Reviewerly/exHarmony huggingface dataset:
Pruner to Predictor: An Efficient Pruning Method for Neural Networks Compression

Channel pruning is an effective way for neural networks compression. However, traditional pruning methods based on rules and regularization usually depend on expert knowledge. Moreover, reinforcement learning and evolutionary algorithms-based pruning methods have low pruning efficiency and are time consuming. In this paper, an efficient pruning method named Pruner to Predictor (P2P) is developed. The pruner which consists of differentiable structural parameters generates a continuous representation of the neural network structure. The predictor which is constructed by neural networks predicts the performance of networks with different structures. As a result, the predictor maps the relationship between the network structure and the performance. Therefore, the gradient descent method is leveraged to optimize the pruner in an end to end manner, which achieves an effective and efficient neural network pruning. Experimental results on CIFAR10 and ImageNet show that the present P2P outperforms many previous state-of-the-art methods.
Public Sentiment Analysis and Topic Modeling Regarding COVID-19’s Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia

The COVID-19 pandemic has affected many aspects of human life.The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior.Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms.To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021.Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment.The changes between these lockdowns were identified, and the latent topics were highlighted.Most of the public sentiment focused on the first lockdown Alamoodi et al.: Public Sentiment Analysis and Topic Modeling Regarding COVID-19's Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysiaas reflected in the large number of latent topics generated during this period.The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative.Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns.Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems.These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.
Application of the sliding window method and Mask-RCNN method to nuclear recognition in oral cytology

Abstract Background We aimed to develop an artificial intelligence (AI)-assisted oral cytology method, similar to cervical cytology. We focused on the detection of cell nuclei because the ratio of cell nuclei to cytoplasm increases with increasing cell malignancy. As an initial step in the development of AI-assisted cytology, we investigated two methods for the automatic detection of cell nuclei in blue-stained cells in cytopreparation images. Methods We evaluated the usefulness of the sliding window method (SWM) and mask region-based convolutional neural network (Mask-RCNN) in identifying the cell nuclei in oral cytopreparation images. Thirty cases of liquid-based oral cytology were analyzed. First, we performed the SWM by dividing each image into 96 × 96 pixels. Overall, 591 images with or without blue-stained cell nuclei were prepared as the training data and 197 as the test data (total: 1,576 images). Next, we performed the Mask-RCNN by preparing 130 images of Class II and III lesions and creating mask images showing cell regions based on these images. Results Using the SWM method, the highest detection rate for blue-stained cells in the evaluation group was 0.9314. For Mask-RCNN, 37 cell nuclei were identified, and 1 cell nucleus was identified as a non-nucleus after 40 epochs (error rate:0.027). Conclusions Mask-RCNN is more accurate than SWM in identifying the cell nuclei. If the blue-stained cell nuclei can be correctly identified automatically, the entire cell morphology can be grasped faster, and the diagnostic performance of cytology can be improved.
Sand dust image visibility enhancement algorithm via fusion strategy

The outdoor images captured in sand dust weather often suffer from poor contrast and color distortion, which seriously interfere with the performance of intelligent information processing systems. To solve the issues, a novel enhancement algorithm based on fusion strategy is proposed in this paper. It includes two components in sequence: sand removal via the improved Gaussian model-based color correction algorithm and dust elimination using the residual-based convolutional neural network (CNN). Theoretical analysis and experimental results show that compared with the prior sand dust image enhancement methods, the proposed fusion strategy can effectively correct the overall yellowing hue and remove the dust haze disturbance, which provides a constructive idea for the future development of sand dust image enhancement.
Exploring the factors influencing users’ satisfaction and continuance intention of MOOCs in China

