Reviewerly/exHarmony huggingface dataset:
ChineseTR: A weakly supervised toponym recognition architecture based on automatic training data generator and deep neural network

Abstract Toponym recognition is used to extract toponyms from natural language texts, which is a fundamental task of ubiquitous geographic information applications. Existing toponym recognition methods with state‐of‐the‐art performance mainly leverage supervised learning (i.e., deep‐learning‐based approaches) with parameters learned from massive, labeled datasets that must be annotated manually. This is a great inconvenience when model training needs to fit different domain texts, especially those of social media messaging. To address this issue, this article proposes a weakly supervised Chinese toponym recognition (ChineseTR) architecture that leverages a training dataset creator that generates training datasets automatically based on word collections and associated word frequencies from various texts and an extension recognizer that employs a basic bidirectional recurrent neural network based on particular features designed for toponym recognition. The results show that the proposed ChineseTR achieves a 0.76 F1 score in a corpus with a 0.718 out‐of‐vocabulary rate and a 0.903 in‐vocabulary rate. All comparative experiments demonstrate that ChineseTR is an effective and scalable architecture that recognizes toponyms.
Towards a Secure and Reliable Federated Learning using Blockchain

Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite these advantages, FL still suffers from several challenges related to reliability (i.e., unreliable participating devices in training), tractability (i.e., a large number of trained models), and anonymity. To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to FL, which uses blockchain features to enable collaborative model training in a fully distributed and trustworthy manner. In particular, we design a secure FL based on the blockchain sharding that ensures data reliability, scalability, and trustworthiness. In addition, we introduce an incentive mechanism to improve the reliability of FL devices using subjective multi-weight logic. The results show that our proposed SRB- FL framework is efficient and scalable, making it a promising and suitable solution for federated learning.
The Role of Blockchain Technologies in the Sustainable Development of Students’ Learning Process

Technological evolution has contributed to considerable changes in the learning methods used today. If a few years ago, before the technological boom, students relied only on their memory capacity, on notes on sheets of paper and written materials (in the form of course materials, books, or textbooks), today the process of learning is significantly facilitated by the means and techniques involved in the use of technology, such as artificial intelligence, smartphones, and tablets, which are rapidly replacing bulky desktop computers and outdated textbooks used in the learning process. Moreover, blockchain technology can also be used as a motivational factor for developing learning abilities by implementing a system that can bring students credit points convertible into cryptocurrencies or online badges. Studies conducted in the field show that students’ motivation for learning and creativity is significantly improved with the help of this reward system. In this article, two specialized studies were conducted (in Romania), one on the perception of teachers in the university environment towards the use of new technologies in the learning process (artificial intelligence, smartphones, and tablets), and the other on the perception of students on the technological integration of blockchain (by implementing a system that can bring students credit points convertible into cryptocurrencies or online badges to certify their knowledge) in the learning process and measuring their satisfaction.
Feasibility study of multi-site split learning for privacy-preserving medical systems under data imbalance constraints in COVID-19, X-ray, and cholesterol dataset

It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven't neglected this trend either. Hospitals are now at the forefront for multi-site medical data sharing to provide ground-breaking advancements in the way health records are shared and patients are diagnosed. Sharing of medical data is essential in modern medical research. Yet, as with all data sharing technology, the challenge is to balance improved treatment with protecting patient's personal information. This paper provides a novel split learning algorithm coined the term, "multi-site split learning", which enables a secure transfer of medical data between multiple hospitals without fear of exposing personal data contained in patient records. It also explores the effects of varying the number of end-systems and the ratio of data-imbalance on the deep learning performance. A guideline for the most optimal configuration of split learning that ensures privacy of patient data whilst achieving performance is empirically given. We argue the benefits of our multi-site split learning algorithm, especially regarding the privacy preserving factor, using CT scans of COVID-19 patients, X-ray bone scans, and cholesterol level medical data.
TimeAutoAD: Autonomous Anomaly Detection With Self-Supervised Contrastive Loss for Multivariate Time Series

