Reviewerly/exHarmony huggingface dataset:
Prediction of Dynamic Virtual Machine (VM) Provisioning in Cloud Computing Using Deep Learning

The increasing usage of remote services and high marketplace competition requires cloud service providers to plan and provision computing resources efficiently, while providing affordable services and managing their data center expenditures. Generally, IaaS cloud resources are managed by predicting either long-term workload or long-term resource utilization pattern. But it does not give any genuine information about the necessary memory/CPU before exposing it to the physical machine. So, the prediction of dynamic virtual machine (VM) provisioning is a challenging problem in cloud computing. In this paper, we explored CPU usage details of VMs in Azure cloud dataset to predict utilization patterns. The dataset is used to train several deep learning models. Training with CPU utilization as the target class, we predict the minimum, maximum, and average CPU utilization values. The results are then analyzed using multiple evaluation metrics. After evaluating the different models, we conclude that the GRU performs better in predicting the CPU utilization.
Novel Unsupervised Learning Architecture for Exposure-Based Classification and Enhancement

Numerous imaging applications are affected by the poor quality of images caused by poor illuminating conditions, contrast degradation, and unwanted noise. These effects create noticeable artifacts in an indeterministic selective manner, where some parts of the image are modified, and some parts of the image are uninfluenced. Thus, the classification of an image into various sections and, then, segmentwise application of imaging algorithms are a preferable solution. This article focuses on classifying an image into three categories as under–well–over exposed regions. This article introduces the concept of multilevel superpixel-based classification. Superpixel stores the local integrity and color similarity of an image; hence, an image is initially classified into an experimentally predetermined number of superpixels. Then, a novel algorithm depending upon the superpixel contrast, entropy, and statistical distribution of illumination classifies it into an under–well–over exposed region. Then, with an increased number of superpixels, we reiterate the whole process. The regions classified into the same category in both iterations perform as the training datasets for the support-vector-machine (SVM) classifier. Finally, the trained SVM classifies the ambiguous regions obtained from multilevel superpixel classification. Both qualitative and visual results show the superior performance of the proposed method over the state-of-the-art methods.
A Lightweight Verification Scheme Based on Dynamic Convolution

Abstract Since Electricity Grid Engineering involves a large number of personnel in the construction process, face recognition algorithms can be used to solve the personnel management problem. The recognition devices used in Electricity Grid Engineering are often mobile, embedded, and other lightweight devices with limited hardware performance. Although a large number of existing face recognition algorithms based on deep convolutional neural networks have high recognition accuracy, they are difficult to run in mobile devices or offline environments due to high computational complexity. In order to maintain the accuracy of face recognition while reducing the complexity of face recognition networks, a lightweight face recognition network based on Dynamic Convolution is proposed. Based on MobileNetV2, this paper introduces the Dynamic Convolution operation. It proposes a Dynamic Inverted Residuals Block, which enables the lightweight neural network to combine the feature extraction and learning ability of large neural networks to improve the recognition accuracy of the model. The experiments prove that the proposed model maintains high recognition accuracy while ensuring lightweight.
A Self-Trained Spatial Graph Convolutional Network for Unsupervised Human-Related Anomalous Event Detection in Complex Scenes

Most of previous works on abnormal event detection take this task as a novelty detection problem, which employ the supervised setting that needs videos containing only normal events for learning normal patterns. However, few works are developed under the unsupervised setting that detects anomaly without labeled normal videos. In this article, we develop a novel unsupervised algorithm using the skeleton feature for detecting human-related anomalous events. Our method applies the idea of self-training regression for iteratively updating the anomaly scores of skeletons for anomaly detection. In detail, each extracted skeleton is first decomposed into global and local feature components. Then, an unsupervised anomaly detector is operated on these two components to generate the initial anomalous and normal skeleton sets. These two sets are utilized to optimize parameters of an anomaly scoring module consisting of a spatial graph convolutional network (SGCN) and fully connected layers. The trained module is then employed to recalculate anomaly scores of all skeletons to update memberships of pseudo anomalous and normal skeletons set for the next training procedure, and this process is performed in an iterative way to get superior anomaly detection performance. Experimental results on two challenging data sets and their subsets that only contain human-related anomalies demonstrate our method outperforms several state-of-the-art supervised methods.
On the Conciliation of Traditional and Computer-Assisted Proofs

