Reviewerly/exHarmony huggingface dataset:
PRTA: A Proxy Re-encryption based Trusted Authorization scheme for nodes on CloudIoT

In CloudIoT platform, the data is collected and shared by different nodes of Internet of Things (IoT), and data is processed and stored based on cloud servers. It has increased the abilities of IoT on information computation. Meanwhile, it also has enriched the resource in cloud and improved integration of the Internet and human world. All of this offer advantages as well as the new challenges of information security and privacy protection. As the energy limitation of the nodes in IoT, they are particularly vulnerable. It is much easier to hijack the nodes than to attack the data center for hackers. Thus, it is a crucial and urgent issue to realize the trusted update of authorization of nodes. When some nodes are hijacked, both of the behaviors to upload data to servers and to download information from servers should be forbidden. Otherwise, it might cause the serious damage to the sensitive data and privacy of servers. In order to solve this problem, we proposed a Proxy Re-encryption based Trusted Authorization scheme for nodes on CloudIoT (PRTA). PRTA is based on the proxy re-encryption (PRE), and the cloud server will play the roles of data storing and re-encrypting, which would reach the full potential of cloud computing and reduce the cost of nodes. The node’s status is taken as one of the parameters for data re-encryption and it is under the authorization servers’ control, which could ensure the security and reliability of the data and be beneficial for the privacy protection in CloudIoT. Also, the authorization servers are divided into the downloading and uploading kinds, which will make the application range much wider.
Multiaspect‐based opinion classification model for tourist reviews

Abstract Tourist reviews on social media websites reflect the tourist's opinions concerning various aspects of a tourist place or service (e.g., “comfortable room” and “terrible service” in hotel reviews). Extracting these aspects from reviews is a challenging task in opinion mining. Therefore, aspect‐based opinion mining has emerged as a new area of social review mining. Existing approaches in this area focus on extracting explicit aspects and classification of opinions around these aspects. However, the implicit and coreferential aspects during aspect extraction are often neglected, and the classification of multiaspect opinions is relatively less emphasized in prior art. In this paper, we propose a model, namely, “enhanced multiaspect‐based opinion classification” that addresses existing challenges by automatically extracting both explicit and implicit aspects and classifying the multiaspect opinions. In this model, first, a probabilistic co‐occurrence‐based method is proposed that utilizes the co‐occurrence between aspects and sentiment words to identify the coreferential aspects and merge them into groups. Second, an implicit aspect extraction method is proposed that associates the sentiment words with suitable aspects to build an aspect‐sentiment hierarchy. Third, a multiaspect opinion classification approach is proposed that employs multilabel classification algorithms to classify opinions into different polarity classes. The effectiveness of the proposed model is evaluated by conducting experiments on benchmark and real‐world datasets. The experimental results revealed the supremacy of multilabel classifiers by achieving 90% accuracy per label on classification when extracting 87% domain‐relevant aspects. A state‐of‐the‐art performance comparison is conducted that also verifies the advantages of the proposed model.
Coal Quality Exploration Technology Based on an Incremental Multilayer Extreme Learning Machine and Remote Sensing Images

This paper proposes a new coal quality exploration method that detects coal quality in coal mining areas and explores and monitors the distribution and change of coal through remote sensing images. First, we collected a large number of coal and noncoal samples such as sandstones, shales, and coal gangues. Second, we measured the actual spectral data of these samples using a spectrometer. For coal mines, we used the chemical analysis method to quantify coal's fixed carbon and categorize the coal mines into three types based on the fixed carbon content present in coal. Third, we collected satellite remote sensing images of coal mining areas and established spectral data relations between the measured spectral data of the samples and the remote sensing images. Fourth, we proposed an incremental multilayer learning machine algorithm and used the algorithm combined with spectral data to build a coal quality classification model to identify coal quality in remote sensing images. Finally, the model accurately described the distribution map of coal quality. Compared with traditional coal exploration methods, this method has the advantages of high speed, high accuracy, and low price.
Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user’s current context (e.g., location and time). Moreover, the advent of Web 2.0 and the growing popularity of social and e-commerce media sites have encouraged users to naturally write texts describing their assessment of items. There are increasing efforts to incorporate the rich information embedded in user’s reviews/texts into the recommender systems. Given the importance of this type of texts and their usage along with opinion mining and contextual information extraction techniques for recommender systems, we present a systematic review on the recommender systems that explore both contextual information and opinion mining. This systematic review followed a well-defined protocol. Its results were based on 17 papers, selected among 195 papers identified in four digital libraries. The results of this review give a general summary of the current research on this subject and point out some areas that may be improved in future primary works.
Mobile Learning—Trends and Practices