Multivariate time series (MTS) data are becoming increasingly ubiquitous in networked systems, e.g., IoT systems and 5G networks. Anomaly detection in MTS refers to identifying time series which exhibit different behaviors from normal status. Building such a system, however, is challenging due to a few reasons: i) labels for anomaly cases are usually unavailable or very rare; ii) most existing approaches rely on manual model-design and hyperparameter tuning, which may cost a huge amount of labor effort. To this end, we propose an autonomous anomaly detection technique for multivariate time series data (TimeAutoAD) based on a novel self-supervised contrastive loss. Specifically, we first present an automatic anomaly detection pipeline to optimize the model configuration and hyperparameters automatically. Next, we introduce three different strategies to augment the training data for generating pseudo negative time series and employ a self-supervised contrastive loss to distinguish the original time series and the generated time series. In this way, the representation learning capability of TimeAutoAD can be greatly enhanced and the anomaly detection performance can thus be improved. Extensive empirical studies on real-world datasets demonstrate that the proposed TimeAutoAD not only outperforms state-of-the-art anomaly detection approaches but also exhibits robustness when training data are contaminated.
Parallel Time-Delay Reservoir Computing With Quantum Dot Lasers

A semiconductor laser with optical feedback and optical injection is an appealing scheme to construct the time-delay reservoir computing (TDRC) networks. Quantum dot (QD) lasers are compatible to the silicon platform, and hence is helpful to develop fully on-chip TDRCs. This work theoretically demonstrates a parallel TDRC based on a Fabry-Perot QD laser with multiple longitudinal modes. These modes act as connected physical neurons, which process the input signal in parallel. The interaction strength of the modes is characterized by the cross-gain saturation effect. We show that the neuron interaction strength affects the performance of various benchmark tasks, including the memory capacity, time series prediction, nonlinear channel equalization, and spoken digit recognition. In comparison with the one-channel TDRC with the same number of nodes, the parallel TDRC runs faster and its performance is improved on multiple benchmark tasks.
Fall Detection from Smart Wearable Sensors using Deep Convolutional Neural Network with Squeeze-and-Excitation Module

Older people are at a higher risk of injury or death due to falls than the general population. As the world's population ages, new fall detection and prevention technology must be developed quickly. The emerging technology sector is devoted to the development of tools that improve the lives of the elderly. The goal of a fall prevention system is to foresee and reduce the risk of a person falling. A fall detection system, on the other hand, tracks the fall. It alerts the user in the event of a fall to assist. The purpose of this study is to demonstrate the efficacy of fall detection utilizing wearable sensors. We developed the SE-DeepConvNet model, a hybrid deep convolutional neural network, to enhance the fall detection capability using squeeze-and-excitation modules. The proposed model uses wearable sensor data from the SisFall dataset, a publicly available benchmark dataset. We performed a series of experiments using various deep learning models, including the proposed approach. The experimental findings demonstrate that the introduced SE-DeepConvNet model performs better than other deep learning models (99.201% accuracy and 99.202% F1-score).
Securing color image by using bit-level modified integer nonlinear coupled chaos model in Fresnel diffraction domains

An optical color image cryptosystem is presented by using bit-level modified integer nonlinear coupled chaos model in Fresnel diffraction domains. Firstly, a bit-level modified integer nonlinear coupled chaos model (INCCM) controlled by linear feedback shift register (LFSR) is designed. Subsequently, a framework of LFSR controlling image encryption is designed, and a bit-level encryption algorithm is desinged. LFSR is used to generate pseudo-random sequence to control bit-scrambling sequence, which improves the security. In addition, this model improves integer nonlinear coupling algorithm saving much running time. The effectiveness and feasibility of the proposed color encryption algorithm are verified by numerical simulation considering various types of attacks.
VR as a choice: what drives learners’ technology acceptance?

Abstract Post-secondary institutions are investing in and utilizing virtual reality (VR) for many educational purposes, including as a discretionary learning tool. Institutions such as vocational schools, community colleges, and universities need to understand what psychological factors drive students’ acceptance of VR for learning in discretionary contexts. The Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al. in MIS Quarterly 27:425–478, 2003) offers a theoretical framework for understanding students’ receptivity to VR for learning. Undergraduate university students ( N = 300) read a description of VR and video training mediums, then indicated which they would choose to learn a novel task. Three psychological variables—performance expectancy, effort expectancy, and social influence—tended to be related to acceptance of VR, which was measured in two ways: (a) rated intentions to use VR and (b) preference for VR over a video-based alternative. Relative weight analyses compared the importance of the three predictors and revealed that performance expectancy tended to be the most influential antecedent of VR acceptance.
Improving data augmentation for low resource speech-to-text translation with diverse paraphrasing

