Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score

Earning Extra Performance from Restrictive Feedbacks

Many machine learning applications encounter a situation where model providers are required to further refine the previously trained model so as to gratify the specific need of local users. This problem is reduced to the standard model tuning paradigm if the target data is permissibly fed to the model. However, it is rather difficult in a wide range of practical cases where target data is not shared with model providers but commonly some evaluations about the model are accessible. In this paper, we formally set up a challenge named \emph{Earning eXtra PerformancE from restriCTive feEDdbacks} (EXPECTED) to describe this form of model tuning problems. Concretely, EXPECTED admits a model provider to access the operational performance of the candidate model multiple times via feedback from a local user (or a group of users). The goal of the model provider is to eventually deliver a satisfactory model to the local user(s) by utilizing the feedbacks. Unlike existing model tuning methods where the target data is always ready for calculating model gradients, the model providers in EXPECTED only see some feedbacks which could be as simple as scalars, such as inference accuracy or usage rate. To enable tuning in this restrictive circumstance, we propose to characterize the geometry of the model performance with regard to model parameters through exploring the parameters' distribution. In particular, for the deep models whose parameters distribute across multiple layers, a more query-efficient algorithm is further tailor-designed that conducts layerwise tuning with more attention to those layers which pay off better. Our theoretical analyses justify the proposed algorithms from the aspects of both efficacy and efficiency. Extensive experiments on different applications demonstrate that our work forges a sound solution to the EXPECTED problem.Comment: Accepted by IEEE TPAMI in April 202

Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets

A vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the transfer learning model and the relevance feedback technique was formulated in this study to optimize the dynamic trade-off between the structural complexity and retrieval performance of the small- and medium-scale content-based image retrieval (CBIR) system. First, the pretrained deep learning model was fine-tuned to extract features from target datasets. Then, the target dataset was clustered into the relative and irrelative image library by exploring the Bayes classifier. Next, the support vector machine (SVM) classifier was used to retrieve similar images in the relative library. Finally, the relevance feedback technique was employed to update the parameters of both classifiers iteratively until the request for the retrieval was met. Results demonstrate that the proposed method achieves 95.87% in classification index F1 - Score, which surpasses that of the suboptimal approach DCNN-BSVM by 6.76%. The performance of the proposed method is superior to that of other approaches considering retrieval criteria as average precision, average recall, and mean average precision. The study indicates that the Bayes + SVM combined classifier accomplishes the optimal quantities more efficiently than only either Bayes or SVM classifier under the transfer learning framework. Transfer learning skillfully excels training from scratch considering the feature extraction modes. This study provides a certain reference for other insights on applications of small- and medium-scale CBIR systems with inadequate samples

EEG-based measurement system for monitoring student engagement in learning 4.0

A wearable system for the personalized EEG-based detection of engagement in learning 4.0 is proposed. In particular, the effectiveness of the proposed solution is assessed by means of the classification accuracy in predicting engagement. The system can be used to make an automated teaching platform adaptable to the user, by managing eventual drops in the cognitive and emotional engagement. The effectiveness of the learning process mainly depends on the engagement level of the learner. In case of distraction, lack of interest or superficial participation, the teaching strategy could be personalized by an automatic modulation of contents and communication strategies. The system is validated by an experimental case study on twenty-one students. The experimental task was to learn how a specific human-machine interface works. Both the cognitive and motor skills of participants were involved. De facto standard stimuli, namely (1) cognitive task (Continuous Performance Test), (2) music background (Music Emotion Recognition-MER database), and (3) social feedback (Hermans and De Houwer database), were employed to guarantee a metrologically founded reference. In within-subject approach, the proposed signal processing pipeline (Filter bank, Common Spatial Pattern, and Support Vector Machine), reaches almost 77% average accuracy, in detecting both cognitive and emotional engagement

