Reviewerly/exHarmony huggingface dataset:
Robust Optimization Model for Primary and Backup Capacity Allocations against Multiple Physical Machine Failures under Uncertain Demands in Cloud

This paper proposes a robust optimization model for probabilistic protection under uncertain capacity demands to minimize the total required capacity against multiple simultaneous failures of physical machines. The proposed model determines both primary and backup virtual machine allocations simultaneously under the probabilistic protection guarantee. To express the uncertainty of capacity demands, we introduce an uncertainty set that considers the upper bound of the total demand and the upper and lower bounds of each demand. The robust optimization technique is applied to the optimization model to deal with two uncertainties: failure event and capacity demand. With this technique, the model is formulated as a mixed integer linear programming (MILP) problem. To solve larger sized problems, a simulated annealing (SA) heuristic is introduced. In SA, we obtain the capacity demands by solving maximum flow problems. Numerical results show that our proposed model reduces the total required capacity compared with the conventional model by determining both primary and backup virtual machine allocations simultaneously. We also compare the results of MILP, SA, and a baseline greedy algorithm. For a larger sized problem, we obtain approximate solutions in a practical time by using SA and the greedy algorithm.
A Pixel Dependent Adaptive Gamma Correction Based Image Enhancement Technique

It is seen that real world photography produces inaccurate colours when displayed on any digital screen. Most computer systems have gamma correction algorithms to increase colour accuracy, which have a number of drawbacks. This paper aims to formulate a novel approach to contrast correct through the use of indigenous pixel values of each individual channel. Allowing the gamma correction algorithm to have a larger pixel dependant intercept aids in evenly balancing contrast in relatively dark (low contrast) and comparatively bright (high contrast) portions of the subject picture. Comparative studies on Low Dynamic Range (LDR) pictures have been done to show the difference in outcomes obtained using the suggested technique, the Pixel Adaptive Gamma Correction (PAGC) methodology. With our suggested strategy, we gained absolute supremacy in the entropy score as well as the colourfulness measure over standard gamma correction and histogram equalisation contrast-adjustment techniques.
Stochastic Marine Predator Algorithm with Multiple Candidates

This work proposes a metaheuristic algorithm that modifies the marine predator algorithm (MPA), namely, the stochastic marine predator algorithm with multiple candidates (SMPA-MC).The modification is conducted in several aspects.The proposed algorithm replaces the three fixed equal size iteration phases with linear probability.Unlike the original MPA, in this proposed algorithm, the selection between exploration and exploitation is conducted stochastically during iteration.In the beginning, the exploration-dominant strategy is implemented to increase the exploration probability.Then, during the iteration, the exploration probability decreases linearly.Meanwhile, the exploitation probability increases linearly.The second modification is in the prey's guided movement.Different from the basic MPA, where the prey moves toward the elite with small step size, several candidates are generated with equal intercandidate distance in this work.Then, the best candidate is chosen to replace the prey's current location.The proposed algorithm is then implemented to solve theoretical mathematic functions and a real-world optimization problem in production planning.The simulation result shows that in the average fitness score parameter, the proposed algorithm is better than MPA, especially in solving multimodal functions.The simulation result also shows that the proposed algorithm creates 9%, 19%, and 30% better total gross profit than particle swarm optimization, marine predator algorithm, and Komodo mlipir algorithm, respectively.
Ensemble3D: Interactive 3D Scene Creation System with Multiple Roles and Devices

We present ensemble3D, a shared virtual world to create 3D content such as video games and CG movies. Generally, 3D content is created by multiple professionals who have different specialties. For instance, creators design 3D objects and scenes, while directors check the designs to develop the quality. However, each task is achieved by different individual workspaces, so much time is spent combining the results and completing the content. Therefore, we designed a shared virtual world that allows creators and directors to share a 3D scene at the same time. In the joint workspace, the director’s comments and requests are smoothly responded to by creators. Moreover, our system supports multiple devices, such as HMD, tablets, and PC. In this paper, we describe our ensemble3D, focusing on the concept, design, and implementation.
Systematic Literature Review Search Query Refinement Pipeline: Incremental Enrichment and Adaptation

