Reviewerly/exHarmony huggingface dataset:
Estimation of Parameters of Finite Mixture of Rayleigh Distribution by the Expectation-Maximization Algorithm

In the lifetime process in some systems, most data cannot belong to one single population. In fact, it can represent several subpopulations. In such a case, the known distribution cannot be used to model data. Instead, a mixture of distribution is used to modulate the data and classify them into several subgroups. The mixture of Rayleigh distribution is best to be used with the lifetime process. This paper aims to infer model parameters by the expectation-maximization (EM) algorithm through the maximum likelihood function. The technique is applied to simulated data by following several scenarios. The accuracy of estimation has been examined by the average mean square error (AMSE) and the average classification success rate (ACSR). The results showed that the method performed well in all simulation scenarios with respect to different sample sizes.
The Feasibility of Enhancing Environmental Awareness using Virtual Reality 3D in the Primary Education

Technology development promotes different learning strategies for varying levels of education. This technology provides opportunities for more engaging learning and encourages motivation to understand concepts and values. This research focuses on the development of 3D virtual reality (VR), which explains the living environment, especially animals, in the original habitat in the context of elementary school learning. This VR application is called Zoo-VR, which can be used in various learning models to achieve learning competencies. Zoo-VR includes 30 types of animals with their original sounds and explanations in audio and text form. The animals move freely in their habitat according to natural conditions. Users can move from one place to another, approach particular objects, and run virtual menus using programs installed on Oculus. The feasibility of Zoo-VR is reviewed from the perception of teachers and students after using it. The snowball effect interview model measured the perceptions of teachers and students. The teacher’s experience is observed in four aspects of the experience: conformity with the subject matter, feasible learning models, competencies, and the adequacy of menus in the application. The student experience is observed in five aspects: immersion, interest, the comfort of the tool, intention to share, and practicality of existing features. This experience gives the basis for a broader analyzing the use of Zoo-VR both in and outside the classroom. The results of the review and observation explain that Zoo-VR is feasible to apply to real learning situations. The challenge of Zoo-VR is integrating this application with learning objectives on various subjects. However, the result from the limited number of participants cannot be applied to generalize comprehensively. Studies on the application of Zoo-VR to actual learning conditions need to be carried out to reveal the real potential of VR.
SAM: A Mechanism to Facilitate Smear-Aware Forensic Analysis of Volatile System Memory

AbstractAbstractPage smear is a phenomenon that occurs when a system's volatile memory dump is obtained in a non-atomic manner; it's more common in systems with a lot of RAM and different workloads. It has a considerable impact on the quality and reliability of the forensic artifacts obtained, as well as the analysis of such snapshots. We present SAM, a timeline-based page table state information collection mechanism that enables a reliable memory analysis. It facilitates visualizing inconsistencies in the page table data structure and provides the investigator with a reliable source of page table information to deal with the inconsistent values.Keywords: Memory forensicspage smeartimeline-based analysisPageDumperLiME Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Later, PageDumper's functionality was extended to work on x86_64-bit Linux with five level paging hierarchy.2 PageDumper saves the time value for each page it acquires in milliseconds. For a more granular level of timestamp collection, it also allows to log the time values in terms of kernel jiffies. In Figure 6, the column KERNEL_TIMER provide the acquisition time for the PTE by PageDumper in kernel jiffies.
Probability models for highly parallel image coding architecture

A key aspect of image coding systems is the probability model employed to code the data. The more precise the probability estimates inferred by the model, the higher the coding efficiency achieved. In general, probability models adjust the estimates after coding every new symbol. The main difficulty to apply such a strategy to a highly parallel coding engine is that many symbols are coded simultaneously, so the probability adaptation requires a different approach. The strategy employed in previous works utilizes stationary estimates collected a priori from a training set. Its main drawback is that statistics are dependent of the image type, so different images require different training sets. This work introduces two probability models for a highly parallel architecture that, similarly to conventional systems, adapt probabilities while coding data. One of the proposed models estimates probabilities through a finite state machine, while the other employs the statistics of already coded symbols via a sliding window. Experimental results indicate that the latter approach improves the performance achieved by the other models, including that of JPEG2000 and High Throughput JPEG2000, at medium and high rates with only a slight increase in computational complexity.
TET: Text emotion transfer

