Reviewerly/exHarmony huggingface dataset:
Enabling Trusted and Privacy-Preserving Healthcare Services in Social Media Health Networks

Social Media Health Networks provide a promising paradigm to attract patients to share and communicate their personal health status with other online patients, and consult healthcare services from online caregivers with social networks. Social Media Health Networks transform healthcare services from time-consuming offline hospital-centered paradigm to the convenient and efficient online paradigm through Internet, which can expand the traditional healthcare services and shorten the information gap between patients and caregivers. However, how to build the trust between patients and caregivers raises a challenging issue due to the openness of the social networks; meanwhile, the personal privacy may be disclosed when sharing personal health information with other patients and caregivers. In this paper, we propose a personalized and trusted healthcare service approach to enable trusted and privacy-preserving healthcare services in social media health networks, which can improve the trustiness between patients and caregivers through authentic ratings toward caregivers and guarantee the patients' privacy. Specifically, we employ the collaborative filtering model to seek appropriate personalized caregivers, bloom filter to extract and map the personal healthcare symptoms, and inner product to compute the similarity between patients for finding patients with similar health symptoms in a privacy-preserving way. Meanwhile, to guarantee authentic ratings and reviews toward caregivers, we develop a sybil attack detection scheme to find patients' fake ratings and reviews using different pseudonyms. Security analysis shows that our proposed approach can preserve the privacy of patients and prevent sybil attacks. Performance evaluation demonstrates that our approach can achieve prominent performance improvement, in terms of personalized caregivers finding and sybil attack resistance.
Avoidance of Convex and Concave Obstacles With Convergence Ensured Through Contraction

This letter presents a closed-form approach to obstacle avoidance for multiple moving convex and star-shaped concave obstacles. The method takes inspiration in harmonic-potential fields. It inherits the convergence properties of harmonic potentials. We prove impenetrability of the obstacles hull and asymptotic stability at a final goal location, using contraction theory. We validate the approach in a simulated co-worker industrial environment, with one KUKA arm engaged in a pick and place grocery task, avoiding in real-time humans moving in its vicinity and in simulation to drive wheel-chair robot in the presence of moving obstacles.
RefineNet: Multi-Path Refinement Networks for Dense Prediction

Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense prediction problems such as semantic segmentation and depth estimation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments on semantic segmentation which is a dense classification problem and achieve good performance on seven public datasets. We further apply our method for depth estimation and demonstrate the effectiveness of our method on dense regression problems.
A recurrent neural network with predefined-time convergence and improved noise tolerance for dynamic matrix square root finding

Zeroing neural network (ZNN, or termed Zhang neural network after its inventors) is an effective approach to dynamic matrix square root (DMSR) finding arising in numerous fields of science and engineering. The conventional ZNN models can obtain the theoretical DMSR in infinitely long time or in finite time. However, in some applications especially the ones that require to fulfill hard time constraints, these ZNN models may be not competent to guarantee a timely convergence. Hence, for solving DMSR, a ZNN model with explicitly and antecedently definable convergence time is more preferable. Being robust to external noises is very significant for a neural network model. Unfortunately, the existing ZNN models exhibit limited noise-tolerance capability and the corresponding steady-state residual errors would be theoretically bounded when the ZNN models are perturbed by dynamic bounded non-vanishing noises. To enhance the existing ZNN models, by using two novel activation functions, this paper for the first time enables the ZNN model to be predefined-time convergent with improved noise-tolerance capability. The convergence time of the accelerated ZNN model can be explicitly defined as a prior constant parameter. More importantly, such a predefined-time convergent ZNN (PTZNN) is capable of theoretically and completely enduring dynamic bounded vanishing and non-vanishing noises. For handling constant noises such as large constant model-implementation errors, the PTZNN can achieve improved noise-tolerance performance as compared with the existing ZNN models. Comparative simulation results demonstrate that the proposed PTZNN delivers superior convergence and robustness performance for solving DMSR in comparison with the existing ZNN models.
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU

