Reviewerly/exHarmony huggingface dataset:
Federated Learning With Spiking Neural Networks

As neural networks get widespread adoption in resource-constrained embedded devices, there is a growing need for low-power neural systems.Spiking Neural Networks (SNNs) are emerging to be an energy-efficient alternative to the traditional Artificial Neural Networks (ANNs) which are known to be computationally intensive.From an application perspective, as federated learning involves multiple energy-constrained devices, there is a huge scope to leverage energy efficiency provided by SNNs.Despite its importance, there has been little attention on training SNNs on a large-scale distributed system like federated learning.In this paper, we bring SNNs to a more realistic federated learning scenario.Specifically, we propose a federated learning framework for decentralized and privacy preserving training of SNNs.To validate the proposed federated learning framework, we experimentally evaluate the advantages of SNNs on various aspects of federated learning with CIFAR10 and CIFAR100 benchmarks.We observe that SNNs outperform ANNs in terms of overall accuracy by over 15% when the data is distributed across a large number of clients in the federation while providing up to 5.3× energy efficiency.In addition to efficiency, we also analyze the sensitivity of the proposed federated SNN framework to data distribution among the clients, stragglers, and gradient noise and perform a comprehensive comparison with ANNs.The source code is available at https://github.com/Intelligent-
Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

Abstract Medical database classification problems can be considered as complex optimization problems to assure the diagnosis support precisely. In healthcare, several computer researchers have employed different deep learning (DL) approaches to enhance the classification performance. Besides, encryption is an effective way to offer secure transmission of medical data over public network. With this motivation, this paper presents new privacy‐preserving encryption with DL based medical data transmission and classification (PPEDL‐MDTC) model. The presented model derives multiple key‐based homomorphic encryption (MHE) technique with sailfish optimization (SFO), called MHE‐SFO algorithm‐based encryption process. In addition, the cross‐entropy based artificial butterfly optimization‐based feature selection technique and optimal deep neural network (ODNN) based classification is carried out. In ODNN model, the hyperparameter optimization of the DNN model is carried out utilizing the use of chemical reaction optimization (CRO) algorithm. The proposed method has been simulated utilizing Python 3.6.5 tool, which is tested using activity recognition and sleep stage dataset. A detailed comparative outcomes analysis makes sure the higher efficiency of the PPEDL‐MDTC on the state of art techniques with the detection accuracy of 0.9813 and 0.9650 on the applied activity recognition and University College Dublin Sleep Stage dataset.
Design and Implementation of Web Multimedia Teaching Evaluation System Based on Artificial Intelligence and jQuery

It is an important reflection of modern education and an overwhelming way to strengthen the quality of teaching. In the current environment, the traditional multimedia classroom management model can no longer adapt to the current rapidly developing network environment. How to manage more and more campus multimedia classrooms is an urgent problem to be solved. The informatization construction and application of multimedia classrooms is the key to the realization of educational informatization. The traditional multimedia classroom management model has not been able to adapt to the rapidly developing network environment, which is mainly manifested in the following aspects: electronic education management personnel cannot discover and process teaching in a timely manner. Equipment failure has not formed a set of standard troubleshooting procedures and cannot accurately record the status, use time, and maintenance records of various teaching pieces of equipment. This will not only affect the teaching quality of colleges and universities but also slow down the process of education informatization. This paper develops a web-based multimedia teaching equipment management system based on artificial intelligence and jQuery, which realizes the centralized control and management of various multimedia teaching equipment. According to the actual needs of multimedia teaching, this paper follows the design and development of software engineering, using artificial intelligence, jQuery, Ajax, and Spring MVC technology to design and develop a web-based multimedia teaching equipment management system. On the basis of realizing the centralized control and management of multiple multimedia teaching equipment, it can also track and record the use status and maintenance content of the multimedia teaching equipment to form an information knowledge base of the multimedia equipment, which is convenient for later maintenance and management. Through the use of this system, management can be systematized, standardized, and automated, reducing the tedious workload of management and maintenance personnel. It can speed up the information management process of multimedia teaching equipment and improve the work efficiency of related managers. A course can be studied online, and an online teaching system has been developed. According to our survey on Mandarin online course training in Northwest China (N = 343), we found that 81.6% of samples are satisfied with the Mandarin online training courses; 21.6% think that they have learned new teaching methods/teaching concepts from the teacher through the Mandarin training; 36.2% think that they have learned the theoretical knowledge of Mandarin through the Mandarin training. Gender, age, ethnicity, and learning experience are related to the difficulty of learning Mandarin online courses. Therefore, we can satisfy learners of different ages, learning foundations, and cultural backgrounds by designing different online course patterns, so as to enhance the high-quality promotion of Mandarin.
Small Object Detection in Traffic Scenes Based on YOLO-MXANet

