Reviewerly/exHarmony huggingface dataset:
Adaptive Multilevel Prediction Method for Dynamic Multimodal Optimization

This study develops an adaptive multilevel prediction (AMLP) method to detect and track multiple global optima over time. First, it formulates a multilevel prediction approach in which a higher level prediction improves the accuracy of the lower level prediction to reduce the prediction error, enabling it to capture more complex patterns in the changes. However, a higher level prediction is more sensitive to input errors and the randomness in the pattern of the change. To overcome this challenge, this study employs an adaptive mechanism which can determine the near-optimal prediction level at each time step. At the same time, AMLP calculates the strength of the diversity introduced after a change based on the estimated prediction error. A successful static multimodal optimizer is augmented with AMLP, for which AMLP determines the location and the mutation strength of the initialized subpopulations. An existing dynamic benchmark generator is improved so that it can generate dynamic test problems with more complex patterns in their changes. In particular, this dynamic benchmark generator allows for controlling the randomness of the pattern in the change to simulate dynamic problems with different degrees of predictability. A few controlled experiments are first performed to provide insight into different components of AMLP. Then, AMLP is compared with some of the most successful prediction methods when they are incorporated into the developed dynamic multimodal optimization method. Eleven dynamic cases with different change severity, change frequency, predictability, problem dimensionality, and the number of global minima are considered. The numerical results show the superiority of AMLP over other prediction methods.
Smart Contracts based on Private and Public Blockchains for the Purpose of Insurance Services

Blockchain technology has evolved into many applications and it has many advantages, especially in insurance business, because the Blockchain solutions minimize the time to process claims for losses and operational expenses. This paper compares the application of private and public Blockchain for insurance services through the experimental implementation of smart contracts based on Hyperledger Fabric and Ethereum. The results from the experiments are presented.
Accurate Interpretation of the Online Learning Model for 6G-Enabled Internet of Things

The next-generation network (6G) has more strict requirements for the online learning ability and high interpretability of the learned systems. Machine learning is expected to be essential to assist in making the networks efficient and adaptable, but most promising methods often are treated as “black boxes” due to the deep structures and high nonlinearity. Therefore, this article attempts to study the interpretations of machine learning algorithms to make them more applicable to the 6G-enabled Internet of things (IoT) networks. Typically, this article focuses on the new model GBDT2NN, which distills the knowledge learned by gradient boosting decision tree (GBDT) into neural network (NN) models to retain the learning ability of numerical data and rise the ability of online learning at the same time, but it loses the interpretability. This article conducts an empirical study on explaining individual prediction of GBDT2NN by taking use of the feature importance learned from GBDT, and then further explores whether the explanation can improve the approximation process. In addition, this article proposes two methods to obtain the interpretations: 1) the independent method and 2) the joint method. The experiments on several data sets of IoT networks show that the proposed methods can achieve better performance on both explanations and predictions.
Smooth twin bounded support vector machine with pinball loss

Abstract The twin support vector machine improves the classification performance of the support vector machine by solving two small quadratic programming problems. However, this method has the following defects: (1) For the twin support vector machine and some of its variants, the constructed models use a hinge loss function, which is sensitive to noise and unstable in resampling. (2) The models need to be converted from the original space to the dual space, and their time complexity is high. To further enhance the performance of the twin support vector machine, the pinball loss function is introduced into the twin bounded support vector machine, and the problem of the pinball loss function not being differentiable at zero is solved by constructing a smooth approximation function. Based on this, a smooth twin bounded support vector machine model with pinball loss is obtained. The model is solved iteratively in the original space using the Newton-Armijo method. A smooth twin bounded support vector machine algorithm with pinball loss is proposed, and theoretically the convergence of the iterative algorithm is proven. In the experiments, the proposed algorithm is validated on the UCI datasets and the artificial datasets. Furthermore, the performance of the presented algorithm is compared with those of other representative algorithms, thereby demonstrating the effectiveness of the proposed algorithm.
Feature extraction from unequal length heterogeneous EHR time series via dynamic time warping and tensor decomposition