To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have been shown by several studies to offer sub-optimal performance. Alternatively, Deep Learning offers state of the art capabilities in certain prediction tasks and research suggests deep neural networks are able to outperform traditional techniques. Nevertheless, a main impediment for the adoption of Deep Learning in healthcare is its reduced interpretability, for in this field it is crucial to gain insight into the why of predictions, to assure that models are actually learning relevant features instead of spurious correlations. To address this, we propose a deep multi-scale convolutional architecture trained on the Medical Information Mart for Intensive Care III (MIMIC-III) for mortality prediction, and the use of concepts from coalitional game theory to construct visual explanations aimed to show how important these inputs are deemed by the network. Results show our model attains a ROC AUC of 0.8735 (± 0.0025) which is competitive with the state of the art of Deep Learning mortality models trained on MIMIC-III data, while remaining interpretable. Supporting code can be found at https://github.com/williamcaicedo/ISeeU.
Vigilance Estimation Using a Wearable EOG Device in Real Driving Environment

Vigilance decrement in driving tasks has been reported to be a major factor in fatal accidents and could severely endanger public transportation safety. However, efficient approaches for estimating vigilance in real driving environment are still lacking. In this paper, we propose a novel approach for implementing continuous vigilance estimation using forehead electrooculograms (EOGs) acquired by wearable dry electrodes in both simulated and real driving environments. To improve the feasibility of this approach for real-world applications, a forehead EOG-based electrode placement with only four electrodes is designed. Flexible dry electrodes and an acquisition board are integrated as a wearable device for recording EOGs. Twenty and ten subjects participated in the simulated and real-world driving environment experiments, respectively. Accurate eye movement parameters from eye-tracking glasses are extracted to calculate the PERCLOS index for vigilance annotation. This is because the vigilance state is a temporally dynamic process, and a continuous conditional random field and a continuous conditional neural field are introduced to construct more accurate vigilance estimation models. To evaluate the efficiency of our system, systematic experiments are performed in real scenarios under various illumination and weather conditions following laboratory simulations as preliminary studies. The experimental results demonstrate that the wearable dry electrode prototype, which has a relatively comfortable forehead setup, can efficiently capture vigilance dynamics. The best mean correlation coefficients achieved by our proposed approach are 71.18% and 66.20% in laboratory simulations and real-world driving environments, respectively. The cross-environment experiments are performed to evaluate the simulated-to-real generalization and a best mean correlation coefficient of 53.96% is achieved.
Pre-processing approaches for imbalanced distributions in regression

Imbalanced domains are an important problem frequently arising in real world predictive analytics. A significant body of research has addressed imbalanced distributions in classification tasks, where the target variable is nominal. In the context of regression tasks, where the target variable is continuous, imbalanced distributions of the target variable also raise several challenges to learning algorithms. Imbalanced domains are characterized by: (1) a higher relevance being assigned to the performance on a subset of the target variable values; and (2) these most relevant values being underrepresented on the available data set. Recently, some proposals were made to address the problem of imbalanced distributions in regression. Still, this remains a scarcely explored issue with few existing solutions. This paper describes three new approaches for tackling the problem of imbalanced distributions in regression tasks. We propose the adaptation to regression tasks of random over-sampling and introduction of Gaussian Noise, and we present a new method called WEighted Relevance-based Combination Strategy (WERCS). An extensive set of experiments provides empirical evidence of the advantage of using the proposed strategies and, in particular, the WERCS method. We analyze the impact of different data characteristics in the performance of the methods. A data repository with 15 imbalanced regression data sets is also provided to the research community.
Efficient Operator Placement for Distributed Data Stream Processing Applications

In the last few years, a large number of real-time analytics applications rely on the Data Stream Processing (DSP) so to extract, in a timely manner, valuable information from distributed sources. Moreover, to efficiently handle the increasing amount of data, recent trends exploit the emerging presence of edge/Fog computing resources so to decentralize the execution of DSP applications. Since determining the Optimal DSP Placement (for short, ODP) is an NP-hard problem, we need efficient heuristics that can identify a good application placement on the computing infrastructure in a feasible amount of time, even for large problem instances. In this paper, we present several DSP placement heuristics that consider the heterogeneity of computing and network resources; we divide them in two main groups: model-based and model-free. The former employ different strategies for efficiently solving the ODP model. The latter implement, for the problem at hand, some of the well-known meta-heuristics, namely greedy first-fit, local search, and tabu search. By leveraging on ODP, we conduct a thorough experimental evaluation, aimed to assess the heuristics' efficiency and efficacy under different configurations of infrastructure size, application topology, and optimization objectives.
Learning deep compact similarity metric for kinship verification from face images

