Reviewerly/exHarmony huggingface dataset:
Discovering Knowledge-Point Importance From the Learning-Evaluation Data

As students in online courses usually show differences in their cognitive levels and lack communication with teachers, it is difficult for teachers to grasp student perceptions of the importance of knowledge-points and to develop personalized teaching. Though recent studies have paid attention to this topic, existing methods fail to calculate the importance of every knowledge-point for each student. Moreover, some studies are based on expert analysis, are not data-driven, and hence are inapplicable to large-scale online scenarios. To address these issues, this article proposes a personal topic rank (PTR) as a solution, which links students and concepts to generate a personalized knowledge concept map. Then, the authors present a novel PTR method to calculate the importance of knowledge-points, wherein student mastery of knowledge-points, student understanding, and the knowledge-point itself are considered simultaneously. This article conducts extensive experiments on a real-world dataset to demonstrate that the method can achieve better results than baselines.
An Analytical Approach Towards Data Stream Processing on Smart Society for Sustainable Development

In real-time processing, stream has to be processed as soon as it’s generated. Data streams generated from IOT sensors are processed into a finite-size window. A sheer window is considered for processing of data streams in a particular time stamp. In these sheer windows, compare reduce aggregate (CRA) algorithm is applied for determining linear relation for multiple feature vectors. A real-time inference pattern is determine using hash-based classification. In this chapter, sheer window hash-based classification using binarised window analytic (SHCUBA) approach is proposed. This approach is beneficial for calculating linear relationship between sheer windows. SHCUBA approach is comprised of transformation and virtualisation. Here, time and space complexity for this approach is $$O(n)$$ and $$O\left(1\right)$$ , respectively. This reduces latency and space requirements for various real-time use cases such as smart applications, sentiment analysis, IOT-based solutions, fraud detection and prevention, stock market prediction, etc. Data generated in smart societies can be correlated using SHCUBA approach for inferring useful decisions.
An efficient deep learning segmentation scheme for cervical collagen and elastin quantification in Mueller matrix polarimetry microscopic images

We present an efficient deep learning-based segmentation approach able to discriminate collagen from elastic fibers from Mueller matrix microscopy images of the mouse cervix. Thanks to the use of a Self-Validating Mueller matrix Micro-Mesoscope (SAMMM) system, Second Harmonic Generation (SHG) and Two Photon Excitation Fluorescence (TPEF) are also acquired and used as the quantitative segmentation ground truth. The method combines a multilayer perceptron and two U-net convolution networks. The accuracy and image quality metrics show improved segmentation results.
Properties of Group Neutro-Topological Space

Unlike traditional algebraic structures, where all operations are well-defined and all axioms are completely true, NeutroAlgebras and AntiAlgebras allow operations to be partially well-defined and axioms to be partially true or fully outer-defined, and axioms to be completely false. These NeutroAlgebras and AntiAlgebras represent a new research subject based on real-world examples. Since an empty set is not a subgroup of a group by observing this, the article leads to learning group neutro-topological space. We introduced the notion of a group neutro-topological space and investigated its properties.
Cognitive Positioning Technologies for IoT Network Devices

Nowadays, wireless technologies are increasingly being used for human needs. Increasingly, technologies are emerging that are used by people not only for the need to transfer data. One of these technologies is the Internet of Things, which often uses wireless sensors with ZigBee data transmission technology as end devices.There are areas that require deployment of these networks on the territory, a large number of sensors are required, which must with sufficient accuracy “know” their position on the deployment area. Usually, devices with built-in GPS modules are used for this, but devices containing this module are significantly more expensive than without it. And if in a large distributed network with many segments, more than 1000 such devices are required, then a device with a GPS module can only be at most one for each segment. Therefore, if this is a forest where there are many thousands of trees and it is necessary to monitor fires at the initial stage, which take place in many US states in the summer, then the cognitive task of teaching those devices, that do not contain a GPS module, to determine their position is relevant. This paper proposes a mathematical formulation of the cognitive task of learning to determine the coordinates of devices in wireless sensor networks. The study of the mathematical model has been carried out. The purpose of these studies was to find new alternative teaching methods for determining the distance between objects of IoT sensor networks, using the function of localizing objects where an emergency occurred.
A New Memorization Method for Japanese Demonstrative Pronouns (kore これ, sore それ, are あれ, kono この, sono その, ano あの) and Adverbs of Place (koko ここ, soko そこ, asoko あそこ)

This article presents a new method of memorization for Japanese demonstrative pronouns and adverbs of place. Rather than their usual ordering of kore これ, sore それ, are あれ; kono この, sono その, ano あの; and koko ここ, soko そこ, asoko あそこ – corresponding to proximal, medial and distal respectively – it is suggested to reverse the order. This order has the same advantage of following the linguistic structure, but has the additional benefit of forming the acronym ASK with the first letters of each word to aid in the memorization of the relative location of the referents.
Digital contact tracking: a threat to civil liberty or a moral obligation?

