Reviewerly/exHarmony huggingface dataset:
Privacy Enhanced Location Sharing for Mobile Online Social Networks

As a primitive function of location-based services (LBSs), the location sharing aims to provide a user's current location information to other designated users. In recent years, LBSs have become one of the most popular services provided by mobile online social networks (mOSNs). As LBSs actively exploit the users' identity and current location information, appropriate approaches have to be utilized to protect the location privacy of the users. Several recent reports have discussed the significance of friendship privacy protection with the goal of hiding the friendship relation of users from unintended entities. However, to the best of our knowledge, there hasn't been an approach for protecting the location sharing with complete privacy of location and friendship connections. To address this issue, we propose a new cryptographic primitive, functional pseudonym, for location sharing in mOSNs that ensures both of them. Unlike many of the existing solutions, our approach does not require a fully trusted server and does not assume pre-established secrets among friends, and therefore is highly practical. Also, the proposed approach significantly reduces computational overhead of users by delegating part of the computations for location sharing to a server, therefore it is highly sustainable. Our primitive can be widely used in many mOSNs to enable LBSs with improved privacy and sustainability. Consequently, it will contribute to proliferate LBSs by eliminating users privacy concerns.
DOOBNet: Deep Object Occlusion Boundary Detection from an Image

Object occlusion boundary detection is a fundamental and crucial research problem in computer vision. Solving this problem is challenging as we encounter extreme boundary/non-boundary class imbalance during the training of an object occlusion boundary detector. In this paper, we propose to address this class imbalance by up-weighting the loss contribution of false negative and false positive examples with our novel Attention Loss function. We also propose a unified end-to-end multi-task deep object occlusion boundary detection network (DOOBNet) by sharing convolutional features to simultaneously predict object boundary and occlusion orientation. DOOBNet adopts an encoder-decoder structure with skip connection in order to automatically learn multi-scale and multi-level features. We significantly surpass the state-of-the-art on the PIOD dataset (ODS F-score of .702) and the BSDS ownership dataset (ODS F-score of .555), as well as improving the detecting speed to as 0.037 s per image on the PIOD dataset.
Toward an Understanding of Adversarial Examples in Clinical Trials

Deep learning systems can be fooled by small, worst-case perturbations of their inputs, known as adversarial examples. This has been almost exclusively studied in supervised learning, on vision tasks. However, adversarial examples in counterfactual modelling, which sits outside the traditional supervised scenario, is an overlooked challenge. We introduce the concept of adversarial patients, in the context of counterfactual models for clinical trials—this turns out to introduce several new dimensions to the literature. We describe how there exist multiple types of adversarial example—and demonstrate different consequences, e.g. ethical, when they arise. The study of adversarial examples in this area is rich in challenges for accountability and trustworthiness in ML–we highlight future directions that may be of interest to the community.
SeSPHR: A Methodology for Secure Sharing of Personal Health Records in the Cloud

The widespread acceptance of cloud based services in the healthcare sector has resulted in cost effective and convenient exchange of Personal Health Records (PHRs) among several participating entities of the e-Health systems. Nevertheless, storing the confidential health information to cloud servers is susceptible to revelation or theft and calls for the development of methodologies that ensure the privacy of the PHRs. Therefore, we propose a methodology called SeSPHR for secure sharing of the PHRs in the cloud. The SeSPHR scheme ensures patient-centric control on the PHRs and preserves the confidentiality of the PHRs. The patients store the encrypted PHRs on the un-trusted cloud servers and selectively grant access to different types of users on different portions of the PHRs. A semi-trusted proxy called Setup and Re-encryption Server (SRS) is introduced to set up the public/private key pairs and to produce the re-encryption keys. Moreover, the methodology is secure against insider threats and also enforces a forward and backward access control. Furthermore, we formally analyze and verify the working of SeSPHR methodology through the High Level Petri Nets (HLPN). Performance evaluation regarding time consumption indicates that the SeSPHR methodology has potential to be employed for securely shar-ing the PHRs in the cloud.
Parallel and distributed association rule mining in life science: A novel parallel algorithm to mine genomics data

