Reviewerly/exHarmony huggingface dataset:
Implementation of Hybrid Blockchain in a Pre-Owned Electric Vehicle Supply Chain

Ecumenical carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) emission emanates from car, bus, taxi, and motorcycle is around 45%. Electric vehicle could be the major contributor to abbreviate the pollution level in a transportation sector. According to the Forbes report the ecumenical passenger electric vehicle demand expected to reach 15% in 2025, and 23% in 2030. This research provides a blockchain predicated solution for a pre-owned electric vehicle market that could engender a trust, transparency, immutable records, and an efficient way to track the entire life cycle of a futuristic electric vehicle supply chain. At present most of the pre-owned electric conveyance purchase transpires through the third-party accommodations, websites, and mobile applications. This does not provide the authentic information on electric conveyance history, charging capabilities of the battery, history of the charging records, and performance of the driver (wear and tear affect battery life). Our solution implemented utilizing hybrid blockchain technology. Each stakeholder assigned with the Ethereum blockchain address to track the vehicle records as a distributed ledger. This design considered electric vehicle manufacturer, charging station, battery manufacturer, road convey ascendancy as major stakeholders. Astute contract designed with solidity programming. Truffle platform used to deploy keenly intellective contract in private blockchain that ascertain the privacy of electric conveyance owners and other stakeholders. Meta mask a mobile application utilized kovan network for tracking vehicle in a public blockchain. This application tested with 0.1 Ether for each transaction. Blockchain Electric Vehicle Cloud of Things (BEVCoT) concept proposed to integrate the IoT-Blockchain application in a cloud environment.
Deep Spatial-Angular Regularization for Light Field Imaging, Denoising, and Super-Resolution

Coded aperture is a promising approach for capturing the 4-D light field (LF), in which the 4-D data are compressively modulated into 2-D coded measurements that are further decoded by reconstruction algorithms. The bottleneck lies in the reconstruction algorithms, resulting in rather limited reconstruction quality. To tackle this challenge, we propose a novel learning-based framework for the reconstruction of high-quality LFs from acquisitions via learned coded apertures. The proposed method incorporates the measurement observation into the deep learning framework elegantly to avoid relying entirely on data-driven priors for LF reconstruction. Specifically, we first formulate the compressive LF reconstruction as an inverse problem with an implicit regularization term. Then, we construct the regularization term with a deep efficient spatial-angular separable convolutional sub-network in the form of local and global residual learning to comprehensively explore the signal distribution free from the limited representation ability and inefficiency of deterministic mathematical modeling. Furthermore, we extend this pipeline to LF denoising and spatial super-resolution, which could be considered as variants of coded aperture imaging equipped with different degradation matrices. Extensive experimental results demonstrate that the proposed methods outperform state-of-the-art approaches to a significant extent both quantitatively and qualitatively, i.e., the reconstructed LFs not only achieve much higher PSNR/SSIM but also preserve the LF parallax structure better on both real and synthetic LF benchmarks. The code will be publicly available at https://github.com/MantangGuo/DRLF.
Teaching Tech to Talk: K-12 Conversational Artificial Intelligence Literacy Curriculum and Development Tools

With children talking to smart-speakers, smart-phones and even smart-microwaves daily, it is increasingly important to educate students on how these agents work—from underlying mechanisms to societal implications. Researchers are developing tools and curriculum to teach K-12 students broadly about artificial intelligence (AI); however, few studies have evaluated these tools with respect to AI-specific learning outcomes, and even fewer have addressed student learning about AI-based conversational agents. We evaluated our Conversational Agent Interface for MIT App Inventor and workshop curriculum with respect to 8 AI competencies from the literature. Furthermore, we analyze teacher (n=9) and student (n=47) feedback from workshops with the interface and recommend that future work (1) leverages design considerations to optimize engagement, (2) collaborates with teachers, and (3) addresses a range of student abilities through pacing and opportunities for extension. We found evidence for student understanding of all 8 competencies, with the most difficult concepts being AI ethics and machine learning. We recommend emphasizing these topics in future curricula.
Split, Embed and Merge: An accurate table structure recognizer