Electronic Health Records (EHR) data is routinely generated patient data that can provide useful information for analytical tasks such as disease detection and clinical event prediction. However, temporal EHR data such as physiological vital signs and lab test results are particularly challenging. Temporal EHR features typically have different sampling frequencies; such examples include heart rate (measured almost continuously) and blood test results (a few times during a patient's entire stay). Different patients also have different length of stays. Existing approaches for temporal EHR sequence extraction either ignore the temporal pattern within features, or use a predefined window to select a section of the sequences without taking into account all the information. We propose a novel approach to tackle the issue of irregularly sampled, unequal length EHR time series using dynamic time warping and tensor decomposition. We use DTW to learn the pairwise distances for each temporal feature among the patient cohort and stack the distance matrices into a tensor. We then decompose the tensor to learn the latent structure, which is consequently used for patient representation. Finally, we use the patient representation for in-hospital mortality prediction. We illustrate our method on two cohorts from the MIMIC-III database: the sepsis and the acute kidney failure cohorts. We show that our method produces outstanding classification performance in terms of AUROC, AUPRC and accuracy compared with the baseline methods: LSTM and DTW-KNN. In the end we provide a detailed analysis on the feature importance for the interpretability of our method.
A cooperative coevolution framework for evolutionary learning and instance selection

Data science becomes an emerging topic in developing computational and artificial intelligence techniques due to the rapid growth of information and data over past decade. Data-driven evaluation, through scanning data to validate the performance of a method, is the crux of effectively retrieving information. Ordinarily, the computational cost at data-driven evaluation is proportional to the volume, determined by the number of instances and the number of attributes, of the data. One straightforward way of reducing the volume of data is instance selection. Past studies have adopted evolutionary algorithm (EA) for instance selection, yet these studies are mainly based on k nearest neighbors classifier due to the high computational cost at learning process. Leveraging EA to learn patterns, models, or classifiers forms a famous branch of learning method known as evolutionary learning (EL). Nevertheless, EL requires immense amount of data-driven fitness evaluations, which detracts EL from efficiency and causes slow convergence. The issue of high computational cost at data-driven evaluation restricts the capability of evolutionary learning and evolutionary instance selection. This study proposes a novel framework called cooperative evolutionary learning and instance selection (CELIS) for addressing the above issues. There are three main features in the proposed CELIS: 1) cooperative evolutionary learning and instance selection in an adaptive manner, 2) fast solution evaluation based on the selected subset of data for evolutionary learning, and 3) effective data evaluation of representativeness as per promising solution for evolutionary instance selection. This study conducts a series of experiments for testing the effectiveness and efficiency of the proposed CELIS on data mining, classification, and symbolic regression problems. The results show that the proposed method can significantly enhance the performance of state-of-the-art EAs on the three types of problems in terms of solution quality and convergence speed. Further analysis also validates that CELIS is able to gain a small and representative subset of data.
Extreme gradient boosting (XGBoost) method in making forecasting application and analysis of USD exchange rates against rupiah

Abstract Economic conditions in Indonesia are still unstable, causing the US dollar exchange rate to increase. This is because most international transactions in Indonesia use US dollars. Prediction or forecasting is chosen as one of the important things in choosing a market to invest in buying and selling. This research will focus on making forecasting applications and analyzing the exchange rate of USD against rupiah based on time series data or temporal datasets from the Investing.com site using machine learning methods, namely Extreme Gradient Boosting (XGBoost). Applications created using the python programming language and streamlit framework. Modeling is carried out using the Knowledge Discovery in Database (KDD) methodology with the stages of dividing the dataset with a 50:50 percentage share into test and train data. The modeling uses hyperparameter tuning values, namely n_estimators = 1000, max_depth = 1, x_colsample_bytree = 0.9894, x_gamma = 0.9989, x_min_child = 1.0, x_reg_lamda = 0.2381, and x_subsample = 0.7063 with best loss or RMSE 451.4151. The values of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) when making the model were 6.61374% and 3.95485%. Meanwhile, when testing the model, the RMSE is 0.23577% and MAPE is 0.11643%.
Handwritten Chinese Font Generation with Collaborative Stroke Refinement

Automatic character generation is an appealing solution for typeface design, especially for Chinese fonts with over 3700 most commonly-used characters. This task is particularly challenging for handwritten characters with thin strokes which are error-prone during deformation. To handle the generation of thin strokes, we introduce an auxiliary branch for stroke refinement. The auxiliary branch is trained to generate the bold version of target characters which are then fed to the dominating branch to guide the stroke refinement. The two branches are jointly trained in a collaborative fashion. In addition, for practical use, it is desirable to train the character synthesis model with a small set of manually designed characters. Taking advantage of content-reuse phenomenon in Chinese characters, we further propose an online zoom-augmentation strategy to reduce the dependency on large size training sets. The proposed model is trained end-to-end and can be added on top of any method for font synthesis. Experimental results on handwritten font synthesis have shown that the proposed method significantly outperforms the state-of-the-art methods under practical setting, i.e. with only 750 paired training samples.
Automatic Calibration of the Fisheye Camera for Egocentric 3D Human Pose Estimation from a Single Image

