Reviewerly/exHarmony huggingface dataset:
An Energy Efficient Task Scheduling Strategy in a Cloud Computing System and its Performance Evaluation using a Two-Dimensional Continuous Time Markov Chain Model

With ongoing energy shortages and rises in greenhouse emissions worldwide, increasing academic attention is being turned towards ways to improve the efficiency and sustainability of cloud computing. In this paper, we present a performance analysis and a system optimization of a cloud computing system with an energy efficient task scheduling strategy directed towards satisfying the service level agreement of cloud users while at the same time improving the energy efficiency in cloud computing system. In this paper, we propose a novel energy-aware task scheduling strategy based on a sleep-delay timer and a waking-up threshold. To capture the stochastic behavior of tasks with the proposed strategy, we establish a synchronous vacation queueing system combining vacation-delay and N-policy. Taking into account the total number of tasks and the state of the physical machine (PM), we construct a two-dimensional continuous-time Markov chain (CTMC), and produce an infinitesimal generator. Moreover, by using the geometric-matrix solution method, we analyze the queueing model in the steady state, and then, we derive the system performance measures in terms of the average sojourn time and the energy conservation level. Furthermore, we conduct system experiments to investigate the proposed strategy and validate the system model according to performance measures. Statistical results show that there is a compromise between the different performance measures when setting strategy parameters. By combining different performance measures, we develop a cost function for the system optimization. Finally, by dynamically adjusting the crossover probability and the mutation probability, and initializing the individuals with chaotic equations, we present an improved genetic algorithm to jointly optimize the sleep parameter, the sleep-delay parameter and the waking-up threshold.
PCA in the context of Face Recognition with the Image Enlargement Techniques

Face recognition has become a field of interest in many applications such as security and entertainments. In surveillance system, the quality of the recoded footage is sometimes insufficient due to the distance and angle of the camera from the scene. This causes the object of interest, e.g. the face of a person in the scene to be of low resolution, which increases the difficulty in recognition process. Image resolution enhancement is a potential solution for enlarging low-resolution images for real time face recognition. An enlarged image is then compared to available database of images to either identify or verify the individuals. However, the optimal performance of face recognition techniques when various image enlargement methods have been applied to them has not been investigated. In this research, the performance of PCA based face recognition method, with the three most well-known image enlargement techniques (Nearest Neighbour, Bilinear, Bicubic) is investigated. First, an input image is down sampled to six different resolutions. The down-sampled image is then enlarged to its original size using the three named image enlargement techniques. The enlarged image is then input to a PCA face recognition system for the recognition process. The simulation results using images from the SCFace database show that PCA based face recognition illustrates superior results when input images enlarged using Nearest Neighbour technique, while the performance of Bicubic and Bilinear techniques is slightly lower than Nearest Neighbour method.
Text Processing of Telugu–English Code Mixed Languages

In social media, code mixed data has increased, due to which there is an enormous development in noisy and inadequate multilingual content. Automation of noisy social media text is one of the existing research areas. This work focuses on extracting sentiments for movie related code mixed Telugu–English bilingual Roman script data. The raw data of size 11250 tweets were extracted using Twitter API. Initially, the data was cleaned and the annotated data was addressed for sentiment extraction through two approaches namely, lexicon based and machine learning based. In lexicon based approach, the language of each word was identified to back transliterate and extract sentiments. In machine learning based approach, sentiment classification was accomplished with uni-gram, bi-gram and skip-gram features using support vector machine classifier. Machine learning performed better in skip-gram with an accuracy of 76.33% as compared to lexicon based approach holding an accuracy of 66.82%.
Augmented Reality in Education: A Study on Preschool Children, Parents, and Teachers in Bangladesh

Augmented reality (AR) is a technology that is being used in various aspects of life, including education. Many studies have been performed to investigate the effectiveness of using AR in educational settings. The purpose of this study is to investigate the effectiveness of AR in teaching preschool children in a developing country such as Bangladesh. To conduct the study, we have developed two AR-based apps for Android using marker-based tracking techniques. We have run our study in a classroom of a school in Bangladesh where 25 students, 13 parents, and three teachers voluntarily participated. We taught students using our AR apps and evaluated their learning improvements through pre- and post-test results. The results show at least 30% learning improvement. We have observed children's reaction and engagement, and surveyed parents and teachers for acceptance of such technology and suggestions for improvement. Our preliminary study finds that AR can be useful for preschool students learning in a developing country such as Bangladesh. Through our study, we also identify a list of requirements for designing and developing an AR app for education.
Comparison of Different Augmentation Techniques for Improved Generalization Performance for Gleason Grading

