Reviewerly/exHarmony huggingface dataset:
Utilizing Two-Phase Processing With FBLS for Single Image Deraining

Rain removal from a single image is a challenging problem and has attracted much attention in recent years. In this paper, we revisit the single image deraining problem, and present a novel solution. The central idea of our solution is to merge the merits of two-phase processing methods and the Fuzzy Broad Learning System (FBLS). Specifically, our solution first uses the dehazing algorithm to preprocess the input rainy image and separates it into the detail layer and the base layer. After that, it puts the Y-channel image of the detail layer into the FBLS to obtain the derained Y channel image, which is then combined with the Cb and Cr channel images to obtain the derained detail layer. Later, it fuses the derained detail layer and the base layer to get a preliminary derained image. Finally, it superimposes the details extracted from the dehazed image with some transparency on the preliminary result, obtaining the final result. Experimental results based on both real and synthetic rainy images demonstrate that our proposed solution can outperform several state-of-the-art algorithms, while it consumes much less running time and training time, compared against the competitors.
Multiview Feature Selection for Single-View Classification

In many real-world scenarios, data from multiple modalities (sources) are collected during a development phase. Such data are referred to as multiview data. While additional information from multiple views often improves the performance, collecting data from such additional views during the testing phase may not be desired due to the high costs associated with measuring such views or, unavailability of such additional views. Therefore, in many applications, despite having a multiview training data set, it is desired to do performance testing using data from only one view. In this paper, we present a multiview feature selection method that leverages the knowledge of all views and use it to guide the feature selection process in an individual view. We realize this via a multiview feature weighting scheme such that the local margins of samples in each view are maximized and similarities of samples to some reference points in different views are preserved. Also, the proposed formulation can be used for cross-view matching when the view-specific feature weights are pre-computed on an auxiliary data set. Promising results have been achieved on nine real-world data sets as well as three biometric recognition applications. On average, the proposed feature selection method has improved the classification error rate by 31 percent of the error rate of the state-of-the-art.
Geometric modelling and calibration of a spherical camera imaging system

Abstract The Ladybug5 is an integrated, multi‐camera system that features a near‐spherical field of view. It is commonly deployed on mobile mapping systems to collect imagery for 3D reality capture. This paper describes an approach for the geometric modelling and self‐calibration of this system. The collinearity equations of the pinhole camera model are augmented with five radial lens distortion terms to correct the severe barrel distortion. Weighted relative orientation stability constraints are added to the self‐calibrating bundle adjustment solution to enforce the angular and positional stability between the Ladybug5’s six cameras. Centimetre‐level 3D reconstruction accuracy can be achieved, with image‐space precision and object‐space accuracy improved by 92 % and 93 % , respectively, relative to a two‐term lens distortion model. Sub‐pixel interior orientation stability and millimetre‐level relative orientation stability were also demonstrated over a 10‐month period.
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach

Recently there has been an increasing interest in the multivariate Gaussian process (MGP) which extends the Gaussian process (GP) to deal with multiple outputs. One approach to construct the MGP and account for non-trivial commonalities amongst outputs employs a convolution process (CP). The CP is based on the idea of sharing latent functions across several convolutions. Despite the elegance of the CP construction, it provides new challenges that need yet to be tackled. First, even with a moderate number of outputs, model building is extremely prohibitive due to the huge increase in computational demands and number of parameters to be estimated. Second, the negative transfer of knowledge may occur when some outputs do not share commonalities. In this paper we address these issues. We propose a regularized pairwise modeling approach for the MGP established using CP. The key feature of our approach is to distribute the estimation of the full multivariate model into a group of bivariate GPs which are individually built. Interestingly pairwise modeling turns out to possess unique characteristics, which allows us to tackle the challenge of negative transfer through penalizing the latent function that facilitates information sharing in each bivariate model. Predictions are then made through combining predictions from the bivariate models within a Bayesian framework. The proposed method has excellent scalability when the number of outputs is large and minimizes the negative transfer of knowledge between uncorrelated outputs. Statistical guarantees for the proposed method are studied and its advantageous features are demonstrated through numerical studies.
Aggregation of preference relations to enhance the ranking quality of collaborative filtering based group recommender system