Association rule mining (ARM) is largely employed in several scientific areas and application domains, and many different algorithms for learning association rules from databases have been introduced. Despite the presence of many existing algorithms, there is still room for the introduction of novel approaches tailored for novel kinds of datasets. Because often the efficiency of such algorithms depends on the type of analyzed dataset. For instance, classical ARM algorithms present some drawbacks for biological datasets produced by microarray technologies in particular containing Single Nucleotide Polymorphisms (SNPs). In particular classical algorithms require large execution times also with small datasets. Therefore the possibility to improve the performance of such algorithms by leveraging parallel computing is a growing research area. The main contributions of this paper are: a comparison among different sequential, parallels and distributed ARM techniques, and the presentation of a novel ARM algorithm, named Balanced Parallel Association Rule Extractor from SNPs (BPARES), that employs parallel computing and a novel balancing strategy to improve response time. BPARES improves performance without loosing in accuracy as well as it handles more efficiently the available computational power and reduces the memory consumption.
Reconstruction of stationary and non-stationary signals by the generalized Prony method

We employ the generalized Prony method in [T. Peter and G. Plonka, A generalized Prony method for reconstruction of sparse sums of eigenfunctions of linear operators, Inverse Problems 29 (2013) 025001] to derive new reconstruction schemes for a variety of sparse signal models using only a small number of signal measurements. By introducing generalized shift operators, we study the recovery of sparse trigonometric and hyperbolic functions as well as sparse expansions into Gaussians chirps and modulated Gaussian windows. Furthermore, we show how to reconstruct sparse polynomial expansions and sparse non-stationary signals with structured phase functions.
Fractional means based method for multi-oriented keyword spotting in video/scene/license plate images

Retrieving desired information from databases containing video, natural scene, and license plate images through keyword spotting is a big challenge to expert systems due to different complexities that occur because of background and foreground variations of texts in real-time environments. To reduce background complexity of input images, we introduce a new model based on fractional means that considers neighboring information of pixels to widen the gap between text and background. To do so, the process obtains text candidates with the help of k-means clustering. The proposed approach explores the combination of Radon and Fourier coefficients to define context features based on regular patterns given by coefficient distributions for foreground and background of text candidates. This process eliminates non-text candidates regardless of different font types and sizes, colors, orientations and scripts, and results in representatives of texts. The proposed approach then exploits the fact that text pixels share almost the same values to restore missing text components using Canny edge image by proposing a new idea of minimum cost path based ring growing, and then outputs keywords. Furthermore, the proposed approach extracts the same above-mentioned features locally and globally for spotting words from images. Experimental results on different benchmark databases, namely, ICDAR 2013, ICDAR 2015, YVT, NUS video data, ICDAR 2013, ICDAR 2015, SVT, MSRA, UCSC, Medialab and Uninusubria license plate data show that the proposed method is effective and useful compared to the existing methods.
New Techniques for Multi-value Input Homomorphic Evaluation and Applications

In this paper, we propose a new technique to perform several homomorphic operations in one bootstrapping call over a multi-value plaintext space. Our construction relies on the FHEW-based gate bootstrapping; we analyze its structure and propose a strategy we call multi-value bootstrapping which allows to bootstrap an arbitrary function in an efficient way. The security of our scheme relies on the LWE assumption over the torus. We give three possible applications: we first describe how to efficiently evaluate an arbitrary boolean function (LUT) and combine LUTs in circuits. We also explain how to apply our procedure to optimize the circuit bootstrapping from (Asiacrypt'2017) which allows to compose circuits in a leveled mode. And we finally present a simple method which makes use of the multi-value bootstrapping to evaluate a encrypted neural network. We have implemented the proposed method and were able to evaluate an arbitrary 6-to-6 LUTs under 1.6 s. Our implementation is based on the TFHE library but can be easily integrated into other homomorphic libraries based on the same structure, such as FHEW (Eurocrypt'2015). The number of LUT outputs does not influence the execution time by a lot, e.g. evaluation of additional 128 outputs on the same 6 input bits takes only 0.05 more seconds.
Computational Intelligence in Automatic Face Age Estimation: A Survey

With the rapid growth of computational intelligence techniques, automatic face age estimation has achieved good accuracy that benefited real-world applications such as access control and monitoring, soft biometrics and information retrieval.Over the past decade, many new algorithms were developed and previous surveys on face age estimation were either outdated or incomplete.Considering the importance of the expanding research in this topic, we aim to provide an up-to-date survey on the face age estimation techniques.First we summarise the stateof-the-art databases and the performance metrics for face age estimation.Then we review the age estimation techniques based on three categories of face features (local, global and hybrid) and discuss different types of age learning algorithms.Finally, we identify the challenges and provide new insights for future research directions of fully automated face age estimation.
Fuzzy bit-plane-dependence image segmentation

