Reviewerly/exHarmony huggingface dataset:
Object semantics sentiment correlation analysis enhanced image sentiment classification

With the development of artificial intelligence and deep learning, image sentiment analysis has become a hotspot in computer vision and attracts more attention. Most of the existing methods focus on identifying the emotions by studying complex models or robust features from the whole image, which neglects the influence of object semantics on image sentiment analysis. In this paper, we propose a novel object semantics sentiment correlation model (OSSCM), which is based on Bayesian network, to guide the image sentiment classification. OSSCM is constructed by exploring the relationships between image emotions and the object semantics combination in the images, which can fully consider the effect of object semantics for image emotions. Then, a convolutional neural networks (CNN) based visual sentiment analysis model is proposed to analyze image sentiment from visual aspect. Finally, three fusion strategies are proposed to realize OSSCM enhanced image sentiment classification. Experiments on public emotion datasets FI and Flickr_LDL demonstrate that our proposed image sentiment classification method can achieve good performance on image emotion analysis, and outperform state of the art methods.
Comparison of the Performance of Machine Learning Algorithms in Breast Cancer Screening and Detection: A Protocol

Background Breast Cancer (BC) is a known global crisis. The World Health Organization reports a global 2.09 million incidences and 627,000 deaths in 2018 relating to BC. The traditional BC screening method in developed countries is mammography, whilst developing countries employ breast self-examination and clinical breast examination. The prominent gold standard for BC detection is triple assessment: i) clinical examination, ii) mammography and/or ultrasonography; and iii) Fine Needle Aspirate Cytology. However, the introduction of cheaper, efficient and noninvasive methods of BC screening and detection would be beneficial. Design and methods We propose the use of eight machine learning algorithms: i) Logistic Regression; ii) Support Vector Machine; iii) K-Nearest Neighbors; iv) Decision Tree; v) Random Forest; vi) Adaptive Boosting; vii) Gradient Boosting; viii) eXtreme Gradient Boosting, and blood test results using BC Coimbra Dataset (BCCD) from University of California Irvine online database to create models for BC prediction. To ensure the models’ robustness, we will employ: i) Stratified k-fold Cross- Validation; ii) Correlation-based Feature Selection (CFS); and iii) parameter tuning. The models will be validated on validation and test sets of BCCD for full features and reduced features. Feature reduction has an impact on algorithm performance. Seven metrics will be used for model evaluation, including accuracy. Expected impact of the study for public health The CFS together with highest performing model(s) can serve to identify important specific blood tests that point towards BC, which may serve as an important BC biomarker. Highest performing model(s) may eventually be used to create an Artificial Intelligence tool to assist clinicians in BC screening and detection.
Pay Attention to Features, Transfer Learn faster CNNs

Deep convolutional neural networks are now widely deployed in vision applications, but the size of training data can bottleneck their performance. Transfer learning offers the chance for CNNs to learn with limited data samples by transferring knowledge from weights pretrained on large datasets. On the other hand, blindly transferring all learned features from the source dataset brings unnecessary computation to CNNs on the target task. In this paper, we propose attentive feature distillation and selection (AFDS) that not only adjusts the strength of regularization introduced by transfer learning but also dynamically determines which are the important features to transfer. When deploying AFDS on ResNet-101, we achieve state-of-the-art computation reduction at the same accuracy budget, outperforming all existing transfer learning methods. On a 10x MACs reduction budget, transfer learned from ImageNet to Stanford Dogs 120, AFDS achieves an accuracy that is 12.51% higher than its best competitor.
Deep multi-granularity graph embedding for user identity linkage across social networks

There have been increasing interests in user identity linkage (UIL) across social networks since it supports many applications such as cross-net recommendation, link prediction, and network fusion. Existing graph embedding based techniques cannot sufficiently model the higher-order structural properties in UIL. Moreover, the very limited supervisory anchor pairs (SAP), which are crucial for the task of UIL across social networks, are not utilized effectively. In this paper, a novel framework named multi-granularity graph embedding (MGGE) is proposed. And as an extension, a deep multi-granularity graph embedding model (DeepMGGE) is further developed. DeepMGGE uses the random walk (RW) to capture the higher-order structural proximities which is ignored by IONE Liu et al. (2016). Besides, DeepMGGE employs a heuristic edge-weighting mechanism given by deep learning to better capture the non-linear SAP-oriented structural properties. Experiments on real social networks demonstrate that the DeepMGGE outperforms state-of-the-art methods.
Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation

In multi-view multi-label learning (MVML), each training example is represented by different feature vectors and associated with multiple labels simultaneously. Nonetheless, the labeling quality of training examples is tend to be affected by annotation noises. In this paper, the problem of multi-view partial multi-label learning (MVPML) is studied, where the set of associated labels are assumed to be candidate ones and only partially valid. To solve the MVPML problem, a two-stage graph-based disambiguation approach is proposed. Firstly, the ground-truth labels of each training example are estimated by disambiguating the candidate labels with fused similarity graph. After that, the predictive model for each label is learned from embedding features generated from disambiguation-guided clustering analysis. Extensive experimental studies clearly validate the effectiveness of the proposed approach in solving the MVPML problem.
Improving Question Generation with Sentence-Level Semantic Matching and Answer Position Inferring

Taking an answer and its context as input, sequence-to-sequence models have made considerable progress on question generation. However, we observe that these approaches often generate wrong question words or keywords and copy answer-irrelevant words from the input. We believe that lacking global question semantics and exploiting answer position-awareness not well are the key root causes. In this paper, we propose a neural question generation model with two general modules: sentence-level semantic matching and answer position inferring. Further, we enhance the initial state of the decoder by leveraging the answer-aware gated fusion mechanism. Experimental results demonstrate that our model outperforms the state-of-the-art (SOTA) models on SQuAD and MARCO datasets. Owing to its generality, our work also improves the existing models significantly.
Revisiting Bilinear Pooling: A Coding Perspective

Bilinear pooling has achieved state-of-the-art performance on fusing features in various machine learning tasks, owning to its ability to capture complex associations between features. Despite the success, bilinear pooling suffers from redundancy and burstiness issues, mainly due to the rank-one property of the resulting representation. In this paper, we prove that bilinear pooling is indeed a similarity-based coding-pooling formulation. This establishment then enables us to devise a new feature fusion algorithm, the factorized bilinear coding (FBC) method, to overcome the drawbacks of the bilinear pooling. We show that FBC can generate compact and discriminative representations with substantially fewer parameters. Experiments on two challenging tasks, namely image classification and visual question answering, demonstrate that our method surpasses the bilinear pooling technique by a large margin.
Blockchain-based mechanism for fine-grained authorization in data crowdsourcing

Data crowdsourcing is a distributed data acquisition method to efficiently collect a sizeable amount of high-quality data from a large network of contributors who participate in data trading activities. However, traditional data crowdsourcing platforms are almost invariably based on a centralized architecture, which tends to give unfair advantages to the platform operator; besides, centralized platforms are obvious targets for cybersecurity attacks and become a single point of failure. Furthermore, a centralized approach with stringent security control also suffers from serious scalability issue. For example, if data owners manage a database with large amounts of valuable data, they have to retrieve data from their database in accordance with certain access policies and encrypt retrieved data for each requester; hence they become bottlenecks in the data trading process when the number of requesters is very large. To address the above issues, we propose a blockchain-based mechanism for fine-grained authorization in data crowdsourcing (BC-FGA-DCrowd). In the BC-FGA-DCrowd scheme, we use a public blockchain to implement cryptocurrencies and payment services as incentive schemes for data trading platform users. With this approach, data owners can employ Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to pre-process the complex encryption workload, and generate the attribute private key for data requester to achieve the fine-grained authorization. In this paper, we also prove that the BC-FGA-DCrowd scheme satisfies the correctness and fairness requirements of data trading, and can effectively withstand malicious activities of internal users and external DDos and Sybil attackers. The approach was tested on a private Ethereum network using Ganache with a local host.
A Context-Aware Click Model for Web Search

To better exploit the search logs, various click models have been proposed to extract implicit relevance feedback from user clicks. Most traditional click models are based on probability graphical models (PGMs) with manually designed dependencies. Recently, some researchers also adopt neural-based methods to improve the accuracy of click prediction. However, most of the existing click models only model user behavior in query level. As the previous iterations within the session may have an impact on the current search round, we can leverage these behavior signals to better model user behaviors. In this paper, we propose a novel neural- based Context-Aware Click Model (CACM) for Web search. CACM consists of a context-aware relevance estimator and an examination predictor. The relevance estimator utilizes session context infor- mation, i.e., the query sequence and clickthrough data, as well as the pre-trained embeddings learned from a session-flow graph to estimate the context-aware relevance of each search result. The examination predictor estimates the examination probability of each result. We further investigate several combination functions to integrate the context-aware relevance and examination probabil- ity into click prediction. Experiment results on a public Web search dataset show that CACM outperforms existing click models in both relevance estimation and click prediction tasks.
RefNet: A Reference-Aware Network for Background Based Conversation