In terms of small objects in traffic scenes, general object detection algorithms have low detection accuracy, high model complexity, and slow detection speed. To solve the above problems, an improved algorithm (named YOLO-MXANet) is proposed in this paper. Complete-Intersection over Union (CIoU) is utilized to improve loss function for promoting the positioning accuracy of the small object. In order to reduce the complexity of the model, we present a lightweight yet powerful backbone network (named SA-MobileNeXt) that incorporates channel and spatial attention. Our approach can extract expressive features more effectively by applying the Shuffle Channel and Spatial Attention (SCSA) module into the SandGlass Block (SGBlock) module while increasing the parameters by a small number. In addition, the data enhancement method combining Mosaic and Mixup is employed to improve the robustness of the training model. The Multi-scale Feature Enhancement Fusion (MFEF) network is proposed to fuse the extracted features better. In addition, the SiLU activation function is utilized to optimize the Convolution-Batchnorm-Leaky ReLU (CBL) module and the SGBlock module to accelerate the convergence of the model. The ablation experiments on the KITTI dataset show that each improved method is effective. The improved algorithm reduces the complexity and detection speed of the model while improving the object detection accuracy. The comparative experiments on the KITTY dataset and CCTSDB dataset with other algorithms show that our algorithm also has certain advantages.
Asynchronous Data Dissemination and its Applications

In this paper, we introduce the problem of Asynchronous Data Dissemination (ADD). Intuitively, an ADD protocol disseminates a message to all honest nodes in an asynchronous network, given that at least t+1 honest nodes initially hold the message where t is the maximum number of malicious nodes. We design a simple and efficient ADD protocol for n parties that is information-theoretically secure, tolerates up to one-third malicious nodes, and has a communication cost of O(n|M|+n2) for disseminating a message M. We then use our ADD protocol to improve many important primitives in cryptography and distributed computing. For asynchronous reliable broadcast (RBC), assuming collision-resistant hash functions, we give a RBC protocol with communication cost O(n|M| + κ n2) where κ is the size of the hash function output. This improves over the prior best scheme with communication cost O(n|M| + κ n2 łog n) under the same setting. Our improved RBC protocol immediately improves the communication cost of asynchronous atomic broadcast and Asynchronous Distributed Key Generation (ADKG) protocols. We also use our improved RBC protocol along with additional new techniques to improve the communication cost of Asynchronous Verifiable Secret Sharing (AVSS), Asynchronous Complete Secret Sharing (ACSS), and dual-threshold ACSS from O(κ n2 łog n) to O(κ n2) without using any trusted setup.
The moral foundations of makerspaces as unconventional sources of innovation: A study of narratives and performance

Makerspaces provide individuals and communities with physical access to costly technologies and empower them to create material things in a physical space. This new form of citizen engagement has the potential to improve autonomy, creativity, and motivation. Consequently, makerspaces can help firms maintain the innovativeness of their employees. Despite the growing popularity of makerspaces, their moral foundations are still far from clear. Our study's purpose is to illuminate the moral foundations of makerspaces from a design perspective, combining previously unconnected (1) technical, (2) social, and (3) cognitive levels of makerspaces. This study reveals configurations of high and low user innovativeness and demonstrates the role of moral foundations in makerspaces. Most importantly, technical facilities can compensate for the lack of moral foundations in small and inexperienced user teams. We discuss our findings and highlight the moral foundations of makerspaces as unconventional sources of innovation.
SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages

This year's iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and crosslingual variation of morphosyntactic features.In terms of the task, we enrich UniMorph with new data for 32 languages from 13 language families, with most of them being under-resourced: Kunwinjku, Classical Syriac, Arabic (Modern Standard, Egyptian, Gulf), Hebrew,
Perceptions on adopting artificial intelligence and related technologies in libraries: public and academic librarians in North America

Purpose This study aims to understand how artificial Intelligence (AI) and related technologies are currently being utilized in public and academic libraries and how librarians perceive the adoption of new technologies in their relative libraries. Design/methodology/approach Using an online survey questionnaire, the authors collected survey responses from both public and academic librarians that were subscribed to information science-centered listservs in North America. The quantitative survey responses ( N = 242) were measured through use of multiple chi-square tests and crosstab analyses. Findings The current use and awareness of AI and related technologies were more reported from academic librarians; however, public librarians' reported perceptions on such technologies were generally more positive. In all, 67% of them responded that AI and related technologies will transform the library's functions, and 68% of librarians reported that they are interested in training. This study discussed the significance of training for preparing librarians for AI and related technologies and the further examination on the role of librarians in the new era. Originality/value This study examined public and academic librarians' perceptions toward the adoption of six emerging technologies which often appear in the LIS literature as well as addressed as the Fourth Industrial Revolution technologies.
Med-BERT: A Pretraining Framework for Medical Records Named Entity Recognition