The recommendation of suitable products/items for a group of users has always been a difficult task. Most of the recommender systems are designed for individual use only. However, there are many scenarios where the recommendations are intended to serve a group of users. Each member of the group has their own set of preferences, and it is challenging to satisfy each member of the group with the recommended list. It has also been observed in recent studies that mere aggregation of preferences (e.g., ratings) does not provide good group recommendations. The quality of the group recommendation depends on two essential things: the ranking quality and the aggregation strategy. The first one confirms that the higher preferred items always appear first in the list, and the second one confirms the agreement among users of the group towards the recommendation list. Hence, this study proposes a method that uses the preference relation based matrix factorization technique to obtain the predicted preference (e.g., ratings) and then uses graph aggregation strategy to aggregate the preferences of the group members. We applied collective rationality during graph aggregation to maintain consistency in preferences among group members. Three benchmark datasets were used to evaluate and compare the proposed model with other baselines in terms of ranking quality of the group recommendation.
Augmented Reality Mobile Application for Museum: A Technology Acceptance Study

The tourism sector in Malaysia is reported for losing its global competitiveness when the nation missed its tourism targets recently. Mirrored at the state level, the tourism performance of Melaka, a heritage city inscribed by the UNESCO, is also experiencing similar downward trend. The museums, which play a pivotal role in showcasing the local culture and history, have seen decreasing number of visitors. Inspired by Tourism 4.0, many museums are now using digital technologies to enrich visitors' experience, such as augmented reality (AR). This is because the tourists are now becoming more tech-savvy and expecting culture and heritage to be presented lively rather than exhibited in dusty glass boxes. In the “When History Comes Alive” Project, an AR mobile app was developed for the People's Museum in Melaka. Although tourism studies have widely discussed the managerial implications of AR, user's acceptance of AR in the tourism context is still in its infancy. Hence, this study aimed to examine the factors affecting museum visitors' behavioural intention to use the AR mobile app. A survey was conducted among 120 museum visitors. The findings showed that playfulness expectancy, content relevance expectancy, performance expectancy, effort expectancy, and social influence positively affect the respondents' behavioural intention to use the AR mobile app with a high explanatory power of 61.2%. Significant managerial and practical implications to the developers of AR mobile app, tourism businesses and policy makers are highlighted in this paper. The limitations of the study and suggestions for future studies are also addressed.
An Efficient Lightweight Cryptographic Technique For IoT based E-healthcare System

IoT based E-healthcare Systems are widely used for remote health monitoring, diagnosis purpose to reduce the cost of health care services. In this system, patient data is stored in health cloud and medical professionals can access the data at any time. Hence secure transferring of health data is essential for treatment or monitoring purposes. Also, while storing the patient's record, it must be kept safe from misuse and modification of data as it can easily be tracked by other devices. Due to the constrained IoT devices, it is very difficult to encrypt the data with high-level cryptographic techniques. Lightweight cryptographic techniques are suitable for this purpose. In this paper, we have proposed a new block cipher technique for the secure transmission of data from these IoT devices.
Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation

Automatically extracting relations between chemicals and diseases plays an important role in biomedical text mining. Chemical-disease relation (CDR) extraction aims at extracting complex semantic relationships between entities in documents, which contain intrasentence and intersentence relations. Most previous methods did not consider dependency syntactic information across the sentences, which are very valuable for the relations extraction task, in particular, for extracting the intersentence relations accurately.In this paper, we propose a novel end-to-end neural network based on the graph convolutional network (GCN) and multihead attention, which makes use of the dependency syntactic information across the sentences to improve CDR extraction task.To improve the performance of intersentence relation extraction, we constructed a document-level dependency graph to capture the dependency syntactic information across sentences. GCN is applied to capture the feature representation of the document-level dependency graph. The multihead attention mechanism is employed to learn the relatively important context features from different semantic subspaces. To enhance the input representation, the deep context representation is used in our model instead of traditional word embedding.We evaluate our method on CDR corpus. The experimental results show that our method achieves an F-measure of 63.5%, which is superior to other state-of-the-art methods. In the intrasentence level, our method achieves a precision, recall, and F-measure of 59.1%, 81.5%, and 68.5%, respectively. In the intersentence level, our method achieves a precision, recall, and F-measure of 47.8%, 52.2%, and 49.9%, respectively.The GCN model can effectively exploit the across sentence dependency information to improve the performance of intersentence CDR extraction. Both the deep context representation and multihead attention are helpful in the CDR extraction task.
Joint representation learning with ratings and reviews for recommendation