Systematic literature reviews (SLRs) are at the heart of evidence-based research, collecting and integrating empirical evidence regarding specific research questions. A leading step in the search for relevant evidence is composing Boolean search queries, which are still at the core of how information retrieval systems work to perform an advanced literature search. Building these queries thus requires going from the general aims of the research questions into actionable search terms that are combined into potentially complex Boolean expressions. Researchers are thus tasked with the daunting and challenging task of building and refining search queries in their quest for sufficient coverage and proper representation of the literature. In this paper, we propose an adaptive Boolean query generation and refinement pipeline for SLR search. Our approach utilizes a reinforcement learning technique to learn the optimal modifications for a query based on the feedback collecting from the researchers about the query retrieval performance. Empirical evaluations with 10 SLR datasets showed our approach to achieve comparable performance to that of queries manually composed by SLR authors.
Sentiment Analysis of Movie Reviews

As human opinions facilitate product efficiency, and as the success or failure of a film depends on the reviews, demand is increasing and an honest sentiment analysis model needs to be developed that classifies film reviews. The analysis used the tokenization to transfer the input string in a word vector, to extract the thought from words by stemming it, to remove the essential words by various functions and finally to classify label reviews as positives or negative ones. Throughout this projected system we have got an inclination to make a straightforward sentiment autoregressive model using machine learning techniques cited as logistic regressive classifier, svc classifier and random forest classifier that is in a position to supply sensible accuracy for predicting the sentiment analysis of movie reviews exploitation of online review information.
CREA-Components Reusability Evaluation and Assessment: An Algorithmic Perspective

Component identification is an important task in evaluating and assessing the reusability of software components. Earlier various approaches were proposed for the component identification process. An exhaustive literature review is conducted and identified the relevant respective research gap on the evaluation and assessment of reusability of software components. In order to fill the identified gap, CREA-Component Reusability Evaluation Assessment with an algorithmic perspective is proposed to overcome the identified limitations and realized the proposed approach and metrics by evaluating and assessing the reusability of software components. Experimental results were presented with the desired dataset consisting of 24 mobile applications to prove the proposed work by analyzing the component reusability score and summarizing the components assigned with the highest score that will need less effort to reuse.
Detect Adversarial Examples by Using Feature Autoencoder

The existence of adversarial samples seriously threatens the security of various deep learning models. Therefore, the detection of adversarial examples is a very important work. Motivated by the comparison with feature maps of adversarial examples and normal examples, we designed an autoencoder to detect the adversarial examples using the feature maps. The feature autoencoder has been evaluated to detect FGSM, DeepFool, JSMA and C&W attacks on CIFAR-10 datasets. The experimental results showed that feature-level detector can detect state-of-art attacks more effectively than at the pixel-level.
VR-based Serious Games Approach for a Virtual Installation of an Ammonia Compressor Pack in the Industrial Refrigeration

Extended Reality technology (xR) contains Augmented, Virtual and Mixed Reality Technology (AR / VR / MR) is one of the key technologies of digital transformation. Thanks to the existing powerful immersive hardware systems, complex technical and natural systems can be digitally represented in a realistic virtual environment. This enables users to completely immerse in the virtual environment and to observe and interact with the systems and objects contained therein without major restrictions, or to augment real products and systems with digital data in runtime. This creates new opportunities to present the behaviour and functionalities of complex systems in a tangible and understandable way. Therefore, the xR technology has the ability to revolutionize learning and training methods, especially in the qualification of specialists and experts. Within the international project “International Cooperation on VR/AR Projects” (IC xR-P) – cooperation between University of Applied Sciences Karlsruhe from Germany, Turku University of Applied Sciences from Finland, and Higher Institute of Computer Science and Multimedia Sfax from Tunisia – the application of the VR technology in the training area in terms of supporting of cognitive skills will be investigated. IC xR-P focuses on the implementation of VR training apps for medical training, rescue and knowledge transfer. This paper presents and discusses the implementation of a VR-Training App for the installation of an industrial refrigeration Ammonia Compressor Pack (ACP). The VR app has been developed in cooperation with the German refrigeration technology manufacturer BITZER Kühlmaschinenbau GmbH. The implemented VR training App has been developed in terms of a feasibility study to demonstrate the aibilty of the VR technology to train Bitzer's customers to install the ACP worldwide. The VR app should support the customers engineers to perform an error-free installation of the ACP based on simplified and tangible virtual process description and virtual instruction. The VR training app comprises four main modules: VR logging and reception room, VR delivery and unloading process for the ACP, VR installation of the ACP, and VR visualization room for the refrigerant flow and technical details of the ACP. The VR app can be used as a single and multiplayer mode. A summary of the app evaluation by the engineers of BITZER will be presented at the end of the paper.
A Novel Weighted Extreme Learning Machine for Highly Imbalanced Multiclass Classification

