Reviewerly/exHarmony huggingface dataset:
Hybrid and blind watermarking scheme in DCuT – RDWT domain

In this paper, a hybrid and blind watermarking scheme is proposed for the protection of copyright of digital images. The scheme based on hybridization of two advance transforms such that discrete curvelet transform (DCuT) and redundant discrete wavelet transform (RDWT). The motivation behind using these two transforms combination to improve the imperceptibility of the watermarking scheme. The imperceptibility requirement of the scheme is achieved using hybrid coefficients which are achieved by applying single level RDWT to the high frequency curvelet coefficients of the cover image. The watermark information is inserted by modifying the coefficients of the wavelet coefficients of LH subband using PN sequences according to watermark bit and gain factor. The security of the proposed scheme is achieved by applying Arnold scrambling to watermark image before embedding. Experiments of the proposed scheme are conducted on various types of natural images. Experiments results show that, compared with existing schemes, the proposed scheme is robust to various attacks while having high imperceptibility. Also, the proposed scheme is performed better than many existing schemes.
Predicting consumer gaze hits: A simulation model of visual attention to dynamic marketing stimuli

The purpose of the present study is to build and test a simulation model for the prediction of gaze hits in the context of dynamic marketing stimuli. Forecasting the attentional effect of dynamic stimuli is of particular interest when it comes to indirect forms of marketing communication such as sponsorship, product placement, or in-game-advertising. Based on large-scale eye tracking data an artificial neural network was trained, providing high predictive accuracy. The model's business applicability is demonstrated with the case of a soccer sponsorship, using media data and color features as model input. The study highlights the value of eye tracking data for the ex-ante valuation of visual communication stimuli which benefits marketing management at the initiation, implementation, and evaluation stages.
A Modular Treatment of Blind Signatures from Identification Schemes

We propose a modular security treatment of blind signatures derived from linear identification schemes in the random oracle model. To this end, we present a general framework that captures several well known schemes from the literature and allows to prove their security. Our modular security reduction introduces a new security notion for identification schemes called One-More-Man In the Middle Security which we show equivalent to the classical One-More-Unforgeability notion for blind signatures. We also propose a generalized version of the Forking Lemma due to Bellare and Neven (CCS 2006) and show how it can be used to greatly improve the understandability of the classical security proofs for blind signatures schemes by Pointcheval and Stern (Journal of Cryptology 2000).
CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study

Prostate cancerRundo, Leonardo is the most common cancer among US men. However, prostateHan, Changhee imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. AlongZhang, Jin with whole prostate gland segmentation, distinguishingHataya, Ryuichiro between the centralNagano, Yudai gland (CG) and peripheralMilitello, Carmelo zone (PZ) can guideFerretti, Claudio toward differential diagnosis, sinceNobile, Marco S. the frequency andTangherloni, Andrea severity of tumors differGilardi, Maria Carla in these regions; however, theirVitabile, Salvatore boundary is often weakNakayama, Hideki and fuzzy. This work presentsMauri, Giancarlo a preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability of convolutional neural networks (CNNs) on two multi-centric MRI prostate datasets. Especially, we compared three CNN-based architectures: SegNet, U-Net, and pix2pix. In such a context, the segmentation performances achieved with/without pre-training were compared in 4-fold cross-validation. In general, U-Net outperforms the other methods, especially when training and testing are performed on multiple datasets.
Consensus Graph Learning for Incomplete Multi-view Clustering

Multi-view data clustering is a fundamental task in current machine learning, known as multi-view clustering. Existing multi-view clustering methods mostly assume that each data instance is sampled in all views. However, in real-world applications, it is common that certain views miss number of data instances, resulting in incomplete multi-view data. This paper concerns the task of clustering of incomplete multi-view data. We propose a novel Graph-based Incomplete Multi-view Clustering (GIMC) to perform this task. GIMC can effectively construct a complete graph for each view with the help of other view(s), and automatically weight each constructed graph to learn a consensus graph, which gives the final clusters. An alternating iterative optimization algorithm is proposed to optimize the objective function. Experimental results on real-world datasets show that the proposed method outperforms state-of-the-art baseline methods markedly.
An effective density-based clustering and dynamic maintenance framework for evolving medical data streams