Mobile learning has become one of the more influential aspects in the field of educational technology given the ubiquity of modern mobile devices and proliferation of educational applications or ‘apps’ for mobile devices. Within this special issue, there are a range of studies and reviews which cover a breadth of current topics in the field, namely user motivations for using mobile learning, issues in evaluation and domain-specific considerations (e.g., use within language learning or audio-based applications). Together these studies represent the synthesis of a range of methods, approaches and applications that highlight benefits and areas of future growth of mobile technologies.
Monotone Interval Fuzzy Inference Systems

In this paper, we introduce the notion of a monotone fuzzy partition, which is useful for constructing a monotone zero-order Takagi-Sugeno-Kang Fuzzy Inference System (ZOTSK-FIS). It is known that a monotone ZOTSK-FIS model can always be produced when a consistent, complete, and monotone fuzzy rule base is used. However, such an ideal situation is not always available in practice, because a fuzzy rule base is susceptible to uncertainties, e.g., inconsistency, incompleteness, and nonmonotonicity. As a result, we devise an interval method to model these uncertainties by considering the minimum interval of acceptability of a fuzzy rule, resulting in a set of monotone interval-valued fuzzy rules. This further leads to the formulation of a Monotone Interval Fuzzy Inference System (MIFIS) with a minimized uncertainty measure. The proposed MIFIS model is analyzed mathematically and evaluated empirically for the Failure Mode and Effect Analysis (FMEA) application. The results indicate that MIFIS outperforms ZOTSK-FIS, and allows effective decision making using uncertain fuzzy rules solicited from human experts in tackling real-world FMEA problems.
The Six-Card Trick: Secure Computation of Three-Input Equality

Secure computation enables parties having secret inputs to compute some function of their inputs without revealing inputs beyond the output. It is known that secure computation can be done by using a deck of physical cards. The five-card trick proposed by den Boer in 1989 is the first card-based protocol, which computes the logical AND function of two inputs. In this paper, we design a new protocol for the three-input equality function using six cards, which we call the six-card trick.
Image-to-Voxel Model Translation with Conditional Adversarial Networks

We present a single-view voxel model prediction method that uses generative adversarial networks. Our method utilizes correspondences between 2D silhouettes and slices of a camera frustum to predict a voxel model of a scene with multiple object instances. We exploit pyramid shaped voxel and a generator network with skip connections between 2D and 3D feature maps. We collected two datasets VoxelCity and VoxelHome to train our framework with 36,416 images of 28 scenes with ground-truth 3D models, depth maps, and 6D object poses. We made the datasets publicly available ( http://www.zefirus.org/Z_GAN ). We evaluate our framework on 3D shape datasets to show that it delivers robust 3D scene reconstruction results that compete with and surpass state-of-the-art in a scene reconstruction with multiple non-rigid objects.
Deep Fusion Network for Splicing Forgery Localization

Digital splicing is a common type of image forgery: some regions of an image are replaced with contents from other images. To locate altered regions in a tampered picture is a challenging work because the difference is unknown between the altered regions and the original regions and it is thus necessary to search the large hypothesis space for a convincing result. In this paper, we proposed a novel deep fusion network to locate tampered area by tracing its border. A group of deep convolutional neural networks called Base-Net were firstly trained to response the certain type of splicing forgery respectively. Then, some layers of the Base-Net are selected and combined as a deep fusion neural network (Fusion-Net). After fine-tuning by a very small number of pictures, Fusion-Net is able to discern whether an image block is synthesized from different origins. Experiments on the benchmark datasets show that our method is effective in various situations and outperform state-of-the-art methods.
The 2nd YouTube-8M Large-Scale Video Understanding Challenge

We hosted the 2nd YouTube-8M Large-Scale Video Understanding Kaggle Challenge and Workshop at ECCV'18, with the task of classifying videos from frame-level and video-level audio-visual features. In this year's challenge, we restricted the final model size to 1 GB or less, encouraging participants to explore representation learning or better architecture, instead of heavy ensembles of multiple models. In this paper, we briefly introduce the YouTube-8M dataset and challenge task, followed by participants statistics and result analysis. We summarize proposed ideas by participants, including architectures, temporal aggregation methods, ensembling and distillation, data augmentation, and more.
Trajectory Protection Schemes Based on a Gravity Mobility Model in IoT