The fact that deep learning based algorithms used for digital pathology tend to overfit to the site of the training data is well-known. Since an algorithm that does not generalize is not very useful, we have in this work studied how different data augmentation techniques can reduce this problem but also how data from different sites can be normalized to each other. For both of these approaches we have used cycle generative adversarial networks (GAN); either to generate more examples to train on or to transform images from one site to another. Furthermore, we have investigated to what extent standard augmentation techniques improve the generalization performance. We performed experiments on four datasets with slides from prostate biopsies, stained with H&E, detailed annotated with Gleason grades. We obtained results similar to previous studies, with accuracies of 77% for Gleason grading for images from the same site as the training data and 59% for images from other sites. However, we also found out that the use of traditional augmentation techniques gave better performance compared to when using cycle GANs, either to augment the training data or to normalize the test data.
Generalized visual cryptography scheme with completely random shares

A modification of the standard Naor-Shamir scheme for visual cryptography and visual secret sharing is proposed. Better statistical properties of shares are obtained at the cost of the slightly worse quality of reconstructed images. Advantages and disadvantages of such an approach are discussed.
A novel SVM parameter tuning method based on advanced whale optimization algorithm

Abstract The classification performance of support vector machine (SVM) algorithm is highly dependent on the careful tuning of hyper-parameters and penalty coefficient. This paper introduces a novel SVM parameter optimization method by using the advanced whale optimization algorithm (AWOA) that is an improved whale of algorithm (WOA) with external archiving strategy. A new framework for SVM parameter optimization based on AWOA is built. To demonstrate the performance of our proposed method, six typical data sets are chosen to evaluate the effect of SVM classification problem. Experimental results show that the higher accuracy and better convergence can be achieved by AWOA compared with other three usual parameter optimization methods (WOA, PSO, and DE).
An Image Forensic Method for AI Inpainting Using Faster R-CNN

With the introduction of AI technology in the field of image inpainting, it makes up for the shortcomings of traditional inpainting methods. Since this technology can achieve object removal without obvious traces by learning visual semantics informations and performs better in complex environment, it brings great challenges to the existing image forensics work. So far, there are few detection methods for AI image inpainting forgery. In order to fill the gap in this field, this paper proposes a deep learning-based detection method. Considering Faster R-CNN model has demonstrated good performance on detecting semantic objects over a range of scales, we migrate it to the forensic field for image inpainting forgery. It turns out that Faster R-CNN model can capture inconsistencies features between the inpainted region and the authentic region. Therefore, this can further enrich the application range of digital image forensics algorithms. We construct a large-scale AI image inpainting dataset based on ImageNet dataset. The experimental results on this dataset demonstrate that our proposed approach achieves good performance.
A new approach to differential lung diagnosis with CT scans based on the Siamese neural network

Abstract A lot of computer-aided diagnosis systems for lung cancer detection have been developed in the last years, but most of them may be not effective when we deal with the differential diagnosis. Only a part of patients with lung cancer have a typical nodular cancer. Some patients have the extremely difficultly recognized lung cancer due to its atypical visualization. A new approach is proposed for differential lung diagnosis and for solving the problem of classifying all types of lung cancer. It is based on the following ideas. First, we apply the length chord and Hounsfield unit value histograms characterizing the inner structure of a tissue and its surrounding as a new feature representation of the tissue. Second, we collect a special dataset which contains a lot of atypical cases of cancer and non-cancer tissues. Third, we propose to use histograms as well as suspicious lung object images for training and using two Siamese neural networks which can be viewed as a key element of the proposed approach and allow us to implement some elements of explainable artificial intelligence systems.
On specific features of the endoscopic image processing

Abstract Main principles of endoscopic image processing are considered. Specific characteristics of such images and complexities in their classification are discussed. We suggest that several features may be regarded as specific signs that can be used to discriminate between benign and malignant loci. The methods of colour histograms, gradients and texture analysis are considered and discussed.
Innovative and Comprehensive Support System for Training People Working in Dangerous Conditions

