Reviewerly/exHarmony huggingface dataset:
Underlying SUSY in a generalized Jaynes–Cummings model

Abstract We present a general qubit-boson interaction Hamiltonian that describes the Jaynes–Cummings model and its extensions as a single Hamiltonian class. Our model includes non-linear processes for both the free qubit and boson field as well as non-linear, multi-boson excitation exchange between them. It shows an underlying algebra with supersymmetric quantum mechanics features allowing an operator based diagonalization that simplifies the calculations of observables. As a practical example, we show the evolution of the population inversion and the boson quadratures for an initial state consisting of the qubit in the ground state interacting with a coherent field for a selection of cases covering the standard Jaynes–Cummings model and some of its extensions including Stark shift, Kerr-like, intensity dependent coupling, multi-boson exchange and algebraic deformations.
Image plagiarism detection using GAN - (Generative Adversarial Network)

In Today’s date plagiarism is a very important aspect because content originality is the client's prior requirement. Many people on the internet use others' images and get publicity while the owner of the image or data won′t get anything out of it. Many users copy the data or image features from the other users and modify it a little bit or create an artificial replica of it. With sufficient computational power and volume of data, the GAN models are capable enough to produce fake images that look very much similar to the real images. These kinds of images are generally not detected by modern plagiarism systems. GAN stands for generative adversarial network. It has two neural networks working inside. The first one is the generator which generates a random image and the second one is the discriminator which identifies whether the image being generated is a real or a fake image. In this paper, we have proposed a system that has been trained on both fake images (GAN Generated images) and real images and will help us in flagging whether the image is plagiarised or a real image.
Saliency-Based Gaze Visualization for Eye Movement Analysis

Gaze movement and visual stimuli have been utilized to analyze human visual attention intuitively. Gaze behavior studies mainly show statistical analyses of eye movements and human visual attention. During these analyses, eye movement data and the saliency map are presented to the analysts as separate views or merged views. However, the analysts become frustrated when they need to memorize all of the separate views or when the eye movements obscure the saliency map in the merged views. Therefore, it is not easy to analyze how visual stimuli affect gaze movements since existing techniques focus excessively on the eye movement data. In this paper, we propose a novel visualization technique for analyzing gaze behavior using saliency features as visual clues to express the visual attention of an observer. The visual clues that represent visual attention are analyzed to reveal which saliency features are prominent for the visual stimulus analysis. We visualize the gaze data with the saliency features to interpret the visual attention. We analyze the gaze behavior with the proposed visualization to evaluate that our approach to embedding saliency features within the visualization supports us to understand the visual attention of an observer.
GP with a Hybrid Tree-vector Representation for Instance Selection and Symbolic Regression on Incomplete Data

Data incompleteness is a pervasive problem in symbolic regression, and machine learning in general. Unfortunately, most symbolic regression methods are only applicable when the given data is complete. One common approach to handling this situation is data imputation. It works by estimating missing values based on existing data. However, which existing data should be used for imputing the missing values? The answer to this question is important when dealing with incomplete data. To address this question, this work proposes a mixed tree-vector representation for genetic programming to perform instance selection and symbolic regression on incomplete data. In this representation, each individual has two components: an expression tree and a bit vector. While the tree component constructs symbolic regression models, the vector component selects the instances that are used to impute missing values by the weighted k-nearest neighbour (WKNN) imputation method. The complete imputed instances are then used to evaluate the GP-based symbolic regression model. The obtained experimental results show the applicability of the proposed method on real-world data sets with different missingness scenarios. When compared with existing methods, the proposed method not only produces more effective symbolic regression models but also achieves more efficient imputations.
Adaptive and Heterogeneity-Aware Coded Cooperative Computation at the Edge

Cooperative computation is a promising approach for localized data processing at the edge, e.g., for Internet of Things(IoT). Cooperative computation advocates that computationally intensive tasks in a device could be divided into sub-tasks, and offloaded to other devices or servers in close proximity. However, exploiting the potential of cooperative computation is challenging mainly due to the heterogeneous and time-varying nature of edge devices. Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a coded cooperative computation framework, which we name Coded Cooperative Computation Protocol (C3P), by taking into account the heterogeneous and time-varying resources of edge devices. C3P dynamically offloads coded sub-tasks to helpers and is adaptive to time-varying resources. We show that (i) task completion delay of C3P is very close to optimal coded cooperative computation solutions, (ii) the efficiency of C3P in terms of resource utilization is higher than 99%, and (iii) C3P improves task completion delay significantly as compared to baselines via both simulations and in a testbed consisting of real Android-based smartphones.
TURKISH SENTIMENT ANALYSIS FOR OPEN AND DISTANCE EDUCATION SYSTEMS

