Reviewerly/exHarmony huggingface dataset:
From Fingerprint to Footprint

With increasing diversity of user interest and preference, personalized location recommendation is essential and beneficial to our daily life. To achieve this, the most critical challenge is the cold-start recommendation problem, for we cannot learn preference from cold-start users without any historical records. In this paper, we demonstrate that it is feasible to make personalized location recommendation by learning user interest and location features from app usage data. By proposing a novel generative model to transfer user interests from app usage behavior to location preference, we achieve personalized location recommendation via learning the interest's correlation between locations and apps. Based on two real-world datasets, we evaluate our method's performance with a variety of scenarios and parameters. The results demonstrate that our method outperforms the state-of-the-art solutions in solving cold-start problem, i.e., when there are 60% cold-start users, we can still achieve a 77.0% hitrate in recommending the top five locations, which is at least 9.6% higher than the baselines. Our study is the first step forward for transferring user interests learning from online fingerprints to offline footprints, which paves the way for better personalized location recommendation services.
Secure Multiparty Computation and Trusted Hardware: Examining Adoption Challenges and Opportunities

When two or more parties need to compute a common result while safeguarding their sensitive inputs, they use secure multiparty computation (SMC) techniques such as garbled circuits. The traditional enabler of SMC is cryptography, but the significant number of cryptographic operations required results in these techniques being impractical for most real-time, online computations. Trusted execution environments (TEEs) provide hardware-enforced isolation of code and data in use, making them promising candidates for making SMC more tractable. This paper revisits the history of improvements to SMC over the years and considers the possibility of coupling trusted hardware with SMC. This paper also addresses three open challenges: (1) defeating malicious adversaries, (2) mobile-friendly TEE-supported SMC, and (3) a more general coupling of trusted hardware and privacy-preserving computation.
Automatic Recognition of Kazakh Speech Using Deep Neural Networks

This article presents a deep neural network (DNN) system based on automatic speech recognition for Kazakh language, developed using the Kaldi speech recognition tool. DNNs are initialized using the restricted Boltzmann machines (RBM) and are trained using cross-entropy as the objective function and the standard back propagation of error. In order to achieve optimal results, the training has been modified based on peculiarities of Kazakh language. A 76 hours-corpus has been used in training. Results are compared for two different sets of values between classical models and various DNN settings.
Joint Optimization of Data-Center Selection and Video-Streaming Distribution for Crowdsourced Live Streaming in a Geo-Distributed Cloud Platform

Empowered by today's rich media generating devices and convenient Internet access, crowdsourced live streaming (CSLS) service has developed rapidly and become one of the most popular Internet services. Large crowdsourced live streaming providers (CSLSPs) are migrating their services to geo-distributed cloud platforms (GDCPs) for lower costs and higher availability. A CSLSP may rent compute and network resources from cloud providers for video transcoding, video delivering, user-requests handling, and other related tasks. However, due to dynamic requests by viewers and widely spread locations of broadcasters and viewers, it is still challenging for a CSLSP to serve demands of users with reasonable resources from the cloud-based geo-distributed data centers. To overcome this challenge cost-effectively, we propose an online algorithm to save operational costs for CSLSPs by jointly and dynamically choosing right data centers for broadcasters and viewers. Mathematical analysis is presented and proves that our proposed online algorithm can ensure operational costs to be within an upper bound above the optimal solution, while guaranteeing the QoE for viewers. We conduct extensive trace-driven illustrative studies and show that the proposed method can achieve suboptimal results and outperforms other alternative methods.
Postcards from the Post-HTTP World: Amplification of HTTPS Vulnerabilities in the Web Ecosystem

HTTPS aims at securing communication over the Web by providing a cryptographic protection layer that ensures the confidentiality and integrity of communication and enables client/server authentication.However, HTTPS is based on the SSL/TLS protocol suites that have been shown to be vulnerable to various attacks in the years.This has required fixes and mitigations both in the servers and in the browsers, producing a complicated mixture of protocol versions and implementations in the wild, which makes it unclear which attacks are still effective on the modern Web and what is their import on web application security.In this paper, we present the first systematic quantitative evaluation of web application insecurity due to cryptographic vulnerabilities.We specify attack conditions against TLS using attack trees and we crawl the Alexa Top 10k to assess the import of these issues on page integrity, authentication credentials and web tracking.Our results show that the security of a consistent number of websites is severely harmed by cryptographic weaknesses that, in many cases, are due to external or related-domain hosts.This empirically, yet systematically demonstrates how a relatively limited number of exploitable HTTPS vulnerabilities are amplified by the complexity of the web ecosystem.
Binary tree-like network with two-path Fusion Attention Feature for cervical cell nucleus segmentation