With the proliferation of the Internet-of-Things (IoT), the users’ trajectory data containing privacy information in the IoT systems are easily exposed to the adversaries in continuous location-based services (LBSs) and trajectory publication. Existing trajectory protection schemes generate dummy trajectories without considering the user mobility pattern accurately. This would cause that the adversaries can easily exclude the dummy trajectories according to the obtained geographic feature information. In this paper, the continuous location entropy and the trajectory entropy are defined based on the gravity mobility model to measure the level of trajectory protection. Then, two trajectory protection schemes are proposed based on the defined entropy metrics to protect the trajectory data in continuous LBSs and trajectory publication, respectively. Experimental results demonstrate that the proposed schemes have a higher level than the enhanced dummy-location selection (enhance-DLS) scheme and the random scheme.
Did We Just Travel to the Past? Building and Evaluating With Cultural Presence Different Modes of VR-Mediated Experiences in Virtual Archaeology

Over the past years, Virtual Archaeology has introduced more experiential elements in virtual reconstructions, therefore going beyond the traditional visualization of 3D architectural models. In the case of dissemination, these experiences equate to a trip in time, in which users witness what the past was like and learn about it. However, due to a lack of explicit theoretical frameworks and/or systematic evaluation focusing on such experiential elements, it is uncertain whether the intended goals are achieved and why. Based on a novel theoretical framework arising from the concept of Cultural Presence, this article will investigate if and how current virtual environments achieve the feeling of traveling to the past. To that end, six different virtual reconstructions of the Neolithic site of Çatalhöyük (Turkey) were built and evaluated in a between-subjects experiment. The results support the role of content meaningfulness, responsive characters, enhanced interaction, and multisensory realism in the achievement of successful Virtual Reality--mediated experiences.
The Correlation between Users’ Cognitive Characteristics and Visualization Literacy

One of the ultimate goals of studies on visualization literacy is to improve users’ visualization literacy through education and training. Even though users’ cognitive characteristics may significantly affect learning and responding processes in general, studies have addressed the relationships between users’ cognitive characteristics and visualization literacy. As a first step toward discovering the relationships, we conducted an empirical study to investigate the correlation between cognitive characteristics and visualization literacy. Our first study focuses on testing the correlation between visualization and three cognitive characteristics: numeracy, need for cognition, and visualizer-verbalizer style. In this study, we measured 178 participants’ visualization literacy and the level of the three cognitive characteristics using the Visualization Literacy Assessment Test (VLAT), the Decision Research Numeracy Test (DRNT), the Need for Cognition Scale (NCS), and the Verbalizer-Visualizer Questionnaire (VVQ) through a crowdsourcing experiment. Our test results confirmed that a correlation exists between visualization literacy and both numeracy and need for cognition. Based on our test results, we discuss the implications for education to enhance visualization literacy and future studies to investigate on related user characteristics.
Stacked Denoising Extreme Learning Machine Autoencoder Based on Graph Embedding for Feature Representation

Extreme learning machine is characterized by less training parameters, fast training speed, and strong generalization ability. It has been applied to obtain feature representations from the complex data in the tasks of data clustering or classification. In this paper, a graph embedding-based denoising extreme learning machine autoencoder (GDELM-AE) is proposed for capturing the structure of the inputs. Specifically, in GDELM-AE, a graph embedding framework that contains an intrinsic graph and a penalty graph constructed by local Fisher discrimination analysis is integrated into the autoencoder. So, it can exploit both local structure and global structure information in extreme learning machine (ELM) spaces. Further, we propose a stacked graph embedded denoising (SGD)-ELM by stacking several GDELM-AEs. The experimental results on several benchmarks validate that GDELM-AE can obtain efficient and robust feature representation of original data; moreover, the stacked GDELM-AE can obtain high-level and noise-robust representations. The comparative results with the state-of-the-art algorithms indicate that the proposed algorithm can obtain better accuracy as well as faster training speed.
Selective Learning Confusion Class for Text-Based CAPTCHA Recognition

Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) recognition is one of the most important branches in CAPTCHA research. The existing CAPTCHA recognition methods based on Deep Convolutional Neural Network (DCNN) have low recognition accuracy in confusion class. To solve this problem, a novel method of selective learning confusion class for text-based CAPTCHA recognition is presented. First, a frame with two-stage DCNN is proposed, which integrates all-class DCNN and confusion-class DCNN. Second, a confusion relation matrix is constructed to show confusion relations among classes, which can be used to analyze the output of all-class DCNN. Third, a set partition algorithm is presented, which can be used to divide a confusion class set into multiple subsets, each one corresponding to a new confusion-class DCNN. Fourth, with a view on improving the recognition accuracy of the confusing characters in confusion-class DCNN, training and validating interactive learning algorithm is proposed. Lastly, the outputs of the two stages were combined as the final recognition result. The experimental results based on real CAPTCHA data sets demonstrate that, compared with the four state-of-the-art attacks, the proposed method could effectively improve text-based CAPTCHA recognition accuracy by 1.4%-39.4%.
Fuzzy metric and its applications in removing the image noise

