Reviewerly/exHarmony huggingface dataset:
Exploring user experience of digital pen and tablet technology for learning chemistry: applying an activity theory lens

Mobile learning technologies are spreading rapidly in educational institutions throughout the world. Although research findings concerning the efficacy of mobile technologies for improving student outcomes are generally promising, there are still significant gaps in the research literature, particularly data from direct observational studies. This empirical investigation focused on how students made use of tablet devices and digital pens for learning Chemistry in an undergraduate university course. Observational data in the form of videos and static images, as well as, interview responses, were the main sources of data collected for the study. Activity theory was employed as the guiding theoretical framework to analyse and interpret the data. Several themes emerged from the data analyses, including the affordances of digital pen technology for facilitating reflective thinking, flexibility, peer collaboration, emerging learning and focused learning. It was also found that the use of these mobile technologies was contextualized, dependent on individual differences, and had challenges, for example, there was limited synchronicity between the operational design of the mobile devices and natural human movement. One of the main implications of the research is that when higher education institutions consider the potential benefits and challenges associated with mobile technologies they should take account of the interactions that occur between components within a system including, students, technological devices, and emerging learning processes.
Hierarchical Image Segmentation Based on Nonsymmetry and Anti-Packing Pattern Representation Model

Image segmentation is the foundation of high-level image analysis and image understanding. How to effectively segment an image into regions that are “meaningful” to the human visual perception and ensure that the segmented regions are consistent at different resolutions is still a very challenging issue. Inspired by the idea of the Nonsymmetry and Anti-packing pattern representation Model in the Lab color space (NAMLab) and the “global-first” invariant perceptual theory, in this paper, we propose a novel framework for hierarchical image segmentation. Firstly, by defining the dissimilarity between two pixels in the Lab color space, we propose an NAMLab-based color image representation approach that is more in line with the human visual perception characteristics and can make the image pixels fast and effectively merge into the NAMLab blocks. Then, by defining the dissimilarity between two NAMLab-based regions and iteratively executing NAMLab-based merging algorithm of adjacent regions into larger ones to progressively generate a segmentation dendrogram, we propose a fast NAMLab-based algorithm for hierarchical image segmentation. Finally, the complexities of our proposed NAMLab-based algorithm for hierarchical image segmentation are analyzed in details. The experimental results presented in this paper show that our proposed algorithm when compared with the state-of-the-art algorithms not only can preserve more details of the object boundaries, but also it can better identify the foreground objects with similar color distributions. Also, our proposed algorithm can be executed much faster and takes up less memory and therefore it is a better algorithm for hierarchical image segmentation.
Literature Review of Supply Chain Finance Based on Blockchain Perspective

Blockchain technology has the characteristics of decentralization and immutability.Since it appears, blockchain technology has received widespread attention and is considered to be the most revolutionary technology with the most promising development after cloud computing, Internet accelerated speed, and big data.Therefore, a wave of application of blockchain technology has set off around the world today.Supply chain finance is based on multi-party, relatively closed environment, and requires controllable and trusted data as supporting factors.Naturally, it has great potential room for combining with blockchain technology.The application of blockchain technology in the field of supply chain finance can provide new solutions to the problems in the development of supply chain finance.Therefore, the research on supply chain finance based on blockchain has become a new research hotspot.This article reviews the current literature on the application of blockchain to supply chain finance.Numerous studies have shown that the application of blockchain technology will open a new door for the development of supply chain finance and promote the vigorous and healthy development of supply chain finance.
Echo state network for two-dimensional turbulent moist Rayleigh-Bénard convection

Recurrent neural networks are machine learning algorithms that are well suited to predict time series. Echo state networks are one specific implementation of such neural networks that can describe the evolution of dynamical systems by supervised machine learning without solving the underlying nonlinear mathematical equations. In this work, we apply an echo state network to approximate the evolution of two-dimensional moist Rayleigh-B\'enard convection and the resulting low-order turbulence statistics. We conduct long-term direct numerical simulations to obtain training and test data for the algorithm. Both sets are preprocessed by a proper orthogonal decomposition (POD) using the snapshot method to reduce the amount of data. Training data comprise long time series of the first 150 most energetic POD coefficients. The reservoir is subsequently fed by these data and predicts future flow states. The predictions are thoroughly validated by original simulations. Our results show good agreement of the low-order statistics. This incorporates also derived statistical moments such as the cloud cover close to the top of the convection layer and the flux of liquid water across the domain. We conclude that our model is capable of learning complex dynamics which is introduced here by the tight interaction of turbulence with the nonlinear thermodynamics of phase changes between vapor and liquid water. Our work opens new ways for the dynamic parametrization of subgrid-scale transport in larger-scale circulation models.
Statistical Assessment of Hybrid Blockchain for SME Sector

