels is not straightforward. We show that such a fast algorithm can be developed by first expressing the filtering in terms of the local histogram and then approximating the latter by an uniform distribution. The distribution in question is along the direction of maximum variance in the RGB space, which we compute from the local covariance (this is done efficiently using power iterations). The local covariances in turn are computed using fast convolutions. To demonstrate the effectiveness of our fast algorithm, we apply it for sharpening, detail enhancement, and deblocking.
Estimation of Emotion Labels via Tensor-Based Spatiotemporal Visual Attention Analysis

This paper presents emotion label estimation via tensor-based spatiotemporal visual attention analysis. It has been reported in the fields of psychology and neuroscience that human emotions are related to two elements, their visual attention change and objects included in a target image. Therefore, the proposed method focuses on the spatiotemporal change of visual attention of human gazing at objects in the target image and constructs two neural networks which enable the emotion label estimation considering both of the above two elements. Specifically, the proposed method newly constructs a fourth-order tensor, gaze and image tensor (GIT) whose modes correspond to the width, the height and the color channel of the target image and the time axis of visual attention which is used for representing the time change. Then the first network, which consists of general tensor discriminant analysis (GTDA) and extreme learning machine (ELM), estimates the emotion label from the fourth-order GIT with concerning their visual attention change. Furthermore, the second network, which consists of pre-trained convolutoinal neural network-based feature extraction, GTDA and ELM, enables the estimation from the second-order GIT including visual features obtained from objects focused at each time. Finally, the proposed method estimates emotion labels based on decision fusion of the outputs from the two networks. Experimental results show the effectiveness of the proposed method.
Online VM Consolidation in Cloud Environments

Dynamic virtual machine (VM) consolidation is considered an effective approach for improving power consumption and computing resource utilization in cloud-based data centers. However, the ever-changing workload in a data center makes it difficult for VM consolidation to prevent service level agreement (SLA) violations and optimize power consumption. The detection of overutilized and underutilized physical machines (PMs) plays a significant role in effective VM consolidation, immediately improving resource utilization, SLA violations, and power consumption. This paper proposes the dynamic threshold-based fuzzy approach (DTFA) for detecting overloaded and underutilized PMs, and the Lowest Interdependence Factor Exponent Multiple Resources Predictive (LIFE-MP) approach for VM placement, by considering multiple computing resources being used simultaneously in a cloud environment. The DTFA is a fuzzy threshold-based approach used to adjust the threshold values of PMs in a cloud environment. The LIFE-MP approach selects a PM at which to place the migrating VM, based on the PM with the lowest correlation coefficient value among the already-running VMs and the migrating VM to reduce performance degradation because of the VM migration. The comparison between LIFE-MP scheme and a power-aware best-fit decreasing (PABFD) scheme shows that the proposed scheme reduces power consumption by 22.52, SLA violations by 45.63, and the number of VM migrations by 56.68.
Big Data Network Flow Processing Using Apache Spark

The increasing amount of traffic flows captured as a part of network monitoring activities makes the analysis more complicated. One of the goals for network traffic analysis is to identify malicious communication. In the paper, we present a new system for big data network flow classification and clustering. The proposed system is based on the popular big data engines such as Apache Spark and Apache Ignite. The conducted experiments demonstrate the feasibility of the proposed approach and show the possible scalability.
CNN-Based Blind Quality Prediction On Stereoscopic Images Via Patch To Image Feature Pooling

In previous quality assessment studies on stereoscopic 3D (S3D) images, researchers have concentrated on deriving manually extracted features which represent the quality of images. These features are based on the human visual system or natural scene statistics, but they have not been revealed as a deterministic function, preventing to guarantee the robustness of features. To solve this problem, we introduce a deep learning method for predicting the quality of S3D images without a reference. A convolutional neural network (CNN) model is trained through two-step learning. First, to overcome the lack of training data, patch-based CNNs are introduced. And then, automatically extracted patch features are pooled into image features. Finally, the trained CNN model parameters are updated iteratively using holistic image labeling, i.e., mean opinion score (MOS). The proposed method represents a significant improvement compared to other no-reference (NR) S3D image quality assessment (IQA) algorithms.