Baldota, Siddhant Aggarwal, DeeptiImbalance of classes in data distributions has proved to be a hindrance for their accurate classification. Remedies for balancing classes such as oversampling and undersampling have resulted in inaccurate delineation of data. A paradigm shift from data level transformation to cost-based learning intends to solve this issue. Classification models such as artificial neural networks tend to have limited performance despite hyperparameter tuning. Extreme learning machines which do not rely on traditional training methods have provided a solution to these shortcomings. Weighted extreme learning machines (WELMs) have handled the issue of class imbalance well. This study proposes two forms of a WELM using penalization and regularization techniques. The proposed methods are compared and contrasted with the existing ones on 45 multiclass and binary datasets from machine learning repositories. The proposed methods were evaluated using the AUC, precision, recall, and F-measure metrics. Friedman tests applied to each of these metrics show that the proposed methods significantly exceed the performance of the existing WELMs, even for multiclass datasets having extremely high imbalance ratios (>850). Thus, the methods proposed in the study serve well for imbalanced multiclass classification problems.
Comparison of K-means-Based Network Partition Algorithms with Different Initial Centers Seeding

Partitioning or clustering of networks can serve many purposes. One of the perspective goals is optimum multi-controller placement. For this purpose, the sum of the shortest paths distance from all nodes to their nearest controller node is minimized using K-means-based algorithms. Various methods of initial centers seeding for a given number of seeds are used, the well-established ones as well as new ones based on k-means++ algorithm and walktrap and spinglass community detection with centroid detection of these communities. For these methods, the achieved sums of shortest paths distances are compared, together with the number of centroid updates and standard deviation of the number of vertices in partitions. Seedings based on community detection methods produce the best results in all the measured parameters.
Study of Load Balancing and Security in Cloud Computing

The demand of cloud computing is increases day by day because now a day’s every institute public or private need privacy and security in their data. Many authors suggested their proposed frameworks with different 2 mechanisms. So that they can protect their data from the access of third party. Two load balancing algorithms min-max and max-max load balancing algorithms are simulated to prove the performance. This paper also discusses about cloud computing and the processes which is used to secure and fast the process. For security, cryptography is used to secure the data. For load balancing, min-min and max-min load balancing algorithms are used. For quality of service, resource scheduling framework in cloud computing is used.
From Synthetic to One-Shot Regression of Camera-Agnostic Human Performances

Capturing accurate 3D human performances in global space from a static monocular video is an ill-posed problem. It requires solving various depth ambiguities and information about the camera’s intrinsics and extrinsics. Therefore, most methods either learn on given cameras or require to know the camera’s parameters. We instead show that a camera’s extrinsics and intrinsics can be regressed jointly with human’s position in global space, joint angles and body shape only from long sequences of 2D motion estimates. We exploit a static camera’s constant parameters by training a model that can be applied to sequences with arbitrary length with only a single forward pass while allowing full bidirectional information flow. We show that full temporal information flow is especially necessary when improving consistency through an adversarial network. Our training dataset is exclusively synthetic, and no domain adaptation is used. We achieve one of the best Human3.6M joint’s error performances for models that do not use the Human3.6M training data.
Exploring the Pertinence of Distance Functions for Nominal Multi-label Data

Data with nominal features constitute a good fraction of multi-label datasets. Dealing with high-dimensional, nominal data is different from the handling of data with numeric features. The key reason being – the distance functions which work good on numeric datasets may not function optimally (without returning the true separations of the points) in a nominal feature space. We have further observed that, in a multi-label dataset, an imbalance exists in the distribution nominal features which further aggravates the learning. In this work, we focus to find the suitability of four different distance functions –euclidean, hamming, jaccard and kulsinski in a binary-nominal context. Additionally, we also propose and explore an ensemble of two classifiers where one classifier is modelled using jaccard distance and the other is modelled on kulsinski distance. An empirical study involving five binary-nominal datasets, four evaluation metrics and three multi-label classifiers is used to evaluate the pertinence of each distance function and the ensemble. We find that the proposed ensemble gives the best outcome across all but one case.
Graphic Icon and Information Presentation Types of Hover Feedback Designs on the User Interface of Shopping Websites