This paper presents a novel fuzzy bit-plane-dependence image segmentation methodology. We propose a probability model for characterizing the distributions of image variations based on bit-plane probabilities and dependencies between bit-planes. Compared with the current state-of-the-art image variation models which assume the distributions have specific structures (e.g., symmetry, monotone and periodicity), the proposed model provides a universal parametric representation that can be used to model random distributions without enforcing any specific restrictions on the distributions. In addition, we show that the maximum likelihood estimators of model parameters are joint sufficient statistics, which, in turn, justify the theoretical basis for their use. To effectively segment images with various textures, we propose a fuzzy bit-plane-dependence image segmentation algorithm. The proposed algorithm integrates the bit-plane-dependence probability model into the agglomerative fuzzy algorithm, and incorporates neighboring information and boundary correction for image segmentation applications. Experiments demonstrate the superior performance of the proposed method.
Data-free metrics for Dirichlet and generalized Dirichlet mixture-based HMMs – A practical study

Approaches to design metrics between hidden Markov models (HMM) can be divided into two classes: data-based and parameter-based. The latter has the clear advantage of being deterministic and faster but only a very few similarity measures that can be applied to mixture-based HMMs have been proposed so far. Most of these metrics apply to the discrete or Gaussian HMMs and no comparative study have been led to the best of our knowledge. With the recent development of HMMs based on the Dirichlet and generalized Dirichlet distributions for proportional data modeling, we propose to design three new parametric similarity measures between these HMMs. Extensive experiments on synthetic data show the reliability of these new measures where the existing ones fail at giving expected results when some parameters vary. Illustration on real data show the clustering capability of these measures and their potential applications.
Test-Oriented RESTful Service Discovery with Semantic Interface Compatibility

Web API/service technology has been attracting considerable attention and the REST (REpresentational State Transfer) architecture is now widely accepted as mainstream technology. Nonetheless, developing the means by which to discover RESTful Web APIs/services is crucial to the further development of this technology. Unfortunately, existing search engines for RESTful Web APIs/services provide only keyword-based or tag-based search functions. A failure to take into account the semantics and/or characteristics (e.g., their interface compatibility) greatly hampers the ability to find suitable APIs/services. In this study, we propose a novel approach to the discovery of RESTful Web API/services, referred to as Test-Oriented API Search with Semantic Interface Compatibility (TASSIC). This scheme involves expanding the terms of Web API/service documents based on DBpedia and WordNet. Unsuitable APIs/services are then filtered out using a systematic process, as follows: 1) calculation of semantic similarity between a set of candidate APIs/services and a user query, 2) analysis of interface compatibility between candidate APIs/services using the Hungarian Algorithm, 3) invocation of candidate APIs/services to verify functionality and availability, and 4) analysis of similarity between the actual response of the candidate services and the expected response specified in the user query. The proposed TASSIC increases the likelihood of matching APIs/services that are semantically equivalent or similar to user queries. Unit test and acceptance test are used to verify that a set of candidate APIs/services are actually available and that they actually meets user requirements. Experiment results demonstrate the efficacy of TASSIC, the accuracy of which is superior to that of existing methods.
From Same Photo: Cheating on Visual Kinship Challenges

With the propensity for deep learning models to learn unintended signals from data sets there is always the possibility that the network can “cheat” in order to solve a task. In the instance of data sets for visual kinship verification, one such unintended signal could be that the faces are cropped from the same photograph, since faces from the same photograph are more likely to be from the same family. In this paper we investigate the influence of this artefactual data inference in published data sets for kinship verification. To this end, we obtain a large data set, and train a CNN classifier to determine if two faces are from the same photograph or not. Using this classifier alone as a naive classifier of kinship, we demonstrate near state of the art results on five public benchmark data sets for kinship verification – achieving over $$90\%$$ accuracy on one of them. Thus, we conclude that faces derived from the same photograph are a strong inadvertent signal in all the data sets we examined, and it is likely that the fraction of kinship explained by existing kinship models is small.
Extreme learning machine-based prediction of uptake of pharmaceuticals in reclaimed water-irrigated lettuces in the Region of Murcia, Spain