Blockchain innovation with its property of immutable data isn't just limited to digital cryptographic forms of money yet in addition for various domains like healthcare, logistics, rentals, entertainment, social media and so on. Ventures are investigating this innovation to adopt in main line of business, which will give certainty on security ceasing data breach. This research paper has built up a hybrid blockchain framework which can be utilized on premise or over the cloud or a combination of both and can be utilized by various departments for protecting transactions in small and medium scale businesses with an uprightness of high security, transparency, remote devices, accessibility and cost adequacy. This created framework compares with the traditional approach model on some significant factors for statistical assessment. The analysis discovers how this hybrid blockchain framework is more productive and secured than the client server approach model and aims to be utilized as platform as a service both in local or hybrid cloud infrastructure
An end-to-end convolutional network for joint detecting and denoising adversarial perturbations in vehicle classification

Abstract Deep convolutional neural networks (DCNNs) have been widely deployed in real-world scenarios. However, DCNNs are easily tricked by adversarial examples, which present challenges for critical applications, such as vehicle classification. To address this problem, we propose a novel end-to-end convolutional network for joint detection and removal of adversarial perturbations by denoising (DDAP). It gets rid of adversarial perturbations using the DDAP denoiser based on adversarial examples discovered by the DDAP detector. The proposed method can be regarded as a pre-processing step—it does not require modifying the structure of the vehicle classification model and hardly affects the classification results on clean images. We consider four kinds of adversarial attack (FGSM, BIM, DeepFool, PGD) to verify DDAP’s capabilities when trained on BIT-Vehicle and other public datasets. It provides better defense than other state-of-the-art defensive methods.
Securing Data: A Study on Different Transform Domain Techniques

Steganography is a technology which is utilized in securing information through hiding data over another data which can be stored or passed over public network without a clue that secret data is hidden in the cover medium. This article focus on securing and hiding the data through transform domain techniques used in Image, Audio and Video steganography. The review study throws light on the algorithm and properties of transform domain techniques and its benefits to utilize in securing data. Parameters such as imperceptibility, capacity and robustness are evaluated for discrete cosine transform (DCT) and discrete wavelet transform (DWT).
A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications

Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene selection algorithms and methods have been introduced, they may suffer from problems such as parameter tuning or low level of performance. To tackle such limitations, in this study, a universal wrapper approach is introduced based on our introduced optimization algorithm and the genetic algorithm (GA). In the proposed approach, candidate solutions have variable lengths, and a support vector machine scores them. To show the usefulness of the method, thirteen classification and regression-based datasets with different properties were chosen from various biological scopes, including drug discovery, cancer diagnostics, clinical applications, etc. Our findings confirmed that the proposed method outperforms most of the other currently used approaches and can also free the users from difficulties related to the tuning of various parameters. As a result, users may optimize their biological applications such as obtaining a biomarker diagnostic kit with the minimum number of genes and maximum separability power.
Virtual Reality Applications for Higher Educations: A Market Analysis

Benefits and applications of virtual reality (VR) in higher education have seen much interest both from research and industry.While several immersive VR applications for higher education have been described, a structured analysis of such applications on the market does not exist.We use design elements from research for applying VR in higher education to analyze available VR apps.The analyzed VR applications were acquired from pertinent online stores to capture the market's state.We analyze the current picture of the available apps by categorizing them based on design elements and learning content.The aims are to map what types of apps are available, to study what expected types cannot (yet) be found, to compare the current state of the literature and the educational VR app market, as well as to scrutinize the most frequently used design elements for VR in education.
How Linguistically Fair Are Multilingual Pre-Trained Language Models?