Text style transfer aims at transforming the style of a piece of text while keeping its primary content. The style of the text is usually defined as a particular writing tone in different categories, such as formality, politeness, sentiment, and political slant. Recently, most of the work in the area has been devoted to the problem of sentiment transfer, which tries to transfer an opinionated text into a positive or negative perspective. It has applications in marketing, political news, chatbots, writing tools, and many others. On the other hand, emotions as the basic forms of sentiments have brought many attentions to different tasks, including image style transfer but they are not well expressed in text style transfer yet. This article presents a text emotion transfer model that transforms the style of a text to each of the predefined ‘anger’, ‘fear’, ‘joy’, and ‘sadness’ emotions. Relying on masked language modeling and transfer learning, the proposed model can perform efficiently on limited amounts of emotion-annotated data. Moreover, the model shows promising experimental results against other existing models considering style transfer accuracy, content preservation, and fluency in the ISEAR and TEC emotion corpora.
Mixed Histories, Augmented Pasts

There is widespread confusion over the terms augmented reality, mixed realityMixed reality, and virtual reality, but there is a further term as well, augmented virtualityAugmented Reality (AR)Augmented virtuality. A new term, Extended Reality, or XRExtended Reality (XR), promises to do away with the confusion by gathering all of the terms under its umbrella label. But there is also the apparently new concept of the MetaverseMetaverse, which has again confused, inspired, or irritated. Rather than attempt to cover all the major developments of this rapidly changing field, this chapter selects highlights that provide particular opportunities and issues of concern for the field of virtual heritage.
Exploring Student-Teacher Interaction and Learner Autonomy Through Writing Online Journals via Emails and Penzu: A Mixed-Method Analysis of Turkish Students’ Perceptions

The prevalence of online education has raised concerns about maintaining student-teacher interaction and learner autonomy. With the outbreak of the COVID-19 pandemic, these concerns have levelled up, so more studies have been called for. Therefore, this chapter presents empirical research on students' interaction with their peers, teacher and content, as well as on students' management and regulation of their learning. The study in the chapter investigated Turkish university-level students' perceptions about their learning through teacher interaction and autonomous learning. The students first interacted with their teacher through writing dialogue journals via emails for 4 weeks and then kept personal writings in Penzu for 4 weeks. Data from questionnaires and interviews showed that the students had positive beliefs on writing dialogue and personal journals since they thought that they improved their language learning skills thanks to their experience in writing emails and Penzu journals. Also, they indicated that Penzu enabled them to regulate their writing process freely. The chapter ends by suggesting that studies should further explore language use and pragmatic features of student writings to understand students' learning deeply; and that programme designers should consider integrating automated writing evaluation tools to enhance autonomous learning more.
How Does the Use of Different Soil Mineralogical Atlases Impact Soluble Iron Deposition Estimates?

Atmospheric deposition is the primary input of soluble iron (Fe) in remote regions of the open ocean. Fe constitutes a fundamental micro-nutrient for marine biota, affecting biogeochemical cycles and ultimately carbon uptake. Fe is emitted to the atmosphere primarily from dust sources. Fe solubility at emission is origin-dependent, being mostly insoluble when associated with dust minerals. Earth System Models (ESMs) commonly assume that dust aerosols have a globally uniform composition, neglecting known regional variations in soil mineralogy. This work assesses the implications of soil composition uncertainties on the bio-available Fe delivery to the ocean by using a state-of-the-art ESM with a detailed atmospheric Fe cycle (EC-Earth3-Iron). We run two 1-year simulations considering two different Soil Mineralogy Atlases (SMAs). Our results reveal a non-negligible impact of soil mineralogy uncertainties on soluble Fe estimates. By only changing the source of information for the soil composition and updating the Fe content in minerals, we find differences of 31.5% in mineral Fe emissions and 26.7% in soluble Fe deposition budgets.
Active learning for ordinal classification based on expected cost minimization

To date, a large number of active learning algorithms have been proposed, but active learning methods for ordinal classification are under-researched. For ordinal classification, there is a total ordering among the data classes, and it is natural that the cost of misclassifying an instance as an adjacent class should be lower than that of misclassifying it as a more disparate class. However, existing active learning algorithms typically do not consider the above ordering information in query selection. Thus, most of them do not perform satisfactorily in ordinal classification. This study proposes an active learning method for ordinal classification by considering the ordering information among classes. We design an expected cost minimization criterion that imbues the ordering information. Meanwhile, we incorporate it with an uncertainty sampling criterion to impose the query instance more informative. Furthermore, we introduce a candidate subset selection method based on the k-means algorithm to reduce the computational overhead led by the calculation of expected cost. Extensive experiments on nine public ordinal classification datasets demonstrate that the proposed method outperforms several baseline methods.
Data Analysis and Interpretation

This chapter entails data analysis and interpretation of results derived from applying relevant statistical tools and techniques to the data. The section provides the numerical data and details about, the significance of findings. Hypotheses testing and data analysis has been done by the using statistical techniques including Factor analysis, Multiple Regression, and ANOVA. Mean, Standard deviation, along with correlation coefficients and reliability test results have been listed in detail in this chapter.
An Experimental Study on Discovering the Value of Private Data in Data Marketplaces