Students' opinions are the most essential source to enhance the quality of education and educational services in Open and Distance education (ODE) Systems.How to access and analyze students' real opinions is a problem for ODE institutions.The purpose of the present study is to conduct a sentiment analysis (SA) on the collected Turkish tweets about an ODE system to monitor students' opinions and sentiments about the system.Firstly, the related 63699 tweets about the ODE system are gathered and analyzed.Later, preprocessing is applied to the dataset.Sentence-based SA is performed with the data provided.The dataset is vectorized using two vector space models to test four classifiers which are Support Vector Machines, K-Nearest Neighbor, Logistic Regression (LR), and Artificial Neural Networks.F-score values obtained with these classifiers are evaluated, and the results are discussed.LR classifier gives the best F-score values with %75 for each vector space model.Through the SA results, students' dissatisfaction, appreciation, and concerns will be learned quickly by the university administration to develop strategies that will increase the quality of education and educational services.
Single Image Super Resolution using ESPCN – With SSIM Loss

We propose a deep learning machine learning model to Enhance Resolution of low-resolution images. We will examine various available machine learning models to decide upon one model to apply and improve upon using ovel ideas on image processing technologies. The model that we propose is a subcategory of Convolutional Neural Network (CNN) models which is an Efficient Sub-pixel Convolutional Neural Network (ESPCN) which is improved upon. The purpose of this paper is to create Super Resolution images from the model output and investigate how we improved and will improve the accuracy of the model by using a different Loss function different from Mean Squared Error (MSE) which is Structural Similarity Index measure (SSIM).
Comparative Analysis of the Process of Training Education Managers in Educational Institutions

Context: A significant part of future specialists receive only practical skills without formal practical exam situation. In this regard, there is a need to train a manager who can adapt and be ready to carry out his/her activities in a specific profile interaction. The aim of the experimental study was to develop and test the advanced working hypothesis about the possibility of effective formation of readiness for future self-realisation of future education managers.Approach: The work uses mathematical methods for testing the hypothesis of testing the readiness of education managers with verification of competency type.Findings: The novelty of the study is determined by what the authors are considering "the possibility of training a manager in accordance" including not only the standards for the provision of educational services to the population, but also the possibility of introducing world-class training standards. The authors show that the basis for such a development may be the readiness to work in a globalised environment. The dynamics of changes in the indicators of the levels of readiness for professional self-realization of students studying in the control and experimental groups from the first to the fourth year were compiled and recorded. The methodology of vocational training of students for the purpose of their self-realisation during training has been specified. The practical significance of the study is determined by the fact that integration with the global educational environment should be based on international standards for the provision of educational services. Conclusion: The results of this research can be useful and interesting to international readers, as the parameters of educational and methodological support have been developed and tested, which contains a set of situational exercises to form readiness for professional self-realisation for future education managers. Also, the conducted analysis indicated serious problems with the employment of university graduates both in the Republic of Kazakhstan and in the Republic of Lithuania.
Activation Functions for Convolutional Neural Networks: Proposals and Experimental Study

Activation functions lie at the core of every neural network model from shallow to deep convolutional neural networks. Their properties and characteristics shape the output range of each layer and, thus, their capabilities. Modern approaches rely mostly on a single function choice for the whole network, usually ReLU or other similar alternatives. In this work, we propose two new activation functions and analyze their properties and compare them with 17 different function proposals from recent literature on six distinct problems with different characteristics. The objective is to shed some light on their comparative performance. The results show that the proposed functions achieved better performance than the most commonly used ones.
Psychological Semantic Parameters of Adolescents’ Conformism in the Measurement of Value Orientations