The paper focuses on the ethical dilemmas related to the technology of digital contact tracing (DCT). The author reviewed existing arguments and offers a new classification of approaches to this problem. Ethical justifications to implementation of DCT can be divided into utilitarian and conditional (deontological) approaches. The author tries to prove that the conditional approach is more effective, and it might become the basis for the ethical analysis of specific public health decisions. In conclusion, it demonstrates that the conditional (deontological) approach to the ethical justification of DCT might be used in the discussion about digital vaccination passports (DVP).
Optimization of Textual Index Construction Using Compressed Parallel Wavelet Tree

Nowadays, large number of information are flowing through Internet that requires better storage and optimization technique to retrieve relevant information. In the past, a lot of researches have been carried out in the field of index optimization for reducing storage space, construction time and retrieval time. In recent times, researchers have used various succinct data structures for indexing the textual data. In this paper, we propose two algorithms for compressing the textual index that reduces the index construction time. In the baseline approach (Algorithm 1), we apply traditional wavelet tree with LZW compression technique (TWL) to construct the index. In the second approach (Algorithm 2), we propose LZW compression with parallel wavelet tree (PWTL) to construct the textual index. The algorithms are evaluated on I3, I5 and I7 processor with multiple sizes of index. The results show that proposed algorithms outperform as compared to traditional wavelet tree in terms of index construction time.
Integrating Visual Exploration and Direct Editing of Multivariate Graphs

A central concern of analyzing multivariate graphs is to study the relation between the graph structure and its multivariate attributes. During the analysis, it can also be relevant to edit the graph data, for example, to correct identified errors, update outdated information, or to experiment with what-if scenarios, that is, to study the influence of certain attribute values on the graph. To facilitate both, the visual exploration and direct editing of multivariate graphs, we propose a novel interactive visualization approach. The core idea is to show the graph structure and calculated attribute similarity in an integrated fashion as a matrix. A table can be attached to the matrix on demand to visualize the graph’s multivariate attributes. To support the visual comparison of structure and attributes at different levels, several mechanisms are provided, including matrix reordering, selection and emphasis of subsets, rearrangement of sub-matrices, and column rotation for detailed comparison. Integrated into the visualization are interaction techniques that enable users to directly edit the graph data and observe the resulting changes on the fly. Overall, we present a novel integrated approach to explore relations between structure and attributes, to edit the graph data, and to investigate changes in the characteristics of their relationships. To demonstrate the utility of the presented solution, we apply it to explore and edit structure and attributes of a network of soccer players.
One-Way ANOVA with Possible Heteroscedasticity of Variances and Non-Normality of Errors

In this chapter, we start out by positing the Behrens-Fisher statistic as the statistic of choice for testing equality of two means. After, numerically showing that the CLT applies to this statistic, we consider the problem of testing equality of more than two means. The FDR (the false discovery rate) is used here for that purpose for adjusting the p-values yielded by all possible pairwise comparisons. Type I errors and power for different null hypotheses and alternatives using the FDR, the classical ANOVA F statistic and also the Kruskal-Wallis statistic are shown.
How Do Online Customer Reviews Impact Online Purchases? The Role of Online Review Examination as a Guilt/Shame Reduction Strategy: An Abstract

Consumers often face situations where they have to choose between emotional gratification and functionality. For example, they have to decide between buying a sports car or a minivan; they have to choose a healthy snack or a tempting dessert. One of the factors that influence consumer choice in this context, is the anticipation of emotions that will be experienced as a result of this choice. Researchers have established the impact of shopping motive (i.e., utilitarian vs. hedonic) on choice and the aversive state of guilt (e.g., Choi et al. 2014; Okada 2005). Okada (2005) found that given a choice between a hedonic and a utilitarian item, consumers have a higher preference for the utilitarian item and that hedonic purchases lead to feelings of guilt unless the shopper can formulate a justification (e.g., I have gone to the gym today, so it is okay if I choose a high-calorie dessert). However, this stream of research commonly ignores the significance of shoppers’ personal values and belief system. In this study, we argue that feelings of guilt are not a consequence of hedonic shopping, but rather a byproduct of the value violation occurring in association with the hedonic shopping motive and purchase behavior. We explore and demonstrate that when a purchase decision does not involve a value violation, consumer purchase intention is similar regardless of shopping motive (i.e., hedonic or utilitarian). However, when a value violation is involved, it leads to anticipation of aversive states such as shame and guilt which in turn reduce purchase intention. In addition, we suggest that online customer reviews provide an ideal context for justification of a value-violating purchase. Our results indicate that as a result of the negative emotional states evoked by the value-violating purchase, customers’ online information search behavior change.
Prediction Model of Internationalized Talent Training Based on Fuzzy Neural Network Algorithm