Data marketplaces, such as Datacoup and Datum, have emerged as a new business paradigm where data are traded as commodities. In a data marketplace, people, or data owners, can monetise their private data. However, it is rarely investigated how much loss that the data owners experience when they sell their data in data marketplaces and it is hard for data marketplace companies to compensate them properly. This paper aims to fill this gap by answering the following research question: How much cost, in monetary terms, do data owners experience when trading their private data, in varying situations regarding the type of data being traded, the level of privacy being preserved, or the volume of data? To answer these questions, we design and conduct laboratory experiments by constructing a simulated data marketplace. The data analysis results show some insights on the private data value of data owners in data trading.
Low-light image enhancement via multistage feature fusion network

The images captured under low-light conditions are characterized by low brightness and poor contrast, which affects the accuracy of computer vision tasks. In recent years, there have been a variety of low-light image enhancement (LLIE) models based on deep learning, but they have not been able to fully extract the multiscale information of multiple stages, resulting in poor generalization performance and instability of the model. Currently, a large number of multistage networks cause color distortion and stylization of images due to excessive transmission of noncritical information. To address these issues, we propose an LLIE via multistage feature fusion network. Our network consists of three stages. In the first two stages of the LLIE, S-UNet, which combines UNet and spatial weighted residual channel attention block (SWRCAB), helps the network extract more critical multiscale information and occupy a small amount of computing resources. In the third stage, we fuse the SWRCAB and a nonlocal sparse block into the original enhancement network to enhance the original resolution pixel-by-pixel. We also propose a fusion attention mechanism, which can provide real and effective supervision and control the transmission of a small amount of critical feature information for each stage. In addition, we add illumination guidance for image segmentation at the beginning of each stage of the network, excepting that the model can better focus on the dark part of the low-light image and avoid overexposure. We conduct experiments on multiple benchmark datasets to qualitatively and quantitatively demonstrate that the proposed method is more competitive than the state-of-the-art methods.
Graph-Clustering Method for Construction of the Optimal Movement Trajectory under the Terrain Patrolling

The method of the optimal movement trajectory construction in the terrain patrolling tasks is proposed. The method is based on the search of the Hamiltonian circuit on the graph of the terrain map and allows automatic construction of the optimal closed path for arbitrary terrain map. The distinguishing feature of the method is the use of the modified algorithm for the Hamiltonian circuit search. The algorithm can be scaled for the maps corresponding to the graphs with a large (more than 100) number of the vertices, for which the standard brute-force algorithm of the Hamiltonian circuit search requires significantly higher execution time than the proposed algorithm. It is demonstrated that the utilized algorithm possesses 17 times less constant of the time complexity growth than the standard brute-force algorithm. It allows more than one order of magnitude (from 30 to 500 vertices, i.e., approximately to the 17 times) increase of the graph vertices that is used for the Hamiltonian circuit search in the real time (0.1–100 s) regime.
A Method of Noise Reduction for Radio Communication Signal Based on RaGAN

Radio signals are polluted by noise in the process of channel transmission, which will lead to signal distortion. Noise reduction of radio signals is an effective means to eliminate the impact of noise. Using deep learning (DL) to denoise signals can reduce the dependence on artificial domain knowledge, while traditional signal-processing-based denoising methods often require knowledge of the artificial domain. Aiming at the problem of noise reduction of radio communication signals, a radio communication signal denoising method based on the relativistic average generative adversarial networks (RaGAN) is proposed in this paper. This method combines the bidirectional long short-term memory (Bi-LSTM) model, which is good at processing time-series data with RaGAN, and uses the weighted loss function to construct a noise reduction model suitable for radio communication signals, which realizes the end-to-end denoising of radio signals. The experimental results show that, compared with the existing methods, the proposed algorithm has significantly improved the noise reduction effect. In the case of a low signal-to-noise ratio (SNR), the signal modulation recognition accuracy is improved by about 10% after noise reduction.
JUNO Project: Deployment and Validation of a Low-Cost Cloud-Based Robotic Platform for Reliable Smart Navigation and Natural Interaction with Humans in an Elderly Institution