The usability of the user interface for shopping websites is a very important design issue pertinent to the user experience. Graphic icons, as a visual presentation aid, provide an excellent interactive experience to the users while interacting with the user interface. The purpose of this study was to explore the usability of graphic icons and information presentation types in the operation of shopping websites, and propose design suggestions for future improvements. A 2 x 2 mixed factorial design was planned to help explore whether the graphic icons (i.e., presence or absence) and different information presentation types (i.e. plain text information and information visualization) may affect users’ task performance and questionnaire of the System Usability Scale (SUS). The experiment adopted the convenience sampling method. A total of 20 participants were recruited to take part in the experiment. The experimental data were collected regarding the participant’s task performance and questionnaire of the System Usability Scale (SUS), as well as semi-structured interviews. The generated results revealed that: (1) Graphic icons significantly affect users’ task performance. The presence of graphic icons improves users’ task performance more than the absence of graphic icons in plain text information. However, the result is opposite in the condition of information visualization. (2) The analysis of SUS showed a significant interaction effect between the graphic icons and the information presentation types. (3) The presence of graphic icons can significantly provide more efficiency than the absence of graphic icons in plain text information. Nonetheless, the result is opposite in the condition of information visualization. It is hoped that the results generated from this study can be a good design reference for future Website designers.Keywords: Shopping website; Mouse hover; Icon symbol; Information visualization; SUS
Randomly Selection of Interior Points in SV Learning Algorithm Uses of Confidence Parameter

Machine learning is a one of the subsets of AI. When a data received in the training set, according to the training dataset to build an algorithmic model based on machine learning concept, it is a specific testable prediction with progressive approach of input and output data that design can be applicable for large dataset in the real-time approach. Support vector machine (SVM) design is in large data applications. In sixties, Russia developed the algorithm based on nonlinear SV which is called generalized portrait algorithm. We evaluate the recognition accuracy of classification of the event using portrait algorithm. The data dependent and data independent show the effectiveness of a support vector machine learning algorithm approach of non-parametric methods to tractable for massive datasets in feature of high dimensional. In this approach, the new set of points, generated to the probability distribution PY((y, f(y))). Y((y. f(y))), represented in the example is a support vector, and the probability distribution controls the marginal value and also corresponding weighted vector value. These weighted vectors belong to the training set but not classified with confidence factor. The active SVM learning based on points and their distance between points and hyperplane and newly enters the confidence factor is known as adaptive factor. The confidence factor value is computed from the availability of information around us by using the principle of the k-nearest neighbor, N dimensional data as a input X in to K dimensional space (feature), then K always greater than N through the mapping function ϕ. SVM algorithm gives both computation efficiency and accuracy in a simple manner. In large date set, we can solve by predictor–corrector method in step by step in this process, the similar dates are combined together and give maximum accuracy and also optimum solution to reduce the training error and test error through probability distribution. Here, SVM gives the efficient idea based for function estimation that covers both dependent and independent data.
Research on Environmental Assessment Method of Meteorological Observation Station Detection Based on Panoramic Image Processing

At present, the meteorological observation station detection environment evaluation is completed by manual method, which is not only inefficient, but also lack of objective factors in the results. In order to solve this problem, this paper proposes a meteorological station detection environment assessment method based on panoramic image processing. SIFT (scale invariant feature transform) algorithm combined with fast (features from accelerated segment test) algorithm is used to extract and process the corresponding information of some overlapping images in adjacent areas. According to the pixel coordinates of the highest obstacle in the image, binocular ranging algorithm is used to build the detection environment model. The experimental results show that this method not only saves manpower, but also meets the needs of ground weather station to detect the environment of meteorological observation station, and can improve the efficiency of meteorological evaluation process.
6G Communication: A Vision on Deep Learning in URLLC

Currently, the focus of wireless science is gradually moving toward 6G communication technology. The goals of 6G are high; however, it is a necessity to make many applications a reality. This paper analyzes the required suggested targets that must be taken to establish the 6G network effectively and efficiently. In addition, the paper also presents some future applications that will use 6G. The rapid advances in artificial intelligence and machine learning will significantly help the 6G to achieve its goal. 6G will integrate artificial intelligence in its every component to transfer the automation from smart to intelligence. One such aspect is using deep learning to achieve Ultra-Reliable and Low-Latency Communication (URLLC) in 6G. This paper has elaborated on the role of deep learning to achieve URLLC.