Medical data stream clustering has become an integral part of medical decision systems since it extracts highly-sensitive information from a tremendous flow of medical data. However, clustering and maintaining of medical data streams is still a challenging task. That is because the evolving of medical data streams imposes various challenges for clustering such as the ability to discover the arbitrary shape of a cluster, the ability to group data streams without a predefined number of clusters, and the ability to maintain the data clusters dynamically.To support the online medical decisions, there is a need to address the clustering challenges. Therefore, in this paper, we propose an effective density-based clustering and dynamic maintenance framework for grouping the patients with similar symptoms into meaningful clusters and monitoring the patients' status frequently.For clustering, we generate a set of initial medical data clusters based on the combination of Piece-wise Aggregate Approximation and the density-based spatial clustering of applications with noise called (PAA+DBSCAN) algorithm. For maintenance, when new medical data streams arrive, we maintain the initially generated medical data clusters dynamically. Since the incremental cluster maintenance is time-consuming, we further propose an Advanced Cluster Maintenance (ACM) approach to improve the performance of the dynamic cluster maintenance.The experimental results on real-world medical datasets demonstrate the effectiveness and efficiency of our proposed approaches. The PAA+DBSCAN algorithm is more efficient and effective than the exact DBSCAN algorithm. Moreover, the ACM approach requires less running time in comparison with the Baseline Cluster Maintenance (BCM) approach using different tuning parameter values in all datasets. That is because the BCM approach tracks all the data points in the cluster.The proposed framework is capable of clustering and maintaining the medical data streams effectively by means of grouping the patients who share similar symptoms and tracking the patients status that naturally tends to be changing over time.
An extensive evaluation of ensemble techniques for software change prediction

Abstract Predicting the areas of the source code having a higher likelihood to change in the future represents an important activity to allow developers to plan preventive maintenance operations. For this reason, several change prediction models have been proposed. Moreover, research community demonstrated how different classifiers impact on the performance of devised models as well as classifiers tend to perform similarly even though they are able to correctly predict the change proneness of different code elements, possibly indicating the presence of some complementarity among them. In this paper, we deeper investigated whether the use of ensemble approaches, ie, machine learning techniques able to combine multiple classifiers, can improve the performances of change prediction models. Specifically, we built three change prediction models based on different predictors, ie, product‐, process‐ metrics‐, and developer‐related factors, comparing the performances of four ensemble techniques (ie, Boosting, Random Forest, Bagging, and Voting) with those of standard machine learning classifiers (ie, Logistic Regression, Naive Bayes, Simple Logistic, and Multilayer Perceptron). The study was conducted on 33 releases of 10 open‐source systems, and the results showed how ensemble methods and in particular Random Forest provide a significant improvement of more than 10% in terms of F measure. Indeed, the statistical analyses conducted confirm the superiority of this ensemble technique. Moreover, the model built using developer‐related factors performed better than the other models that exploit product and process metrics and achieves an overall median of F measure around 77%.
A Pseudo-Spectrum Approach for Weak Target Detection and Tracking

Conventional velocity-filtering-based track-before-detect (VF-TBD) methods integrate the energy of a cell in a frame with that of the cell closest to the predicted target position in the last frame of the processing batch, assuming a certain target velocity. However, the target may not exactly be on the quantized cell and its echo envelope may occupy multiple adjacent cells. This often leads to significant energy loss and echo envelope degradation. In this paper, a novel VF-TBD method based on pseudo-spectrum (PS-VF-TBD) is presented to address this problem. For every cell, a pseudo-spectrum is constructed around the predicted position according to the assumed velocity using a truncated point spread function. Samples of the pseudo-spectrum on the cells that are located in the truncated spread area are added onto the last frame of the processing batch to integrate the target energy within multiple cells. Due to the use of the point spread model and the accurate sampling of the predicted spectrum, energy loss can be mitigated and the echo envelope is well maintained. This approach simultaneously maximizes the signal-to-noise ratio (SNR) gain and enables improved parameter estimation utilizing the envelope characteristics. The procedure for pseudo-spectrum construction and multiframe accumulation is derived in detail and the output SNR is analyzed theoretically. It is found that the proposed PS-VF-TBD can achieve an SNR gain greater than that by the conventional VF-TBD method. To deal with a target with unknown velocity, a bank of pseudo-spectrum-based velocity filters is proposed. The signal gain loss resulting from velocity mismatch is investigated and the μ-width of the envelope in the velocity domain is analyzed. Finally, a method for improved position and velocity estimation is presented. Simulation results demonstrate the superiority of the proposed method in terms of SNR gain, detection probability, and estimation accuracy at the expense of increased computational complexity.
Deep Decision Tree Transfer Boosting