Recently, Massive Open Online Courses (MOOC) have received much attention from users.Users' continuance intention of using MOOC is one of the most important indicators to measure the success of MOOCs.At present, MOOCs are facing a low completion rate and retention rate problems.This calls for the importance in investigating factors that affect MOOC users' satisfaction, thereby enhancing the intention to continue using MOOCs.The purpose of this research was therefore to explore the factors affecting MOOC users' satisfaction and continuance intention towards the Expectation Confirmation Model.In total, 410 questionnaires were collected via an online survey and analysed with the Structural Equation Modelling (SEM).Participants were Chinese employees from different city tiers who had experienced using or now used MOOCs.The analysis indicated a good fit of the structural equation model.The analysis results showed that the confirmation factor has a significant impact on perceived usefulness, perceived service quality, and satisfaction.The analysis results highlighted that perceived service quality has the strongest impact on satisfaction, thereby enhancing the continuance intention in using MOOC.The results also revealed that perceived usefulness and perceived service quality can not only directly influence continuance intention, but also indirectly influenced satisfaction.The findings therefore suggested that MOOC providers should focus on improving MOOCs' overall quality to better meet MOOC users' satisfaction, thereby enhancing the intention to continue using MOOCs.
Group Sparsity Mixture Model and Its Application on Image Denoising

Prior learning is a fundamental problem in the field of image processing. In this paper, we conduct a detailed study on (1) how to model and learn the prior of the image patch group, which consists of a group of non-local similar image patches, and (2) how to apply the learned prior to the whole image denoising task. To tackle the first problem, we propose a new prior model named Group Sparsity Mixture Model (GSMM). With the bilateral matrix multiplication, the GSMM can model both the local feature of a single patch and the relation among non-local similar patches, and thus it is very suitable for patch group based prior learning. This is supported by the parameter analysis which demonstrates that the learned GSMM successfully captures the inherent strong sparsity embodied in the image patch group. Besides, as a mixture model, GSMM can be used for patch group classification. This makes the image denoising method based on GSMM capable of processing patch groups flexibly. To tackle the second problem, we propose an efficient and effective patch group based image denoising framework, which is plug-and-play and compatible with any patch group prior model. Using this framework, we construct two versions of GSMM based image denoising methods, both of which outperform the competing methods based on other prior models, <i>e.g</i>., Field of Experts (FoE) and Gaussian Mixture Model (GMM). Also, the better version is competitive with the state-of-the-art model based method WNNM with about &#x00D7;8 faster average running speed.
Studying Alignment in a Collaborative Learning Activity via Automatic Methods: The Link Between What We Say and Do

A dialogue is successful when there is alignment between the speakers, at different linguistic levels. In this work, we consider the dialogue occurring between interlocutors engaged in a collaborative learning task, where they are evaluated on how well they performed and how much they learnt. Our main contribution is to propose new automatic measures to study alignment; focusing on lexical alignment, and a new alignment context that we introduce termed as behavioural alignment (when an instruction given by one interlocutor was followed with concrete actions in a physical environment by another). Thus we propose methodologies to create a link between what was said, and what was done as a consequence. To do so, we focus on expressions related to the task in the situated activity. These expressions are minimally required by the interlocutors to make progress in the task. We then observe how these local alignment contexts build to dialogue level phenomena; success in the task. What distinguishes our approach from other works, is the treatment of alignment as a procedure that occurs in stages. Since we utilise a dataset of spontaneous speech dialogues elicited from children, a second contribution of our work is to study how spontaneous speech phenomena (such as when interlocutors say "uh", "oh" ...) are used in the process of alignment. Lastly, we make public the dataset to study alignment in educational dialogues. Our results show that all teams lexically and behaviourally align to some degree regardless of their performance and learning, and our measures capture that teams that did not succeed in the task were simply slower to collaborate. Thus we find that teams that performed better, were faster to align. Furthermore, our methodology captures a productive, collaborative period that includes the time where the interlocutors came up with their best solutions. We also find that well-performing teams verbalise the marker "oh" more when they are behaviourally aligned, compared to other times in the dialogue; showing that this marker is an important cue in alignment. To the best of our knowledge, we are the first to study the role of "oh" as an information management marker in a behavioural context (i.e. in connection to actions taken in a physical environment), compared to only a verbal one. Our measures contribute to the research in the field of educational dialogue and the intersection between dialogue and collaborative learning research.
Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries

The purpose of this study is to investigate the effects of contextual features on automatic detection accuracy of online recruitment frauds in Australian job market.In addition, the study aims to unearth the significance of localisation of such approaches.The study first generates a dataset based on a local and semi-structured advertising platform in Australia.The labelled dataset is then used to train a learning model on several content-based and contextual features.The existence of advertising body in relevant government and non-government registries in Australia, along with the internet presence of the advertiser, were considered as contextual features.The extraction process of such contextual features was automated as well.The study concludes that the inclusion of contextual features improves the performance measures of the automated online recruitment fraud detection model.The practical implication of the study is two-folds.Firstly, the contextual feature space generation engine can be used with any dataset, with minimal localisation efforts.Secondly, such learning models can be used in the back end of online job recruitment portals to detect and prevent online recruitment frauds.The study not only demonstrates the positive impact of using contextual features in fraud detection using a real-life dataset, but it also demonstrates how these contextual features can be extracted automatically from the web, based on localised company registries.The generated dataset has been made available for further research in the domain.
Knowledge Guided Feature Aggregation for the Prediction of Chronic Obstructive Pulmonary Disease With Chinese EMRs

The automatic disease diagnosis utilizing clinical data has been suffering from the issues of feature sparse and high probability of missing values. Since the graph neural network is a effective tool to model the structural information and infer the missing values, it is becoming the dominant method for the predictive model construction from electronic medical records. Existing graph neural network based solutions usually adopt the medical concepts (e.g., symptoms) the feature representation of clinical data without considering their underlying semantic relations. The limited discriminative capability of the medical concept cannot provide sufficient indicative information about the disease. This article proposes a knowledge-guided graph attention network for the disease prediction. Beside extracting the attribute-value structure as a large-size medical concept, the mutual information between multiple medical concepts mentioned in the electronic medical records are taken into account in the graph construction. Meanwhile, the defined diseases and their associations with the medical concepts in the medical knowledge graph are incorporated into the graph, which provides the potentials to enhance the indicative impacts of the medical concepts directly related to a target disease. Then, the spatial and attention based graph encoders are employed to aggregate information from directly neighbor nodes to generate node embeddings as the compact features to be used for disease diagnosis. The approach itself is a general one that can utilized to build the predictive model using Chinese EMRs for different diseases. The empirical experiments for its performance evaluation are conducted on the real-world COPD EMR dataset. The comparison study results show that the proposed model outperforms baseline methods, which illustrates the effectiveness of our proposed model.
A Modeling Method of Agents and SOA in Advanced Avionics System Based on AADL

The modeling method of agents and service-oriented architecture (SOA) in avionics systems describes agents and SOA in avionics systems with models. To our knowledge, however, the current modeling methods cannot describe the behavior of agents and SOA accurately and do not fit well with the existing avionics system models. This paper addresses the above problems by presenting a modeling method based on architecture analysis and design language (AADL). In this method, the working states of agents are described by the mode components, with the working process being triggered by the input of agents; and the services are described by the process component. The application of the software system is described by the system components that contain several process components. Moreover, different modes of the system are used to describe different applications, and the transitions of application are triggered by specific application requests. Software architecture of an avionics system is modeled by the proposed method. This case demonstrates that the proposed method can accurately describe how agents and SOA work in a new way and fit well with the existing avionics system models.
Hybrid Visual and Optimal Elliptic Curve Cryptography for Medical Image Security in Iot

In this manuscript, a hybridizing visual cryptography with Optimal Elliptic Curve Cryptography is proposed for medical image security in Internet of Things (IoT). The visual cryptography is generally used to send the secure and confidential medical image to the receiver. Here, the medical image is transmitted as shares and all shares of the medical image are collectively loaded to retrieve the original medical image. Moreover, the multiple shares are created interms of pixel values of medical image and this share is extracted and partioned in blocks. The blocks of every share are encrypted with elliptic curve cryptography (ECC) mechanisms and encrypted image is decrypted using ECC decrypts. In hybridizing visual crypto with optimal elliptic curve crypto, the optimal key will be generated using an imperialist competitive algorithm. Finally, the decrypted output image compares to the original image. The proposed system is executed on MATLAB platform and performance is evaluated with existing method like Score-based Key Enumeration Algorithm (SKEA). The proposed ESEA approach reduces the file size as 45.76%, 24.97%, 15.86%, 33% and 33.86%.And higher PSNR as 29.08%, 25.86%, 23.98%, 25.86% and 42.75%. The proposed ESEA approach achieves 6.89% higher security than existing SKEA method. Furthermore, the simulation outcome demonstrates that the proposed technique can be able to find the optimal global solutions efficiently and accurately than the existing techniques.
Quantifying power and energy fluctuations of photovoltaic systems