Recent advances in kinship verification have shown that learning an appropriate kinship similarity metric on human faces plays a critical role in this problem. However, most of existing distance metric learning (DML) based solutions rely on linearity assumption of the kinship metric model, and the domain knowledge of large cross-generation discrepancy (e.g., large age span and gender difference between parent and child images) has not been considered in metric learning, leading to degraded performance for genetic similarity measure on human faces. To address these limitations, we propose in this work a new kinship metric learning (KML) method with a coupled deep neural network (DNN) model. KML explicitly models the cross-generation discrepancy inherent on parent-child pairs, and learns a coupled deep similarity metric such that the image pairs with kinship relation are pulled close, while those without kinship relation (but with high appearance similarity) are pushed as far away as possible. Moreover, by imposing the intra-connection diversity and inter-connection consistency over the coupled DNN, we introduce the property of hierarchical compactness into the coupled network to facilitate deep metric learning with limited amount of kinship training data. Empirically, we evaluate our algorithm on several kinship benchmarks against the state-of-the-art DML alternatives, and the results demonstrate the superiority of our method.
A secured storage and privacy-preserving model using CRT for providing security on cloud and IoT-based applications

Cloud computing and Internet of Things (IoT) are emerging technologies which have the capability to enhance the human daily lifestyle. Moreover, the combination of Cloud computing and IoT are increasing the productivity of the enormous number of applications in various fields like supply chains, commercial, engineering, manufacturing etc. Presently, security is a very big threat in cloud computing and the Internet of things areas. For that purpose, the various researchers have introduced many privacy-preserving models and security mechanisms in recent years. Even though, they have failed to achieve the latest cloud user's expectation in terms of security level. For satisfying the cloud users, this paper proposes a new Chinese Remainder Theorem (CRT)-based data storage mechanism for storing the user data securely in cloud database. Moreover, a new group key management scheme is also built using CRT to access the encrypted data from the cloud database. In the proposed CRT-based secured storage scheme adopts two encryption schemes which use new formulas for performing the first and second encryption and also introduced a new formula for decrypting the cloud data. In addition, a new formula is introduced in the process of group key generation for accessing the encrypted cloud data from the cloud database in a cloud server. The performance level of the security models has been evaluated by analyzing the experimental results. Finally, it is proven that the proposed data security model is better than other existing models.
Aspect-level Sentiment Analysis using AS-Capsules

Aspect-level sentiment analysis aims to provide complete and detailed view of sentiment analysis from different aspects. Existing solutions usually adopt a two-staged approach: first detecting aspect category in a document, then categorizing the polarity of opinion expressions for detected aspect(s). Inevitably, such methods lead to error accumulation. Moreover, aspect detection and aspect-level sentiment classification are highly correlated with each other. The key issue here is how to perform aspect detection and aspect-level sentiment classification jointly, and effectively. In this paper, we propose the aspect-level sentiment capsules model (AS-Capsules), which is capable of performing aspect detection and sentiment classification simultaneously, in a joint manner. AS-Capsules utilizes the correlation between aspect and sentiment through shared components including capsule embedding, shared encoders, and shared attentions. AS-Capsules is also capable of communicating with different capsules through a shared Recurrent Neural Network (RNN). More importantly, AS-Capsules model does not require any linguistic knowledge as additional input. Instead, through the attention mechanism, this model is able to attend aspect related words and sentiment words corresponding to different aspect(s). Experiments show that the AS-Capsules model achieves state-of-the-art performances on a benchmark dataset for aspect-level sentiment analysis.
A Novel Method for Multilevel Color Image Segmentation Based on Dragonfly Algorithm and Differential Evolution

Multilevel thresholding is a simple and powerful image segmentation method that has received widespread attention in recent years.However, the accuracy and stability of thresholding techniques are still not ideal and cannot meet the needs of engineering problems.Therefore, this paper presents a memetic algorithm of dragonfly algorithm (DA) and differential evolution (DE) for color image segmentation, which is known as improved DA (IDA).On the one hand, DA algorithm has a satisfied capability of avoiding convergence to the local optimum, thus it is served as a global search technique.On the other hand, the DE algorithm is adopted as a local search technique, which can increase the precision of solutions.In this paper, two thresholding techniques, namely, Otsu and minimum cross entropy (MCE) methods are used to determine the optimal threshold values.In order to evaluate the performance of the proposed method, we conduct a series of experiments on color images from the Berkeley database and the results are compared with five stateof-the-art meta-heuristic algorithms.Besides, a non-parametric Wilcoxon's rank sum test is also included for statistical analysis.From the experimental results, it is found that IDA-based method outperforms other compared methods in terms of average fitness values, standard deviation, peak signal to noise ratio, structural similarity index, and feature similarity index.The promising results indicate that the application of IDA-based thresholding technique is potential and meaningful.
Arabic Sentiment Classification Using Convolutional Neural Network and Differential Evolution Algorithm

