Reviewerly/exHarmony huggingface dataset:
Teacher Training in Lifelong Learning—The Importance of Digital Competence in the Encouragement of Teaching Innovation

The vertiginous advance of society in recent years has forced a modification of demands among citizens and educators. The arrival of information and communication technologies to the educational atmosphere has led to the development of the digital competence of teachers, which is one of the educational challenges teacher training has to face now. Based on this, this work intends to carry out an evaluation of the development of digital competence in teachers of the Lifelong Learning stage in the community of Andalusia (Spain). In order to do so, a quantitative and transversal work design was used, with a sample of 142 teachers from different schools in this area. The results showed a deficit of teachers in the five digital dimensions, especially in the creation of digital content. It was also clarified that there was a direct relationship between previous information and communication technology (ICT) training and the dimensions of communication and collaboration, and content creation. Finally, the work here showed that the teachers concerned with preservice ICT training were young and with less than 10 years of experience. In conclusion, the development of digital teaching competence continues to be a challenge for the education system which must therefore be addressed, and it shall continue to be a key issue in the training of current teachers, as it is indeed a fundamental pillar for promoting a new way of teaching, being the only way to develop an areal teaching innovation panorama.
Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic

Sustainable smart city initiatives around the world have recently had great impact on the lives of citizens and brought significant changes to society. More precisely, data-driven smart applications that efficiently manage sparse resources are offering a futuristic vision of smart, efficient, and secure city operations. However, the ongoing COVID-19 pandemic has revealed the limitations of existing smart city deployment; hence; the development of systems and architectures capable of providing fast and effective mechanisms to limit further spread of the virus has become paramount. An active surveillance system capable of monitoring and enforcing social distancing between people can effectively slow the spread of this deadly virus. In this paper, we propose a data-driven deep learning-based framework for the sustainable development of a smart city, offering a timely response to combat the COVID-19 pandemic through mass video surveillance. To implementing social distancing monitoring, we used three deep learning-based real-time object detection models for the detection of people in videos captured with a monocular camera. We validated the performance of our system using a real-world video surveillance dataset for effective deployment.
Dual Shared-Specific Multiview Subspace Clustering

Multiview subspace clustering has received significant attention as the availability of diverse of multidomain and multiview real-world data has rapidly increased in the recent years. Boosting the performance of multiview clustering algorithms is challenged by two major factors. First, since original features from multiview data are highly redundant, reconstruction based on these attributes inevitably results in inferior performance. Second, since each view of such multiview data may contain unique knowledge as against the others, it remains a challenge to exploit complimentary information across multiple views while simultaneously investigating the uniqueness of each view. In this paper, we present a novel dual shared-specific multiview subspace clustering (DSS-MSC) approach that simultaneously learns the correlations between shared information across multiple views and also utilizes view-specific information to depict specific property for each independent view. Further, we formulate a dual learning framework to capture shared-specific information into the dimensional reduction and self-representation processes, which strengthens the ability of our approach to exploit shared information while preserving view-specific property effectively. The experimental results on several benchmark datasets have demonstrated the effectiveness of the proposed approach against other state-of-the-art techniques.
An improved face recognition algorithm and its application in attendance management system

Face Recognition is a computer application that is capable of detecting, tracking, identifying or verifying human faces from an image or video captured using a digital camera. Although lot of progress has been made in domain of face detection and recognition for security, identification and attendance purpose, but still there are issues hindering the progress to reach or surpass human level accuracy. These issues are variations in human facial appearance such as; varying lighting condition, noise in face images, scale, pose etc. This research paper presents a new method using Local Binary Pattern (LBP) algorithm combined with advanced image processing techniques such as Contrast Adjustment, Bilateral Filter, Histogram Equalization and Image Blending to address some of the issues hampering face recognition accuracy so as to improve the LBP codes, thus improve the accuracy of the overall face recognition system. Our experiment results show that our method is very accurate, reliable and robust for face recognition system that can be practically implemented in real-life environment as an automatic attendance management system.
SoK: Layer-Two Blockchain Protocols