Recommender system is an important technique to find the information that the users may be interested by their feedbacks. However, it is still a challenge to model the preference of users due to the sparsity of user feedbacks. To alleviate this problem, many methods are developed by extracting information from various kinds of auxiliary information that are related to the users. In the auxiliary information, review is the popular one, since it can reflect both user preferences and item characteristics. Moreover, the review can generate plausible recommendation explanations in the recommendation results. In this paper, we propose a hybrid deep collaborative filtering model that jointly learns rating embedding and textural feature from ratings and reviews respectively. Specifically, two embedding layers are employed to learn rating embedding for users and items based on the interactions, and two attention-based GRU networks attempt to learn context-aware representation as textural feature for users and items from reviews. To leverage the contribution between rating embedding and textual feature and obtain the fused features for users and items, a proposed gating mechanism is used. Then an interaction-learning layer is adopted to learn the user and item interaction information based on the fused user and item features. The prediction score is obtained with the factorization machine. Experimental results on six real-world datasets demonstrate the superior performance of the proposed method over several state-of-the-art methods.
A Generalized Quantum Protocol for Secure Multiparty Summation

The secure multiparty quantum summation is an important primitive of quantum cryptography. The existing multiparty quantum summation protocols have (n, n) threshold approach, where n out of total n players need compute the summation protocol. In this brief, we present a generalized secure multiparty quantum summation protocol. In our protocol, the linear secret sharing (LSS) and Quantum Fourier Transform (QFT) are used to take the advantages of both classical and quantum summation. We show that the outside and participant attacks are not possible in our protocol. We simulate it using the real quantum processor released by IBM corporation on “cloud”.
Facial Image Synthesis and Super-Resolution With Stacked Generative Adversarial Network

Image synthesis and super-resolution (SR) have always been a hot spot for computer vision and image processing research. Since the development of Deep Learning, especially after the Deep Convolutional Generative Adversarial Network (DC-GAN) methods, facial image synthesis and SR problem had been solved in many circumstances. But most of the existing works were focused on natural-looking of the synthesized result rather than keeping facial information of the original image. Our paper presented an end-to-end method of getting high-resolution photo-realistic facial images from low-resolution (LR) in-the-wild images without losing the facial identity details. The pipeline used a flexible stacked GAN structure for the SR process with different target image resolutions on different upscaling factors. To avoid getting blur or nonsensical image output and realize the flexibility, "U-Net" architecture and upsampling layers with residual learning blocks were stacked. The stacked network structure makes applying different loss functions in different parts of the network possible, which helps to solve the two problems of keeping identical facial details of the LR input image and generating high-quality output images simultaneously. By using 3 different loss functions in different positions of the stacked network separately, through experimental comparison, we found the best stacked residual block parameters which could get the best output image quality. Experimental results also explicated that the network had a good SR ability compare to state of the art methods in different resolution and upscaling factor.
Point cloud coding: A privileged view driven by a classification taxonomy

Humans mainly communicate among them and with the world around them using light and vision, thus implying that visual representation technologies play a central role in human societies. While visual representation has been based on the 2D representation paradigm for many decades, multiple developments are nowadays pressing towards the adoption of more realistic and immersive 3D visual representation models. Point clouds are one of these emerging representation models. However, the huge amount of data involved asks for highly efficient coding solutions, some of which have recently started to be developed by the MPEG and JPEG standardization groups. In this hectic context, this paper proposes a privileged view over the current point cloud coding technologies, driven by a novel, appropriate classification taxonomy. For this purpose, some of the most representative point cloud coding solutions available in the literature will be reviewed to exercise the most relevant classification paths in the proposed taxonomy. It is expected that this type of classification taxonomy and privileged view may help better understanding the point cloud coding landscape for further solid and consistent advancements in this emerging technical area.
A multi-layer fuzzy model based on fuzzy-rule clustering for prediction tasks

Fuzzy systems are widely used for solving complex and non-linear problems that cannot be addressed using precise mathematical models. Their performance, however, is critically affected by how they are constructed as well as their fuzzy rule base. Inspired by neural networks that apply a multi-layer structure to improve their performance, we propose a multi-layer fuzzy model with modified fuzzy rules to improve the approximation ability of fuzzy systems without losing efficiency. In practical applications, the fuzzy rule base extracted from numerical data is often incomplete, which makes a fuzzy system less robust. To address this problem, a non-linear function is used as the consequent of each fuzzy rule based on fuzzy-rule clustering to enhance the approximation ability of the fuzzy rule base. In addition, exact matching of fuzzy rules is employed based on the fuzzy rule's antecedent for prediction. By doing so, only one rule will be triggered in each layer, which is very efficient. Experimental results from two simulated functions and three practical applications confirm that our proposed multi-layer fuzzy model can outperform other well-established fuzzy models in terms of accuracy and robustness without sacrificing efficiency.
Deep embedding of concept ontology for hierarchical fashion recognition