Image Based Attack and Protection on Secure-Aware Deep Learning

In the era of Deep Learning, users are enjoying remarkably based on image-related services from various providers. However, many security issues also arise along with the ubiquitous usage of image-related deep learning. Nowadays, people rely on image-related deep learning in work and business, thus there are more entries for attackers to wreck the image-related deep learning system. Although many works have been published for defending various attacks, lots of studies have shown that the defense cannot be perfect. In this thesis, one-pixel attack, a kind of extremely concealed attacking method toward deep learning, is analyzed first. Two novel detection methods are proposed for detecting the one-pixel attack. Considering that image tempering mostly happens in image sharing through an unreliable way, next, this dissertation extends the detection against single attack method to a platform for higher level protection. We propose a novel smart contract based image sharing system. The system keeps full track of the shared images and any potential alteration to images will be notified to users. From extensive experiment results, it is observed that the system can effectively detect the changes on the image server even in the circumstance that the attacker erases all the traces from the image-sharing server. Finally, we focus on the attack targeting blockchain-enhanced deep learning. Although blockchain-enhanced federated learning can defend against many attack methods that purely crack the deep learning part, it is still vulnerable to combined attack. A novel attack method that combines attacks on PoS blockchain and attacks on federated learning is proposed. The proposed attack method can bypass the protection from blockchain and poison federated learning. Real experiments are performed to evaluate the proposed methods

Disease diagnosis in smart healthcare: Innovation, technologies and applications

To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed

Bimodal Emotion Classification Using Deep Learning

Multimodal Emotion Recognition is an emerging associative field in the area of Human Computer Interaction and Sentiment Analysis. It extracts information from each modality to predict the emotions accurately. In this research, Bimodal Emotion Recognition framework is developed with the decision-level fusion of Audio and Video modality using RAVDES dataset. Designing such frameworks are computationally expensive and require more time to train the network. Thus, a relatively small dataset has been used for the scope of this research. The conducted research is inspired by the use of neural networks for emotion classification from multimodal data. The developed framework further confirmed the fact that merging modality can enhance the accuracy in classifying emotions. Later, decision-level fusion is further explored with changes in the architecture of the Unimodal networks. The research showed that the Bimodal framework formed with the fusion of unimodal networks having wide layer with more nodes outperformed the framework designed with the fusion of narrow unimodal networks having lesser nodes

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.Comment: 20 pages, Transactions on Knowledge and Data Engineerin

Trajectories of land surface evolution in polygonal tundra

In the past three decades, an abrupt acceleration in the thaw of ice wedges has spurred rapid surface deformation (i.e., thermokarst) in polygonal tundra landscapes spanning the Arctic. The ensuing conversion of low-centered polygons (LCPs) and flat terrain into high-centered polygons (HCPs) has profound impacts on regional hydrology and carbon fluxes between the soil and atmosphere. However, pathways of ice wedge degradation and the stability of the deformed terrain are uncertain, complicating efforts to project feedbacks on global climate change. In this dissertation, I explore trajectories of surface deformation in ice wedge polygons, using a combination of calibration-constrained numerical experiments, remote sensing, and machine learning. In the first two chapters, numerical simulations of the soil hydrologic and thermal regimes reveal that, relative to terrain unaffected by thermokarst, the permafrost beneath HCPs tends to be well-buffered against climate extremes, promoting landscape stability. Ice wedges at HCP boundaries are less vulnerable to thaw during warm summers, reinforcing prior field-based observations that thermokarst is typically a self-arresting process. Simultaneously, the cooling of thermokarst-affected ice wedges in winter tends to be inhibited by snow accumulation in degraded troughs, reducing the likelihood of renewed ice wedge cracking and restoration of LCP microtopography. Overall, these results indicate that the microtopography of polygons already affected by thermokarst will likely remain stable over the next century. In the second half of this dissertation, a novel machine-learning-based tool is introduced for delineating and measuring the microtopography associated with ice wedge polygons in high-resolution digital elevation models. The tool is used to map polygonal geomorphology across ~1,000 km² of tundra south of Prudhoe Bay, Alaska, visualizing in unprecedented detail the heterogeneous extent to which thermokarst has affected a modern polygonal landscape. This map of polygonal geomorphology provides useful context for upscaling point- to plot-scale observations of gas exchange in ice wedge polygons to landscape-scale estimates of carbon fluxes. It also provides an extensive baseline dataset for quantifying contemporary rates of land surface deformation, through future surveys at the site.Geological Science