Massively multilingual pre-trained language models, such as mBERT and XLM-RoBERTa, have received significant attention in the recent NLP literature for their excellent capability towards crosslingual zero-shot transfer of NLP tasks. This is especially promising because a large number of languages have no or very little labeled data for supervised learning. Moreover, a substantially improved performance on low resource languages without any significant degradation of accuracy for high resource languages lead us to believe that these models will help attain a fairer distribution of language technologies despite the prevalent unfair and extremely skewed distribution of resources across the world’s languages. Nevertheless, these models, and the experimental approaches adopted by the researchers to arrive at those, have been criticised by some for lacking a nuanced and thorough comparison of benefits across languages and tasks. A related and important question that has received little attention is how to choose from a set of models, when no single model significantly outperforms the others on all tasks and languages. As we discuss in this paper, this is often the case, and the choices are usually made without a clear articulation of reasons or underlying fairness assumptions. In this work, we scrutinize the choices made in previous work, and propose a few different strategies for fair and efficient model selection based on the principles of fairness in economics and social choice theory. In particular, we emphasize Rawlsian fairness, which provides an appropriate framework for making fair (with respect to languages, or tasks, or both) choices while selecting multilingual pre-trained language models for a practical or scientific set-up.
Fault Attacks on CCA-secure Lattice KEMs

NIST’s post-quantum standardization effort very recently entered its final round. This makes studying the implementation-security aspect of the remaining candidates an increasingly important task, as such analyses can aid in the final selection process and enable appropriately secure wider deployment after standardization. However, lattice-based key-encapsulation mechanisms (KEMs), which are prominently represented among the finalists, have thus far received little attention when it comes to fault attacks.Interestingly, many of these KEMs exhibit structural similarities. They can be seen as variants of the encryption scheme of Lyubashevsky, Peikert, and Rosen, and employ the Fujisaki-Okamoto transform (FO) to achieve CCA2 security. The latter involves re-encrypting a decrypted plaintext and testing the ciphertexts for equivalence. This corresponds to the classic countermeasure of computing the inverse operation and hence prevents many fault attacks.In this work, we show that despite this inherent protection, practical fault attacks are still possible. We present an attack that requires a single instruction-skipping fault in the decoding process, which is run as part of the decapsulation. After observing if this fault actually changed the outcome (effective fault) or if the correct result is still returned (ineffective fault), we can set up a linear inequality involving the key coefficients. After gathering enough of these inequalities by faulting many decapsulations, we can solve for the key using a bespoke statistical solving approach. As our attack only requires distinguishing effective from ineffective faults, various detection-based countermeasures, including many forms of double execution, can be bypassed.We apply this attack to Kyber and NewHope, both of which belong to the aforementioned class of schemes. Using fault simulations, we show that, e.g., 6,500 faulty decapsulations are required for full key recovery on Kyber512. To demonstrate practicality, we use clock glitches to attack Kyber running on a Cortex M4. As we argue that other schemes of this class, such as Saber, might also be susceptible, the presented attack clearly shows that one cannot rely on the FO transform’s fault deterrence and that proper countermeasures are still needed.
Human Activity Recognition Using CNN &amp; LSTM

In identifying objects, understanding the world, analyzing time series and predicting future sequences, the recent developments in Artificial Intelligence (AI) have made human beings more inclined towards novel research goals. There is a growing interest in Recurrent Neural Networks (RNN) by AI researchers today, which includes major applications in the fields of speech recognition, language modeling, video processing and time series analysis. Recognition of Human Behavior or the Human Activity Recognition (HAR) is one of the difficult issues in this wonderful AI field that seeks answers. As an assistive technology combined with innovations such as the Internet of Things (IoT), it can be primarily used for eldercare and childcare. HAR also covers a broad variety of real-life applications, ranging from healthcare to personal fitness, gaming, military applications, security fields, etc. HAR can be achieved with sensors, images, smartphones or videos where the advancement of Human Computer Interaction (HCI) technology has become more popular for capturing behaviors using sensors such as accelerometers and gyroscopes. This paper introduces an approach that uses CNN and Long Short-Term Memory (LSTM) to predict human behaviors on the basis of the WISDM dataset.
Tracing CAPEC Attack Patterns from CVE Vulnerability Information using Natural Language Processing Technique

To effectively respond to vulnerabilities, information must not only be collected efficiently and quickly but also the vulnerability and the attack techniques must be understood.A security knowledge repository can collect such information.The Common Vulnerabilities and Exposures (CVE) provides known vulnerabilities of products, while the Common Attack Pattern Enumeration and Classification (CAPEC) stores attack patterns, which are descriptions of the common attributes and approaches employed by adversaries to exploit known weaknesses.Because the information in these two repositories is not directly related, identifying the related CAPEC attack information from the CVE vulnerability information is challenging.One proposed method traces some related CAPEC-ID from CVE-ID through Common Weakness Enumeration (CWE).However, it is not applicable to all patterns.Here, we propose a method to automatically trace the related CAPEC-IDs from CVE-ID using TF-IDF and Doc2Vec.Additionally, we experimentally confirm that TF-IDF is more accurate than Doc2vec.
The impact of the pandemic on the quality of education and the image of a university

