Reviewerly/exHarmony huggingface dataset:
Enhance the Uncertainty Modeling Ability of Fuzzy Grey Cognitive Maps by General Grey Number

In real-life systems, people cannot get precise data. The data are represented in the forms of interval or multiple intervals in many cases. Most intelligent algorithms are designed for precise data in algorithm research. People always use a real number contained in the interval or multiple intervals as the candidate for the precise data. However, such a measure will lose lots of information contained in the interval or multiple intervals. Fuzzy Cognitive Map (FCM) is one of the famous intelligent algorithms. The Fuzzy Grey Cognitive Map (FGCM) was proposed to enable FCM to do interval computation. But FGCM can only deal with a single interval, as for multiple intervals, the FGCM is powerless. Thus, this paper aims to enhance the FGCM to make it can cope with multiple intervals. The paper introduces the general grey number and deduces the new activation functions according to Grey System Theory (GST) and Taylor series. Finally, an industrial process control problem is applied to verify the new algorithm. The results show that the new algorithm is not only compatible with the original FGCM and FCM, but can process more uncertain knowledge and data. In general, the new algorithm inherits most of FGCM's characteristics and can cope with the data expressed by multiple intervals, which means it can be used in environments with more uncertain knowledge and data.
Embracing Physical Keyboards for Virtual Reality

This installment of Computer's series highlighting the work published in IEEE Computer Society journals comes from IEEE Transactions on Visualization and Computer Graphics.
INVESTIGATION OF BLAST FRAGMENTATION MODELS IN A SANDSTONE QUARRY

The existing blast fragmentation models were investigated in a sandstone quarry for bench blasting&#x0D; operations. It was tried to present different perspectives for evaluation of blast fragmentation.&#x0D; Size distribution curves were created for each blast. KuzRam model provided successful results.&#x0D; Kuznetsov-Cunningham-Ouchterlony (KCO) model was useful especially for the fine size&#x0D; predictions. Capability of the models for fine, mean and coarse size predictions were evaluated in&#x0D; detail. Uniformity coefficients proposed by the models were examined. Chung &amp; Katsabanis model&#x0D; was practically tested and a modification implemented for the studied quarry. Model validation was&#x0D; performed using ten different error metrics. Applicability of symmetric errors and scaled errors was&#x0D; investigated for evaluation of blast fragmentation. KuzRam model predicted mean fragment size&#x0D; with an error lower than three centimeters. The results of the modified Chung &amp; Katsabanis model&#x0D; is quite promise.
Reducing the Number of Experiments Required for Modelling the Hydrocracking Process with Kriging Through Bayesian Transfer Learning

Abstract The objective is to improve the learning of a regression model of the hydrocracking process using a reduced number of observations. When a new catalyst is used for the hydrocracking process, a new model must be fitted. Generating new data is expensive and therefore it is advantageous to limit the amount of new data generation. Our idea is to use a second data set of measurements made on a process using an old catalyst. This second data set is large enough to fit performing models for the old catalyst. In this work, we use the knowledge from this old catalyst to learn a model on the new catalyst. This task is a transfer learning task. We show that the results are greatly improved with a Bayesian approach to transfer linear model and kriging model.
PVF-DA: Privacy-preserving, verifiable and fault-tolerant data aggregation in MEC

As an emergent-architecture, mobile edge computing shifts cloud service to the edge of networks. It can satisfy several desirable characteristics for IoT systems. To reduce communication pressure from IoT devices, data aggregation is a good candidate. However, data processing in MEC may suffer from many challenges, such as unverifiability of aggregated data, privacy-violation and fault-tolerance. To address these challenges, we propose PVF-DA: privacy-preserving, verifiable and fault-tolerant data aggregation in MEC based on aggregator-oblivious encryption and zero-knowledge-proof. The proposed scheme can not only provide privacy protection of the reported data, but also resist the collusion between MEC server and corrupted IoT devices. Furthermore, the proposed scheme has two outstanding features: verifiability and strong fault-tolerance. Verifiability can make IoT device to verify whether the reported sensing data is correctly aggregated. Strong fault-tolerance makes the aggregator to compute an aggregate even if one or several IoTs fail to report their data. Finally, the detailed security proofs are shown that the proposed scheme can achieve security and privacy-preservation properties in MEC.
A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank

Accurate blood smear quantification with various blood cell samples is of great clinical importance. The conventional manual process of blood smear quantification is quite time consuming and is prone to errors. Therefore, this paper presents automatic detection of the most frequently occurring condition in human blood-microcytic hyperchromic anemia-which is the cause of various life-threatening diseases. This task has been done with segmentation of blood contents, i.e., Red Blood Cells (RBCs), White Blood Cells (WBCs), and platelets, in the first step. Then, the most influential features like geometric shape descriptors, Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and Gabor features (mean squared energy and mean amplitude) are extracted from each of the RBCs. To discriminate the cells as hypochromic microcytes among other RBC classes, scanning is done at angles (0∘, 45∘, 90∘, and 135∘). To achieve high-level accuracy, Adaptive Synthetic (AdaSyn) sampling for imbalance learning is used to balance the datasets and locality sensitive discriminant analysis (LSDA) technique is used for feature reduction. Finally, upon using these features, classification of blood cells is done using the multilayer perceptual model and random forest learning algorithms. Performance in terms of accuracy was 96%, which is better than the performance of existing techniques. The final outcome of this work may be useful in the efforts to produce a cost-effective screening scheme that could make inexpensive screening for blood smear analysis available globally, thus providing early detection of these diseases.
On the Verification of Smart Contracts: A Systematic Review

Ensuring the correctness of smart contracts is of paramount importance to achieve trust and continuity in the Blockchain-based business process execution. Due to the immutable nature of distributed ledger technology on the blockchain, a smart contract should work as intended before using it. Any bugs or errors will become permanent once published and could lead to huge economic losses. To avoid such problems, verification is required to check the correctness and the security of the smart contract. In this paper, we consider the smart contracts and we investigate the verification of the correctness of the Blockchain-based smart contracts using formal verification methods. We provide an overview of the formal verification of smart contracts and we present the used methods, tools and approaches. We show a description of each method as well as its advantages and limitations.
On Designing a Lesser Obtrusive Authentication Protocol to Prevent Machine-Learning-Based Threats in Internet of Things

In the era of the Internet of Things (IoT), people access many applications through smartphones for controlling smart devices. Therefore, such a centralized node must follow a robust access control mechanism so that an intruder cannot control the connected devices. Recent reports suggest that password can be used as an authentication factor for accessing the smart setups. However, this static information can be compromised under the light of different machine learning (ML)-empowered attack mechanisms. Alarmingly, different sensors used in the IoT setup can also expose this static information to the adversaries. Password-based authentication that uses a challenge-response strategy is an effective solution for handling such threat scenarios. In this article, at first, we show that no existing usable challenge-response protocol is safe to be used in the public area network. Following this, we propose a challenge-response protocol that is more secure to use in the public domain. By using eight classifiers, we show that a learning-based threat specific to our protocol has a marginal impact on the method's security standard. The discussion in this article also suggests that the proposed protocol has usability and security advantages compared to the existing state of the art (e.g., reduces the number of interactions between the user and verifier by a factor of 0.5).
The First Corpus-Driven Lexical Database of Lithuanian as L2

The article presents a new resource for A2-B2 learners of Lithuanian as L2 to improve their lexical competence and language production skills. The lexical database is a lexicographic application of the Lithuanian Pedagogic Corpus which was used both to develop headword lists and to collect word usage information. For this study, we adopt the inductive procedure of Corpus Pattern Analysis which was partly automated using the Lithuanian Sketch Grammar in Sketch Engine. We explain the model for pattern recognition and description, sense division, the selection of examples and give some details concerning the user interface.
Machine Learning-Based Decision Support System for Early Detection of Breast Cancer

Background: Breast cancer is one of the leading causes of death of women in the United States and also one of the most malignant cancer among women worldwide.Early, more accurate detection of breast cancer enables extended longevity at a reduced cost.Towards this, analyzing the available big data using tools, such as Machine learning-based decision support systems can improve the speed and accuracy of early detection of breast cancer.In this paper, we examined the prediction performance of various state-of-theart machine learning models and a decision support system based on these models that provided the predicted category along with a prediction confidence measure.Methods: The various machine learning (ML) algorithms applied include Decision Tree, Naïve Bayes, k-Nearest Neighbors (kNN) and Support Vector Machine (SVM).We also analyzed the effect of multiple feature selection approaches on the prediction performance.We used the Breast Cancer Wisconsin Dataset from Wisconsin Prognostic Breast Cancer (WPBC) with 569 digitized images of a fine needle aspirate (FNA) of breast mass and 10 realvalued feature information.The performance of the ML model was evaluated using the ten-fold cross-validation approach and also on a prediction set comprising of 20% data with the models trained on remaining 80% data.Sensitivity and Specificity were used as the primary measures of performance.Results: Among all five machine learning methods, SVM had the best performance.Except for the kNN algorithm, the performance of the other three algorithms, Logistic Regressions, Naïve Bayes and Decision Trees, were also quite close to SVM.The prediction performance of the decision support system was better than any individual ML model where the prediction confidence was "High" or "Medium".Conclusion: We found that feature selection improved the performance and computation cost for all ML models.By building the ML-based decision support system with the optimal feature subset, the prediction performance for breast cancer can be improved to 96% which means it can provide powerful assistance to doctors and patinets.On the other hand, as the size of the data set increases, the processing of data with a lot of features can increase the computation cost as well as the possibility of classification errors.
Augmented Reality as a Tool to Support the Inclusion of Colorblind People