In recent years, convolutional neural network (CNN) has attracted considerable attention since its impressive performance in various applications, such as Arabic sentence classification. However, building a powerful CNN for Arabic sentiment classification can be highly complicated and time consuming. In this paper, we address this problem by combining differential evolution (DE) algorithm and CNN, where DE algorithm is used to automatically search the optimal configuration including CNN architecture and network parameters. In order to achieve the goal, five CNN parameters are searched by the DE algorithm which include convolution filter sizes that control the CNN architecture, number of filters per convolution filter size (NFCS), number of neurons in fully connected (FC) layer, initialization mode, and dropout rate. In addition, the effect of the mutation and crossover operators in DE algorithm were investigated. The performance of the proposed framework DE-CNN is evaluated on five Arabic sentiment datasets. Experiments' results show that DE-CNN has higher accuracy and is less time consuming than the state-of-the-art algorithms.
Wavelet-TSK-Type Fuzzy Cerebellar Model Neural Network for Uncertain Nonlinear Systems

In this paper, a novel fuzzy neural network structure for uncertain nonlinear systems is proposed. This network is called wavelet Takagi-Sugeno-Kang (TSK) fuzzy cerebellar model neural network, which includes the framework of a cerebellar model neural network (CMNN) and the wavelet-function-based TSK fuzzy inference model. In order to effectively solve the uncertainty problem of nonlinear systems, a new structure is proposed where the wavelet function is used in the consequent parts of TSK-type fuzzy CMNN instead of the linear combination of the input variables in the traditional TSK fuzzy systems. This structure combines the advantages of the wavelet function, the CMNN and the TSK fuzzy inference system; thus, it is a more effective model for the uncertain nonlinear systems. In order to provide fast training, parameter update laws of the proposed model are derived based on the gradient descent method in which the learning-rates are online adapted. Furthermore the Lyapunov function is used to analyze the convergence of the considered systems. Finally, four different types of applications are applied to demonstrate the effectiveness of the proposed model. The simulation comparisons with other neural network models have verified the effectiveness of the new model.
Integrating spherical video-based virtual reality into elementary school students’ scientific inquiry instruction: effects on their problem-solving performance

Developing the problem-solving abilities of elementary school students has been recognized as an important educational objective. Science curricula, because of their practical and experimental characteristics, are thought to be an important way to develop students’ problem-solving ability. The use of Virtual Reality (VR) technology in teaching practices provides new possibilities for the development of problem-solving abilities by providing students with a richer situation, making the learning process more interesting and interactive, improving students’ motivation and attention, and helping them to discover and explore their own knowledge. In this research, we conducted a study on the use of exploratory scientific practice activities based on Spherical Video-based Virtual Reality (SVVR) in science classes, and verified the impact of such activities on the problem-solving abilities of elementary school students. The results show that integrating SVVR into exploratory scientific practice instructions has a significant effect on students’ learning outcomes and problem-solving abilities. The results also reveal that the integration of SVVR has different influences on students with different learning attitudes. Relatively speaking, students with poorer learning attitudes benefitted more from the scientific inquiry activity than those students with more positive attitudes.
Extended File Hierarchy Access Control Scheme with Attribute-Based Encryption in Cloud Computing

In cloud computing, attribute based encryption (ABE) is often used to solve the challenging issue in secure data storage. In order to lighten the burden of authority center, hierarchical ABE schemes is a very effective way. File hierarchy attribute based encryption (FH-CP-ABE) is a scheme, which both saves storage space of ciphertext and reduces the computation overhead of encryption. However, it's impossible to encrypt multiple files on the same access level in existing FH-CP-ABE scheme. The scheme is obviously not practical. In this paper, an efficient extended file hierarchy CP-ABE scheme (EFH-CP-ABE) is proposed, which can encrypt multiple files on the same access level. Our scheme is very practical especially for those big institutions or companies which have many hierarchical sectors, since it greatly saves storage space and computation cost for them on the cloud servers. Furthermore, our solution also achieves secure and flexible access control for users in cloud storage. We formally prove the security for our new scheme under the standard model. Finally, we implement the corresponding experiment for EFH-CP-ABE scheme and achieve desirable experimental results.