This paper introduces the notions of the fuzzy T-metric and fuzzy S-metric and considers their duality and their links to standard metrics. Numerous examples of fuzzy T-metrics are given in the paper, and some of their properties are proved. A procedure for constructing new fuzzy metrics as a composition of the given fuzzy metrics and norms is provided. An application of the fuzzy T-metrics in image filtering is given. The proposed filtering algorithm is a modification of the algorithm presented by Valentin, Morillas and Sapena in Valentin G et al. (Fuzzy Sets Syst 170:95–111, 2011). Instead of the fuzzy metric that is used in the paper of Valentin G et al. 2011, a new fuzzy T -metric is applied in the algorithm. The aim was to improve the sharpness and the quality of the image, measured by the image quality index UIQI. It is shown that the image filtered by this modified algorithm has better quality and greater sharpness than the image filtered by the median filter. Fuzzy T-metric parameters that give the best image quality and sharpness are determined experimentally.
Online Feature Selection for Streaming Features Using Self-Adaption Sliding-Window Sampling

In recent years, online feature selection has been a research topic on streaming feature mining, as it can reduce the dimensionality of the streaming features by removing the irrelevant and redundant features in real time. There are many representative research efforts on the online feature selection with streaming features, i.e., alpha - investing, online streaming feature selection (OSFS), and scalable and accurate online approach (SAOLA) for feature selection. In these studies, alpha-investing has limited prediction accuracy and a large number of selected features. SAOLA sometimes offers outstanding efficiency in running time and prediction accuracy but possesses a large number of selected features. OSFS offers high prediction accuracy in many datasets, but its running time increases exponentially with an increasing number of features with low redundancy and high relevance. To address the limitations of the above-mentioned works, we propose an online learning algorithm named OSFAS, which samples streaming features in real-time by a self-adaption sliding-window and discards the irrelevant and redundant features by conditional independence. The OSFAS obtains an approximate Markov blanket with high prediction accuracy, meanwhile reducing the number of selected features. The efficiency of the proposed OSFASW algorithm was validated in a performance test on widely used datasets, e.g., NIPS2003 and causality workbench. Through the extensive experimental results, we demonstrate that OSFAS significantly improves the prediction accuracy and requires a smaller number of selected features than alpha - investing, OSFS, and SAOLA.
Improvement of the hourly global solar model and solar radiation for air-conditioning design in China

Hourly global solar radiation is a significant parameter for building thermal simulation and building cooling design. Most of the solar radiation models focus on the monthly or daily values, but very few studies have been carried out on the hourly values. The Zhang and Huang model was developed for estimating the hourly solar radiation based on the dry-bulb temperature difference, relative humidity and cloud cover. In this study, a new model is proposed for all-sky conditions based on the hourly/daily radiation ratio retrieved from the Zhang and Huang model and the measured daily solar radiation dataset. Compared with the Zhang and Huang model, the proposed model is more accurate (the average of R2, 0.90) and the average of RMSE is improved by 29.54%. The latest hourly solar radiation dataset for 17 locations in China from 2006 to 2016 is subsequently established using the proposed model, which can be used to generate the latest Typical Meteorological Year (TMY). As one of the significant applications to buildings, two kinds of hourly solar radiation datasets (the frequency levels of 95% and 97.5%) for 17 locations are also developed, which are usable for air-conditioning design.
On the Hamming distances of repeated-root constacyclic codes of length <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e2312" altimg="si309.gif"><mml:mn>4</mml:mn><mml:msup><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msup></mml:math>

Let p be an odd prime, s, m be positive integers, γ,λ be nonzero elements of the finite field Fpm such that γps=λ. In this paper, we show that, for any positive integer η, the Hamming distances of all repeated-root λ-constacyclic codes of length ηps can be determined by those of certain simple-root γ-constacyclic codes of length η. Using this result, Hamming distances of all constacyclic codes of length 4ps are obtained. As an application, we identify all MDS λ-constacyclic codes of length 4ps.
An Efficient Similarity Measure for Collaborative Filtering

In the field of recommendation system, the memory-based Collaborative filtering has been proven to be useful in lots of practices. Similarity measures like Pearson correlation coefficient tend to only focus on improving as much as possible the accuracy. Handling datasets with different features, exiting measures cannot apply to different types of data simultaneously. In this paper, an improved similarity measure Common Pearson Correlation Coefficient (COPC) was proposed. Unlike existing measures, it strongly depends on chosen distance function, which adhere to the natural property of monotonicity and utilize consensus evaluation measure to capture an optimal value to improve PCC measure. To mitigate sparse problem, we also introduce the Hellinger Distance (Hg) as global similarity to lower the impact of lacking co-rated items. Experimental results on real-world datasets demonstrates that our measure outperformed the existing schemes of predicting ratings.