Color blindness is a condition that affects the cones in the eyes, it can be congenital or acquired and is considered a medium disability that affects about 8.5% of the world population and it occurs in children, who have special difficulties to the newly enter an educational environment with materials developed for people with normal vision, this work focuses on the development one technology, to allow people with a visual disability known as color blindness, to improve their daily activities which in turn leads to a better inclusion in the social and educational environment.
Strange bedfellows: Design research and behavioral design

Behavioral design-the application of behavioral economics principles to real-world challenges-has achieved success across a variety of domains, yet its scale and effectiveness has been limited by its narrow focus on behavioral change to frame problems.While some have suggested that joining forces with the social sciences and other analytical problem-solving methodologies might help overcome its perceived deficits, the more generative and synthetic discipline of design research is particularly well positioned to be a complementary partner due to design's systems orientation and humanity-centered perspective to issues of context, evidence, and problem framing.This disciplinary integration has the potential to be particularly valuable with regard to so-called "wicked problems," which tend to resist analytical efforts.In contributing a more expansive lens with which to surface and develop potential hypotheses, design research shows great promise in its ability to partner with behavioral design to take ground on these complex challenges.
Depth Vision Guided Human Activity Recognition in Surgical Procedure using Wearable Multisensor

The increasing complexity of modern surgery rooms brings many challenges.Human activity recognition (HAR) plays a significant part in healthcare, telemedicine, long-term treatment, and even surgery by using wearable inertial sensors or depth cameras.Although the development of artificial intelligence techniques provide various machine learning (ML) methods to identify activities, it is a timeconsuming implementation and high work burden to collect and label the large data set.To fascinate efficient data collection and labeling, we propose a novel depth vision-guided HAR architecture to obtain the labels of the collected raw data from the inertial measurement unit (IMU) sensors using depth data automatically.Experimental results show that the novel depth vision-guided interface can be utilized for identifying activities without labeling the data in advance.
A Simultaneous Cartoon‐Texture Image Segmentation and Image Decomposition Method

Image segmentation and image decomposition are fundamental problems in image processing. Image decomposition methods for separating images into cartoon and texture components can effectively serve different image processing tasks because different components can be respectively treated in more effective way. However, image decomposition methods are currently simply taken as an independent preprocessing step, and particularly in image segmentation different effects of cartoon and texture components have not been considered. This paper presents a novel simultaneous cartoon-texture image segmentation and image decomposition method to boost the performance of both segmentation and decomposition. We design a fast alternating optimization algorithm to solve the proposed model. Experimental results demonstrate the outstanding performance of the proposed method on both image segmentation and image decomposition.
Creating Sustainable Knowledge Systems: Towards a Risk and Threat Assessment Framework

Knowledge is the most important asset to a company. It is key to strategic innovation and alignment, and must be made sustainable. To keep knowledge sustainable, it must be safeguarded. However, knowledge management (KM) and knowledge systems are different than traditional information systems with different threats and operational requirements. Information security professionals recognize that risk assessment is the cornerstone to information security. We build on this perspective and propose that risk assessment techniques need to be applied to KM. We discuss risk assessment frameworks and build on a KM/knowledge system risk assessment framework with a KM/knowledge system specific threat assessment.
A simple, general, and efficient method for sequential hypothesis testing: The independent segments procedure.

We propose a new sequential hypothesis testing procedure in which data are collected and analyzed in a series of independent segments. As in fixed-sample hypothesis testing and in previous sequential procedures, the overall α level can be set to any desired value. Like other sequential procedures, the independent segments procedure generally requires smaller samples than fixed-sample procedures-often approximately 30% smaller-to achieve the same α level and statistical power. Relative to other sequential procedures, the new method has the advantages that it is simpler to use, requires fewer assumptions, and can be used with a wider array of statistical tests. Thus, in some circumstances the independent segments procedure may provide an attractive option for increasing the efficiency of statistical testing. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
DIGITALIZATION OF EDUCATION IN THE CONTEXT OF DIGITAL INEQUALITY