This paper describes the main results of the JUNO project, a proof of concept developed in the Region of Murcia in Spain, where a smart assistant robot with capabilities for smart navigation and natural human interaction has been developed and deployed, and it is being validated in an elderly institution with real elderly users. The robot is focused on helping people carry out cognitive stimulation exercises and other entertainment activities since it can detect and recognize people, safely navigate through the residence, and acquire information about attention while users are doing the mentioned exercises. All the information could be shared through the Cloud, if needed, and health professionals, caregivers and relatives could access such information by considering the highest standards of privacy required in these environments. Several tests have been performed to validate the system, which combines classic techniques and new Deep Learning-based methods to carry out the requested tasks, including semantic navigation, face detection and recognition, speech to text and text to speech translation, and natural language processing, working both in a local and Cloud-based environment, obtaining an economically affordable system. The paper also discusses the limitations of the platform and proposes several solutions to the detected drawbacks in this kind of complex environment, where the fragility of users should be also considered.
Study on non-iterative algorithms for center-of-sets type-reduction of Takagi–Sugeno–Kang type general type-2 fuzzy logic systems

Abstract The paper performs the center-of-sets (COS) type-reduction (TR) and de-fuzzification for Takagi–Sugeno–Kang (TSK) type general type-2 fuzzy logic systems (GT2 FLSs) on the basis of the $$\alpha$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -planes expression of general type-2 fuzzy sets. Actually, comparing the popular Karnik–Mendel (KM) algorithms with other non-iterative algorithms is an important question in T2 society. Here the modules of fuzzy inference, COS TR, and de-fuzzification for TSK type GT2 FLSs are discussed by means of non-iterative Nagar–Bardini (NB) algorithms, Nie–Tan (NT) algorithms, and Begian–Melek–Mendel (BMM) algorithms. Simulation instances are constructed to illustrate the performances of three types of non-iterative algorithms compared with the KM algorithms. It is proved that, the proposed non-iterative algorithms can enhance the computational efficiencies significantly, which afford the potential application value for designers of GT2 FLSs.
Spatial variation and pollution risk assessment of heavy metals in industrial soil based on geochemical data and GIS-A case of an iron and steel plant in Beijing，China

The precise range of contamination determines the cost of treating heavy metals contamination of soils at industrial sites and was a key aspect of soil heavy metals contamination management. The pollution level and spatial correlation of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in one hundred and thirty topsoil samples from a typical contaminated site was analysed. Geochemical analysis showed that Hg pollution was the most serious in the site, followed by Cd, Pb, Zn, Cu and Cr. The contents of As and Ni in the soil did not exceed the background values. The interpolation accuracy, interpolation results and spatial distribution of inverse distance weight method (IDW), local polynomial method (LPI), radial basis function method (RBF) and ordinary kriging method (OK) were compared, respectively. The results show that the range, mean and coefficient of variation of the four interpolation methods were generally lower than that of the sampled data, which was mainly caused by smoothing effect. The smoothing effect of LPI and OK were the most serious, while the smoothing effect of IDW and RBF were not obvious. The optimal interpolation method of Cd, Cr, Hg, Pb and Zn in polluted sites was RBF and the optimal interpolation method for As, Cu and Ni was IDW. The evaluation of polluted area by IDW and RBF interpolation method was ranked as IDW > RBF, which was much larger than that calculated by sampling point. Both interpolation methods could increased the area of polluted area. The probability kriging (PK) method was similar to that based on sampling points, which can greatly improve the calculation accuracy of heavy metals polluted area in industrial contaminated sites. Geochemical data and spatial interpolation results show that phytoremediation techniques was the most suitable for remediation of heavy metal contaminated soils at this site.
Improved Mask R-CNN Segmentation and Bayesian Interactive Adaboost CNN Classification for Breast Cancer Detection on Bach Dataset

Breast cancer is considered as the predominant type of cancer that affects more than ten percentage of the worldwide female population. Though microscopic evaluation remains to be a significant method for diagnosing, time and cost complexity seeks alternative and effective computer aided design for rapid and more accurate detection of the disease. As DL (Deep Learning) possess a significant contribution in accomplishing machine automation, this study intends to resolve existing problems with regard to lack of accuracy by proposing DL based algorithms. The study proposes Improved-Mask R CNN (I-MRCNN) method for segmentation. In this process, RPN (Region Proposal Network), predicts the objectless scores and object bound at every position. Here, (RoI Align) Region of interest Align is used for feature extraction as it is capable of resolving the conventional RoI pooling issues by attaining high accuracy for small objects and also eliminates quantization issues. Further, classification is performed using the proposed Bayesian Interactive Adaboost CNN classifier (B-IAB- CNN) that integrates the advantages of CNN, Bayesian and Adaboost classifier. The advantages of the three classifier enable optimum classification of the input Bach dataset that is confirmed through the results of performance analysis of the proposed system. Outcomes reveal that, average accuracy for segmentation is 96.32%, while, the classification accuracy is exposed to be 96%. As Timely prediction is significant, high prediction rate of the proposed system will assist the medical practitioners to detect breast cancer quickly which is the important practical implication from this study for diagnosing breast cancer.