Instance transfer approaches consider source and target data together during the training process, and borrow examples from the source domain to augment the training data, when there is limited or no label in the target domain. Among them, boosting-based transfer learning methods (e.g., TrAdaBoost) are most widely used. When dealing with more complex data, we may consider the more complex hypotheses (e.g., a decision tree with deeper layers). However, with the fixed and high complexity of the hypotheses, TrAdaBoost and its variants may face the overfitting problems. Even worse, in the transfer learning scenario, a decision tree with deep layers may overfit different distribution data in the source domain. In this paper, we propose a new instance transfer learning method, i.e., Deep Decision Tree Transfer Boosting (DTrBoost), whose weights are learned and assigned to base learners by minimizing the data-dependent learning bounds across both source and target domains in terms of the Rademacher complexities. This guarantees that we can learn decision trees with deep layers without overfitting. The theorem proof and experimental results indicate the effectiveness of our proposed method.
Huffman-code based retrieval for encrypted JPEG images

This paper proposes a retrieval scheme for encrypted JPEG images, based on Huffman code in the JPEG bitstream. Three parties are involved: content owner, cloud server, and authorized user. First, the content owner produces encrypted images by jointly using a stream cipher, permutation cipher, then uploads them to a cloud server. Especially, the conversion between encrypted quantization tables is still valid. With the same secret key, the authorized user submits the encrypted query image to the server. Second, the server extracts Huffman-code histogram from the encrypted image as a feature. Although the Huffman-code histogram is changed during encryption, encrypted images with similar content to the query image are returned to the user after feature comparison. Finally, through decryption and hash verification, the user can obtain authenticated plaintext images. Experimental results show that the proposed scheme ensures confidentiality, integrity and format compatibility, while image retrieval of different quality factors is still effective.
Ensemble clustering based on evidence extracted from the co-association matrix

The evidence accumulation model is an approach for collecting the information of base partitions in a clustering ensemble method, and can be viewed as a kernel transformation from the original data space to a co-association matrix. However, cluster structure information may be partially lost in this transformation; hence, some methods proposed in the literature try to find the lost information and return it to the ensemble process. In this paper, an interesting phenomenon is introduced: remove some evidences from the co-association matrix, which can result in more accurate clustering results. The intuitive explanation for this is that some evidences in the original co-association matrix could be noise, with negative effects on the final clustering. However, it is difficult to detect those evidences practically, let alone remove them from the matrix. To remedy this problem, we remove multiple level evidences having low occurrence frequencies, because negative evidences do not normally occur regularly in the base partitions. Subsequently, we use normalized cut to achieve multiple clustering results. To discriminate the optimal ensemble result, an internal validity index, which uses only the co-association matrix, is specially designed for the clustering ensemble. The experimental results on 16 datasets demonstrate that the proposed scheme outperforms some state-of-the-art clustering ensemble approaches.
Fog-based Secure Communications for Low-power IoT Devices

Designing secure, scalable, and resilient IoT networks is a challenging task because of resource-constrained devices and no guarantees of reliable network connectivity. Fog computing improves the resiliency of IoT, but its security model assumes that fog nodes are fully trusted. We relax this latter constraint by proposing a solution that guarantees confidentiality of messages exchanged through semi-honest fog nodes thanks to a lightweight proxy re-encryption scheme. We demonstrate the feasibility of the solution by applying it to IoT networks of low-power devices through experiments on microcontrollers and ARM-based architectures.
Computational Cognitive Assessment: Investigating the Use of an Intelligent Virtual Agent for the Detection of Early Signs of Dementia

The ageing population has caused a marked increased in the number of people with cognitive decline linked with dementia. Thus, current diagnostic services are overstretched, and there is an urgent need for automating parts of the assessment process. In previous work, we demonstrated how a stratification tool built around an Intelligent Virtual Agent (IVA) eliciting a conversation by asking memory-probing questions, was able to accurately distinguish between people with a neuro-degenerative disorder (ND) and a functional memory disorder (FMD). In this paper, we extend the number of diagnostic classes to include healthy elderly controls (HCs) as well as people with mild cognitive impairment (MCI). We also investigate whether the IVA may be used for administering more standard cognitive tests, like the verbal fluency tests. A four-way classifier trained on an extended feature set achieved 48% accuracy, which improved to 62% by using just the 22 most significant features (ROC-AUC: 82%).
Learning Voice Source Related Information for Depression Detection