A proof of a mathematical proposition or a program specification obtained by a formal verification process, using an interactive theorem prover, can be questioned as a true demonstration or as having the same purposes of a traditional pencil-and-paper proof. However, in our opinion the verification process of a software component exhibits the same construction phases as a purely mathematical one. A correspondence between both kinds of proofs enables us to give a proposal of what we call transitional proofs, a concept that outlines a conciliation between traditional paper-and-pencil and computer-assisted proofs, which can be useful in philosophical problems surrounding formalized mathematics and program verification with proof-assistants.
Automatic Detection and Classification for Damaged Vehicle Using Enhanced Deep Learning Algorithm

Claims leakage costs insurance companies millions of dollars each year because of disparity amid the cost spent by allowance business and the accurate quantity that must be reimbursed. According to experts, the company's mislays are driven by ineffectual allegations rectifying and fallacious making. Hence, processing claims for identifying and classifying car damages is time-consuming and costly for insurance companies. Therefore, deep learning algorithms have been utilized to clarify such issues, mitigate their pessimistic implications, and automate this process to save time and money. In this context, we offer brutalize as well as well-organized deep knowledge-based constructions for detecting, localizing, and classification vehicle damage using enhanced Mask R-CNN method which integrates deep learning, instance segmentation, and convey determining methods for attribute withdrawal, damage recognition, location, classification, and visualization car’s defects using car damaged images. Moreover, comparative performance measure analyses are presented for Precision, Recall, F1 Score, Accuracy, Loss function, as well the confusion matrices using the three pretrained models, i.e., VGG16, VGG19, and Inception-ResNetV2, with Inception-ResNetV2 stands out to perform the best. The empirical results reveal that the proposed method not only recognizes damaged automobiles, but also locates them and determines their severity level and fulfills our goal to determine vehicle damage immediately, find it, analyze its extremity levels, and portray it by outlining its accurate site and severity levels. This method demonstrates a valuable value for the insurance industry in combating claims leakage issues to save time and money.
Iterative Residual Feature Refinement Network for Bit-Depth Enhancement

Bit-depth enhancement (BDE) restores high bit-depth (HBD) images from low bit-depth (LBD) ones, which has important applications. Recently, residual-optimized BDE algorithms based on convolutional neural networks (CNNs) have achieved top performance. However, they fail to use a single model to accurately recover all frequency information encoded by missing significant bits at one time on challenging large bit-depth recovery tasks. In this paper, we redefine BDE residual recovery from the perspective of image frequency characteristics. On this basis, we propose an iterative residual feature optimization strategy, which provides an implicit error correction mechanism and improves training and inference efficiency. Furthermore, we design a simple but effective iterative residual feature refinement network (IRFRN). By linking model complexity with the recovery of different frequency information, IRFRN enables a single model to simultaneously recover the missing low and high frequency information. Extensive experiments indicate that our method achieves the state-of-the-art quantitative and qualitative performance on large bit-depth recovery tasks.
Environmental Geology and Isotopic Evaluation of Springs within the Central Part of the Sierra Cerro De La Silla, Northeastern Mexico

The Metropolitan Area of Monterrey (ZMM), in the state of Nuevo León, Mexico has seen a significant increase in its population and associated urban footprint through the decades. From a population standpoint, ZMM represents the second largest urban area in Mexico. This growth has required increased infrastructure for necessary services including public access to potable water which will continue to increase over time. The supply of water resources for the ZMM is derived, in large part, from underground sources that are subject to certain conditions influenced by its geographical location, physiographical characteristics and the geological environment that directly impact the water recharge system of the regional aquifers. Nuevo León State government has developed a water resource master plan outlined investment through 2050 which includes construction of new dams and installation of additional shallow and deep production wells. With continued one to five-percent annual population growth, additional stresses and vulnerability of this valuable resource will occur. Based on the changing demographics and additional stresses to water resources, it is critical to understand the localized natural groundwater regime extending from the precipitation recharge zones to the individual aquifers that serve the needs of the population. The following study evaluates specific aspects of the water regime (recharge zones and springs) in an area approximately 25 km south of central Monterrey in the Santiago area of the ZMM which is expected to have significant urban expansion in the future. The central portion of the Sierra del Cerro de la Silla, which constitutes the study area is a catchment and infiltration zone for meteoric water. Within the study area, five springs were identified and were sampled during the dry season and the collected spring water samples were submitted for geochemical analyses. Field surveys, data collection and interpretation have contributed to the understanding of the water origin and associated physical-chemical characteristics during infiltration through the geological sequences and ultimate incorporation into the aquifer. The characterization of the regime through a series of field survey and spring water characterization has established the presence of a low enthalpy geothermal system at depth that has the potential to impact shallow sweet-water regimes. Beyond contributing to the knowledge of the local hydrogeological system, this study can assist in understanding the vulnerability of the natural resource due to human activity and serve to support concepts of optimal use and protection.
Design COVID-19 Ontology: A Healthcare and Safety Perspective