Existing conversational systems tend to generate generic responses. Recently, Background Based Conversation (BBCs) have been introduced to address this issue. Here, the generated responses are grounded in some background information. The proposed methods for BBCs are able to generate more informative responses, however, they either cannot generate natural responses or have difficulties in locating the right background information. In this paper, we propose a Reference-aware Network (RefNet) to address both issues. Unlike existing methods that generate responses token by token, RefNet incorporates a novel reference decoder that provides an alternative way to learn to directly select a semantic unit (e.g., a span containing complete semantic information) from the background. Experimental results show that RefNet significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, indicating that RefNet can generate more appropriate and human-like responses.
Channel Pruning Guided by Classification Loss and Feature Importance

In this work, we propose a new layer-by-layer channel pruning method called Channel Pruning guided by classification Loss and feature Importance (CPLI). In contrast to the existing layer-by-layer channel pruning approaches that only consider how to reconstruct the features from the next layer, our approach additionally take the classification loss into account in the channel pruning process. We also observe that some reconstructed features will be removed at the next pruning stage. So it is unnecessary to reconstruct these features. To this end, we propose a new strategy to suppress the influence of unimportant features (i.e., the features will be removed at the next pruning stage). Our comprehensive experiments on three benchmark datasets, i.e., CIFAR-10, ImageNet, and UCF-101, demonstrate the effectiveness of our CPLI method.
Importance Gaussian Quadrature

Importance sampling (IS) and numerical integration methods are usually employed for approximating moments of complicated target distributions. In its basic procedure, the IS methodology randomly draws samples from a proposal distribution and weights them accordingly, accounting for the mismatch between the target and proposal. In this work, we present a general framework of numerical integration techniques inspired by the IS methodology. The framework can also be seen as an incorporation of deterministic rules into IS methods, reducing the error of the estimators by several orders of magnitude in several problems of interest. The proposed approach extends the range of applicability of the Gaussian quadrature rules. For instance, the IS perspective allows us to use Gauss-Hermite rules in problems where the integrand is not involving a Gaussian distribution, and even more, when the integrand can only be evaluated up to a normalizing constant, as it is usually the case in Bayesian inference. The novel perspective makes use of recent advances on the multiple IS (MIS) and adaptive (AIS) literatures, and incorporates it to a wider numerical integration framework that combines several numerical integration rules that can be iteratively adapted. We analyze the convergence of the algorithms and provide some representative examples showing the superiority of the proposed approach in terms of performance.
Exploiting Web Images for Multi-Output Classification: From Category to Subcategories

Studies present that dividing categories into subcategories contributes to better image classification. Existing image subcategorization works relying on expert knowledge and labeled images are both time-consuming and labor-intensive. In this article, we propose to select and subsequently classify images into categories and subcategories. Specifically, we first obtain a list of candidate subcategory labels from untagged corpora. Then, we purify these subcategory labels through calculating the relevance to the target category. To suppress the search error and noisy subcategory label-induced outlier images, we formulate outlier images removing and the optimal classification models learning as a unified problem to jointly learn multiple classifiers, where the classifier for a category is obtained by combining multiple subcategory classifiers. Compared with the existing subcategorization works, our approach eliminates the dependence on expert knowledge and labeled images. Extensive experiments on image categorization and subcategorization demonstrate the superiority of our proposed approach.
Complex Security Policy? A Longitudinal Analysis of Deployed Content Security Policies

The Content Security Policy (CSP) mechanism was developed as a mitigation against script injection attacks in 2010.In this paper, we leverage the unique vantage point of the Internet Archive to conduct a historical and longitudinal analysis of how CSP deployment has evolved for a set of 10,000 highly ranked domains.In doing so, we document the longterm struggle site operators face when trying to roll out CSP for content restriction and highlight that even seemingly secure whitelists can be bypassed through expired or typo domains.Next to these new insights, we also shed light on the usage of CSP for other use cases, in particular, TLS enforcement and framing control.Here, we find that CSP can be easily deployed to fit those security scenarios, but both lack wide-spread adoption.Specifically, while the underspecified and thus inconsistently implemented X-Frame-Options header is increasingly used on the Web, CSP's well-specified and secure alternative cannot keep up.To understand the reasons behind this, we run a notification campaign and subsequent survey, concluding that operators have often experienced the complexity of CSP (and given up), utterly unaware of the easy-to-deploy components of CSP.Hence, we find the complexity of secure, yet functional content restriction gives CSP a bad reputation, resulting in operators not leveraging its potential to secure a site against the non-original attack vectors.
A Novel Random Number Generator Based on Fractional Order Chaotic Chua System

