Reviewerly/exHarmony huggingface dataset:
Groundwater Level Prediction Using Modified Linear Regression

The non-stop decline of the groundwater stage is one of the crucial factors that affect the development of the countrywide economy and society. Based on the linear regression with PLS regression, the predicting model of the groundwater level is presented. The precision of the version is checked using the tracking facts within the Texas vicinity. Using this approach, a clean equation is generated and on evaluating one of a kind algorithms we selected the first-rate configuration and trained the version of the use of the Texas dataset. The case study shows that the precision of the version is instead excessive and its popularization importance is higher than the alternative models and has some practical fee when being used in the dynamic groundwater level analysis. Groundwater is one of the most significant characteristic asset so as to full fill the water necessities from irrigation, domestic, industrial and research needs. Gauge of the ground water level isn't only the pre-basic for long haul forecast of slant strength in repository bank, yet what's more the key for ensuring the sheltered activity of the supply. In this paper, linear regression with PLS regression is a compelling method for forecast of groundwater level in Texas.
Video object tracking and segmentation with box annotation

This paper presents a two-stage approach, track and then segment, to perform semi-supervised video object segmentation (VOS) with only bounding box annotations. The proposed reverse optimization for VOS (ROVOS) which leverages a fully convolutional Siamese network performs tracking and segmentation in the tracker. The segmentation cues are able to reversely optimize the location of the tracker and the object segmentation masks are produced by the two-branch system online. The experimental results on DAVIS 2016 and DAVIS 2017 demonstrate significant improvements of the proposed algorithm over the state-of-the-art methods.
Effects of visual information presented by augmented reality on children’s behavior

The effects on children's behavior of visual information presented by augmented reality (AR) were investigated. A human-like AR character was presented standing in one of two physical pathways to children aged 5-7 years old and 8-10 years old before they completed a filler task. After the task, the children were required to walk through one of the two pathways to obtain a reward. Both the 5-7- and 8-10-year-olds chose the pathway that was not associated with the AR character more frequently than the pathway that was. Subsequently, adult participants tested in a similar manner showed no significant bias in pathway selection. Taken together, these results suggest that the presentation of an AR character within the present experimental setting affected the behavior of children aged from 5-10 years but not that of adults. The results are discussed in the context of developmental changes in sensitivity to insubstantial agents (e.g., imaginary companion), the reality of information displayed by AR technology, and differences in the methods of AR presentation (e.g., hand-held devices vs. head-mounted devices).
OPTIMAL AUXILIARY PRIORS AND REVERSIBLE JUMP PROPOSALS FOR A CLASS OF VARIABLE DIMENSION MODELS

This article develops a Markov chain Monte Carlo (MCMC) method for a class of models that encompasses finite and countable mixtures of densities and mixtures of experts with a variable number of mixture components. The method is shown to maximize the expected probability of acceptance for cross-dimensional moves and to minimize the asymptotic variance of sample average estimators under certain restrictions. The method can be represented as a retrospective sampling algorithm with an optimal choice of auxiliary priors and as a reversible jump algorithm with optimal proposal distributions. The method is primarily motivated by and applied to a Bayesian nonparametric model for conditional densities based on mixtures of a variable number of experts. The mixture of experts model outperforms standard parametric and nonparametric alternatives in out of sample performance comparisons in an application to Engel curve estimation. The proposed MCMC algorithm makes estimation of this model practical.
A secure and efficient certificateless batch verification scheme with invalid signature identification for the internet of things

With the continuously developing wireless communication technique, the Internet of Things (IoT) has been deployed in various domains. In the IoT, numerous smart devices exchange information transparently and seamlessly via the open channel to provide intelligent and convenient services for the citizens. Due to the vulnerable nature of the communication channel, ensuring data authenticity of the transmitted information is a challenging issue. Certificateless signature (CLS) is regarded as an appropriate cryptographical primitive to protect data authenticity in the IoT. However, the existing CLS schemes are infeasible for the practical IoT systems, since individual verification causes network congestion and service delay in the face of massive service requests. To improve the verification efficiency, plenty of CLS schemes with batch verification have been investigated to verify multiple signatures quickly at once. Despite the improvement of verification efficiency, these schemes have poor efficiency or security issue. Furthermore, the batch verification failure cannot be settled in these schemes, which reduces the advantage of batch verification significantly. Motivated by the above problems, this paper presents a secure and efficient CLS scheme with batch verification and invalid signature identification. The proposed scheme is provably secure under the random oracle model. The comprehensive comparison analysis demonstrates that the presented scheme is superior to the related works in security and performance.
Image Encryption Based on Parallel Algorithm via Zigzag Manner with a New Chaotic System