We propose a method for egocentric 3D human pose estimation from a single image captured by a fisheye camera. The problem of estimating the egocentric 3D pose for a fisheye camera is that images may be subject to strong image distortions (e.g. 2D poses on the image plane that pass through the line of sight of the fisheye lens).Therefore, in this paper, we approach this problem by an automatic calibration module. Given a single image, our network first estimates 3D joint locations of a human in camera coordinates. To alleviate the impact of image distortions on 3D human pose estimation, we then use the automatic calibration to further regularize the 3D predictions. Experimental results demonstrate that the proposed method achieves state-of-the-art performance.
EXAM: A Framework of Learning Extreme and Moderate Embeddings for Person Re-ID

Person re-identification (Re-ID) is challenging due to host of factors: the variety of human positions, difficulties in aligning bounding boxes, and complex backgrounds, among other factors. This paper proposes a new framework called EXAM (EXtreme And Moderate feature embeddings) for Re-ID tasks. This is done using discriminative feature learning, requiring attention-based guidance during training. Here “Extreme” refers to salient human features and “Moderate” refers to common human features. In this framework, these types of embeddings are calculated by global max-pooling and average-pooling operations respectively; and then, jointly supervised by multiple triplet and cross-entropy loss functions. The processes of deducing attention from learned embeddings and discriminative feature learning are incorporated, and benefit from each other in this end-to-end framework. From the comparative experiments and ablation studies, it is shown that the proposed EXAM is effective, and its learned feature representation reaches state-of-the-art performance.
Modularizing Deep Learning via Pairwise Learning With Kernels

By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces, leading to a kernel machine interpretation. Based on this construction, we then propose a provably optimal modular learning framework for classification that does not require between-module backpropagation. This modular approach brings new insights into the label requirement of deep learning (DL). It leverages only implicit pairwise labels (weak supervision) when learning the hidden modules. When training the output module, on the other hand, it requires full supervision but achieves high label efficiency, needing as few as ten randomly selected labeled examples (one from each class) to achieve 94.88% accuracy on CIFAR-10 using a ResNet-18 backbone. Moreover, modular training enables fully modularized DL workflows, which then simplify the design and implementation of pipelines and improve the maintainability and reusability of models. To showcase the advantages of such a modularized workflow, we describe a simple yet reliable method for estimating reusability of pretrained modules as well as task transferability in a transfer learning setting. At practically no computation overhead, it precisely described the task space structure of 15 binary classification tasks from CIFAR-10.
Factors Influencing Tourists’ Intention to Use COVID-19 Contact Tracing App

Abstract The purpose of this study was to develop and test a model that explores the antecedents of tourists’ acceptance of COVID-19 contact tracing app (CTA). Data was obtained from a crowdsourcing platform (Pollfish), in which 400 respondents answered the questionnaire. We used SmartPLS to analyse the data. Results reveal that trust and structural assurance have the strongest relationship. Furthermore, the relationship between trust and destination safety was positive. Finally, self-efficacy moderated the relationship between trust and intention, implying that trust was stronger for tourists who have higher levels of self-efficacy. Recommendations are offered.
Optimal Investment in Cyber-Security under Cyber Insurance for a Multi-Branch Firm

Investments in security and cyber-insurance are two cyber-risk management strategies that can be employed together to optimize the overall security expense. In this paper, we provide a closed form for the optimal investment under a full set of insurance liability scenarios (full liability, limited liability, and limited liability with deductibles) when we consider a multi-branch firm with correlated vulnerability. The insurance component results to be the major expense. It ends up being the only recommended approach (i.e., setting zero investments in security) when the intrinsic vulnerability is either very low or very high. We also study the robustness of the investment choices when our knowledge of vulnerability and correlation is uncertain, concluding that the uncertainty induced on investment by either uncertain correlation or uncertain vulnerability is not significant.
Advanced Glove for Deaf and Dumb with Speech and Text Message on Android Cell Phone

Most of us are blessed with natural ability to see, listen and interact. However, few people do not have this ability. Due to various reasons like birth defects, accidents, oral diseases, and environmental impacts the number of such people is rapidly increasing in the recent years. Most of the deaf and dumb use gesture and sign symbol for the communication. Sign language is the form of communication where shapes, arms or body, orientation and movement of hands, and facial expressions are systematically combined. But, sign language is ineffective in many cases. It may not be sufficient when such people are involved in the educational, social and professional environments where they want to interact with others. Therefore, there is an immediate need to have an advanced sign language detection and gesture recognition system. The need, design methodology and implementation strategies of a smart glove are discussed in this paper. The main objective of the work is to bring the technology for the help of differently abled people to communication effectively and in most meaningful form. The hand glove converts the hand/finger gestures in to meaningful voice messages over an Android phone. An app is designed using Android Studio for the said purpose. There are several works carried out across the globe for converting hand and finger gesture or sign language into speech. The work described in this paper uses NodeMCU as processing and wireless communication of data and it is most advanced form of all the hand glove designs so far.
A Novel Blind mmWave Channel Estimation Algorithm Based on ML-ELM