Blockchains have the potential to revolutionize markets and services. However, they currently exhibit high latencies and fail to handle transaction loads comparable to those managed by traditional financial systems. Layer-two protocols, built on top of (layer-one) blockchains, avoid disseminating every transaction to the whole network by exchanging authenticated transactions off-chain. Instead, they utilize the expensive and low-rate blockchain only as a recourse for disputes. The promise of layer-two protocols is to complete off-chain transactions in sub-seconds rather than minutes or hours while retaining asset security, reducing fees and allowing blockchains to scale. We systematize the evolution of layer-two protocols over the period from the inception of cryptocurrencies in 2009 until today, structuring the multifaceted body of research on layer-two transactions. Categorizing the research into payment and state channels, commit-chains and protocols for refereed delegation, we provide a comparison of the protocols and their properties. We provide a systematization of the associated synchronization and routing protocols along with their privacy and security aspects. This Systematization of Knowledge (SoK) clears the layer-two fog, highlights the potential of layer-two solutions and identifies their unsolved challenges, indicating propitious avenues of future work.
Applications of Explainable Artificial Intelligence in Diagnosis and Surgery

In recent years, artificial intelligence (AI) has shown great promise in medicine. However, explainability issues make AI applications in clinical usages difficult. Some research has been conducted into explainable artificial intelligence (XAI) to overcome the limitation of the black-box nature of AI methods. Compared with AI techniques such as deep learning, XAI can provide both decision-making and explanations of the model. In this review, we conducted a survey of the recent trends in medical diagnosis and surgical applications using XAI. We have searched articles published between 2019 and 2021 from PubMed, IEEE Xplore, Association for Computing Machinery, and Google Scholar. We included articles which met the selection criteria in the review and then extracted and analyzed relevant information from the studies. Additionally, we provide an experimental showcase on breast cancer diagnosis, and illustrate how XAI can be applied in medical XAI applications. Finally, we summarize the XAI methods utilized in the medical XAI applications, the challenges that the researchers have met, and discuss the future research directions. The survey result indicates that medical XAI is a promising research direction, and this study aims to serve as a reference to medical experts and AI scientists when designing medical XAI applications.
Differentiable Convex Optimization Layers

Recent work has shown how to embed differentiable optimization problems (that is, problems whose solutions can be backpropagated through) as layers within deep learning architectures. This method provides a useful inductive bias for certain problems, but existing software for differentiable optimization layers is rigid and difficult to apply to new settings. In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific languages (DSLs) for convex optimization. We introduce disciplined parametrized programming, a subset of disciplined convex programming, and we show that every disciplined parametrized program can be represented as the composition of an affine map from parameters to problem data, a solver, and an affine map from the solver’s solution to a solution of the original problem (a new form we refer to as affine-solver-affine form). We then demonstrate how to efficiently differentiate through each of these components, allowing for end-to-end analytical differentiation through the entire convex program. We implement our methodology in version 1.1 of CVXPY, a popular Python-embedded DSL for convex optimization, and additionally implement differentiable layers for disciplined convex programs in PyTorch and TensorFlow 2.0. Our implementation significantly lowers the barrier to using convex optimization problems in differentiable programs. We present applications in linear machine learning models and in stochastic control, and we show that our layer is competitive (in execution time) compared to specialized differentiable solvers from past work.
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters

We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa.Existing methods typically update the original parameters of pre-trained models when injecting knowledge.However, when multiple kinds of knowledge are injected, the historically injected knowledge would be flushed away.To address this, we propose K-ADAPTER, a framework that retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused model.Taking RoBERTa as the backbone model, K-ADAPTER has a neural adapter for each kind of infused knowledge, like a plug-in connected to RoBERTa.There is no information flow between different adapters, thus multiple adapters can be efficiently trained in a distributed way.As a case study, we inject two kinds of knowledge in this work, including (1) factual knowledge obtained from automatically aligned texttriplets on Wikipedia and Wikidata and (2) linguistic knowledge obtained via dependency parsing.Results on three knowledge-driven tasks, including relation classification, entity typing, and question answering, demonstrate that each adapter improves the performance and the combination of both adapters brings further improvements.Further analysis indicates that K-ADAPTER captures versatile knowledge than RoBERTa.
Snow White: Robustly Reconfigurable Consensus and Applications to Provably Secure Proof of Stake

