Reviewerly/exHarmony huggingface dataset:
3D Point Cloud Attribute Compression Using Geometry-Guided Sparse Representation

3D point clouds associated with attributes are considered as a promising paradigm for immersive communication. However, the corresponding compression schemes for this media are still in the infant stage. Moreover, in contrast to conventional image/video compression, it is a more challenging task to compress 3D point cloud data, arising from the irregular structure. In this paper, we propose a novel and effective compression scheme for the attributes of voxelized 3D point clouds. In the first stage, an input voxelized 3D point cloud is divided into blocks of equal size. Then, to deal with the irregular structure of 3D point clouds, a geometry-guided sparse representation (GSR) is proposed to eliminate the redundancy within each block, which is formulated as an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm regularized optimization problem. Also, an inter-block prediction scheme is applied to remove the redundancy between blocks. Finally, by quantitatively analyzing the characteristics of the resulting transform coefficients by GSR, an effective entropy coding strategy that is tailored to our GSR is developed to generate the bitstream. Experimental results over various benchmark datasets show that the proposed compression scheme is able to achieve better rate-distortion performance and visual quality, compared with state-of-the-art methods.
Improved continuous Ant Colony Optimization algorithms for real-world engineering optimization problems

The Ant Colony Optimization (ACO) algorithm is a well-known optimization method that has been successfully applied to solve many difficult discrete optimization problems. A decade ago, a variant of ACO, called ACOR, was developed for continuous search spaces. This work proposes two new variants of ACOR; namely, IACOR and LIACOR, with improved performance in solving real-world engineering optimization problems. The IACOR uses a success-based random-walk selection that chooses between Brownian motion and Lévy flights. Thus, trying to balance exploitation and exploration, respectively. The LIACOR, on the other hand, is a memetic version of IACOR where a local search is used to enhance solutions in the colony. Furthermore, the ACOR is tested on the 22 real-world engineering optimization problems of the IEEE CEC 2011. The proposed variants are also tested on the same set of problems against five state-of-the-art optimization methods. The proposed IACOR and LIACOR outperform the original ACOR on most problems. In addition, the results of the comparative analysis show the superiority of LIACOR compared to the other tested algorithms.
Image encryption using block cipher and chaotic sequences

Abstract Encryption of data with high correlation, such as images, is a challenge for block ciphers, since patterns of the original image may remain after encryption. This is due to the deterministic mapping performed by the cipher. To overcome this limitation, a block cipher is used in an adequate mode of operation, such as cipher block chaining, counter mode. It is presented in this work randomized block ciphers inspired by the Rijndael architecture employing chaotic maps as an entropy source. It is shown that the proposal achieves good security and robustness indicators with fewer rounds compared to that obtained with the Rijndael algorithm.
Blockchain‐based security aspects in heterogeneous Internet‐of‐Things networks: A survey

Abstract Blockchain is a key technology that enables cryptocurrencies such as Bitcoin, Litecoin, etc. In recent years, researchers have ventured into tapping the potential of blockchain‐based ecosystems beyond cryptocurrencies. This paper explores the convergence of blockchain with the Internet of things (IoT) and its improved versions like software‐defined network (SDN)–based IoT, which are upcoming technologies in the area. This paper gives a brief description of the IoT and its challenges, especially the security aspects. It also discusses how blockchain concept can further resolve these challenges on the SDN‐based IoT platform. This paper first discussed the evolution of conventional IoT to the SDN‐based IoT, which can resolve many drawbacks of a conventional IoT system. Also, it is focused on how the concept of blockchain can be converged with SDN‐based IoT system to further improve its security aspects. Although the convergence of these different technologies has resulted into many advantages, many associated challenges still remain. This paper highlighted these challenges and opportunities to discover many other research issues that need to be resolved. The details of current literature on the same issue has also been highlighted in this paper.
Membership Inference Attacks Against Adversarially Robust Deep Learning Models