A large amount of data is generated every day with the development of Internet medical care, which is of great significance for the clinical decision support system and medical real-world research. Medical records named entity recognition (NER) is important on the aforementioned research topics under the premise of protecting patients’ private information. In this article, we propose a medical dictionary enhanced bidirectional encoder representations from transformers (BERT), dubbed Med-BERT, to achieve better representations of long medical entities. On Med-BERT, we propose a span flat-lattice transformer (Span-FLAT) method on medical records NER, and the entity types include private information such as names and addresses, as well as medical information such as patient symptoms, signs, and diseases. Experimental results on two benchmark medical datasets show the effectiveness of Med-BERT, and the proposed Med-BERT-based Span-FLAT method remarkably outperforms the state-of-the-art methods on medical NER task.
A Systematic Analysis of Applications Of Blockchain in Healthcare

advent of the internet and its impact on healthcare domain made it possible to store, access and update medical records anywhere and anytime. The term “Electronic Health Record(EHR)” refers to a digital format in which patient information is kept. Because the records are patient-centered, any authorised user can access them from any location, at any time, and from any device. Blockchain is a new technology, booming in most of the industries. Blockchain contains a number of built-in characteristics, including distributed ledger, decentralized storage, authenticity, confidentiality, and immutability and it has progressed actual applications in industries like healthcare. Blockchain achieves fortifying and averting the modification of electronic health records. Blockchain supports the quality of healthcare services to the benefits of both patients and hospitals. This paper investigates the existing techniques used in healthcare blockchain system in the domain of authentication, data integrity, and confidentiality.
Toward data privacy preservation with ciphertext update and key rotation for IoT

Abstract Fine‐grained access control become a research spotlight in the scenario of the smart home with Internet‐of‐Things (IoTs). However, most of the existing works could only realize ciphertext outsourcing with an unchangeable encryption key in the cloud. That is because the trivial solution needs great communication overhead. To overcome this challenge, we propose an updatable encryption based on SM9 (SM9‐UE) scheme for the IoT smart home scenario, where SM9 is an identity and cryptographic standard adopted by GM/T0044. SM9‐UE realizes secure and lightweight ciphertext updating through using the token generated by the data owner, which is information independent with the plaintext. We give the formal security definition and prove it to be adaptive update indistinguishability (IND‐UPD) secure. Theoretical comparison and analysis show that our scheme is efficient in computation and storage overhead. Experimental results also indicate that SM9‐UE can be practically applied to IoT smart home.
Self-Regulation in E-Learning Environment

The COVID-19 pandemic has contributed to the accelerated spread of e-learning around the world. In e-learning, self-regulation becomes more relevant than ever. Reducing the influence of traditional features of the face-to-face learning environment and increasing the impact of the e-environment place high demands on students’ self-regulation. The author’s self-regulation e-learning model emphasizes the position of e-learning at the intersection of the electronic environment and the learning environment. We observe a collision of the concepts of these two environments. The Internet is a more common environment that provokes the use of unacceptable tools and hints, which is a logical consequence of such behavior to pass the test, and not to gain knowledge. Therefore, the most important thing is that students have their own goals and strategies, and use the large resources of the electronic environment for development, and not for cheating. The authors conducted a survey (N = 767), which showed that students rate their self-efficacy of online learning higher in the e-environment than in the offline learning environment. Self-regulation indicators are the highest in the field of environment, and the lowest when setting goals and in time management.
LDAS: Local density-based adaptive sampling for imbalanced data classification