Early detection of cervical lesion is of great significance in reducing mortality from cervical cancer, and segmentation of cervical cell nuclei plays an important role in screening for cervical lesion. Compared with traditional algorithms, several deep learning methods can improve the segmentation; however, due to blurred boundaries and complex gradients of medical images, the segmentation remains unsatisfactory. In addition, the existing datasets used by cervical cell nucleus segmentation research are lacking in terms of both quantity and diversity, so methods based on those datasets cannot effectively cope with challenging cases. This paper releases a new cervical cell dataset and proposes a network named Binary Tree-like Network with Two-path Fusion Attention Feature (BTTFA). The simplified diagram of BTTFA is similar to a binary tree structure and combines ResNeXt's last four layers of information by integrating adjacent pairs of layers each time; therefore, the information of multilayers can be fully integrated, and the information lost by the pooling layers can be recovered. BTTFA also applies a two-path fusion attention to selectively utilize information close to the root nodes, thereby taking full advantage of low-level detail and high-level semantic information and selectively emphasizing important features while suppressing less useful ones. Meanwhile, at some nodes of the binary tree-like network, focal loss is imposed to calculate the loss between the ground truth and the feature map during the training process. Experiments demonstrate that BTTFA performs better than the existing technology on our dataset and another public dataset.
Encrypted LQG using labeled homomorphic encryption

We consider the problem of implementing a Linear Quadratic Gaussian (LQG) controller on a distributed system, while maintaining the privacy of the measurements, state estimates, control inputs and system model. The component sub-systems and actuator outsource the LQG computation to a cloud controller and encrypt their signals and matrices. The encryption scheme used is Labeled Homomorphic Encryption, which supports the evaluation of degree-2 polynomials on encrypted data, by attaching a unique label to each piece of data and using the fact that the outsourced computation is known by the actuator. We write the state estimate update and control computation as multivariate polynomials in the encrypted data and propose an extension to the Labeled Homomorphic Encryption scheme that achieves the evaluation of low-degree polynomials on encrypted data, with degree larger than two. We showcase the numerical results of the proposed protocol for a temperature control application that indicates competitive online times.
Breast Cancer Prognostic Factors in the Digital Era: Comparison of Nottingham Grade using Whole Slide Images and Glass Slides

To assess reproducibility and accuracy of overall Nottingham grade and component scores using digital whole slide images (WSIs) compared to glass slides.Two hundred and eight pathologists were randomized to independently interpret 1 of 4 breast biopsy sets using either glass slides or digital WSI. Each set included 5 or 6 invasive carcinomas (22 total invasive cases). Participants interpreted the same biopsy set approximately 9 months later following a second randomization to WSI or glass slides. Nottingham grade, including component scores, was assessed on each interpretation, providing 2045 independent interpretations of grade. Overall grade and component scores were compared between pathologists (interobserver agreement) and for interpretations by the same pathologist (intraobserver agreement). Grade assessments were compared when the format (WSI vs. glass slides) changed or was the same for the two interpretations.Nottingham grade intraobserver agreement was highest using glass slides for both interpretations (73%, 95% confidence interval [CI]: 68%, 78%) and slightly lower but not statistically different using digital WSI for both interpretations (68%, 95% CI: 61%, 75%; P= 0.22). The agreement was lowest when the format changed between interpretations (63%, 95% CI: 59%, 68%). Interobserver agreement was significantly higher (P < 0.001) using glass slides versus digital WSI (68%, 95% CI: 66%, 70% versus 60%, 95% CI: 57%, 62%, respectively). Nuclear pleomorphism scores had the lowest inter- and intra-observer agreement. Mitotic scores were higher on glass slides in inter- and intra-observer comparisons.Pathologists' intraobserver agreement (reproducibility) is similar for Nottingham grade using glass slides or WSI. However, slightly lower agreement between pathologists suggests that verification of grade using digital WSI may be more challenging.
Cross-Lingual Transfer of Semantic Roles: From Raw Text to Semantic Roles

We describe a transfer method based on annotation projection to develop a dependency-based semantic role labeling system for languages for which no supervised linguistic information other than parallel data is available.Unlike previous work that presumes the availability of supervised features such as lemmas, part-of-speech tags, and dependency parse trees, we only make use of word and character features.Our deep model considers using character-based representations as well as unsupervised stem embeddings to alleviate the need for supervised features.Our experiments outperform a state-of-the-art method that uses supervised lexico-syntactic features on 6 out of 7 languages in the Universal Proposition Bank.
An evolutionary approach to build ensembles of multi-label classifiers