Abstract The high share of power generation based on fluctuating renewable energy sources, especially wind and solar, has increased the levels of variability and uncertainty in power systems. The aim of this study is to develop a method for quantifying the variability of photovoltaic (PV) systems. The developed method investigates the power measurements of a PV system and quantifies its power and energy fluctuations in three steps. The first includes a classification of days into three classes according to the variability and power output of the PV system. The second consists of computing the empirical cumulative distribution functions of PV power ramps for the given classes. The third calculates the PV daily energy fluctuations based on predicted PV power output. This method of PV variability quantification was then applied to seven rooftop PV systems in different locations that feature different installed capacities, years of installation, orientations and solar cell types. The testing of this method indicates that it can be used to quantify the power and energy fluctuations of different PV systems, independent of their locations and technical characteristics. The proposed method was developed as part of a general approach for quantifying the flexibility potential of buildings and city districts with PV systems.
Asymmetric double-image encryption based on chaotic random phase encoding

An asymmetric double-image encryption scheme based on chaotic random phase encoding (CRPE) is proposed. In this proposed encryption scheme, two grayscale images to be encrypted are first Fresnel transformed and combined into a complex image. Then, the amplitude and phase components are obtained by conducting phase-amplitude truncation on the complex image. Finally, the amplitude component is again Fresnel transformed and encrypted into a noise-like pattern by the CRPE in the Fresnel domain. Since the initial values and control parameters of the chaotic map can replace the random phase masks to serve as secret keys, the management and transmission of secret keys will become more convenient in the proposed encryption scheme. Furthermore, the Fresnel transform parameters and phase keys derived from the complex image's phase component can also act as secret keys during the decryption process. Numerical simulations have demonstrated the feasibility, security, and robustness of the proposed encryption scheme.
A Prediction of Power Demand using Weather Forecasting and Machine Learning: A Case of a Clinic in Japan

There is a need to accelerate research and development for an energy saving and a demand response using a renewable energy system because the supply and demand of a power is tight worldwide since 2021. Since hospitals and clinics are heavy power demand, there are promote energy conservation by using distributed energy that actively utilizes renewable energy. Recently, clinics installing a photovoltaic (PV) in combination with a diesel generator (DG) for an energy saving and/or a peek cut of a demand have been more. So, it's necessary for medical staff to predict a power demand, assuming an energy saving and/or a short-term operation. Therefore, this paper proposes the prediction method of a power demand for medical facilities using the weather forecasting data. A neural network (NN) of the prediction method is used the weather forecasting data in the area of medical facilities announced by Japan Meteorological Agency (JMA) are gathered for a long-term as an input. As a result, it is shown that power demand can be predicted with high accuracy.
Removal of phase artifacts from high-contrast texture for 3D fringe projection system

Digital fringe projection (DFP) methods are commonly used to obtain high-accuracy shape measurements. However, many measured objects may have high-contrast texture caused by edges of black- and white-colored sections of the object. In these high-contrast areas, there has consistently been a phase artifact, which in turn creates measurement error, sometimes referred to as “discontinuity-induced measurement artefacts” (DMA). Our study indicated that this error is generally shaped like a Gaussian curve. Based on this finding, we developed a method for removing this error via Gaussian curve fitting on the affected regions. These regions can be found by locating large spikes in the image intensity gradient, which directly correspond to the edge of the Gaussian artifact. We propose to use this error removal method in two ways: to remove errors on a checkerboard calibration target in order to increase calibration accuracy; and to directly remove errors in high-contrast areas to decrease shape measurement error. Experimental results demonstrate that the proposed method can successfully work for decreasing calibration error for a checkerboard calibration target, and shape measurement error can also be significantly decreased as well.