Abstract The COVID-19 pandemic has flooded a vast amount of information into the world. To help control this situation, good utilization of the overflow in data is required. However, data come in different forms, posing numerous challenges in subsequent processing. Therefore, a uniform knowledge representation of COVID-19 information is needed, and ontology can play a role. The ontology will model patient healthcare-related data, ranging from symptoms to side effects and medical conditions, and the necessary precautions, especially for healthcare workers, to obtain protection from the COVID-19 virus. We followed Sánchez’s methodology to build the vocabularies, which include current ontology concepts, W3C standards RDF, OWL and SWRL. This work shows promising results that can be applied by different organizations.
Single Image DnCNN Visibility Improvement (SImDnCNNVI)

The presence of fog, haze, or atmospheric particles reduces visibility which is an under-constrained challenging classical problem due to ambiguous scene radiance and transmission. Consequently, digital images captured under such conditions suffer from poor recognition. In this work, a fast single image physics based inversion scattering model is adopted to overcome these limitations. Denoising convolutional neural networks (DnCNNs) model is well suited for blind Gaussian denoising in a learning framework at hidden layers. With the DnCNN blind denoised depth map, high-quality transmission is estimated and finally by inverting scattering image formation model, a clear image is obtained along with tuned haziness factor. The proposed algorithm performs well compared to sixteen state-of-the-art methods qualitatively and quantitatively on Ground Truth (GT) O-Haze dataset. Output images are appealing, halo free, edge preserved, colour balanced, clear.
Introduction of machine learning and neural networks

Environmental perception is currently one of the most significant challenges in unmanned driving systems. To make the driving behavior of unmanned vehicles similar to that of human drivers, the target obstacles need to be identified. This identification process is a typical pattern recognition process. Traditional computer vision often requires different artificially designed features for different tasks. However, artificially designed features often have omissions, which may lead to serious security consequences. This chapter introduces some basic concepts of machine learning and expounds on deep learning, including machine learning and the application of deep learning in the field of unmanned driving.
Task Scheduling Using Deep Q-Learning

Process scheduling is a very crucial task of operating systems. Effective scheduling ensures system efficiency and minimizes wastage of resources and cost overall, enhancing productivity. Most commonly, it is an exhaustive task to select the most accurate resources in executing these tasks. The solution for this effective job scheduling and resource management would preferably be dependent on the nature of the workload and adapt to any given environment compared to an algorithmic one. Thus, to meet this rising demand for an automated, self-assigning system, a deep Q-learning (Reinforcement learning technique)-based implementation has been done, which schedules tasks to maximize CPU utilization and memory utilization.
Evaluation of Draw and Use Buttons Methods for Curve Plot Using a Facial Tracker

This paper presents the evaluation of two methods to plot three dimensional polygonal curves with the aid of a face tracker (eViacam). We compared a pointing method to select points forming polygons on a 3D grid, called “Draw” and the other method was denominated “Use Buttons” and used to type three coordinates and plot the curves. The experiment evaluates the new feature of “Use Buttons” in contrast with the original “Draw” method. A total of forty-six volunteers including six users with disabilities participated in the tests which have taken place in São Paulo University and Federal University of ABC. We evaluated average time, the amount of errors and effort of participants to draw previously defined figures along three sessions such as training and affinity could be carried on through sessions. The obtained results show that the method “Use Buttons” does not imply better usability, despite being an interesting alternative input method. Although time demand is greater with this method than the original one, it is still not a big problem when volunteers focus on the activity.
Mathematical Model of Cognition With Cartoon Film Movement, Contrast Sensitivity, and Complementary Color Afterimages: The Ability To Convert Digital to Analog Information by Damping Oscillation

Verdigris appears after red disappears. Goethe discovered this phenomenon of complementary color afterimages. In addition, there are two illusions of cognition as shown below: an image of a cartoon film that continuously moves, and a black background that makes a gray circle look lighter as well as a white background that makes it look darker. However, no studies have explained these using a mathematical model. It was also reported that among living creatures, "smooth continuation" is the most important. In this report, three illusions of cognition are explained by a mathematical hypothesis in which digital information is converted to continuous analog information through a damping oscillation equation. According to this hypothesis, one feels that its rolling width is bigger than its true width during changes in digital information as in a cartoon film movie. It proves that this hypothesis is right.
Invarint Relations for Affine Loops