The aim is to theoretically substantiate and empirically study the psychological semantic parameters of conformism of adolescence and to establish the relationship with values. Value orientations as an important regulatory mechanism of personality behavior are revealed. Conformism is interpreted through social desirability as the individual’s desire for positive and constructive interaction, exchange with society, during which the individual obeys the latter, presenting himself in a favorable light. It is noted that young people often change their values under the pressure of “significant others” and society, presenting themselves in a favorable light, implementing conformal behavior. It is established that conformism correlates with value orientations (p&lt;.01; p&lt;.05). It is noted that respondents with a high level of conformism are characterized by a change in values under the influence of the environment and people with low levels – this trend is not observed. It has been studied that in adolescence there is a shift in the orientation of the individual from focusing on other people to focusing on their own meanings and values. It is determined that a person with a high level of conformism can neglect his own values, and therefore change his own behavior, which can be regarded as a “challenge to the environment”. It is offered to apply the received empirical results in training and to implement in educational and professional preparation of students-psychologists.
Introducing an Intelligent Goods Service Framework

With the increasing diffusion of Internet of Things (IoT) technologies, the transportation of goods sector is in a position to adopt novel intelligent services that cut across the otherwise highly fragmented and heterogeneous market, which today consists of a myriad of actors. Legacy systems that rely upon direct integration between all actors involved in the transportation ecosystem face considerable challenges for information sharing. Meanwhile, IoT based services, which are designed as devices that follow goods and communicate directly to cloud-based backend systems, may provide services that previously were not available. For the purposes of this paper, we present a theoretical framework for classification of such intelligent goods systems based on a literature study. The framework, labelled as the Intelligent Goods Service (IGS) framework, aims at increasing the understanding of the actors, agents, and services involved in an intelligent goods system, and to facilitate system comparisons and the development of new innovative solutions. As an illustration of how the IGS framework can be used and contribute to research in this area, we provide an example from a direct industry-academia collaboration.
Researching Blockchain Technology and its Usefulness in Higher Education

The current paper focuses on the potential of using Blockchain Technology (BCT) in the Higher Education Domain and explores its usefulness in solving Higher Education issues. This research discusses the Blockchain features, challenges and its benefits in education, followed by review of some current Blockchain Higher Education applications. This paper reviews the Blockchain Technology (BCT) and its implementation in Higher Education. This research used a quantitative methodology and stratified clustered simple random sampling approach. Data has been gathered through an online survey instrument and the partial least squares structural equation modelling (PLS-SEM) technique applied to 383 responses. Blockchain technology has its unique features, benefits that can solve Education system requirements, and its successful implementation issues discussed. An effort made to gather enough consensus to build future implementation. The integrated model of Blockchain features matched to the needs of the Education System by agreement of the experts (discussions), and a survey conducted involving the students, teachers, educationists, Blockchain experts, and professionals, is tested and validated by SEM using PLS.
Fighting Game Commentator with Pitch and Loudness Adjustment Utilizing Highlight Cues

This paper presents a commentator for providing real-time game commentary in a fighting game. The commentary takes into account highlight cues, obtained by analyzing scenes during gameplay, as input to adjust the pitch and loudness of commentary to be spoken by using a Text-to-Speech (TTS) technology. We investigate different designs for pitch and loudness adjustment. The proposed AI consists of two parts: a dynamic adjuster for controlling pitch and loudness of the TTS and a real-time game commentary generator. We conduct a pilot study on a fighting game, and our result shows that by adjusting the loudness significantly according to the level of game highlight, the entertainment of the gameplay can be enhanced.
Privacy for 5G-Supported Vehicular Networks

Vehicular networks allow billions of vehicular users to be connected to report and exchange real-time data for offering various services, such as navigation, ride-hailing, smart parking, traffic monitoring, and vehicular digital forensics. Fifth generation (5G) is a new radio access technology with greater coverage, accessibility, and higher network density. 5G-supported Vehicular Networks (5GVNs) have attracted plenty of attention from both academia and industry. Geared with new features, they are expected to revolutionize the mobility ecosystem to empower a portfolio of new services. Meanwhile, the development of such communication capabilities, along with the development of sensory devices and the enhancement of local computing powers, have lead to an inevitable reality of massive data (e.g., identity, location, and trajectory) collection from vehicular users. Unfortunately, 5GVN are still confronted with a variety of privacy threats. Such threats are targeted at users' data, identity, location, and trajectory. If not properly handled, such threats will cause unimaginable consequences to users. In this survey, we first review the state-of-the-art of survey papers. Next, we introduce the architecture, features, and services of 5GVN, followed by the privacy objectives of 5GVN and privacy threats to 5GVN. Further, we present existing privacy-preserving solutions and analyze them in-depth. Finally, we define some future research directions to draw more attention and down-to-earth efforts into this new architecture and its privacy issues.
Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection

This study presents a novel framework based on a heterogeneous ensemble method and a hybrid dimensionality reduction technique for spam detection in micro-blogging social networks. A hybrid of Information Gain (IG) and Principal Component Analysis (PCA) (dimensionality reduction) was implemented for the selection of important features and a heterogeneous ensemble consisting of Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR) and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) classifiers based on Average of Probabilities (AOP) was used for spam detection. The proposed framework was applied on MPI_SWS and SAC’13 Tip spam datasets and the developed models were evaluated based on accuracy, precision, recall, f-measure, and area under the curve (AUC). From the experimental results, the proposed framework (that is, Ensemble + IG + PCA) outperformed other experimented methods on studied spam datasets. Specifically, the proposed method had an average accuracy value of 87.5%, an average precision score of 0.877, an average recall value of 0.845, an average F-measure value of 0.872 and an average AUC value of 0.943. Also, the proposed method had better performance than some existing methods. Consequently, this study has shown that addressing high dimensionality in spam datasets, in this case, a hybrid of IG and PCA with a heterogeneous ensemble method can produce a more effective method for detecting spam contents.
Proof-of-Activity Consensus Algorithm Based on K-Medoids Clustering

K-medoids cluster-based Proof of Action consensus algorithm (KPoA) is proposed by us in order to mainly decrease the computing power wast and Malicious information dissemination in Proof of Activity consensus algorithm (PoA). In PoA, offline participant nodes cause block headers to be discarded, then the terrible wastage of computing power takes place. Moreover, the efficiency of consensus is greatly affected because the malicious nodes are not handled in time. KPoA uses K-medoids clustering and follow-the-satoshi mechanism to select participant nodes and accounting nodes successively to ensure the unpredictability of accounting nodes. All nodes are grouped by K-medoids clustering, which help to achieve transaction blocks' hierarchical consensus and reduce the spread of malicious information. Multiple accounting nodes take turns to create transaction blocks in the same block header, which nicely increases the number of transaction blocks and reduces the waste of computing power. In order to improve the activity of nodes, the reward and punishment scheme based on credit is set up. The credit value is used to adjust the probability of a node creating blocks. Also malicious nodes can be eliminated in time according to credit value. Experimental results show that KPoA creates transaction blocks at an acceleration of about 2.5 times that of PoA. And its stability is better than PoA.
Extension of luminance component based demosaicking algorithm to 4- and 5-band multispectral images

Multispectral imaging systems are currently expanding with a variety of multispectral demosaicking algorithms. But these algorithms have limitations due to the remarkable presence of artifacts in the reconstructed image. In this paper, we propose a powerful multispectral image demosaicking method that focuses on the G band and luminance component. We've first identified a relevant 4-and 5-band multispectral filter array (MSFA) with the dominant G band and then proposed an algorithm that consistently estimates the missing G values and other missing components using a convolution operator and a weighted bilinear interpolation algorithm based on the luminance component. Using the considered MSFA patterns, we've also demonstrated that our algorithm outperforms existing approaches both visually and quantitatively in terms of the PSNR and SSIM.
A multiphase texture-based model of active contours assisted by a convolutional neural network for automatic CT and MRI heart ventricle segmentation

Left and right ventricle automatic segmentation remains one of the more important tasks in computed aided diagnosis. Active contours have shown to be efficient for this task, however they often require user interaction to provide the initial position, which drives the tool substantially dependent on a prior knowledge and a manual process.We propose to overcome this limitation with a Convolutional Neural Network (CNN) to reach the assumed target locations. This is followed by a novel multiphase active contour method based on texture that enhances whole heart patterns leading to an accurate identification of distinct regions, mainly left (LV) and right ventricle (RV) for the purposes of this work.Experiments reveal that the initial location and estimated shape provided by the CNN are of great concern for the subsequent active contour stage. We assessed our method on two short data sets with Dice scores of 93% (LV-CT), 91% (LV-MRI), 0.86% (RV-CT) and 0.85% (RV-MRI).Our approach overcomes the performance of other techniques by means of a multiregion segmentation assisted by a CNN trained with a limited data set, a typical issue in medical imaging.