Reviewerly/exHarmony huggingface dataset:
Open, Distance, and Digital Education (ODDE) – An Equity View

Abstract Understanding how equity manifests in open, distance, and digital education (ODDE) requires us to grapple with several coexisting trends, including the changing forms of teaching and learning provision, the advent of a post-digital society and education, the datafication of education, inequality in society at large, and digital inequities. Most of these trends are social in nature, yet they shape, and are shaped by, the educational sector. It is at the intersection of these coexisting trends that equity issues in ODDE are raised and become apparent, reinforced by the uneven distribution of technology in society, and with deep roots in economic and social inequities. Current scholarship foregrounds these nested relationships and entanglements, as well as their intersection with power relations and contestations which play out across ODDE at macro, meso, and micro levels.
Specialty Grand Challenge: Quantum engineering

SPECIALTY GRAND CHALLENGE article Front. Quantum. Sci. Technol., 23 September 2022Sec. Quantum Engineering https://doi.org/10.3389/frqst.2022.1029525
Emotion-Cause Pair Extraction via Transformer-Based Interaction Model with Text Capsule Network

The goal of Emotion-Cause Pair Extraction is to extract the emotion clause and the corresponding cause clause from the unmarked document level text. The two steps solution has the disadvantage of error propagation and fails to make full use of context information. In this paper, we propose an end to end neural network. The model uses Transformer with different granularity to extract hierarchical text features, and uses the text capsule network to model the relationship between the local text features of emotion cause pair and the overall causal relationship. We also utilize a filter mechanism to alleviate the problem of sample imbalance of ECPE task. The experimental results show that our model has achieved great performance improvement, which is higher than most baseline methods on F1 score.
Cultivating Locally Transformative Digital Pedagogies: The Need for Formative-Intervention Research

This chapter discusses the pressing need to re-orientate digital pedagogy research and development in Southern Africa. In reviewing contemporary research, I illustrate how these approaches are missing deeper contextual considerations and educators’ agency in informing the transformational digital practices they describe. I argue if we are to find truly innovative digital pedagogies, a critical reform emphasising educators as change agents of their teaching and learning practices is integral. Using insights gained from a Change Laboratory formative-intervention, I discuss how dialectical understandings of development, which centre on educators’ transformational capacities, can offer locally transformative digital pedagogies that work for educators and their unique contexts.
Blockchain utility in renewable energy

The tightly regulated energy market has taken notice of blockchain technology as it prepares for an energy business model transition in which providers and consumers will create and sell electricity. Blockchain could provide a secure, low-cost mechanism to record and confirm operating and financial transactions along a decentralized system with no central authority. In addition to the smart grid, the Internet of Energy (IoE), which integrates many kinds of energies, has become a prominent innovation feature in the energy generation and distribution landscape. Given the establishing of a distributed smart grid infrastructure, it follows that IoE leverages the Internet to gather, control, optimize, and organize network energy information from various edge devices. Customers and suppliers use sensors and modern communication systems to collect the required data and forecast demand and supply. The present energy sector began changing towards distributed and decentralized approaches with smart grid energy utilities like electric vehicles, smart grid, and vehicle-to-grid technologies. The founding principles of blockchain, such as secrecy, decentralization, and transparency, have sparked an interest in the energy domain for this technology which provides automatic data interchange, complicated energy trades, improved service control, and distributed energy trading, among other things, by utilizing smart contracts. As decentralized renewable resources and smart grid networks are implemented and exploited, blockchain plays a critical role in developing the IoE market.
Impact of Blockchain-IoE on economy

Blockchain is popular among the technologically inclined people looking to use the computer to send and receive data while paying attention to data safety and privacy concerns. Blockchains have the ability to maintain digital data, in its transparent form as well as unaltered one, with the help of distributed computing along with cryptography and hashing techniques. Such a blockchain technology, while incorporated with Supply Chain Management (SCM), is creating a huge impact on the economy. SCM is the technique to maintain the process transformation flow from the purchase of raw materials to the production of final products and plays a vital role in economy streamlining the business activities by aligning those with customer satisfaction while keeping an eye open to the competition. The significance of blockchain in the circular economy is more because it considers not only the business parameters but also the society and the environment too. Blockchain-enabled smart contracts are employed to ensure trustworthiness and reliability in the economy. Blockchain will still be more strengthened by integrating the recent technologies such as the internet of things and machine learning in economy-related applications. An optimal and neutral finance management system will be developed by the proper blending of the aforementioned technologies. The case studies of blockchain applications in various industrial fields, at various acceptance levels, and for various organizational purposes are discussed at the end of the chapter.
Augmented Reality–Assisted Design of Local Flaps

