Reviewerly/exHarmony huggingface dataset:
Silhouette-free interference-based multiple-image encryption using cascaded fractional Fourier transforms

An optical approach of silhouette-free multiple-image encryption based on interference is proposed, with two layers to enhance the level of security. In the first layer, a group of plain images are encoded into an amplitude distribution, called interim, using the cascaded fractional Fourier transforms. In the second layer, the interim is encrypted into two noisy ciphertext images. The two layers are compactly combined into a single whole to assure that the inherent silhouette problem is removed through the involvement of a random phase-only mask as the built-in function of cryptosystem. In addition to the phase mask keys generated in the process of encryption, the security is enhanced further by considering the fractional order as an additional key. Numerical simulations are given to demonstrate the feasibility and validity of this approach.
A lightweight anonymous user authentication and key establishment scheme for wearable devices

The paradigm of the Internet of Things (IoT) is a system of interconnected objects that can be accessed globally with the help of the Internet. IoT has numerous applications including healthcare wherein the wearable devices can sense the data from the body of the patient and sent it to the concerned doctor for remote monitoring. It is essential that these devices should operate in real time and generate the precise data as life-critical decisions are made based upon the data received from these devices. The infrastructure of IoT is very heterogeneous and dynamic therefore vulnerable to the threat of security and privacy. One of the most significant challenges in IoT is the authentication of the devices before sending the data so that we can have confidence in the received data. As IoT devices are resource constraint, there is a need for lightweight authentication scheme for them. In this paper, we introduce a new authentication technique for communication on the Internet of Things (IoT) using simple XOR and one-way Cryptographic hash function. The proposed protocol not only provides security but also maintains equilibrium between the efficiency and communication cost. The security of the proposed protocol is evaluated using BAN-Logic, the widely accepted AVISPA tool and the informal security analysis. Then, the comparison of the proposed protocol with the existing related schemes is shown concerning security features, communication, and computational cost.
Hybrid binary Coral Reefs Optimization algorithm with Simulated Annealing for Feature Selection in high-dimensional biomedical datasets

The last decades have witnessed accumulation in biomedical data. Though they can be analyzed to enhance assessment of at-risk patients and improve the diagnosis, a major challenge associated with biomedical data analysis is the so-called “curse of dimensionality”. For the issue, an improved Coral Reefs Optimization algorithm for selecting the best feature subsets has been proposed. Tournament selection strategy is adopted to increase the diversity of initial population individuals. The KNN classifier is used to evaluate the classification accuracy. Experimental results on thirteen public medical datasets show proposed BCROSAT outperforms other state-of-the-art methods.
Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean

Although deep learning breakthroughs in NLP are based on learning distributed word representations by neural language models, these methods suffer from a classic drawback of unsupervised learning techniques. Furthermore, the performance of general-word embedding has been shown to be heavily task-dependent. To tackle this issue, recent researches have been proposed to learn the sentiment-enhanced word vectors for sentiment analysis. However, the common limitation of these approaches is that they require external sentiment lexicon sources and the construction and maintenance of these resources involve a set of complexing, time-consuming, and error-prone tasks. In this regard, this paper proposes a method of sentiment lexicon embedding that better represents sentiment word's semantic relationships than existing word embedding techniques without manually-annotated sentiment corpus. The major distinguishing factor of the proposed framework was that joint encoding morphemes and their POS tags, and training only important lexical morphemes in the embedding space. To verify the effectiveness of the proposed method, we conducted experiments comparing with two baseline models. As a result, the revised embedding approach mitigated the problem of conventional context-based word embedding method and, in turn, improved the performance of sentiment classification.
Dynamic Saliency-Aware Regularization for Correlation Filter-Based Object Tracking

With a good balance between tracking accuracy and speed, correlation filter (CF) has become one of the best object tracking frameworks, based on which many successful trackers have been developed. Recently, spatially regularized CF tracking (SRDCF) has been developed to remedy the annoying boundary effects of CF tracking, thus further boosting the tracking performance. However, SRDCF uses a fixed spatial regularization map constructed from a loose bounding box and its performance inevitably degrades when the target or background show significant variations, such as object deformation or occlusion. To address this problem, we propose a new dynamic saliency-aware regularized CF tracking (DSAR-CF) scheme. In DSAR-CF, a simple yet effective energy function, which reflects the object saliency and tracking reliability in the spatial-temporal domain, is defined to guide the online updating of the regularization weight map using an efficient level-set algorithm. Extensive experiments validate that the proposed DSAR-CF leads to better performance in terms of accuracy and speed than the original SRDCF.
Learning Deep Ship Detector in SAR Images From Scratch