Information safety has become a vital issue and data encryption and decryption have lately been vastly researched and developed. There is a need for a powerful encryption and decryption which is very tough to crack, so cryptography plays a major role to fulfill these demands. Moreover, parallel algorithm is rapidly becoming cost and resource-efficient as a result of a unified approach to solve computer problems and wide-ranging problems encountered in applied sciences and engineering applications. In this paper, a parallel environment has been utilized to construct a new encryption system, based on involving the so-called 'zig-zag' ordering that is used in JPEG data compression. A new chaotic system of three dimensions will be produced to eliminate the common encryption systems problems.
Survey on visual rhythms: A spatio-temporal representation for video sequences

Strategies for representing spatial and temporal information from video sequences in images are very convenient due to the demand for efficient tools for storing, browsing, retrieving, indexing and transmitting multimedia content. Visual rhythm, also known as temporal slice, summarizes relevant information present in a video through a space-time representation by means of one or a few images. In this work, we provide a comprehensive review of the main methods and techniques for constructing visual rhythms, classify the approaches into different categories, and identify new trends. Additionally, we discuss some limitations, challenges and applications related to the visual rhythm representation, as well as outline some directions for future research.
Method for Project Execution Control based on Soft Computing and Machine Learning

To support decision-making, organizations employ dissimilar tools during their projects execution control. However, they are still insufficient in environments with uncertain information and changing conditions in management styles. Deficiencies in systems for controlling the projects execution, affects the quality of their classification in aiding decision-making. An alternative solution is the introduction of soft computing techniques, which provide robustness, efficiency and adaptability at tools. This research proposes a method for project execution control based on soft computing and machine learning, which contributes to improve the project management. The proposed method allows the machine learning and adjusting of fuzzy inference systems to the project evaluation. The results are obtained from the execution of seven algorithms, which are based on space partitioning, neural networks, gradient descent and genetic algorithms. Validation of the proposed system, integrated to a project management tool, ratifies an improvement in the quality of project evaluation. The obtained result provides a contribution to the perfection of tools to support the decision-making in project management organization.
Automated OpenCL GPU kernel fusion for Stan Math

We developed an OpenCL GPU kernel fusion library for the Stan software for Bayesian statistics. The library automatically combines kernels, optimizes computation, and is simple to use. The practical utility of the library is that it speeds up the development of new GPU kernels while keeping the performance of automatically combined kernels comparable to hand crafted kernels. We demonstrate this with experiments on basic operations and a linear regression model likelihood.
Progressive Learning of Low-Precision Networks for Image Classification

Recent years have witnessed a great advance of deep learning in a variety of vision tasks. Many state-of-the-art deep neural networks suffer from large size and high complexity, which makes them difficult to deploy in resource-limited platforms such as mobile devices. To this end, low-precision neural networks are widely studied that quantize weights or activations into the low-bit format. Although efficient, low-precision networks are usually difficult to train and encounter severe accuracy degradation. In this paper, we propose a new training strategy based on progressive learning for image classification. First, we equip each low-precision convolutional layer with an ancillary full-precision convolutional layer based on a low-precision network structure. Second, a decay method is introduced to reduce the output of the added full-precision convolution gradually, which keeps the resulting topology structure the same as the original low-precision convolution. Extensive experiments on SVHN, CIFAR and ILSVRC-2012 datasets reveal that the proposed method can bring faster convergence and higher accuracy for low-precision neural networks.
Adaptive Feature Selection Siamese Networks for Visual Tracking

Recently, template based discriminative trackers, especially Siamese network based trackers have shown great potential in terms of balanced accuracy and tracking speed. However, it is still difficult for Siamese models to adapt the target variations from offline learning. In this paper, we introduced an Adaptive Feature Selection Siamese (AFS-Siam) network to learn the most discriminative feature information for better tracking. Features from different layers contain complementary information for discrimination. Proposed adaptive feature selection module selects the most useful feature information from different convolutional layers while suppresses the irrelevant ones. Proposed tracking algorithm not only alleviates the over-fitting problem but also increases the discriminative ability. The proposed tracking framework is trained end-to-end. And extensive experimental results over OTB50, OTB100, TC-128, and VOT2017 demonstrate that our tracking algorithm exhibits favorable performance compared to other state-of-the-art methods.
Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics

Multi-label classification in deep learning is a practical yet challenging task, because class overlaps in the feature space means that each instance is associated with multiple class labels. This requires a prediction of more than one class category for each input instance. To the best of our knowledge, this is the first deep learning study which quantifies uncertainty and model interpretability in multi-label classification; as well as applying it to the problem of recognising proteins expressed in cell types in testes based on immunohistochemically stained images. Multi-label classification is achieved by thresholding the class probabilities, with the optimal thresholds adaptively determined by a grid search scheme based on Matthews correlation coefficients. We adopt MC-Dropweights to approximate Bayesian Inference in multi-label classification to evaluate the usefulness of estimating uncertainty with predictive score to avoid overconfident, incorrect predictions in decision making. Our experimental results show that the MC-Dropweights visibly improve the performance to estimate uncertainty compared to state of the art approaches.
Enhanced Missing Object Detection System using YOLO

This framework identifies objects utilizing You Only Look Once (YOLO) approach and to distinguish the missing article continuously and alert the client. Since the items are been investigated in each frame, this framework has immaterial dormancy. YOLO calculation has a few favorable circumstances when contrasted with other item identification calculations. In a single assessment, bounding boxes and class probabilities can be predicted through a Solitary neural system directly from the full picture. The framework can be improved truly by distinguishing proof execution, because the full framework is a unique pipeline. In various estimations like Deformable parts, the model uses a sliding window approach where the classifier runs at similarly partitioned territories over the image. Strategies like R-CNN, first creates bounding boxes and afterward run the classifier on the crates.
A Philosophy of Security Architecture Design

Abstract Digital systems are almost always vulnerable, yet we increasingly depend on these systems. There will be many threats towards these system. In a fully networked system, the vulnerabilities will literally be exposed to the whole world. The exposed vulnerabilities may be transformed into attacks. To counter this state of “vulnerability”, the standard remedy is to conduct security requirements analysis and security threat modeling. Threats are assessed, and various countermeasures are devised. The totality of these measures may be described as a security architecture. The goal of a security architecture will largely be to make the system robust and resilient in the face of an adversary. However, we shall argue that this is not enough. Security architecture designs should go one step further, and actually improve the defenses when faced with hostile actions. That is, the security architectures must become antifragile.
Hands-on Artificial Evolution Through Brain Programming

This paper is about the evolution of a bio-inspired methodology http://evovision.cicese.mx that mimics the cortical visual pathways. The methodology has been extensively tested on problems with different levels of complexity with outstanding results. After a review of the main works, the problem of classification of digitized art is introduced. An image database of five classes downloaded from the Kaggle web site is used as a benchmark for evolutionary learning. A comparison with convolutional neural network from scratch and the well-known AlexNet is provided to illustrate the quality of the proposal in comparison with the state-of-the-art.
Autonomic Elasticity Control for Multi-Server Queues Under Generic Workload Surges in Cloud Environments

Cloud computing environments and Internet datacenters consist of a multitude of servers that process user requests. Performance and scalability can suffer greatly when the workload surges to levels that cause a system to become unstable (i.e., when the arrival rate of requests exceeds the system’s capacity to process them). This article presents a detailed design and evaluation of an autonomic elasticity controller for surges of any shape. This controller uses an analytical model, derived by the authors, of a single-queue multiple-server system (G/G/c) subject to workload surges that cause the system to become unstable during finite time intervals. The controller is evaluated through extensive simulations and by using publicly available Google traces. The controller is further extended to take into account VM startup delays. The article also illustrates how fudge factors can be used to more aggressively react to surges at the expense of additional resources. Finally, our controller is compared with a hypothetical oracle controller that knows the exact shape of the surge when it starts to occur.
Sentiment Analysis On Product Review: A Survey