Table structure recognition is an essential part for making machines understand tables. Its main task is to recognize the internal structure of a table. However, due to the complexity and diversity in their structure and style, it is very difficult to parse the tabular data into the structured format which machines can understand, especially for complex tables. In this paper, we introduce Split, Embed and Merge (SEM), an accurate table structure recognizer. SEM is mainly composed of three parts, splitter, embedder and merger. In the first stage, we apply the splitter to predict the potential regions of the table row/column separators, and obtain the fine grid structure of the table. In the second stage, by taking a full consideration of the textual information in the table, we fuse the output features for each table grid from both vision and text modalities. Moreover, we achieve a higher precision in our experiments through providing additional textual features. Finally, we process the merging of these basic table grids in a self-regression manner. The corresponding merging results are learned through the attention mechanism. In our experiments, SEM achieves an average F1-Measure of 97.11% on the SciTSR dataset which outperforms other methods by a large margin. We also won the first place of complex tables and third place of all tables in Task-B of ICDAR 2021 Competition on Scientific Literature Parsing. Extensive experiments on other publicly available datasets further demonstrate the effectiveness of our proposed approach.
Using Blockchain to Ensure Trust between Donor Agencies and NGOs in Under-Developed Countries

Non-governmental organizations (NGOs) in under-developed countries are receiving funds from donor agencies for various purposes, including relief from natural disasters and other emergencies, promoting education, women empowerment, economic development, and many more. Some donor agencies have lost their trust in NGOs in under-developed countries, as some NGOs have been involved in the misuse of funds. This is evident from irregularities in the records. For instance, in education funds, on some occasions, the same student has appeared in the records of multiple NGOs as a beneficiary, when in fact, a maximum of one NGO could be paying for a particular beneficiary. Therefore, the number of actual beneficiaries would be smaller than the number of claimed beneficiaries. This research proposes a blockchain-based solution to ensure trust between donor agencies from all over the world, and NGOs in under-developed countries. The list of National IDs along with other keys would be available publicly on a blockchain. The distributed software would ensure that the same set of keys are not entered twice in this blockchain, preventing the problem highlighted above. The details of the fund provided to the student would also be available on the blockchain and would be encrypted and digitally signed by the NGOs. In the case that a record inserted into this blockchain is discovered to be fake, this research provides a way to cancel that record. A cancellation record is inserted, only if it is digitally signed by the relevant donor agency.
Effects of an Immersive Virtual Reality Exergame on University Students’ Anxiety, Depression, and Perceived Stress: Pilot Feasibility and Usability Study

Background In recent years, there has been an increase in the number of students with depression, anxiety, and perceived stress. A solution that has been increasingly used for improving health and well-being is exergaming. The effects and acceptability of exergames have been studied widely but mostly with older adults. The feasibility and usability of exergames among university students, especially those of immersive virtual reality (iVR) exergames, remain unexplored. Objective This study aimed to explore the feasibility of a 6-week iVR exergame–based intervention in reducing anxiety, depression, and perceived stress among university students and to examine the usability and acceptability of such games. Methods A total of 31 university students were recruited to participate in a 6-week study in which they needed to play a boxing-style iVR exergame called FitXR (FitXR Limited) twice per week (30 minutes per session). Their anxiety (Beck Anxiety Inventory), depression (Beck Depression Inventory-II), and perceived stress (Perceived Stress Scale) levels were measured before and after intervention. Results A total of 15 participants completed the 6-week study. Our results suggested that participants’ mean depression scores decreased significantly from 8.33 (SD 5.98) to 5.40 (SD 5.14) after the intervention (P=.01). In addition, most participants (14/15, 93%) believed that the iVR exergame has good usability. Furthermore, most participants (14/15, 93%) were satisfied with the iVR gameplay experience and would play the iVR exergame again in the future. Of the 15 participants, 11 (73%) would recommend the iVR exergame to their friends. Conclusions The results gained from this study show that the iVR exergame has good usability, is highly acceptable, and has the potential to reduce depression levels among university students.
HSDLM

Imbalanced data presents many difficulties, as the majority of learners will be prejudice against the majority class, and in severe cases, may fully disregard the minority class. Over the last few decades, class inequality has been extensively researched using traditional machine learning techniques. However, there is relatively little analytical research in the field of deep learning with class inequality. In this article, the authors classify the imbalanced data with the combination of both sampling method and deep learning method. They propose a novel sampling-based deep learning method (HSDLM) to address the class imbalance problem. They preprocess the data with label encoding and remove the noisy data with the under-sampling technique edited nearest neighbor (ENN) algorithm. They also balance the data using the over-sampling technique SMOTE and apply parallelly three types of long short-term memory networks, which is a deep learning classifier. The experimental findings indicate that HSDLM is a promising and fruitful solution to working with strongly imbalanced datasets.
Supply Chain Inventory Sharing Using Ethereum Blockchain and Smart Contracts