In recent years, the research community has increasingly focused on understanding the security and privacy challenges posed by deep learning models. However, the security domain and the privacy domain have typically been considered separately. It is thus unclear whether the defense methods in one domain will have any unexpected impact on the other domain. In this paper, we take a step towards enhancing our understanding of deep learning models when the two domains are combined together. We do this by measuring the success of membership inference attacks against two state-of-the-art adversarial defense methods that mitigate evasion attacks: adversarial training and provable defense. On the one hand, membership inference attacks aim to infer an individual's participation in the target model's training dataset and are known to be correlated with target model's overfitting. On the other hand, adversarial defense methods aim to enhance the robustness of target models by ensuring that model predictions are unchanged for a small area around each sample in the training dataset. Intuitively, adversarial defenses may rely more on the training dataset and be more vulnerable to membership inference attacks. By performing empirical membership inference attacks on both adversarially robust models and corresponding undefended models, we find that the adversarial training method is indeed more susceptible to membership inference attacks, and the privacy leakage is directly correlated with model robustness. We also find that the provable defense approach does not lead to enhanced success of membership inference attacks. However, this is achieved by significantly sacrificing the accuracy of the model on benign data points, indicating that privacy, security, and prediction accuracy are not jointly achieved in these two approaches.
A memetic algorithm using emperor penguin and social engineering optimization for medical data classification

Gene selection and classification of microarray data play an important role in cancer diagnosis and treatment. One of the most popular and faster classification model is support vector machine (SVM). However, the major challenge in SVM lies in the selection of its two parameters, namely, regularization parameter C and kernel parameter γ. Attempts have been made to improve the performance of SVM by tuning these two parameters with the help of metaheuristics. Although existing metaheuristics can search the promising regions of the search space, they are unable to explore the global optimum efficiently. In this paper, a memetic algorithm-based SVM (M-SVM) is presented for simultaneous feature selection and optimization of SVM parameters. The memetic algorithm is a fusion of local search strategy using social engineering optimizer (SEO) and global optimization framework using emperor penguin optimizer (EPO). The idea of embedding SEO in EPO is to enhance the exploitation capability of EPO. The performance of our algorithm is evaluated on seven standard benchmark datasets. To prove the efficacy of the method, it is compared with particle swarm optimization based SVM (PSO-SVM), genetic algorithm based SVM (GA-SVM), and fifteen other state-of-the-art methods. The experimental results confirm that the proposed method significantly outperforms other existing techniques in terms of accuracy and number of selected genes. The proposed method is validated using a statistical analysis, namely, ANOVA.
Effective privacy preserving data publishing by vectorization

As smart devices and cloud services are rapidly expanding, a large amount of location information can easily be gathered. However, there is a conflict between collecting location data and protecting personal data since obtaining and utilizing the data may be restricted due to privacy concerns. Various methods for anonymity and on the original location data have been studied, but these methods have excessively reduced data utility while stressing highly on privacy preservation. In this article, we suggest a novel model to overcome this fundamental dilemma via a surrogate vector based on the grid environment. Compared to the existing approaches, our model shows a new theoretical advancement in privacy protection, and outstanding performance with respect to time complexity and data utility has been achieved.
Eye, Head and Torso Coordination During Gaze Shifts in Virtual Reality

Humans perform gaze shifts naturally through a combination of eye, head and body movements. Although gaze has been long studied as input modality for interaction, this has previously ignored the coordination of the eyes, head and body. This article reports a study of gaze shifts in virtual reality aimed to address the gap and inform design. We identify general eye, head and torso coordination patterns and provide an analysis of the relative movements’ contribution and temporal alignment. We quantify effects of target distance, direction and user posture, describe preferred eye-in-head motion ranges and identify a high variability in head movement tendency. Study insights lead us to propose gaze zones that reflect different levels of contribution from eye, head and body. We discuss design implications for HCI and VR, and in conclusion argue to treat gaze as multimodal input, and eye, head and body movement as synergetic in interaction design.
Diagnostic concordance between whole slide imaging and conventional light microscopy in cytopathology: A systematic review