Augmented reality (AR) is a new and promising technology that has been incorporated into several aspects of our everyday life, including surgery. Examples of its application throughout various fields of surgery, although most of them are in experimental stage, have already been demonstrated and published. In this article, we describe the utilization of AR to design effectively and accurately surgical flap markings just with the help of an AR-compatible mobile phone and a low-cost application. We believe that, due to its ease of use, AR will play a pivotal role to the education and the training in the field of plastic surgery and burns, and other surgical specialties alike.
Defending against Poisoning Backdoor Attacks on Federated Meta-learning

Federated learning allows multiple users to collaboratively train a shared classification model while preserving data privacy. This approach, where model updates are aggregated by a central server, was shown to be vulnerable to poisoning backdoor attacks : a malicious user can alter the shared model to arbitrarily classify specific inputs from a given class. In this article, we analyze the effects of backdoor attacks on federated meta-learning , where users train a model that can be adapted to different sets of output classes using only a few examples. While the ability to adapt could, in principle, make federated learning frameworks more robust to backdoor attacks (when new training examples are benign), we find that even one-shot attacks can be very successful and persist after additional training. To address these vulnerabilities, we propose a defense mechanism inspired by matching networks , where the class of an input is predicted from the similarity of its features with a support set of labeled examples. By removing the decision logic from the model shared with the federation, the success and persistence of backdoor attacks are greatly reduced.
Survey on Image and Point-Cloud Fusion-Based Object Detection in Autonomous Vehicles

With the improvements in sensor performance (cameras, Lidars) and the application of deep learning in object detection, autonomous vehicles (AVs) are gradually becoming more notable. After 2019, AV has produced a wave of enthusiasm, and many papers on object detection were published, boasting both practicality and innovation. Due to hardware limitations, it is difficult to accomplish accurate and reliable environment perception using a single sensor. However, multi-sensor fusion technology provides an acceptable solution. Considering the AV cost and object detection accuracy, both the traditional and existing literature on object detection using image and point-cloud was reviewed in this paper. Additionally, for the fusion-based structure, the object detection method was categorized in this paper based on the image and point-cloud fusion types: early fusion, deep fusion, and late fusion. Moreover, a clear explanation of these categories was provided including both the advantages and limitations. Finally, the opportunities and challenges the environment perception may face in the future were assessed.
Output Prediction Attacks on Block Ciphers Using Deep Learning

In this paper, we propose deep learning-based output prediction attacks in a blackbox setting. As preliminary experiments, we first focus on two toy SPN block ciphers (small PRESENT-[4] and small AES-[4]) and one toy Feistel block cipher (small TWINE-[4]). Due to its small internal structures with a block size of 16 bits, we can construct deep learning models by employing the maximum number of plaintext/ciphertext pairs, and we can precisely calculate the rounds in which full diffusion occurs. Next, based on the preliminary experiments, we explore whether the evaluation results obtained by our attacks against three toy block ciphers can be applied to block ciphers with large block sizes, e.g., 32 and 64 bits. As a result, we demonstrate the following results, specifically for the SPN block ciphers: (1) our attacks work against a similar number of rounds that the linear/differential attacks can be successful, (2) our attacks realize output predictions (precisely ciphertext prediction and plaintext recovery) that are much stronger than distinguishing attacks, and (3) swapping or replacing the internal components of the target block ciphers affects the average success probabilities of the proposed attacks. It is particularly worth noting that this is a deep learning specific characteristic because swapping/replacing does not affect the average success probabilities of the linear/differential attacks. We also confirm whether the proposed attacks work on the Feistel block cipher. We expect that our results will be an important stepping stone in the design of deep learning-resistant symmetric-key ciphers.
A Hybrid Accuracy- and Energy-Aware Human Activity Recognition Model in IoT Environment

Personalised health and fitness provide users with information regarding their wellbeing and an opportunity to inform healthcare services for better patient outcomes. Underpinning this industry sector is the need to establish human activity recognition (HAR) in a ubiquitous manner. For example, through the use of smartwatches and/or mobile phones gathering information such as heart rates, movement, and steps of a user. The engineering challenge is providing accurate, informative, and timely data without rapidly depleting the mobile device's battery life. This problem is compounded as a number of algorithms used to process such data require substantial, cloud-based resources, to achieve higher accuracy. Therefore, a balance is required between battery depletion, accuracy of data, and timely delivery of results through a mixture of cloud and local algorithmic execution. In this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AE-HAR (Accuracy and Energy Aware-HAR)</i> model that delivers engineered solutions which approach optimal combinations in the consideration of energy consumption, accuracy, and timeliness of results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AE-HAR</i> introduces a “light-weight” <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">machine learning</i> on-device component identifying the probabilistic accuracy of data together with energy consumption identification requirements. A heuristic is then adopted to determine if cloud-enabled calculations are required while including possible performance costs related to the analysis of networking infrastructures. Our model is validated in a real-world environment through experimentation that demonstrates accuracy in excess of 93% and energy consumption savings in excess of 94%.
RATLS: Integrating Transport Layer Security with Remote Attestation