Since entering the 21st century, the network information age, China’s rapid integration into the global economy, the rapid development of international economy, science and technology level unceasing enhancement, the computer communication network technology, computer network virtual simulation technology, cloud computing technology, the Internet of things technology and multiple wireless sensor network technology under the background of the fast development. The cooperation and competition between countries promote the cultivation of international talents, and the prediction model of national talent cultivation plays an important role in talent cultivation. At the same time, with the support of technological development, the international talent training mode based on fuzzy neural network algorithm appears in a creative image, which is of great significance for the accuracy of the prediction model of international talent training. In this paper, based on the fuzzy relation analysis, using the BP algorithm based on fuzzy neural network algorithm for internationalized talents cultivation model and internationalization of traditional personnel training efficiency and accuracy of forecasting model to calculate, it is concluded that based on fuzzy neural network prediction model of internationalized talents training algorithm at 5.3% and 3.1% respectively in terms of accuracy and efficiency higher than traditional internationalized talents cultivation prediction model.
The Role of Artificial Neural Network in Word Sense Disambiguation (WSD)—A Survey

The chore of reducing ambiguity in distinct sense of words is known as word sense disambiguation (WSD). It’s a key area of research in computational linguistics to deal with the senses being assigned automatically to the words in a particular circumstances (Yuan et al. in Semi-supervised word sense disambiguation with neural models [1]). Human are naturally excellent at WSD and can tell the difference between senses utilized in the vocabulary through verbal language. On the contrary, computers have a hard time distinguishing between proper and incorrect meanings of words. Knowledge-based, Supervised, Semi-Supervised, and Unsupervised techniques have all been used to make progress in the problem of disambiguation. A better knowledge of human language will aid to computer performance in a variety of applications, including search and retrieval. The major goal of the paper is to describe a supervised neural network model that uses multiple strategies to maximize sense detection accuracy. The neural network’s input layer will be made up of binary valued nodes based on whether or not frequently recurring context words connected to the ambiguous phrases are present. Amount of nodes in the outer layer will be equal to the amount of senses of the ambiguous word.
Single Image Fog Removal Using WLS Smoothing Filter Combining CLAHE with DWT

A clean picture may be restored from a damaged image utilising fog removal methods. The degree of haze or fog that influences the original picture may be determined using the image’s depth information. In this paper, WLS (Weighted Least Square)-based filtering is used to enhance the image. In the post-processing stage, further refining is done, such as local contrast enhancement and increasing fine detail throughout the recovered picture, so that the item in the image can be seen clearly. After that, we have utilised CLAHE (Contrast limited adaptive histogram equalisation) which is a well-known local contrast enhancement approach for improving tiny features in a picture. It might result in over-enhancement and noise production in some sections of a picture. The CLAHE-DWT strategy, which combines CLAHE with DWT (Discrete Wavelet Transform), is employed to overcome these difficulties. As a consequence, more visible image features with sharper objects, which is ideal for human visual perception is obtained. In both subjective and objective evaluations, our suggested strategy outperforms existing methods.
Robust Zero Watermarking Algorithm for Medical Volume Data Based on LBP

With the gradual rise of telemedicine, a large number of images will be transmitted on the network during network consultation, which may cause privacy disclosure. How to more effectively ensure the information security of patients and the copyright of medical images has attracted great attention of scholars in relevant fields. This paper proposed a robust watermarking method for medical volume data based on local binary pattern (LBP) transformation. Firstly, taking the Medical Volume Data as the research object, the low frequency feature vector of the Medical Volume Data is extracted by combining local binary mode transform and discrete cosine transform. Then hash perception was performed. Finally, we use zero watermark technology to embed the hash sequence and chaotic technology to encrypt watermark. Experimental prove that the proposed algorithm has strong anti-attack abilities.
Breast cancer detection using optimized hidden convolutional neural network

Breast cancer is the deadlier ailment, and it is the vital cause for augmented death rates in women. Mammography is the principal method for diagnosing breast cancer. Currently, diagnosing breast cancer using mammogram images is a versatile task. Here, the proposed system develops a novel scheme for detecting breast cancer. Initially, erosion and dilation are done during pre-processing. Then the features, including Local Binary Pattern (LBP) and Gray Level Run-Length Matrix (GLRM), are extracted. Further, to resolve the issue of dimensionality, the features are chosen optimally via a new algorithm termed Lion Integrated FireFly Algorithm (LI-FF). Then, an optimized CNN is introduced in the detection phase that portrays the detected results precisely. Remarkably, the same LI-FF model fine-tuned CNN&apos;s weights and hidden neuron counts. At last, the superiority of the developed approach is proved on various measures.
Low-Carbon Economic Life and Landscape Design Analysis and Research