We present the a provably secure proof-of-stake protocol called Snow White. The primary application of Snow White is to be used as a “green” consensus alternative for a decentralized cryptocurrency system with open enrollement. We break down the task of designing Snow White into the following core challenges: Snow White was publicly released in September 2016. It provides the first formal, end-to-end proof of a proof-of-stake system in a truly decentralized, open-participation network, where nodes can join at any time (not necessarily at the creation of the system). We also give the first formal treatment of a well-known issue called “costless simulation” in our paper, proving both upper- and lower-bounds that characterize exactly what setup assumptions are needed to defend against costless simulation attacks. We refer the reader to our detailed chronological notes on a detailed comparison of Snow White and other prior and concurrent works, as well as how subsequent works (including Ethereum’s proof-of-stake design) have since extended and improved our ideas.
Blockchain, business and the fourth industrial revolution: Whence, whither, wherefore and how?

Blockchain is one the most remarkable technological innovations of the 21st century. The most notable application of blockchain is in the development and operation of cryptocurrencies (e.g. bitcoin, ethereum, among others). Besides the financial services industry, blockchain is also considered in other sectors such as international trade, taxation, supply chain management, business operations and governance. However, blockchain has not been examined comprehensively in all areas of relevant literature. This article conducts a survey of the literature to gain an understanding of the opportunities and issues presented by blockchain in various business functions. The article begins by providing a discussion regarding how the blockchain technology operates. The paper takes a broad focus in its analysis of the prospects of blockchain for various business functions, including banking and the capital markets, corporate governance, international trade, and taxation. The paper demonstrates how organisations and regulators can leverage blockchain to upscale business operations, enhance efficiency and reduce operational costs. The key drawbacks of blockchain that stakeholders need to bear in mind before adopting the technology are also highlighted. The article also reflects on how organisations can tap into blockchain to reap the full potential of the fourth industrial revolution.
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning

Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other.They can only utilize single-modal data (i.e., text or image) or limited multi-modal data (i.e., image-text pairs).In this work, we propose a UNIfied-MOdal pre-training architecture, namely UNIMO, which can effectively adapt to both single-modal and multi-modal understanding and generation tasks.Large scale of free text corpus and image collections are utilized to improve the capability of visual and textual understanding, and crossmodal contrastive learning (CMCL) is leveraged to align the textual and visual information into a unified semantic space, over a corpus of image-text pairs augmented with related images and texts.With the help of rich non-paired single-modal data, our model is able to learn more generalizable representations, by allowing textual knowledge and visual knowledge to enhance each other in the unified semantic space.The experimental results show that UNIMO greatly improves the performance of several singlemodal and multi-modal downstream tasks.
Diffusion of blockchain technology

Purpose Although blockchain is often discussed, its actual diffusion seems to be varying for different industries. The purpose of this paper is to explore the blockchain technology diffusion in different industries through a combination of academic literature and social media (Twitter). Design/methodology/approach The insights derived from the academic literature and social media have been used to classify industries into five stages of the innovation-decision process, namely, knowledge, persuasion, decision, implementation and confirmation (Rogers, 1995). Findings Blockchain is found to be diffused in almost all industries, but the level of diffusion varies. The analysis highlights that manufacturing industry is at the knowledge stage. Further public administration is at persuasion stage. Subsequently, transportation, communications, electric, gas and sanitary services and trading industry had reached to the decision stage. Then, services industries have reached to implementation stage while finance, insurance and real estate industries are the innovators of blockchain technologies and have reached the confirmation stage of innovation-decision process. Practical implications Actual implementations of blockchain technology are still in its infancy stage for most of the industries. The findings suggest that specific industries are developing specific blockchain applications. Originality/value To the best of the authors’ knowledge this is the first study which is using social media data for investigating the diffusion of blockchain in industries. The results show that the combination of Twitter and academic literature analysis gives better insights into diffusion than a single data source.
Jointly Learning Entity and Relation Representations for Entity Alignment