We present RATLS, a companion library for OpenSSL that integrates the Trusted Computing concept of Remote Attestation into Transport Layer Security (TLS). RATLS builds upon handshake extensions that are specified in version 1.3 of the TLS standard. It therefore does not require any changes to the TLS protocol or the OpenSSL library, which offers a suitable API for handshake extensions. RATLS supports remote attestation as part of a complete TLS handshake for new connections and it augments session resumption by binding session tickets to the platform state of TLS peers. We demonstrate that RATLS enables both client and server to attest their respective software stacks using widely-used Trusted Platform Modules. Our evaluation shows that the number of round trips during handshake is the same as for traditional TLS and that session resumption can reduce cryptographic overhead caused by remote attestation for frequently communicating peers.
Go-Sharing: A Blockchain-Based Privacy-Preserving Framework for Cross-Social Network Photo Sharing

The evolution of social media has led to a trend of posting daily photos on online Social Network Platforms (SNPs). The privacy of online photos is often protected carefully by security mechanisms. However, these mechanisms will lose effectiveness when someone spreads the photos to other platforms. In this article, we propose Go-sharing, a blockchain-based privacy-preserving framework that provides powerful dissemination control for cross-SNP photo sharing. In contrast to security mechanisms running separately in centralized servers that do not trust each other, our framework achieves consistent consensus on photo dissemination control through carefully designed smart contract-based protocols. We use these protocols to create platform-free dissemination trees for every image, providing users with complete sharing control and privacy protection. Considering the possible privacy conflicts between owners and subsequent re-posters in cross-SNP sharing, we design a dynamic privacy policy generation algorithm that maximizes the flexibility of re-posters without violating formers’ privacy. Moreover, Go-sharing also provides robust photo ownership identification mechanisms to avoid illegal reprinting. It introduces a random noise black box in a two-stage separable deep learning process to improve robustness against unpredictable manipulations. Through extensive real-world simulations, the results demonstrate the capability and effectiveness of the framework across a number of performance metrics.
DuEE-Fin: A Large-Scale Dataset for Document-Level Event Extraction

To tackle the data scarcity problem of document-level event extraction, we come up with a large-scale benchmark, DuEE-Fin, which consists of 15,000+ events categorized into 13 event types, and 81,000+ event arguments mapped in 92 argument roles. We constructed DuEE-Fin from real-world Chinese financial news, which allows one document to contain several events, multiple arguments to share the same argument role and one argument to play different roles in different events. Therefore, it presents some considerable challenges in document-level event extraction task such as multi-event recognition and multi-value argument identification, that are referred to as key issues for document-level event extraction task. Along with DuEE-Fin, we also hosted an open competition, which has attracted 1,690 teams and achieved exciting results. We performed experiments on DuEE-Fin with most popular document-level event extraction systems. However, results showed that even some SOTA models performed poorly with our data. Facing these challenges, we found it necessary to propose more effective methods.
Joint Optimization of Multi-vector Representation with Product Quantization

Dense retrieval models represent queries and documents with one or multiple fixed-width vectors and retrieve relevant documents via nearest neighbor search. Recently these models have shown improvement in retrieval performance and have drawn increasing attention from the IR community. Among a variety of dense retrieval models, the models that employ multiple vectors to represent texts achieve the state-of-the-art ranking performance. However, the multi-vector representation schema imposes tremendous storage overhead compared with single-vector representation, which may hinder its application in practical scenarios. We therefore intend to apply vector compression methods such as Product Quantization (PQ) to reduce the storage cost and improve retrieval efficiency. However, the gap between the original embeddings and the quantized vectors may degenerate retrieval performance. Recently, improved dense retrieval models such as JPQ have been proposed to reduce storage space while maintaining ranking effectiveness by jointly training the encoder and PQ index. They have achieved promising improvement in the single-vector dense retrieval scenario. We therefore try to introduce this joint optimization framework to tackle the storage overhead of the multi-vector models. The key idea is to Jointly optimize Multi-vector representations with Product Quantization (JMPQ). JMPQ prevents effectiveness degeneration by leveraging a joint optimization framework for the query encoding and index compressing processes. We evaluate the performance of JMPQ on publicly available ad-hoc retrieval benchmarks. Extensive experimental results show that JMPQ substantially reduces the memory footprint while achieving ranking effectiveness on par with or even better than its uncompressed counterpart.
Adversarial Transfer for Classical Chinese NER with Translation Word Segmentation

