Reviewerly/exHarmony huggingface dataset:
Secure Sensor Prototype Using Hardware Security Modules and Trusted Execution Environments in a Blockchain Application: Wine Logistic Use Case

The security of Industrial Internet of Things (IIoT) systems is a challenge that needs to be addressed immediately, as the increasing use of new communication paradigms and the abundant use of sensors opens up new opportunities to compromise these types of systems. In this sense, technologies such as Trusted Execution Environments (TEEs) and Hardware Security Modules (HSMs) become crucial for adding new layers of security to IIoT systems, especially to edge nodes that incorporate sensors and perform continuous measurements. These technologies, coupled with new communication paradigms such as Blockchain, offer a high reliability, robustness and good interoperability between them. This paper proposes the design of a secure sensor incorporating the above mentioned technologies—HSMs and a TEE—in a hardware device based on a dual-core architecture. Through this combination of technologies, one of the cores collects the data extracted by the sensors and implements the security mechanisms to guarantee the integrity of these data, while the remaining core is responsible for sending these data through the appropriate communication protocol. This proposed approach fits into the Blockchain networks, which act as an Oracle. Finally, to illustrate the application of this concept, a use case applied to wine logistics is described, where this secure sensor is integrated into a Blockchain that collects data from the storage and transport of barrels, and a performance evaluation of the implemented prototype is provided.
Effective Event Extraction Method via Enhanced Graph Convolutional Network Indication with Hierarchical Argument Selection Strategy

As one of foundation technologies for massive data processing for AI, event mining is attracting more and more attention, mainly including event detection (event trigger identification and event classification) and argument extraction. At present, EE-GCN is one of the most effective methods for event detection. However, since EE-GCN only focuses on event detection, complete event multi-tuple extraction needs to be improved. Inspired by the EE-GCN event detection method, this paper proposes an effective event extraction method via graph convolutional network indication with a hierarchical argument selection strategy. The method mainly includes the following steps. (1) Based on the ACE2005 argument extraction template, a new argument extraction template is established for the Baidu event extraction dataset. (2) The trigger events and event classification detected by EE-GCN are used as indicators to determine the argument extraction template, and the alternative arguments are extracted via named entity recognition based on the determined template. (3) Making full use of the side information of EE-GCN graph to solve the local and global correlation degree, and based on the local and global correlation degrees, the final argument multi-tuple is determined. (4) Finally, several experiments are conducted on the Baidu event extraction dataset to compare the proposed method with other methods. The experimental results show that the proposed method has improved the accuracy and completeness of the event extraction compared to other existing methods.
An End-to-End Online Traffic-Risk Incident Prediction in First-Person Dash Camera Videos

Predicting traffic risk incidents in first-person helps to ensure a safety reaction can occur before the incident happens for a wide range of driving scenarios and conditions. One challenge to building advanced driver assistance systems is to create an early warning system for the driver to react safely and accurately while perceiving the diversity of traffic-risk predictions in real-world applications. In this paper, we aim to bridge the gap by investigating two key research questions regarding the driver’s current status of driving through online videos and the types of other moving objects that lead to dangerous situations. To address these problems, we proposed an end-to-end two-stage architecture: in the first stage, unsupervised learning is applied to collect all suspicious events on actual driving; in the second stage, supervised learning is used to classify all suspicious event results from the first stage to a common event type. To enrich the classification type, the metadata from the result of the first stage is sent to the second stage to handle the data limitation while training our classification model. Through the online situation, our method runs 9.60 fps on average with 1.44 fps on standard deviation. Our quantitative evaluation shows that our method reaches 81.87% and 73.43% for the average F1-score on labeled data of CST-S3D and real driving datasets, respectively. Furthermore, the proposed method has the potential to assist distribution companies in evaluating the driving performance of their driver by automatically monitoring near-miss events and analyzing driving patterns for training programs to reduce future accidents.
LDWS-net: A learnable deep wavelet scattering network for RGB salient object detection

In the salient object detection task, convolutional neural network (CNN) based models have been extensively used. However, preserving a variety of boundary features of objects is equally important while detecting the salient objects. Detecting salient objects with poor boundaries have a significant detrimental effect on the salient object detection (SOD) models' robustness and accuracy. The proposed method leverages a unique and novel edge-directed salient object detection network, which combines wavelet scattering network features with CNN-based features. This integration enables the network to capture both textural and high-level semantic information, leading to improved SOD performance. Additionally, a learnable wavelet scattering network allows for the efficient collection and preservation of textural aspects of objects. This network is seamlessly embedded into the encoder section of the proposed architecture, enhancing the discriminative power of the model. Furthermore, a weighted feature integration module (WFIM) is proposed in the decoder section to adaptively integrate linked nearby features by evaluating their relevance, resulting in improved representation and discrimination capabilities. Extensive testing on well-known benchmark SOD datasets demonstrates that the proposed LDWS-Net outperforms state-of-the-art techniques, exhibiting accurate identification of salient objects and efficient detection of their edges.
Software defect prediction method based on the heterogeneous integration algorithm