The millimeter wave (mmWave) with wide frequency spectrum can fulfill the demand of escalating communication system capacity. However, the large bandwidth of the mmWave channel leads to a large increase in the dimension of the received signal, which results to the increased computational complexity for channel estimation. In this letter, we propose a novel blind channel estimation algorithm based on Manifold Learning-Extreme Learning Machine (ML-ELM) in mmWave communication system. In the proposed ML-ELM algorithm, Manifold Learning (ML) is employed to reduce the feature dimension of received signal, and Extreme Learning Machine (ELM) with one-shot training is applied for the estimated Channel State Information (CSI). The ELM channel estimator is trained using the channel fading features extracted by ML algorithm. The CSI estimated by ML-ELM algorithm is more accurate, and the computational complexity satisfied the real-time requirements. Simulation results show that the proposed ML-ELM algorithm without any pilot aided achieves better MSE performance of CSI compared with the non-blind channel estimation algorithms in higher SNR scenarios, and better compared with pilot-based LS algorithm in lower SNR scenarios.
Data-Driven Epidemic Intelligence Strategies Based on Digital Proximity Tracing Technologies in the Fight against COVID-19 in Cities

In a modern pandemic outbreak, where collective threats require global strategies and local operational defence applications, data-driven solutions for infection tracing and forecasting epidemic trends are crucial to achieve sustainable and socially resilient cities. Indeed, the need for monitoring, containing, and mitigating the ongoing COVID-19 pandemic has generated a great deal of interest in Digital Proximity Tracing Technology (DPTT) on smartphones, as well as their function and effectiveness and insights of population acceptance. This paper introduces and compares different Data-Driven Epidemic Intelligence Strategies (DDEIS) developed on DPTTs. It aims to clarify to what extent DDEIS could be effective and both technologically and socially suitable in reaching the objective of a swift return to normality for cities, guaranteeing public health safety and minimizing the risk of epidemic resurgence. It assesses key advantages and limits in supporting both individual decision-making and policy-making, considering the role of human behaviour. Specifically, an online survey carried out in Italy revealed user preferences for DPTTs and provided preliminary data for an SEIR (Susceptible–Exposed–Infectious–Recovered) epidemiological model. This was developed to evaluate the impact of DDEIS on COVID-19 spread dynamics, and results are presented together with an evaluation of potential drawbacks.
Land Registration System Using Block-chain

In the traditional Land Registration system practiced in India, there exists a middle man (broker) who establishes a contact between the buyer and seller, for instance if an individual wants to buy or sell a property, the broker will create and assemble all the obligatory physical documents with regards to an agreement as a proof of property. Brokers will ensure that the land/property would be registered by an authorised government office where all the attributes are noted down in a ledger and thereafter the whole transaction and purchasement between the two parties takes place. In this scenario, there are chances of losing or tampering of the documents as anyone with certain powers can access or alter the papers easily which in turn threatens this concrete proof of land. Thus, this type of system as compared to our proposed system in which we make use of a smart contract to deal with the assets and transactions among the participants, is relatively time consuming, less secure and unsynchronized where activities including corruption and fraudulence might be associated during the execution of the required process. With an amalgam of inspection and analysis regarding the old accustomed way and considering that Blockchain has an increased transparency and integrity maintenance along with the portability factor, we put forward a blockchain based land registration system which provides a transparent, secured and decentralised method for execution of transactions between the participants by employing the concept of hyper ledger.
Bidirectional teleportation for underwater quantum communications

In this work, we evaluate the performance of a bidirectional teleportation protocol on an IBM Q Experience quantum processor of six or more qubits. Moreover, we analyze the viability in the implementation of the mentioned protocol in a configuration composed by two submerged nuclear submarines on opposite sides of the ocean and a satellite that generates and distributes entangled pairs, and transmits optical bits of disambiguation to those buoys located on the sea surface, and associated to those submerged submarines. The results of the implementations on a 16-qubits Melbourne IBM Q processor turned out to be extremely satisfactory with a low margin of error considering that this processor does not belong to the Premium family of IBM Q, which implies a high presence of decoherence and sensitivity to flip errors even in few-gate quantum circuits.