The problem of forming digital literacy of society is considered through the created so­cio-cultural prerequisites for the implementation of digitalization programs in different countries. It is shown that the existing methods for the analy­sis of digital involvement allow fixing the existing «digital gap», but do not answer the question of ways to preserve human capital in peripheral re­gions. In particular, it is shown that the implemen­tation of the «knowledge society»concept becomes a reality only for those countries whose manage­ment system has been coordinated at the state and regional levels, and has been able to implement the principles of openness and accessibility in the field of education and information technolo­gy. The implementation of these principles makes it possible to build a more flexible education sys­tem, which will create socio-cultural prerequisites for remote and less developed regions to solve the country»s new tasks of creating universal digital literacy and neutralizing social problems caused by increasing digital inequality. The interdisci­plinary research uses institutional, functional and socio-cultural approaches. The conclusions are il­lustrated by the results of quantitative assessment of the main parameters used to determine the level of digitalization of society.
Detection of breast cancer presenting as a mass in women with dense breasts — digital breast tomosynthesis versus full-field digital mammography

Summary. Aim: To evaluate the sensitivity and specificity of digital breast tomosynthesis compared with full-field digital mammography in detection of breast cancer presenting as a mass in women with dense breasts. Маterials and Methods: This study included 347 asymptomatic and symptomatic patients with dense breasts who underwent full-field digital mammography, digital breast tomosynthesis and hand-held full breast ultrasound. 57 core-needle biopsies were performed. Pathology included 31 invasive cancers and 26 non-cancerous lesions. Results: Sensitivity of full-field digital mammography was 61.3% [0.422–0.789] and sensitivity of digital breast tomosynthesis was 77.4% [0.589–0.904]. Specificity of full-field digital mammography was 92.7% [0.893–0.953] that was 2.2% lower than the specificity of digital breast tomosynthesis — 94.9% [0.919–0.971]. Conclusion: Results of our study showed superior sensitivity and specificity of digital breast tomosynthesis compared to full-field digital mammography for detection of malignant masses in women with dense breasts.
Taking Justice Into Their Own Hands: Predictors of Netilantism Among Cyber Citizens in Hong Kong

This research examines the characteristics and predicting indicators of netizens who contribute to “Human Flesh Searching” and internet vigilantism. Human Flesh Searching (HFS) is a form of collective online behaviour where netizens contribute information to social media and/or networking platforms about a certain event or a target individual or group to achieve what they regard as justice. It has been used to identify and investigate crime. Some netizens go further and take justice into their own hands by punishing alleged criminals and deviants through online shaming. Using the results of a survey conducted in Hong Kong, the research found both gender and time spent online are not significant variables to predict netizens’ intention to contribute to HFS. A positive attitude towards HFS was the strongest predictor of HFS intention. Vigilantism was also a strong predictor of HFS intention. Vigilantism not only affects HFS intention directly, but also indirectly through a positive attitude on HFS. Fairness might negatively influence people’s HFS intention and attitude towards HFS; however, this influence was found to be weak in the present study. Social Justice might not affect HFS intention directly, yet it might exert its effect via a positive attitude towards HFS. That is, netizens who intend to contribute to HFS are those who have less confidence in the criminal justice system and believe highly that people should take justice into their own hands.
CAP’NN: Class-Aware Personalized Neural Network Inference

We propose CAP’NN, a framework for Class-Aware Personalized Neural Network Inference. CAP’NN prunes an already-trained neural network model based on the preferences of individual users. Specifically, by adapting to the subset of output classes that each user is expected to encounter, CAP’NN is able to prune not only ineffectual neurons but also miseffectual neurons that confuse classification, without the need to retrain the network. CAP’NN achieves up to 50% model size reduction while actually improving the top-l(5) classification accuracy by up to 2.3%(3.2%) when the user only encounters a subset of VGG-16 classes.
Chinese fingerspelling sign language recognition using a nine-layer convolutional neural network

INTRODUCTION: Sign language is a form of communication and exchange of ideas by people who are hearing-impaired or unable to speak.Chinese fingerspelling is an important component of Chinese sign language, which is suitable for denoting terminology and using as the basis of gesture sign language learning.OBJECTIVES: We propose a nine-layer convolutional neural network (CNN) for the classification of Chinese sign language.METHODS: With self-learning and self-organization abilities, CNN is committed to processing data with similar network structure.CNN has a good application prospect in the aspect of image classification and plays a very important role in the classification of Chinese sign language.RESULTS : Through experiments on 1320 data samples of 30 categories, the results show that the classification accuracy based on the nine-layer convolutional neural network can reach up to 89.69± 2.10 %, it can be seen that this method can effectively classify Chinese gestures.CONCLUSION: We proposed a nine-layer convolutional neural network (CNN) that can classify Chinese sign language.