A software defect is a common cyberspace security problem, leading to information theft, system crashes, and other network hazards. Software security is a fundamental challenge for cyberspace security defense. However, when researching software defects, the defective code in the software is small compared with the overall code, leading to data imbalance problems in predicting software vulnerabilities. This study proposes a heterogeneous integration algorithm based on imbalance rate threshold drift for the data imbalance problem and for predicting software defects. First, the Decision Tree-based integration algorithm was designed following sample perturbation. Moreover, the Support Vector Machine (SVM)-based integration algorithm was designed based on attribute perturbation. Following the heterogeneous integration algorithm, the primary classifier was trained by sample diversity and model structure diversity. Second, we combined the integration algorithms of two base classifiers to form a heterogeneous integration model. The imbalance rate was designed to achieve threshold transfer and obtain software defect prediction results. Finally, the NASA-MDP and Juliet datasets were used to verify the heterogeneous integration algorithm’s validity, correctness, and generalization based on the Decision Tree and SVM.
Mineral prospectivity mapping based on Support vector machine and Random Forest algorithm – A case study from Ashele copper–zinc deposit, Xinjiang, NW China

The utilization of machine learning (ML) techniques in conjunction with multi-source geoscience datasets for comprehensive metallogenic prognosis (MP) has emerged as a novel means of geological prospecting. Nevertheless, the representativeness of features and composition of datasets employed in constructing the ML model for MP may substantially impact the prediction model's overall performance and augment the metallogenic prediction's uncertainty. In this study, the Ashele copper-zinc deposit was chosen to conduct the case study to resolve these challenges. In order to improve the representativeness of features employed in ML modeling, the ore-controlling geological and rock geochemical exploration criteria were digitized and subjected to spatial analysis to extract information closely linked to mineralization. To investigate the impact of differences in dataset composition on the performance of predictive models, multiple labeled datasets were generated by randomly sampling from known boreholes. The predictive SVM and RF models were trained using the labeled datasets, validated using 10-fold cross-validation, and assessed using multiple metrics. The evaluation findings indicate that all ML models, subsequent to the enhancement of feature representativeness, exhibited satisfactory performance, as evidenced by a consistent predictive accuracy of approximately 0.88. Additionally, the study revealed that variations in the labeled datasets could adversely affect the predictive models' performance. However, this effect could be alleviated by selecting merit-based predictive models constructed from diverse dataset compositions. A comparative analysis of the ML predictive models revealed that the RBF kernel SVM and RF models demonstrated exceptional performance, with an AUC of 0.972 and 0.968, respectively. As a result, an integrated prediction model was developed by overlaying the RBF kernel SVM and RF models, successfully identifying 98% of the mineralized boreholes in the top 15% of the study area. The spatial distribution of gravity and magnetic anomalies in the study area corresponded well with the prospecting areas identified by the machine learning predictive model, further validating the efficacy of ML methods in MP.
Unfavorable epidemiological situation as an intensifier of digitalization of higher education

The relevance of the article is determined by the need to analyze the changes that occurred in higher education institutions in the area of the use of digital technology during and after the pandemic. Within the framework of this research, we conducted a content-analysis of the websites and curricula of Volgograd higher education institutions and a questionnaire survey of students in order to identify the advantages and disadvantages of the use of digital technology and e-learning in the modern context. Digitalization came into the process of the management of higher education institutions long ago, however, rapid changes in e-learning have occurred in the last two years, which was confirmed in the results of our study. 70-80% of students' independent work was transferred to electronic form, the classroom workload is replaced with online lessons, the teaching methods change: the use of mixed learning as well as blended and flipped learning increases. The results of the study make us think about the excessive enthusiasm for e-learning, possibilities and risks of its use. Digital education brings significant changes in the role of professors and students in the process of learning, which requires appropriate adaptation and causes changes in the management of the organizations and the introduction of new actors in the educational process. At the institutional level, our conclusions indicate the need to support the academic staff in the area of methodological and technical assistance since the process of digital transformation of higher education institutions is irreversible and its intensiveness continues to grow.
Prediction of Global Solar Radiation Based on Sunshine Hours in Ethiopia’s Amhara Region