With the rapid development of the economy, people’s living standard is constantly improving, the pursuit of low-carbon economic life and healthy life is more and more obvious. As a basic project in the construction of urbanization, urban garden landscape is closely related to the daily life of urban residents. The purpose of this paper is to apply the low-carbon concept to urban landscape design, to provide a good environment for people’s low-carbon life, and to provide ideas for many current landscape designers. This paper mainly analyzes the concept of low carbon and takes qiushui park in A city as A practical case to actively explore the research on China’s low-carbon garden landscape design. With low carbon ecological technology, low-carbon landscape construction and construction related theory as the foundation, to autumn landscape engineering status as the breakthrough point, for A city region of ecological green space landscape construction by the thorough analysis, to explore A low-carbon landscape in related theory and technology problems in practical application, and through the investigation and analysis from the theoretical level, technical level and promote research and analytical design practices. It is concluded that the design and construction of low-carbon garden landscape in region A has good resource conditions and highly feasible technical means. In addition, compared with the previous landscape design, the low-carbon landscape design scheme obtained 93% satisfaction.
Designer Interface for a Class of Wearable Computer Applications

Wearable computing systems are a large and important new class of embedded systems. Within this broad class is an important subclass, namely those systems which monitor various signals, both within the body and external to the body, and possibly perform some action in response to various events. Many of these applications require conditions and actions that are beyond those described in our prior work. This work describes four such extensions. First, sensor data can be aggregated (integrated) over time. Second, actuator controls can be controlled over time with monitoring. Third, inputs from external sources can be used in the same way that sensor data can be used. Finally, messages sent to the outside word can include expressions involving sensor data as well as status of actuators. This paper describes the designer interface that allows professionals without computer expertise to include these features in the definition of a new wearable product. The definitions collected by the designer interface can be used to automatically generate the computer code that runs on the wearable computing device.
A Comparative Analysis to Advancing the National Cybersecurity Strategy in Saudi Arabia

Cyberspace has dramatically expanded due to technological advancement. Nowadays, cyberspace is part of daily life experiences and socio-economical activities. Countries all over the world need to have their own National Cybersecurity Strategies (NCSS) to be protected from cyber risks and threats. NCSS states the strength of a given country’s cybersecurity strength concerning the objectives, aims, vision, and cybersecurity mission of a country in question. Previously, many researchers have conducted studies on NCSS by contrasting the National Cybersecurity Strategy between different nations primarily for intercontinental teamwork and coordination of cybersecurity challenges globally. Purposefully, one of the main objectives is to evaluate and assess policy frameworks in various countries to combat the prevailing cyber threats. As a result, from the comparison of many policy frameworks on NCSS of many countries, it was discovered that more effort should put into National Cybersecurity of Saudi Arabia. This paper compares the cybersecurity strategy of Saudi Arabia with the NCSS of other fifteen countries such as the United States of America, Singapore, India, Japan, Malaysia, Kuwait, Canada, UK, China, Egypt, Bahrain, Hong Kong, Russia, Korea, and France. Saudi Arabia rank in cybersecurity has risen to be in the second rank in 2020. Compared to other developed countries, the results found that Saudi Arabia appears to be on the right track in ensuring the safety of its cyberspace.
Assessment of Solar Ramp Management of a 20 Mwp Solar Power Plant in Bangladesh Using a Fuzzy Logic Approach

Penetration of Variable Renewable Energy (VRE) with short-term variation is a challenge for safe grid integration. The fluctuations in the output power of the SPV plant, which originated from the input variation, are caused by the dynamic change of load flow in the interconnection area of the utility network. A time-series solar irradiance data has been collected to assess the short-term variation where Chittagong University of Engineering and Technology (CUET), Bangladesh is the case study site. Based on the analysis, short term variation of solar irradiance is significant especially from April to September. A fuzzy logic-based model has been used to understand the Energy Storage System (ESS) support amount to minimize the solar ramp rate and ramp level. An 8 MWh ESS with an 8 MW capacity has been identified as ramp management support for a 20 MWp solar plant at the case study site where the cost will be increased to approximately 14%. The daily required support amount and surplus amount in terms of energy and power are close to each other but not the same. The result could be varied in a few amounts due to the smoothing amount, geographical dispersion, cloud enhancement, and similar other effects.