Information sharing is essential for coordination and stakeholder integration and is crucial to achieving sustainable supply chain operations. Furthermore, improved information sharing assists retailers by reducing the time spent looking for alternative suppliers to meet the unexpected demand surge during pandemics. Recent advances in networking and distributed ledger technologies have enabled secure, improved information communication and broader connectivity. However, due to system interoperability, information quality, confidentiality, and trust, information cannot be exchanged seamlessly, resulting in the inefficiency of managing supply chain operations. In this paper, we propose a blockchain-based inventory sharing approach based on smart contracts using a private Ethereum network to link suppliers and retailers. Our approach combines blockchain technology with decentralized storage to increase transparency, trust, and security of supply chain transactions. We present a generalized mechanism for secure information sharing that includes comprehensive algorithms to capture supply chain stakeholder interactions that enhance trust among participating entities. The smart contract was developed and validated using the Remix IDE, and the code is made publicly available on Github. We analyze the solution for various security vulnerabilities and provide a detailed cost analysis for various stakeholder transactions in the supply chain. Our solution demonstrates that a blockchain-based approach reduces inefficiencies, is economical, commercially viable, and provides improved information connectivity among supply chain stakeholders in a trusted and secure way.
A systematic review of augmented reality in chemistry education

Abstract Augmented reality (AR) technology is increasingly present in chemistry teaching and learning processes. It is seen as an important pedagogical resource that allows enhancing the understanding of complex concepts at most educational levels. However, even though numerous benefits related to AR in education have been reported, no systematic review has been conducted specifically addressing the use of this technology in chemistry education. Thus, a systematic literature review was carried out in order to grasp the particularities of this scenario, such as those related to the main topics in chemistry in which AR is used, the most frequently used devices and the main advantages and challenges of this technology. Forty‐nine papers were analysed, recovered from five databases, between the years 2011 and 2020. Among the results, an increase in publications was identified in the last three years, with smartphones and tablets being the predominant devices. Molecular structures were the topic of chemistry to which AR has been most frequently applied for teaching and learning, and the advantages of using this technology were concentrated in the affective learning domain. Regarding the challenges identified, they were related to students’ difficulty of use, distraction and technical problems. The research conclusions also address gaps in research and present suggestions and directions for future work.
BCSE: Blockchain-based trusted service evaluation model over big data

The blockchain, with its key characteristics of decentralization, persistence, anonymity, and auditability, has become a solution to overcome the overdependence and lack of trust for a traditional public key infrastructure on third-party institutions. Because of these characteristics, the blockchain is suitable for solving certain open problems in the service-oriented social network, where the unreliability of submitted reviews of service vendors can cause serious security problems. To solve the unreliability problems of submitted reviews, this paper first proposes a blockchain-based identity authentication scheme and a new trusted service evaluation model by introducing the scheme into a service evaluation model. The new trusted service evaluation model consists of the blockchain-based identity authentication scheme, evaluation submission module, and evaluation publicity module. In the proposed evaluation model, only users who have successfully been authenticated can submit reviews to service vendors. The registration and authentication records of users' identity and the reviews for service vendors are all stored in the blockchain network. The security analysis shows that this model can ensure the credibility of users' reviews for service vendors, and other users can obtain credible reviews of service vendors via the review publicity module. The experimental results also show that the proposed model has a lower review submission delay than other models.
Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective

In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex nonlinear learning models, such as deep neural networks. Gaining a better understanding is especially important, e.g., for safety-critical ML applications or medical diagnostics and so on. Although such explainable artificial intelligence (XAI) techniques have reached significant popularity for classifiers, thus far, little attention has been devoted to XAI for regression models (XAIR). In this review, we clarify the fundamental conceptual differences of XAI for regression and classification tasks, establish novel theoretical insights and analysis for XAIR, provide demonstrations of XAIR on genuine practical regression problems, and finally, discuss challenges remaining for the field.
An integrated mediapipe-optimized GRU model for Indian sign language recognition