Recently, deep learning-based methods have brought new ideas for ship detection in synthetic aperture radar (SAR) images. However, several challenges still exist: 1) deep models contain millions of parameters, whereas the available annotated samples are not sufficient in number for training. Therefore, most deep detectors have to fine-tune networks pre-trained on ImageNet, which incurs learning bias due to the huge domain mismatch between SAR images and ImageNet images. Furthermore, it has a little flexibility to redesign the network structure; and 2) ships in SAR images are relatively small in size and densely clustered, whereas most deep detectors have poor performance with small objects due to the rough feature map used for detection and the extreme foreground–background imbalance. To address these problems, this paper proposes an effective approach to learn deep ship detector from scratch. First, we design a condensed backbone network, which consists of several dense blocks. Hence, earlier layers can receive additional supervision from the objective function through the dense connections, which makes it easy to train. In addition, feature reuse strategy is adopted to make it highly parameter efficient. Therefore, the backbone network could be freely designed and effectively trained from scratch without using a large amount of annotated samples. Second, we improve the cross-entropy loss to address the foreground–background imbalance and predict multi-scale ship proposals from several intermediate layers to improve the recall rate. Then, position-sensitive score maps are adopted to encode position information into each ship proposal for discrimination. The comparison results on the Sentinel-1 data set show that: 1) learning ship detector from scratch achieved better performance than ImageNet pre-trained model-based detectors and 2) our method is more effective than existing algorithms for detecting the small and densely clustered ships.
An experimental approach for characterization of fracture process zone in concrete

In this work, an attempt has been made to understand the evolution of fracture process zone in plain concrete member utilizing Digital Image Correlation (DIC) technique. Series of experiments have been performed in centre point bending under the action of monotonic and repetitive loadings and DIC technique is used during the entire test process. A systematic procedure has been developed to monitor the evolution and propagation of the fracture process zone and traction free crack. Size of fully developed FPZ has been found to be approximately equal in both static and fatigue loading cases. It has been also seen that traction free crack propagation has occurred only in medium and large size beams. DIC analysis results have been compared with the measured CMOD values through validation study and both are found to be in good agreement. An analysis has been performed for evaluating the resolution and standard uncertainty in the DIC measured displacements and strains.
Personalized explanations for hybrid recommender systems

Recommender systems have become pervasive on the web, shaping the way users see information and thus the decisions they make. As these systems get more complex, there is a growing need for transparency. In this paper, we study the problem of generating and visualizing personalized explanations for hybrid recommender systems, which incorporate many different data sources. We build upon a hybrid probabilistic graphical model and develop an approach to generate real-time recommendations along with personalized explanations. To study the benefits of explanations for hybrid recommender systems, we conduct a crowd-sourced user study where our system generates personalized recommendations and explanations for real users of the last.fm music platform. We experiment with 1) different explanation styles (e.g., user-based, item-based), 2) manipulating the number of explanation styles presented, and 3) manipulating the presentation format (e.g., textual vs. visual). We apply a mixed model statistical analysis to consider user personality traits as a control variable and demonstrate the usefulness of our approach in creating personalized hybrid explanations with different style, number, and format.
Consensus Clustering-Based Undersampling Approach to Imbalanced Learning

Class imbalance is an important problem, encountered in machine learning applications, where one class (named as, the minority class) has extremely small number of instances and the other class (referred as, the majority class) has immense quantity of instances. Imbalanced datasets can be of great importance in several real-world applications, including medical diagnosis, malware detection, anomaly identification, bankruptcy prediction, and spam filtering. In this paper, we present a consensus clustering based-undersampling approach to imbalanced learning. In this scheme, the number of instances in the majority class was undersampled by utilizing a consensus clustering-based scheme. In the empirical analysis, 44 small-scale and 2 large-scale imbalanced classification benchmarks have been utilized. In the consensus clustering schemes, five clustering algorithms (namely, k -means, k -modes, k -means++, self-organizing maps, and DIANA algorithm) and their combinations were taken into consideration. In the classification phase, five supervised learning methods (namely, naïve Bayes, logistic regression, support vector machines, random forests, and k -nearest neighbor algorithm) and three ensemble learner methods (namely, AdaBoost, bagging, and random subspace algorithm) were utilized. The empirical results indicate that the proposed heterogeneous consensus clustering-based undersampling scheme yields better predictive performance.
A General Framework for Adversarial Examples with Objectives