In recent years, the multi-label classification task has gained the attention of the scientific community given its ability to solve problems where each of the instances of the dataset may be associated with several class labels at the same time instead of just one. The main problems to deal with in multi-label classification are the imbalance, the relationships among the labels, and the high complexity of the output space. A large number of methods for multi-label classification has been proposed, but although they aimed to deal with one or many of these problems, most of them did not take into account these characteristics of the data in their building phase. In this paper we present an evolutionary algorithm for automatic generation of ensembles of multi-label classifiers by tackling the three previously mentioned problems, called Evolutionary Multi-label Ensemble (EME). Each multi-label classifier is focused on a small subset of the labels, still considering the relationships among them but avoiding the high complexity of the output space. Further, the algorithm automatically designs the ensemble evaluating both its predictive performance and the number of times that each label appears in the ensemble, so that in imbalanced datasets infrequent labels are not ignored. For this purpose, we also proposed a novel mutation operator that considers the relationship among labels, looking for individuals where the labels are more related. EME was compared to other state-of-the-art algorithms for multi-label classification over a set of fourteen multi-label datasets and using five evaluation measures. The experimental study was carried out in two parts, first comparing EME to classic multi-label classification methods, and second comparing EME to other ensemble-based methods in multi-label classification. EME performed significantly better than the rest of classic methods in three out of five evaluation measures. On the other hand, EME performed the best in one measure in the second experiment and it was the only one that did not perform significantly worse than the control algorithm in any measure. These results showed that EME achieved a better and more consistent performance than the rest of the state-of-the-art methods in MLC.
Secure computation protocols under asymmetric scenarios in enterprise information system

It has become acentral question to guarantee the security of sensitive data in enterprise information systems. Actually, different enterprises may grasp asymmetric information due to their scales. How to guarantee the computation security under such scenario is acrucial problem for enterprise information system. In this paper, we propose asecure computation protocol under asymmetric information scenario in the presence of rational parties. We also work towards the achievement of security properties in enterprise information systems. Our new definition of utilities is proved to be coincident (and compatible) with the former definitions under asymmetric information scenario.
A secure image tampering detection and self-recovery scheme using POB number system over cloud

Cloud can be a promising platform to tackle the dramatic increasing demands on fast multimedia content computation and storage. However, the confidentiality of multimedia may be threatened by the semi-trusted cloud. One feasible solution is to encrypt the content to unreadable form before distributing it to the cloud based servers. In this work, we propose an encryption scheme based on a Permutation Ordered Binary (POB) number system, which distributes an image into multiple totally random secret shares. Further, prior to distribution, a SVD based detection watermark is embedded along with a recovery watermark. Thus, in addition to the capability of localizing the tampered regions by the authentic entity in the case of attacking, the main advantage of the proposed scheme is capable of self-recovering the tampered areas by the authentic entity possessing the secret keys. From a perspective of practicality, the detection rate and the quality of the recovered image can be improved by merging the neighboring tampered blocks. Extensive experimental results verify the efficacy of the proposed scheme in terms of the accuracy of tampering detection and the quality of the recovered image against various kinds of possible attacks.
Prewarping Siamese Network: Learning Local Representations for Online Signature Verification

We propose a neural network-based framework for learning local representations of multivariate time series, and demonstrate its effectiveness for online signature verification. In contrast to related works that optimize a global distance objective, we incorporate a Siamese network into dynamic time warping (DTW), leading to a novel prewarping Siamese network (PSN) optimized with a local embedding loss. PSN learns a feature space that preserves the temporal location-wise distances of local structures. Local embedding, along with the alignment conditions of DTW, imposes a temporal consistency constraint on the sequence-level distance measure while achieving invariance as regards non-linear distortions. Validation on online signature verification datasets demonstrates the advantage of our framework over existing techniques that use either handcrafted or learned feature representations.
Pathological Speech Intelligibility Assessment Based on the Short-time Objective Intelligibility Measure

Impaired speech intelligibility in motor speech disorders arising due to neurological diseases negatively affects the communication ability and quality of life of patients. Reliable and cost-effective measures to automatically assess speech intelligibility are necessary for the management of such disorders. In this paper, we propose to automatically assess the intelligibility of pathological speech based on short-time objective intelligibility measures typically used in speech enhancement, which however require a reference signal that is time-aligned to the test signal. We propose a method to create an utterance-dependent reference signal of intelligible speech from multiple healthy speakers. In order to assess intelligibility, the pathological speech signal is aligned to the created reference signal using dynamic time warping and the divergence between the two signals is quantified using either the short-time or the spectral correlation. Experiments on databases of English and French patients suffering from Cerebral Palsy and Amyotrophic Lateral Sclerosis show that the proposed intelligibility measures can obtain a high correlation with subjective intelligibility ratings, outperforming several state-of-the-art pathological speech intelligibility measures.
Continual Learning for Anomaly Detection with Variational Autoencoder