Online reviews are considered as one of the most essential sources of client opinion. In current scenario, consumers can learn about the products and services using online review resources to make decisions. The customer reviews for numerous products plays a very vital role not only for purchasers, But also for the firms. Consumer reviews are used by companies as feedback in their product development strategies and in the management of consumer relations. Due to their disorganized nature, consumer reviews with valuable information still face difficulties in navigating information. Driven by a need for profit, some organizations may generate spam reviews regarding different products or their own product which may mislead customers to buying unworthy product. Because of its promising commercial benefits, Sentiment Analysis has become one of the most interesting subjects in text analysis. One of its main problem is the detection of false negate reviews. In addition, the user reviews can be classified as positive or negative reviews so that a consumer can use the false review to select a product. In this paper, we examine various levels of Sentiment Analysis and compare different existing techniques.
Reconciling Time Slice Conflicts of Virtual Machines With Dual Time Slice for Clouds

The proliferation of system virtualization poses a new challenge for the coarse-grained time sharing techniques for consolidation, since operating systems are running on virtual CPUs. The current system stack was designed under the assumption that operating systems can seize CPU resources at any moment. However, for the guest operating system on a virtual machine (VM), such assumption cannot be guaranteed, since virtual CPUs of VMs share a limited number of physical cores. Due to the time-sharing of physical cores, the execution of a virtual CPU is not contiguous, with a gap between the virtual and real time spaces. Such a virtual time discontinuity problem leads to significant inefficiency for lock and interrupt handling, which rely on the immediate availability of CPUs whenever the operating system requires computation. To reduce scheduling latencies of virtual CPUs, shortening time slices can be a straightforward strategy, but it may lead to the increased overhead of context switching costs across virtual machines for some workloads. It is challenging to determine a single time slice to satisfy all the VMs. In this article, we propose to have dual time slice to resolve the time slice conflict problem occurred in different types of virtual machines.
Encryption of the Images on the Basis of Two Chaotic Systems with the Use of Fuzzy Logic

Recently, new perspective areas of chaotic encryption have evolved, including fuzzy logic encryption. The presented work proposes an image encryption system based on two chaotic mapping that uses fuzzy logic. The paper also presents numerical calculations of some parameters of statistical analysis, such as, histogram, entropy of information and correlation coefficient, which confirm the efficiency of the proposed algorithm.
Hybrid deep emperor penguin classifier algorithm‐based image quality assessment for visualisation application in HDR environments

One of the main open challenges in visualisation applications such as cathode ray tube (CRT) monitor, liquid-crystal display (LCD), and organic light-emitting diode (OLED) display is the robustness for high dynamic range (HDR) environs. This is due to the imperfections in the sensor and the incapability to track interest points successfully because of the brightness constancy in visualisation applications. To address this problem, different tone mapping operators are required for visualising HDR images on standard displays. However, these standard displays have different dynamic ranges. Thus, there is a need for a new model to find the best quality tone mapped image for specific kinds of visualisation applications. The authors propose a hybrid deep emperor penguin classifier to accurately classify the tone mapped images for different visualisation applications. Here, a selective deep neural network is trained to predict the quality of a tone-mapped image. Based on this quality, a decision is made as to the suitability of the image for CRT monitor, LCD display or OLED display. Also, they evaluate the proposed model on the TMIQD database and the simulation results prove that the proposed model outperforms the state-of-the-art image quality assessment methods.
Earn More Social Attention: User Popularity Based Tag Recommendation System

Enhancing social popularity of a post (i.e., the number of views or likes) on social network services is important for both ordinary users and companies who want to promote themselves. In this paper, we have implemented an online tagging support system to achieve this using an algorithm that recommends appropriate hashtags considering not only content popularity but also user popularity. The effectiveness of this technology has been verified by actually uploading photos with recommended hashtags on a real social network service.
Improved MR image denoising via low‐ rank approximation and Laplacian‐of‐Gaussian edge detector

The low rank approximation for MR image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In spite of the great success of existing low rank approximation methods, these tend to lose the subtle edge texture when removing noise. It could degrade the image visual quality and affect the final clinical diagnosis. In this paper, a novel MR image denoising approach is proposed based on low rank approximation model and the Laplacian-of-Gaussian edge detector. In the proposed approach, a similarity evaluation scheme for noisy patch is employed to avoid the effect of the noise in the patch matching, and the details of the edge texture are preserved by the Laplacian-of-Gaussian edge detector. Experimental results show that the proposed approach is efficient and superior to some of the existing approaches in both objective criterion and visual fidelity. The proposed method can retrieve a clear MR image from the noisy one, with the detail of the edge texture, which could be very important in the clinical diagnosis.