High quality end-to-end speech translation model relies on a large scale of speech-to-text training data, which is usually scarce or even unavailable for some low-resource language pairs. To overcome this, we propose a target-side data augmentation method for low-resource language speech translation. In particular, we first generate large-scale target-side paraphrases based on a paraphrase generation model which incorporates several statistical machine translation (SMT) features and the commonly used recurrent neural network (RNN) feature. Then, a filtering model which consists of semantic similarity and speech-word pair co-occurrence was proposed to select the highest scoring source speech-target paraphrase pairs from candidates. Experimental results on English, Arabic, German, Latvian, Estonian, Slovenian and Swedish paraphrase generation show that the proposed method achieves significant and consistent improvements over several strong baseline models on PPDB datasets (http://paraphrase.org/). To introduce the results of paraphrase generation into the low-resource speech translation, we propose two strategies: audio-text pairs recombination and multiple references training. Experimental results show that the speech translation models trained on new audio-text datasets which combines the paraphrase generation results lead to substantial improvements over baselines, especially on low-resource languages.
BinBRO: Binary Battle Royale Optimizer algorithm

Stochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. Optimization algorithms benefit from stochastic methods; however, they do not guarantee to obtain the optimal solution. Many optimization algorithms have been proposed for solving problems with continuous nature; nevertheless, they are unable to solve discrete or binary problems. Adaptation and use of continuous optimization algorithms for solving discrete problems have gained growing popularity in recent decades. In this paper, the binary version of a recently proposed optimization algorithm, Battle Royale Optimization, which we named BinBRO, has been proposed. The proposed algorithm has been applied to two benchmark datasets: the uncapacitated facility location problem, and the maximum-cut graph problem, and has been compared with 6 other binary optimization algorithms, namely, Particle Swarm Optimization, different versions of Genetic Algorithm, and different versions of Artificial Bee Colony algorithm. The BinBRO-based algorithms could rank first among those algorithms when applying on all benchmark datasets of both problems, UFLP and Max-Cut.
Intelligent framework for automated failure prediction, detection, and classification of mission critical autonomous flights

Autonomous flights are the major industry contributors towards next-generation developments in pervasive and ubiquitous computing. Modern aerial vehicles are designed to receive actuator commands from the primary autopilot software as input to regulate their servos for adjusting control surfaces. Due to real-time interaction with the actual physical environment, there exists a high risk of control surface failures for engine, rudder, elevators, and ailerons etc. If not anticipated and then timely controlled, failures occurring during the flight can have severe and cataclysmic consequences, which may result in mid-air collision or ultimate crash. Humongous amount of sensory data being generated throughout mission-critical flights, makes it an ideal candidate for applying advanced data-driven machine learning techniques to identify intelligent insights related to failures for instant recovery from emergencies. In this paper, we present a novel framework based on machine learning techniques for failure prediction, detection, and classification for autonomous aerial vehicles. The proposed framework utilizes long short-term memory recurrent neural network architecture to analyze time series data and has been applied at the AirLab Failure and Anomaly flight dataset, which is a comprehensive publicly available dataset of various fault types in fixed-wing autonomous aerial vehicles' control surfaces. The proposed framework is able to predict failure with an average accuracy of 93% and the average time-to-predict a failure is 19 s before the actual occurrence of the failure, which is 10 s better than current state-of-the-art. Failure detection accuracy is 100% and average detection time is 0.74 s after happening of failure, which is 1.28 s better than current state-of-the-art. Failure classification accuracy of proposed framework is 100%. The performance analysis shows the strength of the proposed methodology to be used as a real-time failure prediction and a pseudo-real-time failure detection along with a failure classification framework for eventual deployment with actual mission-critical autonomous flights.
Artificial intelligence changes the way we work: A close look at innovating with chatbots

Abstract An enhanced understanding of the innovative use of artificial intelligence (AI) is essential for organizations to improve work design and daily business operations. This study's purpose is to offer insights into how AI can transform organizations' work practices through diving deeply into its innovative use in the context of a primary AI tool, a chatbot, and examining the antecedents of innovative use by conceptualizing employee trust as a multidimensional construct and exploring employees' perceived benefits. In particular, we have conceptualized employee trust in chatbots as a second‐order construct, including three first‐order variables: trust in functionality, trust in reliability, and trust in data protection. We collected data from 202 employees. The results supported our conceptualization of trust in chatbots and showed that three dimensions of first‐order trust beliefs have relatively the same level of importance. Further, both knowledge support and work–life balance enhance trust in chatbots, which in turn leads to innovative use of chatbots. Our study contributes to the existing literature by introducing the new conceptualization of trust in chatbots and examining its antecedents and outcomes. The results can provide important practical insights regarding how to support innovative use of chatbots as the new way we organize work.
EvoRecSys: Evolutionary framework for health and well-being recommender systems