Sign language recognition is challenged by problems, such as accurate tracking of hand gestures, occlusion of hands, and high computational cost. Recently, it has benefited from advancements in deep learning techniques. However, these larger complex approaches cannot manage long-term sequential data and they are characterized by poor information processing and learning efficiency in capturing useful information. To overcome these challenges, we propose an integrated MediaPipe-optimized gated recurrent unit (MOPGRU) model for Indian sign language recognition. Specifically, we improved the update gate of the standard GRU cell by multiplying it by the reset gate to discard the redundant information from the past in one screening. By obtaining feedback from the resultant of the reset gate, additional attention is shown to the present input. Additionally, we replace the hyperbolic tangent activation in standard GRUs with exponential linear unit activation and SoftMax with Softsign activation in the output layer of the GRU cell. Thus, our proposed MOPGRU model achieved better prediction accuracy, high learning efficiency, information processing capability, and faster convergence than other sequential models.
Blockchain technology for cybersecurity: A text mining literature analysis

Blockchain, the technology infrastructure behind the famous cryptocurrency bitcoin, can take away the notion of trust from centralized organizations to a decentralized platform that is mathematically verifiable and cryptographically secure. It is gaining more significant momentum exponentially and disrupts the way businesses function beyond the digital currency aspects. This work presents a text mining literature analysis of research articles published in major digital libraries on blockchain technology and cybersecurity. This literature analysis employs automated text mining approaches such as topic modeling and keyphrase extraction for unearthing the themes from a vast body of literature. This analysis highlights the multidisciplinary nature of blockchain technology within the cybersecurity domain. The findings also show the cyber threats and vulnerabilities that evolve with blockchain technology developments. This analysis also showcases the computer security research community’s vulnerabilities and provides future research dimensions that are crucial for designing secure blockchain applications and platforms.
Application of Artificial Intelligence in Pathology: Trends and Challenges

Given the recent success of artificial intelligence (AI) in computer vision applications, many pathologists anticipate that AI will be able to assist them in a variety of digital pathology tasks. Simultaneously, tremendous advancements in deep learning have enabled a synergy with artificial intelligence (AI), allowing for image-based diagnosis on the background of digital pathology. There are efforts for developing AI-based tools to save pathologists time and eliminate errors. Here, we describe the elements in the development of computational pathology (CPATH), its applicability to AI development, and the challenges it faces, such as algorithm validation and interpretability, computing systems, reimbursement, ethics, and regulations. Furthermore, we present an overview of novel AI-based approaches that could be integrated into pathology laboratory workflows.
Action Elimination and Plan Neighborhood Graph Search: Two Algorithms for Plan Improvement

Compared to optimal planners, satisficing planners can solve much harder problems but may produce overly costly and long plans. Plan quality for satisficing planners has become increasingly important. The most recent planning competition IPC-2008 used the cost of the best known plan divided by the cost of the generated plan as an evaluation metric. This paper proposes and evaluates two simple but effective methods for plan improvement: Action Elimination improves an existing plan by repeatedly removing sets of irrelevant actions. Plan Neighborhood Graph Search finds a new, shorter plan by creating a plan neighborhood graph PNG(π) of a given plan π, and then extracts a shortest path from PNG(π). Both methods are implemented in the Aras postprocessor and are empirically shown to improve the result of several planners, including the top four planners from IPC-2008, under competition conditions.
Recommendations-based on semantic analysis of social networks in learning environments

The expansion of social utilization within web applications provides a set of social media that allows users to contribute freely and interact with each other. Consequently, E-learning systems have benefited greatly from the concepts of the social web and the emerging technologies of the web semantic, where students and teachers are living and interacting mainly in the world of Web 2.0 and social networks. Especially, the Web semantic provides content that is understandable by machines and Web 2.0, which help to develop collaborative services. Further it can be integrated in a single social structure semantics that could be a good tool to improve the quality of learning. Our goal is to show that these technologies can be adopted to improve the e-Learning user experience and to provide a full automatic learning platform called iLearn. We present a learning environment formulated as a social network, in order to carry out an automatic semantic reasoning including the interactions between users as well as their relationships with the provided learning resources. We merge the analysis of social networks with the web semantics to go beyond the analysis of social graphs by integrating a treatment semantics of the knowledge that they contain and designed by the ontology formalism. iLearndevelops two ontologies, the first helps to understand the feeling of users versus resources and recommendations, whereas the second categorizes the different resources. In particular, we present an interactive method of detecting communities to provide students belonging to the same learning community the best learning strategies, the strong collaborators candidates, and the relevant resources that meet well within their needs.