The availability of water resources is limited, and rising consumption has increased pressure on natural resources. Therefore, reclaimed water represents an alternative option for use in urban areas, industry and, in particular, agriculture. Recent research has shown that some pharmaceutical compounds are not fully removed by wastewater treatment plants (WWTPs) and may eventually be released into agricultural systems through the application of wastewater-based resources (sludge and effluent). The present study develops an intelligent expert system (based on a feedforward neural network trained via an extreme learning machine algorithm) for predicting the carbamazepine (CBZ) and diclofenac (DCF) content in lettuce tissues irrigated with reclaimed water from WWTPs. This reduces laboratory costs, mitigates the negative impacts on the environment and leads to more effective, safer decisions on the use of reclaimed water in agriculture. The results obtained, which were validated through statistical testing, demonstrate that the intelligent expert system is well calibrated and reliable. Finally, this system was used to predict maps of CBZ and DCF accumulation in lettuce crops if they were watered with effluent from 10 WWTPs located in the Region of Murcia (Spain). In conclusion, our system provides highly accurate predictions of the amount of CBZ and DCF contained in different lettuce tissues (roots and leaves) and the predicted concentrations do not present any health risk.
Synthesis of Realistic Facial Expressions Using Expression Map

For synthesis of realistic facial expressions displaying emotions, we need an efficient representation of pure (e.g., surprise) as well as mixed (e.g., happily surprised) emotional expressions. In this paper, we train an expression map (XM) that efficiently represents the emotional expressions. We propose an algorithm that utilizes the XM to synthesize emotional expressions, tailor-made for the facial structure of the target person. The proposed method can also control the proportions of different basic emotional expressions of those, when mixed together, to generate realistic emotional facial expressions. Unlike many existing methods, our expression synthesis model requires only one expression-neutral face image of the target person. Both qualitative and quantitative tests on four data sets show promising results. On average, we have achieved 92.4% correct validation of the expressions synthesized by our method. We also show that for both basic and mixed emotional expressions, our method generates finer expression details compared to existing state-of-the-art works.
Constant-Time Sliding Window Framework with Reduced Memory Footprint and Efficient Bulk Evictions

The fast evolution of data analytics platforms has resulted in an increasing demand for real-time data stream processing. From Internet of Things applications to the monitoring of telemetry generated in large data centers, a common demand for currently emerging scenarios is the need to process vast amounts of data with low latencies, generally performing the analysis process as close to the data source as possible. Stream processing platforms are required to be malleable and absorb spikes generated by fluctuations of data generation rates. Data is usually produced as time series that have to be aggregated using multiple operators, being sliding windows one of the most common abstractions used to process data in real-time. To satisfy the above-mentioned demands, efficient stream processing techniques that aggregate data with minimal computational cost need to be developed. In this paper we present the Monoid Tree Aggregator general sliding window aggregation framework, which seamlessly combines the following features: amortized <inline-formula><tex-math notation="LaTeX">$O(1)$</tex-math></inline-formula> time complexity and a worst-case of <inline-formula><tex-math notation="LaTeX">$O(\log {n})$</tex-math></inline-formula> between insertions; it provides both a window aggregation mechanism and a window slide policy that are user programmable; the enforcement of the window sliding policy exhibits amortized <inline-formula><tex-math notation="LaTeX">$O(1)$</tex-math></inline-formula> computational cost for single evictions and supports bulk evictions with cost <inline-formula><tex-math notation="LaTeX">$O(\log {n})$</tex-math></inline-formula> ; and it requires a local memory space of <inline-formula><tex-math notation="LaTeX">$O(\log {n})$</tex-math></inline-formula> . The framework can compute aggregations over multiple data dimensions, and has been designed to support decoupling computation and data storage through the use of distributed <i>Key-Value Stores</i> to keep window elements and partial aggregations.
KeyD: Secure Key-Deduplication with Identity-Based Broadcast Encryption

Deduplication, which can save storage cost by enabling us to store only one copy of identical data, becomes unprecedentedly significant with the dramatic increase in data stored in the cloud. For the purpose of ensuring data confidentiality, they are usually encrypted before outsourced. Traditional encryption will inevitably result in multiple different ciphertexts produced from the same plaintext by different users' secret keys, which hinders data deduplication. Convergent encryption makes deduplication possible since it naturally encrypts the same plaintexts into the same ciphertexts. One attendant problem is how to reliably and effectively manage a huge number of convergent keys. Several deduplication schemes have been proposed to deal with the convergent key management problem. However, they either need to introduce key management servers or require interaction between data owners. In this paper, we design a novel client-side deduplication protocol named KeyD without such an independent key management server by utilizing the identity-based broadcast encryption (IBBE) technique. Users only interact with the cloud service provider (CSP) during the process of data upload and download. Security analysis demonstrates that KeyD ensures data confidentiality and convergent key security, and well protects the ownership privacy simultaneously. A thorough and detailed performance comparison shows that our scheme makes a better tradeoff among the storage cost, communication and computation overhead.