The objective of the article is to identify the nature of the impact of the pandemic on the quality of education and the image of a university that uses distance education and artificial intelligence. The research methodology includes a complex of general scientific and special methods. A questionnaire and detailed interview have been formed with regard to the indicators of the leading rating systems of higher education. The data obtained have been summarized and analyzed using a focus group. The regression analysis has been used to examine the connection between students' motives for making life easier in the context of the pandemic and distance education and the dangers of the professor-student-administration system. The sustainable links and tendencies of changes in the quality of education and the image of a university have been revealed. The following traps for students are referred to the stable connections: the absence of a valid system of control over the authorship of the executed works; the possibility of the unauthorized use of various electronic sources during the answer; the coronavirus quarantine leads to the erasure of the boundaries between the study and everyday life, personal space and social environment of students; the growth of students' desire to have high marks in the subjects with the decrease of interest to study. Tendencies: the increase of the workload of professors and supporting (technical) staff; constant redistribution of time spent on the performance of official duties with a different degree of efficiency; the growth of willingness, a desire of the administration and university professors to improve the competitiveness of the university as a high-tech educational organization, included in the world map of science; the growth of dissatisfaction with the distance form of education, the degree of efficiency of return on investment in human development; the decrease in the responsibility of students to acquire knowledge through distance learning. The practical relevance of the work lies in the possibility of considering the identified sustainable links and tendencies to improve the education system at both national and global levels. This will enable attention to problem areas at the local level to achieve their overcoming with minimal financial and human losses through proactive actions. The theoretical relevance of the work lies in adding scientific and methodological elements to the theoretical foundations of the concept of development of higher education. The authors studied for the first time the problems of the impact of the COVID-19 pandemic on the quality of education and the image of a university. The originality of the research is determined by the original methodological model, which allowed the participants of the educational process to assess the essence of the ongoing changes associated with the coronavirus quarantine and with the help of an expert group to identify sustainable links and tendencies in changes in the quality of education and the image of a university
Multi-layer perceptron-based tectonic discrimination of basaltic rocks and an application on the Paleoproterozoic Xiong'er volcanic province in the North China Craton

The geochemistry of basaltic rocks is widely used to investigate the tectonic setting of magmatism. The limitation of traditional two-dimensional tectonic discrimination diagrams is mainly risen from the fact that they can only simultaneously use the information of two (x-y plots) or three (ternary diagrams) elements (or element ratios) for discrimination. This obstacle can be overcome with the assistance of machine learning method, which shows great performances in classification of multidimensional datasets. In this study, we present a neural network-based model that uses whole rock major and trace elements to discriminate basaltic rocks (SiO2 45–55 wt%) from a wide range of tectonic settings, including continental arc basalt (CAB), island arc basalt (IAB), intra-oceanic arc basalt (IOAB), mid-ocean ridge basalt (MORB), oceanic plateau basalt (OPB), oceanic island basalt (OIB), continental flood basalt (CFB) and continental rift basalt (CRB). Using a modified method of cross validation, it is estimated that the model can discriminate the tectonic setting with an average accuracy of ~86%, and ~98% in discriminating the major tectonic regimes (arc, spreading center, or within-plate magmatism). This discrimination model was programed as a stand-alone Microsoft Excel spreadsheet that can be directly used by pasting the whole rock data into it. The discriminator was then applied to investigate the geodynamic background of the Paleoproterozoic (~1.75 Ga) Xiong'er volcanism in the southern margin of the North China Craton (NCC). It has long been debated whether this magmatism took place in a continental arc or within-plate rift environment. The discrimination result shows that both the Xiong'er Group volcanic rocks and coeval intrusive rocks have CFB affinities, indicating that they were products of mantle plume activity and defines a large igneous province (LIP) in a within-plate setting. This, along with previous studies, constrains the breakup of NCC, a part of the Columbia supercontinent, not later than ~1.79 Ga, and supports the idea that the fragmentation of the Columbia was triggered by mantle plume impingement.