One of the practical applications of chaotic systems is the design of a random number generator. In the literature, generally random number generators are designed using discrete time chaotic systems. The reason for the use of the discrete time chaotic systems in the design architecture is that the latter have a simpler structure than the continuous time chaotic systems. In order to observe chaos in continuous time systems, the system must have at least three degrees. It is shown that for fractional order chaotic systems chaos can be observed even in a lower system degree. The aim of this study is to develop a random number generator using a fractional order chaotic Chua system. The proposed generator is analysed using various randomness tests. The analysis results show that the proposed generator passes the random requirements successfully. On the one hand, this study is important because it demonstrates the practical application of fractional order chaotic systems. On the other hand, it provides an alternative to designs based on discrete time chaotic systems.
Robust reversible audio watermarking based on high-order difference statistics

This paper proposes a robust reversible audio watermarking scheme by introducing a high-order difference statistical model. In the proposed scheme, the high-order difference statistical model is constructed to compute a statistical histogram. The histogram can be regarded as a robust feature and shifted for embedding a watermark sequence. Firstly, an original audio file is divided into a number of frames, then a high-order difference statistic in a frame can be calculated to construct a histogram of the high-order difference statistics. With a hiding key, the watermark can be embedded into the audio file by shifting the histogram. In the process of watermark extraction, with the hiding key, the watermark can be correctly extracted from the shifted histogram. Furthermore, by using the inverse operation of histogram shifting, the original audio file can be restored non-destructively. In addition, the watermark can still be correctly extracted from the watermarked audio file that is attacked by some signal processing operations to some extent, such as MP3 compression of 48 Kbps and Gaussian noise of 25 dB. Compared with the existing several robust reversible audio watermarking schemes, the proposed scheme has better performance in audio quality and robustness. Experimental results have shown the effectiveness of the proposed scheme.
Uniqueness of the Phase Transition in Many-Dipole Cavity Quantum Electrodynamical Systems

The possibility of a superradiant phase transition in light-matter systems is the subject of much debate, due to numerous apparently conflicting no-go and counter no-go theorems. Using an arbitrary-gauge approach we show that a unique phase transition does occur in archetypal many-dipole cavity QED systems, and that it manifests unambiguously via a macroscopic gauge-invariant polarization. We find that the gauge choice controls the extent to which this polarization is included as part of the radiative quantum subsystem and thereby determines the degree to which the abnormal phase is classed as superradiant. This resolves the long-standing paradox of no-go and counter no-go theorems for superradiance, which are shown to refer to different definitions of radiation.
Phase-Space Inequalities Beyond Negativities

We derive a family of inequalities involving different phase-space distributions of a quantum state which have to be fulfilled by any classical state. The violation of these inequalities is a clear signature of nonclassicality. Our approach combines the characterization of nonclassical effects via negativities in phase-space distributions with inequality conditions usually being formulated for moments of physical observables. Importantly, the obtained criteria certify nonclassicality even when the involved phase-space distributions are non-negative. Moreover, we show how these inequalities are related to correlation measurements. The strength of the derived conditions is demonstrated by different examples, including squeezed states, lossy single-photon states, and even coherent states.
Deep Learning for Software Vulnerabilities Detection Using Code Metrics

Software vulnerability can cause disastrous consequences for information security.Earlier detection of vulnerabilities minimizes these consequences.Manual detection of vulnerable code is very difficult and very costly in terms of time and budget.Therefore, developers must use automatic vulnerabilities prediction (AVP) tools to minimize costs.Recent works on AVP begin to use techniques of deep learning (DL).All the proposed approaches are based on techniques of feature extraction inspired by previous applications of DL such as automatic language processing.Code metrics were widely used as features to build AVP models based on classic machine learning.This study bridges the gap between deep learning and machine learning features and discusses a deep-learning-based approach to finding vulnerabilities in code using code metrics.Obtained results show that code metrics are very good but not the better to use as features in DL-based AVP.
Image Encryption Based on Improved Lorenz System

This paper proposes a new method to strengthen the nonlinear kinetic complexity and ergodicity of Lorenz system. Through the analysis of auto-correlation, frequency distribution, approximate entropy and information entropy, the improved Lorenz system has better dynamical properties than Lorenz system. According to NIST-800-22 test results, the chaotic sequences generated from proposed system have passed all random tests, which denotes that the improved Lorenz system is applicable to chaotic encryption. Once the plaintext image is color, the size of scramble image is three times as big as plaintext image, so that a lot more pixel information participant in permutation and diffusion to get better encryption results. Simulation results show that the image encryption scheme provides good security and high capacity to resist common attacks.