Class imbalance poses a great challenge to traditional classifiers in machine learning as they strongly favor the majority class while ignoring the minority class. Synthetic over-sampling methods deal with this problem by generating synthetic examples to balance the distribution of data. However, most existing methods prefer to generate synthetic examples in a specific area without considering the complexity of imbalance distribution, which may result in the over-emphasis of learning model on some data difficulty factors. To this end, we propose a local density-based adaptive sampling method (LDAS) for imbalanced data. LDAS first assigns a local density for each minority example, then a new cleaning strategy is proposed to remove the overlapping majority examples. Finally, it weighs each minority example based on its approaching degree of decision boundary and the corresponding local density. This is done in such a way that synthetic examples are generated in the safe area and the border area simultaneously according to the weight of minority examples. Extensive experiments on KEEL datasets demonstrate the effectiveness of the proposal LDAS.
Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Image copy detection is an important technology of copyright protection. This paper proposes an efficient hashing method for image copy detection using 2D-2D (two-directional two-dimensional) PCA (Principal Component Analysis). The key is the discovery of the translation invariance of 2D-2D PCA. With the property of translation invariance, a novel model of extracting rotation-invariant low-dimensional features is designed by combining PCT (Polar Coordinate Transformation) and 2D-2D PCA. The PCT can convert an input rotated image to a translation matrix. Since the 2D-2D PCA is invariant to translation, the low-dimensional features learned from the translation matrix are rotation-invariant. Moreover, vector distances of low-dimensional features are stable to common digital operations and thus hash construction with the vector distances is of robustness and compactness. Three open image datasets are exploited to conduct various experiments for validating efficiencies of the proposed method. The results demonstrate that the proposed method is much better than some representative hashing methods in the performances of classification and copy detection.
A survey Of learning-Based control of robotic visual servoing systems

Major difficulties and challenges of modern robotics systems focus on how to give robots self-learning and self-decision-making ability. Visual servoing control strategy is an important strategy of robotic systems to perceive the environment via the vision. The vision can guide new robotic systems to complete more complicated tasks in complex working environments. This survey aims at describing the state-of-the-art learning-based algorithms, especially those algorithms that combine with model predictive control (MPC) used in visual servoing systems, and providing some pioneering and advanced references with several numerical simulations. The general modeling methods of visual servo and the influence of traditional control strategies on robotic visual servoing systems are introduced. The advantages of introducing neural-network-based algorithms and reinforcement-learning-based algorithms into the systems are discussed. Finally, according to the existing research progress and references, the future directions of robotic visual servoing systems are summarized and prospected.
Cryptocurrency as a safe haven for investment portfolios amid COVID-19 panic cases of Bitcoin, Ethereum and Litecoin

Purpose This article examines the susceptibility of cryptocurrencies to coronavirus disease 2019 (COVID-19) induced panic in comparison with major stock indices. Design/methodology/approach The author employs the Bayesian structural vector autoregression to examine the phenomenon in Bitcoin, Ethereum and Litecoin from 2nd January 2020 to 30th June 2021. A similar analysis is conducted for major stock indices, namely S&amp;P 500, FTSE 100 and SSE Composite for comparison purposes. Findings The results suggest that cryptocurrencies returns suffered immensely in the early days of the COVID-19 outbreak following declarations of the disease as a global health emergency and eventually a pandemic in March 2020. However, the returns for all three cryptocurrencies recovered by April 2020 and remained resistant to further COVID-19 panic shocks. The results are dissimilar to those of S&amp;P 500, FTSE 100 and SSE Composite values which were vulnerable to COVID-19 panic throughout the timeframe to June 2021. The results further reveal strong predictive power of Bitcoin on prices of other cryptocurrencies. Research limitations/implications The article provides evidence to support the cryptocurrency as a safe haven during COVID-19 school of thought given their resistance to subsequent shocks during COVID-19. Thus, the author stresses the need for diversification of investment portfolios by including cryptocurrencies given their uniqueness and resistance to shocks during crises. Originality/value The author makes use of the novel corona virus panic index to examine the magnitude of shocks in prices of cryptocurrencies during COVID-19.
Text-instance graph: Exploring the relational semantics for text-based visual question answering

It is time to stop neglecting the text around your world. In VQA, the surrounding text helps humans to understand complete visual scenes and reason question semantics efficiently. Here, we address the challenging Text-based Visual Question Answering (TextVQA) problem, which requires a model to answer the VQA questions with text reading ability. Existing TextVQA methods mainly focus on the latent relationships between detected object instances and scene texts with the given question, but ignore spatial location relationships and complex relational semantics between visual object instances and OCR texts (e.g. the A of B on C). To deal with these challenges, we propose a novel Text-Instance Graph (TIG) network for TextVQA. The TIG builds an OCR-OBJ graph for overlapping relationships modeling, where each node of graph is updated by utilizing relative objects or OCR texts. To deal with the question with complex logic, we propose a dynamic OCR-OBJ graph network to extend the perception space of graph nodes, which grasps the information of non-directly adjacent node features. Considering a scene about “the brand of the computer on the table”, the model would build correlations between “brand” and “table” using “the computer” node as the intermediate node. Extensive experiments on three benchmarks demonstrate the effectiveness and superiority of the proposed method. In addition, our TIG achieves 0.505 ANLS on ST-VQA challenge leaderboard and sets a new state-of-the-art.