The natural concept ontology structure of clothes has enabled easy management of large quantities of fashion images for online retailers and it is meaningful to study how to automatically recognize fashion images for both commercial promotion and academic research. In this paper, a new hierarchical approach is developed for large-scale fashion recognition. We first embed concept ontology into deep convolutional neural network (CNN) by adopting multiple deep CNN branches to learn node-specific features and classifiers explicitly. Then, we introduce a hierarchical knowledge distillation method to further improve the performance of fashion recognition. Finally, we employ the proposed approach for fashion recommendation. To deal with hierarchical deep learning constrains, we leverage back propagation to simultaneously refine the shared deep CNNs and the diverse CNN branches for relevant node features and classifiers by using our joint objective function. The main advantages of this paper lie in (1) providing an effective way for recognizing rich semantic explanations of fashion images without training large or multiple networks, and (2) saving the storage&time costs by learning personalized features and classifiers for each tree node. The experimental results on both our organized fashion dataset and the public DeepFashion dataset have verified the effectiveness and efficiency of the proposed approach on both hierarchical fashion recognition and within category fine-grained fashion recommendation.
Unifying Offline and Online Multi-Graph Matching via Finding Shortest Paths on Supergraph

This paper addresses the problem of multiple graph matching (MGM) by considering both offline batch mode and online setting. We explore the concept of cycle-consistency over pairwise matchings and formulate the problem as finding optimal composition path on the supergraph, whose vertices refer to graphs and edge weights denote score function regarding consistency and affinity. By our theoretical study we show that the offline and online MGM on supergraph can be converted to finding all pairwise shortest paths and single-source shortest paths respectively. We adopt the Floyd algorithm [1] and shortest path faster algorithm (SPFA) [2] , [3] to effectively find the optimal path. Extensive experimental results show our methods surpass state-of-the-art MGM methods, including CAO [4] , MISM [5], IMGM [6] , and many other recent methods in offline and online settings. Source code will be made publicly available.
Reversible Biosignal Steganography Approach for Authenticating Biosignals Using Extended Binary Golay Code

We present a reversible biosignal steganography method to authenticate the source of biosignal in this paper. Cloud is being a popular platform for storing a large volume of biosignals such as an electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmogram (PPG). However, outsourcing biosignals to the cloud may introduce authenticity issues. For instance, patient data can be altered, or fake patient data can be inserted by the dishonest cloud service provider or attacker for giving benefits to business organizations such as insurance service providers. Steganography approaches can be used to hide data source's identification data before outsourcing to the cloud for maintaining authenticity. Existing biosignal steganography approaches fail to reconstruct original biosignal after applying a reverse data hiding technique. In other words, current biosignal steganography approaches are irreversible. Reversible biosignal steganography method is required for protecting biosignal data from deterioration and efficient use by its stakeholders. In this work, we develop a reversible biosignal steganography approach using the Extended Binary Golay Code based error correction method. Our proposed method embeds secret authentication message as an error within different types of biosignals such as ECG, PPG, and EEG. Extended Binary Golay Code based error correction method is used to extract the secret message, and reconstruct original biosignal. We conduct a set of experiments for evaluating the performance of our proposed method.
A Secure Data Dynamics and Public Auditing Scheme for Cloud Storage

Cloud computing is an evolving technology that provides data storage and highly fast computing services at a very low cost. All data stored in the cloud is handled by their cloud service providers or the caretaker of the cloud. The data owner is concerned about the authenticity and reliability of the data stored in the cloud as the data owners. Data can be misappropriated or altered by any unauthorized user or person. This paper desire to suggest a secure public auditing scheme applying third party auditors to authenticate the privacy, reliability, and integrity of data stored in the cloud. This proposed auditing scheme composes the use of the AES-256 algorithm for encryption, SHA-512 for integrity check and RSA-15360 for public-key encryption. And perform data dynamics operation which deals with mostly insertion, deletion, and, modification.