Invariant relations are used to analyze while loops; while their primary application is to derive the function of a loop, they can also be used to derive loop invariants, weakest preconditions, strongest postconditions, sufficient conditions of correctness, necessary conditions of correctness, and termination conditions of loops. In this paper we present two generic invariant relations that capture the semantics of loops whose loop body applies affine transformations on numeric variables.
Psychological and pedagogical issues in formation of personal and business qualities among military personnel performing professional functions related to the secret service

We analyze the specifics of the training of future officers performing professional functions related to the secret service. The analysis of national and foreign studies allows establishing the theoretical provisions defining personal and business qualities and the system of purposeful education of future officers. The military professional service in the field of secrecy has a specific nomenclature of requirements that regulate the necessary list and the level of formed qualities and abilities of the personality of military specialists, so scientifically based educational process in military educational institutions is necessary. When studying theoretical information we use methods of analysis, concretization and generalization. In the experimental part of the research, we use methods of expert evaluation, correlation analysis and design. As a result of the study, the main directions of the process of professional education of the future officer were identified, forming his personal and business qualities as a military specialist. The list of personal and business qualities of military personnel performing professional functions related to the secret service has been clarified. We consider the process of professional education of future officers in military educational institutions of the Ministry of Defense of the Russian Federation, providing training for secret service officers, its internal and external subsystems. The conducted research proves the need to consider the problem of the formation of personal and business qualities of military personnel performing professional functions related to the secret service, as well as the construction of an educational process aimed at the development of these qualities. The presented results can be applied in the process of research and educational work in higher military educational institutions.
Comparative Analysis on Fraud Detection in Credit Card Transaction Using Different Machine Learning Algorithms

Fraud detection in credit card transaction is an important area of study which attains its importance in the increasing fraud risk posed on credit card by fraudsters to steal money from others' accounts. In the last several years, to identify fraud in credit card transaction the applications of machine learning has shown optimistic result; however, machine learning in the credit card transaction also has certain challenges. There are numerous proposed machine learning algorithms that can be used to detect fraud cases; however, not every model has been proved to be the best since each technique works well with a certain data set, and fraudsters improve themselves to avoid detection by current fraud detection systems. This paper presents a comparative analysis of the credit card fraud detection methods based on supervised and unsupervised learning. Precision, recall, and F1-Score were used to evaluate each performance of all the models, as well as a ROC curve and AUC score for comparison. Since we compared nearly five different models, this report can be utilized as a research tool.
Understanding Individual Neurons of ResNet Through Improved Compositional Formulas

Compositions of concepts from human-annotated datasets, e.g., “chair OR table”, have been shown to approximate latent representations of image classifiers better than single concepts. In this work, we introduce the Close Algorithm, which improves performance according to the IoU metric by utilizing the non-logical connectors CLOSE-TO, WITH, and EXPAND. We consider the shortcomings of current approaches, discuss possible causes, and review a small user study we have run to collect evidence on this point. We also introduce a metric that discourages the reliance on scene-level annotations. (The code to replicate the technical results (along with additional sample images) can be accessed at https://github.com/rafaelharth/indres ).
Information Theory and Entropy

Let $$(\Omega ,{\mathcal A},\Pr )$$ be a fixed probability space. Let S = {s 1, s 2, s 3, …, s n} be a finite set, and let X : Ω → S be a random variable with distribution function f X : S → [0, 1], $$f_X(s_i) = \Pr (X=s_i)$$ .
Let’s Team Up with AI! Toward a Hybrid Intelligence System for Online Customer Service

Customers desire convenient, fast, and personalized service encounters. Hence, service companies deploy self-service technology for online customer service. However, as solutions based on Artificial Intelligence cannot reliably answer the full range of requests and the demands on service employees (SEs) in live chat interaction are high, Hybrid Intelligence Systems (HIS) provide great potential to overcome current pitfalls by combining the complementary strengths of artificial and human intelligence. To ensure optimal performance of this socio-technical ensemble, human-centered design approaches are needed to realize real-time augmentation of decision-making in chat-based service encounters. Following a Design Science Research approach, we generate theory-based design principles (DPs) and implement them in a web-based HIS prototype. We contribute to Hybrid Intelligence research with results showing that the DPs enable task mastery and decision efficiency and provide avenues for future research.