Images perturbed subtly to be misclassified by neural networks, called adversarial examples, have emerged as a technically deep challenge and an important concern for several application domains. Most research on adversarial examples takes as its only constraint that the perturbed images are similar to the originals. However, real-world application of these ideas often requires the examples to satisfy additional objectives, which are typically enforced through custom modifications of the perturbation process. In this paper, we propose adversarial generative nets (AGNs), a general methodology to train a generator neural network to emit adversarial examples satisfying desired objectives. We demonstrate the ability of AGNs to accommodate a wide range of objectives, including imprecise ones difficult to model, in two application domains. In particular, we demonstrate physical adversarial examples---eyeglass frames designed to fool face recognition---with better robustness, inconspicuousness, and scalability than previous approaches, as well as a new attack to fool a handwritten-digit classifier.
Moderation Practices as Emotional Labor in Sustaining Online Communities

We examine how and why Asian American and Pacific Islander (AAPI) moderators on Reddit shape the norms of their online communities through the analytic lens of emotional labor. We conduct interviews with 21 moderators who facilitate identity work discourse in AAPI subreddits and present a thematic analysis of their moderation practices. We report on their challenges to sustaining moderation, which include burning out from volunteer work, navigating hierarchical structures, and balancing unfulfilled expectations. We then describe strategies that moderators employ to manage emotional labor, which involve distancing away from drama, building solidarity from shared struggles, and integrating an ecology of tools for self-organized moderation. We provide recommendations for improving moderation in online communities centered around identity work and discuss implications of emotional labor in the design of Reddit and similar platforms.
Siamese graph convolutional network for content based remote sensing image retrieval

This paper deals with the problem of content-based image retrieval (CBIR) of very high resolution (VHR) remote sensing (RS) images using the notion of a novel Siamese graph convolution network (SGCN). The GCN model has recently gained popularity in learning representations for irregular domain data including graphs. In the same line, we argue the effectiveness of region adjacency graph (RAG) based image representations for VHR RS scenes in terms of localized regions. This technique captures important scene information which can further aid in a better image to image correspondence. However, standard GCN features, in general, lacks discriminative property for fine-grained classes. These features may not be optimal for the task of CBIR in many cases with coherent local characteristics. As a remedy, we propose the SGCN architecture for assessing the similarity between a pair of graphs which can be trained with the contrastive loss function. Given the RAG representations, the aim is to learn an embedding space that pulls semantically coherent images closer while pushing dissimilar samples far apart. In order to ensure a quick response while performing the retrieval using a given similarity measure, the embedding space is kept constrained. We implement the proposed embeddings for the task of CBIR for RS data on the popular UC-Merced dataset and the PatternNet dataset where improved performance can be observed.
A Survey of Techniques for Mobile Service Encrypted Traffic Classification Using Deep Learning

The rapid adoption of mobile devices has dramatically changed the access to various networking services and led to the explosion of mobile service traffic.Mobile service traffic classification has been a crucial task that attracts strong interest in mobile network management and security as well as machine learning communities for past decades.However, with more and more adoptions of encryption over mobile services, it brings a lot of challenges about mobile traffic classification.Although classical machine learning approaches can solve many issues that port and payload-based methods cannot solve, it still has some limitations, such as time-consuming, costly handcrafted features, and frequent features update.With the excellent ability of automatic feature learning, Deep Learning (DL) undoubtedly becomes a highly desirable approach for mobile services traffic classification, especially encrypted traffic.This survey paper looks at emerging research into the application of DL methods to encrypted traffic classification of mobile services and presents a general framework of DL-based mobile encrypted traffic classification.Moreover, we review most of the recent existing work according to dataset selection, model input design, and model architecture.Furthermore, we propose some noteworthy issues and challenges about DL-based mobile services traffic classification.
Deepside: A general deep framework for salient object detection

Deep learning-based salient object detection techniques have shown impressive results compared to conventional saliency detection by handcrafted features. Integrating hierarchical features of Convolutional Neural Networks (CNN) to achieve fine-grained saliency detection is a current trend, and various deep architectures are proposed by researchers, including “skip-layer” architecture, “top-down” architecture, “short-connection” architecture and so on. While these architectures have achieved progressive improvement on detection accuracy, it is still unclear about the underlying distinctions and connections between these schemes. In this paper, we review and draw underlying connections between these architectures, and show that they actually could be unified into a general framework, which simply just has side structures with different depths. Based on the idea of designing deeper side structures for better detection accuracy, we propose a unified framework called Deepside that can be deeply supervised to incorporate hierarchical CNN features. Additionally, to fuse multiple side outputs from the network, we propose a novel fusion technique based on segmentation-based pooling, which severs as a built-in component in the CNN architecture and guarantees more accurate boundary details of detected salient objects. The effectiveness of the proposed Deepside scheme against state-of-the-art models is validated on 8 benchmark datasets.
Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges

This survey focuses on deep learning-based aspect-level sentiment classification (ASC), which aims to decide the sentiment polarity for an aspect mentioned within the document. Along with the success of applying deep learning in many applications, deep learning-based ASC has attracted a lot of interest from both academia and industry in recent years. However, there still lack a systematic taxonomy of existing approaches and comparison of their performance, which are the gaps that our survey aims to fill. Furthermore, to quantitatively evaluate the performance of various approaches, the standardization of the evaluation methodology and shared datasets is necessary. In this paper, an in-depth overview of the current state-of-the-art deep learning-based methods is given, showing the tremendous progress that has already been made in ASC. In particular, first, a comprehensive review of recent research efforts on deep learning-based ASC is provided. More concretely, we design a taxonomy of deep learning-based ASC and provide a comprehensive summary of the state-of-the-art methods. Then, we collect all benchmark ASC datasets for researchers to study and conduct extensive experiments over five public standard datasets with various commonly used evaluation measures. Finally, we discuss some of the most challenging open problems and point out promising future research directions in this field.
Attending to Discriminative Certainty for Domain Adaptation

In this paper, we aim to solve for unsupervised domain adaptation of classifiers where we have access to label information for the source domain while these are not available for a target domain. While various methods have been proposed for solving these including adversarial discriminator based methods, most approaches have focused on the entire image based domain adaptation. In an image, there would be regions that can be adapted better, for instance, the foreground object may be similar in nature. To obtain such regions, we propose methods that consider the probabilistic certainty estimate of various regions and specific focus on these during classification for adaptation. We observe that just by incorporating the probabilistic certainty of the discriminator while training the classifier, we are able to obtain state of the art results on various datasets as compared against all the recent methods. We provide a thorough empirical analysis of the method by providing ablation analysis, statistical significance test, and visualization of the attention maps and t-SNE embeddings. These evaluations convincingly demonstrate the effectiveness of the proposed approach.
Toward an Interoperability Architecture for Blockchain Autonomous Systems

There is considerable interest today in the use of blockchain technology to provide better visibility into shared information among a number of participants and systems arranged in a decentralized peer-to-peer topology. Several challenges in blockchain technology remain to be addressed, including the interoperability, survivability, and manageability of blockchain systems. Crucial to answering these challenges is the need to understand aspects of the Internet architecture that has made it scalable, resilient, and a commercial success as a global connectivity infrastructure. In this paper, we discuss a design philosophy for interoperable blockchain systems, using the design philosophy of the Internet architecture as the basis to identify key design principles. We recast some of the challenges faced in the design of the Internet architecture to that of the design of an interoperable blockchain architecture. We emphasize interoperability as a crucial requirement for the survivability and manageability of blockchain systems. The goal is to define an interoperable blockchain architecture, in which common components of the blockchain architecture can begin to be standardized, leading to lowering of development costs, better reusability, and higher degree of interoperability.
Short-term solar power forecast with deep learning: Exploring optimal input and output configuration

The volatility of cloud movement introduces significant uncertainty in short-term solar power forecasting, which can complicate the operation of modern power systems. This work proposes a specialized convolutional neural network (CNN) “SUNSET” to predict 15-min ahead minutely-averaged PV output. The model is characterized by its usage of hybrid input, temporal history and strong regularization. On a 1-year database, the “baseline” model achieves 16.3% forecast skill in cloudy conditions and 15.7% in all weather conditions, relative to a smart persistence forecast. Optimal input and output configurations are explored and suggestions are given. In terms of input, both sky images and PV output history are found to be crucial. Output-wise, training against PV output significantly outperforms using clear sky index (CSI). Careful down-sampling can reduce the training time by as much as 83% without affecting accuracy. For lag term configurations, using the same length of history as the forecast horizon is a good heuristic, while using slightly shorter history yielded a modest 0.5–0.9% improvement in this case. To ensure reproducibility and facilitate future works, the code base of this work is available at Github/YuchiSun/SUNSET.