Global solar radiation data is required for a variety of applications, including the design of solar energy conversion systems. However, these data are not recorded and readily available in many developing countries. On the other hand, sunshine durations are registered in many metrological stations of developing countries. Scientists have developed several equations that can be used to estimate global solar radiation from hours of sunshine over time. The Angstrom-Prescott linear model and its derivatives, such as power, exponential, logarithmic, quadratic, and cubic, are among the most commonly used. In this study, seven models are used to estimate the global solar radiation from hours of sunshine duration for 16 metrological stations in the Amhara Region of Ethiopia. The estimated values were compared with the measured values based on the statistical results such as coefficient of determination (R2), root mean square error (RMSE), percentage error (PE), mean bias error (MBE), mean absolute bias error (MABE), mean percentage error (MPE), and mean absolute percentage error (MAPE). According to the statistical test results, the cubic model is the most accurate at forecasting the total solar radiation at the 16 stations in the Amhara Region. As a result, it can be used to estimate the region’s global solar radiation.
Dualities for codes over finite Abelian groups

We describe dualities for codes over finite abelian groups and give MacWilliams relations for these dualities.We prove foundational results involving the orthogonal code for both symmetric and non-symmetric dualities and we describe all possible dualities for any finite abelian group.Additionally, we give an inner-product for any mixed alphabet.
Construction of nonlinear component of block cipher using coset graph

&lt;abstract&gt; &lt;p&gt;In recent times, the research community has shown interest in information security due to the increasing usage of internet-based mobile and web applications. This research presents a novel approach to constructing the nonlinear component or Substitution Box (S-box) of block ciphers by employing coset graphs over the Galois field. Cryptographic techniques are employed to enhance data security and address current security concerns and obstacles with ease. Nonlinear component is a keystone of cryptography that hides the association between plaintext and cipher-text. Cryptographic strength of nonlinear component is directly proportional to the data security provided by the cipher. This research aims to develop a novel approach for construction of dynamic S-boxes or nonlinear components by employing special linear group $ PSL(2, \mathbb{Z}) $ over the Galois Field $ GF\left({2}^{10}\right) $. The vertices of coset diagram belong to $ GF\left({2}^{10}\right) $ and can be expressed as powers of α, where α represents the root of an irreducible polynomial $ p\left(x\right) = {x}^{10}+{x}^{3}+1 $. We constructed several nonlinear components by using $ {GF}^{*}\left({2}^{10}\right) $. Furthermore, we have introduced an exceptionally effective algorithm for optimizing nonlinearity, which significantly enhances the cryptographic properties of the nonlinear component. This algorithm leverages advanced techniques to systematically search for and select optimal S-box designs that exhibit improved resistance against various cryptographic attacks.&lt;/p&gt; &lt;/abstract&gt;
Hybrid Algorithm Based on Phasor Particle Swarm Optimization and Bacterial Foraging Optimization

In response to the issues of premature convergence and instability of the phasor particle swarm optimization (PPSO) for solving function optimization problems, a new hybrid algorithm called bacteria PPSO (BPPSO) was proposed which combines the chemotaxis operation of the bacterial foraging optimization (BFO) algorithm with PPSO. In BPPSO, all individuals undergo tumbling and swimming strategies when the chemotaxis condition is met. New coefficients are introduced to update the positions of particles in BPPSO, achieving complementary advantages of BFO and PPSO. Finally, BPPSO is validated using eight benchmark functions, demonstrating its fast convergence speed, high computational accuracy, and good stability, making it a powerful global optimization algorithm.
Reinforced Vision-and-Language Navigation Based on Historical BERT

Vision-and-language navigation(VLN) task needs an agent navigate to a target location under the guidance of natural language instructions given by humans in novel 3D environments. We mainly study the vision-and-language navigation task in indoor scenes. It is difficult to obtain a large number of labeled training data, thus traditional multi-modal methods perform poorly in the data-scarce environment. We introduce pre-trained BERT model into vision-and-language navigation task to extract environment features and reduce the computation consumption by fixing the instruction features during navigation. Meanwhile, we design Gated Recurrent Vision-and-Language BERT to effectively memorize and transmit historical information to alleviate memory loss problem during navigation. In addition, we conduct reinforcement learning and imitation learning simultaneously to address the exposure bias of imitation learning. Empirically, we conduct experiments in discrete environment and analyze experimental results to verify the effectiveness of our proposed method.
Publication Patterns of U.S. Academic Librarians and Libraries, 2013–2017 with Comparison to Preceding Studies