Abstract In recent years, recommender systems have been employed in domains like e-commerce, tourism, and multimedia streaming, where personalising users’ experience based on their interactions is a fundamental aspect to consider. Recent recommender system developments have also focused on well-being, yet existing solutions have been entirely designed considering one single well-being aspect in isolation, such as a healthy diet or an active lifestyle. This research introduces EvoRecSys, a novel recommendation framework that proposes evolutionary algorithms as the main recommendation engine, thereby modelling the problem of generating personalised well-being recommendations as a multi-objective optimisation problem. EvoRecSys captures the interrelation between multiple aspects of well-being by constructing configurable recommendations in the form of bundled items with dynamic properties. The preferences and a predefined well-being goal by the user are jointly considered. By instantiating the framework into an implemented model, we illustrate the use of a genetic algorithm as the recommendation engine. Finally, this implementation has been deployed as a Web application in order to conduct a users’ study.
Feature Selection With Maximal Relevance and Minimal Supervised Redundancy

Feature selection (FS) for classification is crucial for large-scale images and bio-microarray data using machine learning. It is challenging to select informative features from high-dimensional data which generally contains many irrelevant and redundant features. These features often impede classifier performance and misdirect classification tasks. In this article, we present an efficient FS algorithm to improve classification accuracy by taking into account both the relevance of the features and the pairwise features correlation in regard to class labels. Based on conditional mutual information and entropy, a new supervised similarity measure is proposed. The supervised similarity measure is connected with feature redundancy minimization evaluation and then combined with feature relevance maximization evaluation. A new criterion max-relevance and min-supervised-redundancy (MRMSR) is introduced and theoretically proved for FS. The proposed MRMSR-based method is compared to seven existing FS approaches on several frequently studied public benchmark datasets. Experimental results demonstrate that the proposal is more effective at selecting informative features and results in better competitive classification performance.
Enhanced sine cosine algorithm using opposition learning, adaptive evolution and neighborhood search strategies for multivariable parameter optimization problems

Sine cosine algorithm (SCA), an emerging metaheuristic method, is usually limited by the local convergence and search stagnation defects in multivariable optimization problems. To improve the SCA performance, this study proposes an enhanced sine cosine algorithm (ESCA) using several modified strategies, including the opposition learning strategy for enlarging search range, the adaptive evolution strategy for improving global exploration, the neighborhood search strategy for increasing population diversity, and the greedy selection strategy for guaranteeing solution quality. ESCA and several metaheuristics methods are used to solve a group of numerical optimization problems. The experimental results indicate that in terms of solution efficiency and convergence rate, ESCA outperforms several traditional methods for multivariable parameter optimization problems. Then, several engineering optimization problems are employed to further test the feasibility of the ESCA method in practical applications. The simulations show that for various performance evaluation indexes, ESCA can produce high-quality solutions with better objective values compared to the control methods. Thus, a simple but powerful tool is developed to address the complex multivariable parameter optimization problems.
Quadrature photonic spatial Ising machine

As a computing accelerator, a large-scale photonic spatial Ising machine has great advantages and potential due to its excellent scalability and compactness. However, the current fundamental limitation of a photonic spatial Ising machine is the configuration flexibility for problem implementation in the accelerator model. Arbitrary spin interactions are highly desired for solving various non-deterministic polynomial (NP)-hard problems. In this paper, we propose a novel quadrature photonic spatial Ising machine to break through the limitation of the photonic Ising accelerator by synchronous phase manipulation in two sections. The max-cut problem solution with a graph order of 100 and density from 0.5 to 1 is experimentally demonstrated after almost 100 iterations. Our work suggests flexible problem solving by the large-scale photonic spatial Ising machine.
Deep face recognition for dim images

The performance of many state-of-the-art deep face recognition models deteriorates significantly for images captured under low illumination, mainly because the features of dim probe face images cannot match well with those of normal-illumination gallery images. The issue cannot be satisfactorily addressed by enhancing the illumination of face images and performing face recognition on the resulted images alone. We propose a novel deep face recognition framework that consists of a feature restoration network, a feature extraction network, and an embedding matching module. The feature restoration network adopts a two-branch structure based on the convolutional neural network to generate a feature image from the raw image and the illumination-enhanced image. The feature extraction network encodes the feature image into an embedding, which is then used by the embedding matching module for face verification and identification. The overall verification accuracy is improved from 1.1% to 6.7% when tested on the Specs on Faces (SoF) dataset. For face identification, the rank-1 identification accuracy is improved by 2.8%.