Detecting anomalies using a variational autoencoder (VAE) suffers from catastrophic forgetting when trained on a continually growing set of normal data where only the most recently added data is available. Solving this problem would allow the use of the VAE for anomaly detection in settings where it is difficult or even impossible to retain all normal data at the same time. We propose an efficient extension of a method for continual learning which alleviates catastrophic forgetting for anomaly detection using a VAE. We show on some anomaly detection problems that the definition of normal data can be continually expanded without requiring all previously seen data.
Advanced SMT techniques for weighted model integration

Weighted model integration (WMI) is a recent formalism generalizing weighted model counting (WMC) to run probabilistic inference over hybrid domains, characterized by both discrete and continuous variables and relationships between them. WMI is computationally very demanding as it requires to explicitly enumerate all possible truth assignments to be integrated over. Component caching strategies which proved extremely effective for WMC are difficult to apply in this formalism because of the tight coupling induced by the arithmetic constraints. In this paper we present a novel formulation of WMI, which allows to exploit the power of SMT-based predicate abstraction techniques in designing efficient inference procedures. A novel algorithm combines a strong reduction in the number of models to be integrated over with their efficient enumeration. Experimental results on synthetic and real-world data show drastic computational improvements over the original WMI formulation as well as existing alternatives for hybrid inference.
Defining and guaranteeing dynamic service levels in clouds

In this paper, we introduce SLAC, a SLA definition language specifically devised for clouds as a formalism to support the whole SLA lifecycle. The main novelty of the language is the possibility of capturing within the SLA the dynamic aspects of the environment by defining the conditions and actions to change service levels at runtime. SLAC permits to make the most of cloud elasticity, reduces the need for renegotiation and provides guarantees for dynamic scenarios. The language has formal syntax and semantics, and it comes with effective software tools supporting the whole SLA management lifecycle. The impact of our language and of its software tools is assessed by considering a series of experiments that provide empirical evidences of the advantages of SLAC.
Design and Implementation of a Facial Character Analysis Algorithm for Humanoid Robots

Summary Humanoid robots (HR) equipped with a sophisticated facial character analysis (FCA) algorithm can able to initiate crucial improvements in human–robot interactions. This paper, for the first time in the literature, proposes a three-stage FCA algorithm for the HR. At the initial stage of this algorithm, the HR detects the face with the Viola–Jones algorithm, and then important facial distance measurements are obtained with the geometric-based facial distance measurement technique. Finally, the measured facial distances are evaluated with the physiognomy science to reveal the characteristic properties of a person. Even though the proposed algorithm can be implemented to all HR, in this paper, it has been specifically applied to NAO HR. The reliability of the developed FCA algorithm is verified by analyzing each terminal decision about the character and its connection with the measured facial distances in the anatomy science.
A Conceptual Replication on Predicting the Severity of Software Vulnerabilities

Software vulnerabilities may lead to crucial security risks in software systems. Thus, prioritization of the vulnerabilities is an important task for security teams, and assessing how severe the vulnerabilities are would help teams during fixing and maintenance activities. We replicated a prior work which aims to predict the severity of software vulnerabilities by grouping vulnerabilities into different severity levels. We follow their approach on feature extraction using word embeddings, and on prediction model using Convolutional Neural Networks (CNNs). In addition, Long Short Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) models are used. We also extend the replicated work by aiming to predict severity scores rather than levels. We carried out two experiments for predicting severity levels and severity scores of 82,974 vulnerabilities. On predicting the severity levels, our LSTM and CNN models perform similarly with an F1 score of 0.756 F1 score and 0.752, respectively. On predicting the severity scores, LSTM, CNN and XGBoost models perform 16.14%, 17.03%, 18.91% MAPE values, respectively.
Connected and fearful? Exploring fear of online financial crime, Internet behaviour and their relationship

Although it is generally acknowledged that the development of the Internet created new criminal opportunities, the ways this is experienced by the general population has received limited attention. The current study seeks to explore the degrees to which people report fear of online crime, its correlates, and how online fear is related to protective and avoidance behaviour. We focus on online financial crimes. Results based on a large, representative, survey indicate an intermediate level of fear of online crime among the general Dutch population. Various sociodemographic characteristics and victimization experiences are shown to predict fear of online financial crime. We also find online fear to be a strong predictor of avoidance behaviour, given negative relationships with online purchasing and banking, thereby impeding individuals’ perceived online freedom and opportunities. Finally, the results found no indications that fear may stimulate protective behaviour of one’s computer.