Classical Chinese NER aims to automatically identify named entities in classical texts, which can effectively help people understand the content of classical texts. However, due to the difficulty of annotating classical Chinese texts, the scale of existing datasets seriously restricts the development of classical Chinese NER. To address this challenge, we propose an Adversarial Transfer for Classical Chinese NER (AT-CCNER) model, which transfers features learned from large-scale translation word segmentation to assist recognize classical Chinese named entities. In addition, to reduce the feature differences between modern and classical Chinese texts, AT-CCNER utilizes the adversarial method to better apply to classical Chinese texts. We experimentally demonstrate the effectiveness of our method on the open-source classical Chinese NER dataset C-CLUE. What's more, we compare the effects of translation text of different scales on the experimental results. Our method improves Precision, Recall, and F1 by 3.61%, 3.45%, and 3.54%, respectively, compared to the BiLSTM-CRF model.
Image Denoising Using a Deep Auto-encoder Approach Based on Beetle Antennae Search Algorithm

Image will be polluted by different kinds of noise in the process of acquisition, compression, and transmission, resulting in interference to subsequent image segmentation, feature extraction and other processing. With the development of deep convolutional neural network (DCNN), quite a few effective DCNNs have been designed and have made remarkable progress in image denoising. Gradient descent algorithm is generally used for DCNN training. However, due to the complex mathematical properties of the high-dimensional and non-convex loss optimization surface, there are often many local optimal points, saddle points or large range of gradient gentle regions, which affects training effect of the gradient descent algorithm. Although intelligent algorithms such as evolutionary algorithm have global optimization capability, they often have large computing resource requirements and slow convergence speed, which limit its application in DCNN training which is a high-dimensional optimization problem. Beetle antennae search (BAS) algorithm is a simple and efficient bionic intelligent optimization algorithm, which has global search ability. In this paper, the gradient descent method and BAS method are combined as a hybrid method for deep auto-encoder (DAE) denoising network training. Experimental results show that the proposed method accelerates the training speed of the DAE denoising network, reduces the blurring of edge details and improves the visual effect of the restored image.
Performance Evaluation of Low-Precision Quantized LeNet and ConvNet Neural Networks

Low-precision neural network models are crucial for reducing the memory footprint and computational density. However, existing methods must have an average of 32-bit floating-point (FP32) arithmetic to maintain the accuracy. Floating-point numbers need grave memory requirements in convolutional and deep neural network models. Also, large bit-widths cause too much computational density in hardware architectures. Moreover, existing models must evolve into deeper network models with millions or billions of parameters to solve today’s problems. The large number of model parameters increase the computational complexity and cause memory allocation problems, hence existing hardware accelerators become insufficient to address these problems. In applications where accuracy can be traded-off for the sake of hardware complexity, quantization of models enable the use of limited hardware resources to implement neural networks. From hardware design point of view, quantized models are more advantageous in terms of speed, memory and power consumption than using FP32. In this study, we compared the training and testing accuracy of the quantized LeNet and our own ConvNet neural network models at different epochs. We quantized the models using low precision int-4, int-8 and int-16. As a result of the tests, we observed that the LeNet model could only reach 63.59% test accuracy at 400 epochs with int-16. On the other hand, the ConvNet model achieved a test accuracy of 76.78% at only 40 epochs with low precision int-8 quantization.
Contribution to the methods of indexing Arabic textual documents to improve the performance of IRS

Indexing is a key stage of an IRS. Several studies have been conducted on the indexing phase to enhance the performance of the IRS. Currently, the IRS for English and French has introduced some enhancements to the indexation phase. This book proposes a new indexing method based on association rules. This method applies to Arabic. Through the generation of concept maps, the arcs linking the concepts are quantified by approximations. This value stands for the similarity between the concepts used to test this method. The dataset has passed the pre-processing step and then the association rule step, in which we extract the most used words (the most common ones), so this list of frequent elements is the input variable in the next step (model LDA), which will generate a graph representing the topics found in each document, and finally apply the similarity measurement equation to calculate the similarity between the different topics. Finally, to try out this new representation, we are creating a deep learning model for text classification. we found that this novel approach improves the performance of the IRS by 16% in the accuracy of the classification model.