This study adds to a series investigating the publication patterns of refereed articles in Library and Information Science (LIS) journals by United States academic librarians (USALs).The first study covered 1993-97, and subsequent studies continued in five-year increments.This study presents data and metrics for 2013-17 from fifty-two journals: thirty studied since 1998, seven added in 2003, and fifteen added in 2013.Over the years, the proportion of articles by USALs has decreased, despite evidence that USAL publishing is increasing.This difference suggests that other segments of LIS publishing are increasing faster than USAL publishing.The percentages of coauthorship and USALs who publish three or more articles in five years have increased.Large public research universities with librarians who have faculty status and tenure continue to be the most productive, but evidence suggests an increasing number of academic libraries are contributing to the LIS journal literature.The percentages of USAL and non-USAL articles in the journals studied since 1998 and those studied since 2003 or 2013 point to differences in growth among journals, the importance of new journals, and changes in affiliations of USAL authors and where USALs publish.
Building Intelligence in the Mechanical Domain—Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks

Herein, the cognitive capability of a simple, paper‐based Miura‐ori—using the physical reservoir computing framework—is experimentally examined to achieve different information perception tasks. The body dynamics of Miura‐ori (aka its vertices displacements), which is excited by a simple harmonic base excitation, can be exploited as the reservoir computing resource. By recording these dynamics with a high‐resolution camera and image processing program and then using linear regression for training, it is shown that the origami reservoir has sufficient computing capacity to estimate the weight and position of a payload. It can also recognize the input frequency and magnitude patterns. Furthermore, multitasking is achievable by simultaneously applying two targeted functions to the same reservoir state matrix. Therefore, it is demonstrated that Miura‐ori can assess the dynamic interactions between its body and ambient environment to extract meaningful information—an intelligent behavior in the mechanical domain. Given that Miura‐ori has been widely used to construct deployable structures, lightweight materials, and compliant robots, enabling such information perception tasks can add a new dimension to the functionality of such a versatile structure.
Covid-19 WhatsApp sticker memes as public signs in Oman

Abstract In this article, we identify a new form of public discourse that takes the shape of WhatsApp sticker memes personally created and nationally shared on WhatsApp by citizens in Oman during the Covid-19 pandemic to concomitantly act as carriers of information and as promoters of relational work. The data consist of 67 public sign stickers culled from a larger set amassed as part of an ethnographic project on Arabs and Covid-19. Drawing upon interpersonal pragmatics and visual semiotics, we examine the interplay of textual and visual modes in featured stickers, demonstrating how Omanis translated official Covid-19 instructions into three types of terse memes to facilitate communal distribution. We suggest that to encourage public cooperation, the stickers struck a culturally-imperative balance between keeping traditional norms intact while committing socially offensive acts. The article contributes to research on digital discourse by exploring sticker use as public signs in the understudied Arabic context.
Autoencoder and Deep Convolutional Generative Adversarial Network in Improving the Performance of Bangla Handwritten Character Recognition

Outliers and class imbalances in datasets can be great adversaries to the classifier performance. The task of recognizing Bangla handwritten characters is also no different than that. This chapter has addressed these two aspects of a large Bangla handwritten characters dataset. An Autoencoder-based model has been proposed to perform outlier detection. After removing the outliers, the deep convolutional generative adversarial network (DCGAN) has been employed for the imbalanced classes. ResNet-50 classifiers have been trained on the dataset after removing the outliers as well as after adding generated images. In both cases, the classifiers have demonstrated better test accuracy than that of the original dataset. On the outlier removed and the balanced datasets, ResNet-50 has yielded 97.95% and 97.92% test accuracy, respectively whereas the test accuracy on the original dataset has been 97.63%. These methods can help us to obtain a robust Bangla handwritten classifier.
Brain-Metaverse Interaction for Anxiety Regulation

Metaverse has become a powerful tool for conducting research in many domains, including education, social science, and healthcare. It mixes the virtual and physical environments and can produce various stimuli for users to experience and be immersed in the virtual-real environment. However, at present, these stimuli are preset and immobile, not responding to the user’s changing requirements. In addition, it lacks studies on how brain signals might suggest the demand or preference for specific VR content and if/how VR can interact with users’ brains directly, hands-free, and without verbal instructions. As metaverse’s natural association with learning and brain activities, receiving signals directly from the user’s brain will offer a firm edge to explore mental health issues. This research proposes a new framework, namely Brain-Metaverse Interaction (BMI), which enables the direct interaction between users’ brain signals and the adaptation of VR content in an iterative and evolving manner. Our experiment based on this framework shows promising results, although suffering from the typical limitations of hardware devices and data acquisition, such as signal noise of EEG data and sensitivity and latency of the EEG device.