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
An Improved Authentication Scheme for Internet of Vehicles Based on Blockchain Technology

Thanks to the rapid development in mobile vehicles and wireless technologies, the Internet of Vehicles (IoV) has become an attractive application that can provide a large number of mobile services for drivers.Vehicles can be informed of the mobile position, direction, speed, and other real-time information of nearby vehicles to avoid traffic jams and accidents.However, the environments of IoV could be dangerous in the absence of security protections.Due to the openness and self-organization of IoV, there are enormous malicious attackers.To guarantee the safety of mobile services, we propose an effective decentralized authentication mechanism for IoV on the basis of the consensus algorithm of blockchain technology.The simulation under the veins framework is carried out to verify the feasibility of the scheme in reducing the selfish behavior and malicious attacks in IoV.
Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering

Commonsense question answering aims to answer questions which require background knowledge that is not explicitly expressed in the question. The key challenge is how to obtain evidence from external knowledge and make predictions based on the evidence. Recent studies either learn to generate evidence from human-annotated evidence which is expensive to collect, or extract evidence from either structured or unstructured knowledge bases which fails to take advantages of both sources simultaneously. In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence. Specifically, we extract evidence from both structured knowledge base (i.e. ConceptNet) and Wikipedia plain texts. We construct graphs for both sources to obtain the relational structures of evidence. Based on these graphs, we propose a graph-based approach consisting of a graph-based contextual word representation learning module and a graph-based inference module. The first module utilizes graph structural information to re-define the distance between words for learning better contextual word representations. The second module adopts graph convolutional network to encode neighbor information into the representations of nodes, and aggregates evidence with graph attention mechanism for predicting the final answer. Experimental results on CommonsenseQA dataset illustrate that our graph-based approach over both knowledge sources brings improvement over strong baselines. Our approach achieves the state-of-the-art accuracy (75.3%) on the CommonsenseQA dataset.
Perceptual Limits of Optical See-Through Visors for Augmented Reality Guidance of Manual Tasks

Objective:The focal length of available optical seethrough (OST) head-mounted displays (HMDs) is at least 2 m therefore, during manual tasks, the user eye cannot keep in focus both the virtual and real content at the same time.Another perceptual limitation is related to the vergence-accommodation conflict (VAC), this latter being present in binocular vision only.This paper investigates the effect of incorrect focus cues on the user performance, visual comfort and workload during the execution of augmented reality (AR) guided manual task with one of the most advanced OST HMD, the Microsoft HoloLens.Methods: An experimental study was designed to investigate the performance of 20 subjects in a connect-the-dots task, with and without the use of AR.The following tests were planned: AR guided monocular and binocular; Naked-eye monocular and binocular.Each trial was analyzed to evaluate the accuracy in connecting dots; NASA Task Load Index and Likert questionnaires were used to assess the workload and the visual comfort.Results: No statistically significant differences were found in the workload, and in the perceived comfort between the AR guided binocular and monocular test.User performances were significantly better during the Naked eye tests.No statistically significant differences in performances were found in the monocular and binocular tests.The maximum error in AR tests was 5.9 mm.Conclusion: Even if there is a growing interest in using commercial OST-HMD, for guiding high-precision manual tasks, attention should be paid to the limitations of the available technology not designed for the peripersonal space.
Federated learning and differential privacy for medical image analysis

Abstract The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly available multi-centric and diverse datasets mainly stems from confidentiality and privacy concerns around sharing medical data. To demonstrate a feasible path forward in medical image imaging, we conduct a case study of applying a differentially private federated learning framework for analysis of histopathology images, the largest and perhaps most complex medical images. We study the effects of IID and non-IID distributions along with the number of healthcare providers, i.e., hospitals and clinics, and the individual dataset sizes, using The Cancer Genome Atlas (TCGA) dataset, a public repository, to simulate a distributed environment. We empirically compare the performance of private, distributed training to conventional training and demonstrate that distributed training can achieve similar performance with strong privacy guarantees. We also study the effect of different source domains for histopathology images by evaluating the performance using external validation. Our work indicates that differentially private federated learning is a viable and reliable framework for the collaborative development of machine learning models in medical image analysis.