The visuals used in the virtual environment keep the trainees interested and facilitates the memorization of information as well as consolidates their skills. Wireless HMD (Head Mounted Display) and the motion capture technology are used in order to involve muscle memory and improve the effectiveness of trainings. All training scenarios are fully integrated with ICT training assessment system. The possibility of practical application of the VR-based training into the educational process of workers can have a significant impact on improving collective and individual work safety. VR-based training is a particularly valuable for people working in dangerous conditions, where the accident rate is clearly higher than in other branches of the economy. Moreover VR-based training allows to support the rehabilitation and physiotherapy process in order to accelerate the return to work after injuries.
Cloud-based Reversible Dynamic Secure Steganography Model for embedding pathological report in medical images

(2021). Cloud-based Reversible Dynamic Secure Steganography Model for embedding pathological report in medical images. International Journal of Computers and Applications: Vol. 43, No. 10, pp. 1002-1010.
Spectral Normalization and Relativistic Adversarial Training for Conditional Pose Generation with Self-Attention

We address the problem of conditional image generation of synthesizing a new image of an individual given a reference image and target pose. We base our approach on generative adversarial networks and leverage deformable skip connections to deal with pixel-to-pixel misalignments, self-attention to leverage complementary features in separate portions of the image, e.g., arms or legs, and spectral normalization to improve the quality of the synthesized images. We train the synthesis model with a nearest-neighbour loss in combination with a relativistic average hinge adversarial loss. We evaluate on the Market-1501 dataset and show how our proposed approach can surpass existing approaches in conditional image synthesis performance.
Heatmapping of People Involved in Group Activities

This paper proposes a method for heatmapping people who are involved in a group activity. Such people grouping is useful for understanding group activities. In prior work, people grouping is performed based on simple inflexible rules and schemes (e.g., based on proximity among people and with models representing only a constant number of people). Our proposed heatmapping method can group any number of people who dynamically change their deployment. A deep network for this method consists of two input streams (i.e., RGB and human bounding-box images). This network outputs a heatmap representing pixelwise confidence values of the people grouping. Extensive exploration of appropriate parameters was conducted in order to optimize the input bounding-box images. As a result, we demonstrate the effectiveness of the proposed method for heatmapping people involved in group activities.
MiniForest: Distributed and Dynamic Multicasting in Datacenter Networks

The emerging cloud applications require group communications. For these applications, multicast is a better choice than unicast, because it can significantly improve the performance by eliminating the duplicated packets generated by servers. However, existing multicast schemes for datacenters are either based on IP multicast or centralized scheduling. IP multicast is inefficient for datacenters as it cannot take full advantage of the multipath property. And centralized schemes suffer from single-point failure and scalability problems. To solve these problems, we propose MiniForest, a distributed multicast framework for large-scale datacenter networks. It consists of new routing algorithms and a dynamic group management mechanism. A new address mapping solution is then designed for compatibility to existing upper-layer applications. Based on the mapping solution, we propose an efficient load balancing strategy, with which a minimal forest is constructed for all multicast trees. To study the performance of the new multicast scheme in theory, we further provide an analytical model for Clos-based datacenter networks and analyze the overloading behaviors from a new perspective. We show that the distributed scheme can be used in any size of datacenters. It has much lower complexity and better performance than centralized schemes.
Projected Augmented Reality Intelligent Model of a City Area with Path Optimization

Augmented Reality is increasingly used for enhancing user experiences in different tasks. The present paper describes a model combining augmented reality and artificial intelligence algorithms in a 3D model of an area of the city of Coimbra, based on information extracted from OpenStreetMap. The augmented reality effect is achieved using a video projection over a 3D printed map. Users can interact with the model using a smart phone or similar device and simulate itineraries which are optimized using a genetic algorithm and A*. Among other applications, the model can be used for tourists or travelers to simulate travels with realism, as well as virtual reconstructions of historical places or remote areas.
Preventing Timing Attacks Against RQC Using Constant Time Decoding of Gabidulin Codes

This paper studies the resistance of the code-based encryption scheme RQC to timing attacks. We describe two chosen ciphertext timing attacks that rely on a correlation between the weight of the error to be decoded and the running time of Gabidulin code’s decoding algorithm. These attacks are of theoretical interest as they outperform the best known algorithm to solve the rank syndrome decoding problem in term of complexity. Nevertheless, they are quite impracticable in real situations as they require a huge number of requests to a timing oracle. We also provide a constant-time algorithm for the decoding of Gabidulin codes that prevent these attacks without any performance cost for honest users.
Digital Forensics: Focusing on Image Forensics