During depression neurophysiological changes can occur, which may affect laryngeal control i.e. behaviour of the vocal folds. Characterising these changes in a precise manner from speech signals is a non trivial task, as this typically involves reliable separation of the voice source information from them. In this paper, by exploiting the abilities of CNNs to learn task-relevant information from the input raw signals, we investigate several methods to model voice source related information for depression detection. Specifically, we investigate modelling of low pass filtered speech signals, linear prediction residual signals, homomorphically filtered voice source signals and zero frequency filtered signals to learn voice source related information for depression detection. Our investigations show that subsegmental level modelling of linear prediction residual signals or zero frequency filtered signals leads to systems better than the state-of-the-art low level descriptor based systems and deep learning based systems modelling the vocal tract system information.
Detection of fake online reviews using semi-supervised and supervised learning

Online reviews have great impact on today's business and commerce. Decision making for purchase of online products mostly depends on reviews given by the users. Hence, opportunistic individuals or groups try to manipulate product reviews for their own interests. This paper introduces some semi-supervised and supervised text mining models to detect fake online reviews as well as compares the efficiency of both techniques on dataset containing hotel reviews.
Deep Recurrent Neural Networks with Layer-wise Multi-head Attentions for Punctuation Restoration

Punctuation restoration is a post-processing task of automatic speech recognition to generate the punctuation marks on un-punctuated transcripts. This paper proposes a deep recurrent neural network architecture with layer-wise multi-head attentions towards better modelling of the contexts from a variety of perspectives in putting punctuations by human writers. The experimental results show that our proposed model significantly outperforms previous state-of-the-art methods in punctuation restoration performances on IWSLT dataset.
The 2016 and 2017 QBF solvers evaluations (QBFEVAL'16 and QBFEVAL'17)

After a break of about five years, in 2016 the classical QBFEVAL has been revived. QBFEVAL is a competitive evaluation of solvers for quantified Boolean formulas (QBF), the extension of propositional formulas with existential and universal quantifiers over the propositional variables. Due to the enormous interest in QBFEVAL'16, more recently, QBFEVAL'17 was organized. Both competitions were affiliated to the respective editions of the International Conference on Theory and Applications of Satisfiability Testing (SAT'16 and SAT'17), the major conference in research on SAT and related areas. In this paper we report about the 2016 and 2017 competitive evaluations of QBF solvers (QBFEVAL'16 and QBFEVAL'17), the two most recent events in a series of competitions established with the aim of assessing the advancements in reasoning about QBFs. This report gives an overview of the setup of these two events, on their participants and on the results of the experiments that were performed for evaluating the participating systems.
Toward secure and accountable data transmission in Narrow Band Internet of Things based on blockchain

The developments of Narrow Band Internet of Things have attracted much attention from both academia and industry nowadays. While Narrow Band Internet of Things is gradually designed for commercial applications, it also confronts with several security concerns such as identification and privacy. Blockchain technique is equipped with authentication, auditing, and accountability, and thus it can serve as a promising tool for providing secure data communication on the network. To enhance the security level and achieve reliable data authentication and accountability for Narrow Band Internet of Things system, in this article, we combine the merits of blockchain technique and propose a blockchain-based architecture for Narrow Band Internet of Things. The presented architecture consists of three layers which are sensing layer, transport layer, and transaction layer. We describe the interaction processes between each layer in detail and construct the algorithms which are used to conduct data authentication and blockchain generation. By evaluation, our scheme is demonstrated to obtain favorable performances in terms of privacy, authentication, accountability, and efficiency, which makes it an appropriate method to be applied for secure data communication in Narrow Band Internet of Things systems.
Large Context End-to-end Automatic Speech Recognition via Extension of Hierarchical Recurrent Encoder-decoder Models

This paper describes a novel end-to-end automatic speech recognition (ASR) method that takes into consideration long-range sequential context information beyond utterance boundaries. In spontaneous ASR tasks such as those for discourses and conversations, the input speech often comprises a series of utterances. Accordingly, the relationships between the utterances should be leveraged for transcribing the individual utterances. While most previous end-to-end ASR methods only focus on utterance-level ASR that handles single utterances independently, the proposed method (which we call "large-context end-to-end ASR") can explicitly utilize relationships between a current target utterance and all preceding utterances. The method is modeled by combining an attention-based encoder-decoder model, which is one of the most representative end-to-end ASR models, with hierarchical recurrent encoder-decoder models, which are effective language models for capturing long-range sequential contexts beyond the utterance boundaries. Experiments on Japanese discourse speech tasks demonstrate the proposed method yields significant ASR performance improvements compared with the conventional utterance-level end-to-end ASR system.