Many studies have examined the diagnostic concordance of whole slide imaging (WSI) and light microscopy (LM) for surgical pathology. In cytopathology, WSI use has been more limited, mainly because of technical issues. The aim of this study was to review the literature and determine the overall diagnostic concordance of WSI and LM in cytopathology. A systematic search of PubMed, Scopus, and the Cochrane Library was performed, with data extracted from the included articles. A quality assessment of studies was performed with a modified Quality Assessment of Diagnostic Accuracy Studies 2 tool. The primary outcome was concordance for the diagnoses rendered by WSI and LM as shown by the concordance rate with the original diagnosis, intra‐observer and interobserver concordance with the κ coefficient, or a percentage. Secondary outcomes included the time taken to reach a diagnosis and the quality and perception of WSI. A descriptive survey was provided. Among 1867 publications, a total of 19 studies (1%) were included. Overall, the concordance between WSI and the original diagnosis was 84.1%, the intra‐observer concordance between WSI and LM was 92.5% with a κ coefficient of 0.66, and the interobserver κ coefficient was 0.69. The time to reach a diagnosis was longer with WSI in all studies. The quality of WSI was good, but diagnostic confidence and cytologist preference were higher for LM. In conclusion, the concordance of WSI with LM is acceptable and in line with systematic reviews in surgical pathology. However, the time required for scanning and technical issues represent barriers to complete adoption. It is foreseeable that technical advances and rigorous validation study design will help to improve the diagnostic concordance of WSI with LM in cytopathology.
Understanding the apparent superiority of over-sampling through an analysis of local information for class-imbalanced data

Data plays a key role in the design of expert and intelligent systems and therefore, data preprocessing appears to be a critical step to produce high-quality data and build accurate machine learning models. Over the past decades, increasing attention has been paid towards the issue of class imbalance and this is now a research hotspot in a variety of fields. Although the resampling methods, either by under-sampling the majority class or by over-sampling the minority class, stand among the most powerful techniques to face this problem, their strengths and weaknesses have typically been discussed based only on the class imbalance ratio. However, several questions remain open and need further exploration. For instance, the subtle differences in performance between the over- and under-sampling algorithms are still under-comprehended, and we hypothesize that they could be better explained by analyzing the inner structure of the data sets. Consequently, this paper attempts to investigate and illustrate the effects of the resampling methods on the inner structure of a data set by exploiting local neighborhood information, identifying the sample types in both classes and analyzing their distribution in each resampled set. Experimental results indicate that the resampling methods that produce the highest proportion of safe samples and the lowest proportion of unsafe samples correspond to those with the highest overall performance. The significance of this paper lies in the fact that our findings may contribute to gain a better understanding of how these techniques perform on class-imbalanced data and why over-sampling has been reported to be usually more efficient than under-sampling. The outcomes in this study may have impact on both research and practice in the design of expert and intelligent systems since a priori knowledge about the internal structure of the imbalanced data sets could be incorporated to the learning algorithms.
An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images

Abstract Background Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. Results To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. Conclusions We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. Our method also can facilitate downstream nuclei morphological analyses as it provides high-quality features extracted from histopathology images.
Beyond Scalar Neuron: Adopting Vector-Neuron Capsules for Long-Term Person Re-Identification

Current person re-identification (re-ID) works mainly focus on the short-term scenario where a person is less likely to change clothes.However, in the long-term re-ID scenario, a person has a great chance to change clothes.A sophisticated re-ID system should take such changes into account.To facilitate the study of long-term re-ID, this paper introduces a large-scale re-ID dataset called "Celeb-reID" to the community.Unlike previous datasets, the same person can change clothes in the proposed Celeb-reID dataset.Images of Celeb-reID are acquired from the Internet using street snap-shots of celebrities.There is a total of 1,052 IDs with 34,186 images making Celeb-reID being the largest long-term re-ID dataset so far.To tackle the challenge of cloth changes, we propose to use vector-neuron (VN) capsules instead of the traditional scalar neurons (SN) to design our network.Compared with SN, one extra-dimensional information in VN can perceive cloth changes of the same person.We introduce a well-designed ReIDCaps network and integrate capsules to deal with the person re-ID task.Soft Embedding Attention (SEA) and Feature Sparse Representation (FSR) mechanisms are adopted in our network for performance boosting.Experiments are conducted on the proposed long-term re-ID dataset and two common short-term re-ID datasets.Comprehensive analyses are given to demonstrate the challenge exposed in our datasets.Experimental results show that our ReIDCaps can outperform existing state-of-the-art methods by a large margin in the long-term scenario.The new dataset and code will be released to facilitate future researches.
Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection

Feature selection aims to eliminate redundant or irrelevant variables from input data to reduce computational cost, provide a better understanding of data and improve prediction accuracy. Majority of the existing filter methods utilize a single feature-ranking technique, which may overlook some important assumptions about the underlying regression function linking input variables with the output. In this paper, we propose a novel feature selection framework that combines clustering of variables with multiple feature-ranking techniques for selecting an optimal feature subset. Different feature-ranking methods typically result in selecting different subsets, as each method has its own assumption about the regression function linking input variables with the output. Therefore, we employ multiple feature-ranking methods having disjoint assumption about the regression function. The proposed approach has a feature ranking module to identify relevant features and a clustering module to eliminate redundant features. First, input variables are ranked using regression coefficients obtained by training $L1$ regularized Logistic Regression, Support Vector Machine and Random Forests models. Those features which are ranked lower than a certain threshold are filtered-out. The remaining features are grouped into clusters using an exemplar-based clustering algorithm, which identifies data-points that exemplify the data better, and associates each data-point with an exemplar. We use both linear correlation coefficients and information gain for measuring the association between a data-point and its corresponding exemplar. From each cluster the highest ranked feature is selected as a delegate, and all delegates from the three ranked lists are combined into the final feature set using union operation. Empirical results over a number of real-world data sets confirm the hypothesis that combining features selected using multiple heterogeneous methods results in a more robust feature set and improves prediction accuracy. As compared to other feature selection approaches evaluated, features selected using linear correlation-based multi-filter feature selection achieved the best classification accuracy with 98.7%, 100%, 92.3% and 100% for Ionosphere, Wisconsin Breast Cancer, Sonar and Wine data sets respectively.
Towards a Taxonomy of Social VR Application Design

The number of social VR applications-or applications that support social interaction between users in virtual reality-has grown considerably in recent years. A consequence of this growth is that the state of social VR application design has become increasingly obfuscated, which complicates identification of design trends, best practices, and uncommon features that are perhaps worthy of wider adoption. To help address this problem, this paper presents a taxonomy of social VR application design choices as informed by 29 commercial and prototypical applications in the literature. Discussion of the taxonomy highlights novel features of research prototypes that could potentially enrich the social experience in commercially available applications. The paper concludes by considering how the taxonomy can guide future design of social VR applications, and next steps for refining the taxonomy.
Many Task Learning With Task Routing

Typical multi-task learning (MTL) methods rely on architectural adjustments and a large trainable parameter set to jointly optimize over several tasks. However, when the number of tasks increases so do the complexity of the architectural adjustments and resource requirements. In this paper, we introduce a method which applies a conditional feature-wise transformation over the convolutional activations that enables a model to successfully perform a large number of tasks. To distinguish from regular MTL, we introduce Many Task Learning (MaTL) as a special case of MTL where more than 20 tasks are performed by a single model. Our method dubbed Task Routing (TR) is encapsulated in a layer we call the Task Routing Layer (TRL), which applied in an MaTL scenario successfully fits hundreds of classification tasks in one model. We evaluate on 5 datasets and the Visual Decathlon (VD) challenge against strong baselines and state-of-the-art approaches.
Semantic Object Accuracy for Generative Text-to-Image Synthesis

Generative adversarial networks conditioned on textual image descriptions are capable of generating realistic-looking images. However, current methods still struggle to generate images based on complex image captions from a heterogeneous domain. Furthermore, quantitatively evaluating these text-to-image models is challenging, as most evaluation metrics only judge image quality but not the conformity between the image and its caption. To address these challenges we introduce a new model that explicitly models individual objects within an image and a new evaluation metric called Semantic Object Accuracy (SOA) that specifically evaluates images given an image caption. The SOA uses a pre-trained object detector to evaluate if a generated image contains objects that are mentioned in the image caption, e.g. whether an image generated from "a car driving down the street" contains a car. We perform a user study comparing several text-to-image models and show that our SOA metric ranks the models the same way as humans, whereas other metrics such as the Inception Score do not. Our evaluation also shows that models which explicitly model objects outperform models which only model global image characteristics.