The world is continuously developing, and people's needs are increasing as well; so too are the number of thieves increasing, especially electronic thieves. For that reason, companies and individuals are always searching for experts who will protect them from thieves, and these experts are called digital investigators. Digital forensics has a number of branches and different parts, and image forensics is one of them. The budget for the images branch goes up every day in response to the need. In this paper we offer some information about images and image forensics, image components and how they are stored in digital devices and how they can be deleted and recovered. We offer general information about digital forensics, focusing on image forensics.
Subspace Clustering for Interpretable Botnet Traffic Analysis

With the growth of the Internet of Things (IoT), massively connected devices are exposed to cyber-attacks and are becoming active as bots. To protect IoT devices efficiently from increasing threats, in addition to honeypot-based approaches, we need to gain more complete understanding of IoT botnets and potential victims including purposes of attacks and events which they are participating in. In this paper, we propose a two-step subspace clustering method to cluster botnets and clarify their types (functionalities). For each target host, the proposed method separates features into several subspaces and generates a sub-label in each subspace to represent partial characteristic (e.g., low-size flows or high TCP-SYN rate). Major combinations of sub-labels presents different partial characteristic, and the proposed method classify bots and interprets the whole behaviors for each bot group. In the evaluation, we reveal the scale and variety of botnets in the wild through two real-world datasets.
The Impact of Motion on Individual Simulator Sickness in a Moving Base VR Simulator with Head-Mounted Display (HMD)

Simulators are increasingly used for training applications. Therefore, it is essential to consider negative side effects like simulator sickness. Influencing factors of simulator sickness are related to the individual, to the system, or to the training design. Our empirical study investigates some of these factors in a 30-min virtual helicopter flight with HMD. 30 subjects (Mage = 19.3 years; SD = 4.02) completed the MSSQ (motion sickness susceptibility questionnaire) and the SSQ (simulator sickness questionnaire) before exposure and the SSQ after exposure. The participants received the same treatment on two consecutive days: One day without real motion and the other day with real motion realized utilizing a motion platform. Results show that symptoms of simulator sickness significantly increased directly after VR-exposure. One hour after exposure, the symptoms of simulator sickness are comparable to symptoms before exposure. A difference between the two conditions with real motion and without could not be observed after exposure. Individual motion sickness susceptibility has been identified as a predictor for experiencing simulator sickness. Implications of our findings for the training of helicopter crews and theoretical implications in terms of simulator sickness are discussed.
Parallel particle swarm optimization classification algorithm variant implemented with Apache Spark

Summary With the rapid development of technologies such as the internet, the amount of data that are collected or generated in many areas such as in the agricultural, biomedical, and finance sectors poses challenges to the scientific community because of the volume and complexity of the data. Furthermore, the need of analysis tools that extract useful information for decision support has been receiving more attention in order for researchers to find a scalable solution to traditional algorithms. In this paper, we proposed a scalable design and implementation of a particle swarm optimization classification (SCPSO) approach that is based on the Apache Spark framework. The main idea of the SCPSO algorithm is to find the optimal centroid for each target label using particle swarm optimization and then assign unlabeled data points to the closest centroid. Two variants of SCPSO, SCPSO‐F1 and SCPSO‐F2, were proposed based on different fitness functions, which were tested on real data sets in order to evaluate their scalability and performance. The experimental results revealed that SCPSO‐F1 and SCPSO‐F2 scale very well with increasing data set sizes and the speedup of SCPSO‐F2 is almost identical to the linear speedup while the speedup of SCPSO‐F1 is very close to the linear speedup. Thus, SCPSO‐F1 and SCPSO‐F2 can be efficiently parallelized using the Apache Spark framework.
A Framework of Information Security Integrated with Human Factors

Information systems support organizations to achieve strategic competitiveness over other organizations and assist senior management in the decision-making process. In addition, they help organizations in timely implementation of projects and effective risk management. A reliable and coherent Information System requires a solid security framework that ensures Confidentiality, Integrity, Availability, Authenticity and Auditability of the critical information assets; therefore, managing security is essential for organizations doing business in a globally networked and competitive environment whilst seeking to achieve their objectives and goals and ensuring the continuity of business. This paper provides an integrated framework that classifies and holistic view of challenges in Information Security Systems, and their interrelationships. The framework is expected to provide a basis that can be used to evaluate individual organizational members’ behavior and the adequateness of existing security measures.