Reviewerly/exHarmony huggingface dataset:
A Key-Policy Attribute-Based Temporary Keyword Search scheme for Secure Cloud Storage

Temporary keyword search on confidential data in a cloud environment is the main focus of this research. The cloud providers are not fully trusted. So, it is necessary to outsource data in the encrypted form. In the attribute-based keyword search (ABKS) schemes, the authorized users can generate some search tokens and send them to the cloud for running the search operation. These search tokens can be used to extract all the ciphertexts which are produced at any time and contain the corresponding keyword. Since this may lead to some information leakage, it is more secure to propose a scheme in which the search tokens can only extract the ciphertexts generated in a specified time interval. To this end, in this paper, we introduce a new cryptographic primitive called key-policy attribute-based temporary keyword search (KP-ABTKS) which provide this property. To evaluate the security of our scheme, we formally prove that our proposed scheme achieves the keyword secrecy property and is secure against selectively chosen keyword attack (SCKA) both in the random oracle model and under the hardness of Decisional Bilinear Diffie-Hellman (DBDH) assumption. Furthermore, we show that the complexity of the encryption algorithm is linear with respect to the number of the involved attributes. Performance evaluation shows our scheme's practicality.
Differentially private model publishing in cyber physical systems

With the development of Cyber Physical Systems, privacy issues become an important topics in the past few years. It is worthwhile to apply differential privacy, one of the most influential privacy definitions, in cyber physical system. However, as the essential idea of differential privacy is to release query results rather than entire datasets, a large volume of noise has to be introduced. To provide high quality services we need to decrease the correlation between large sets of queries, while to predict on newly entered queries. This paper transfers the data publishing problem in cyber physical systems to a machine learning problem, in which a prediction model will be shared with clients. The predict model is used to answer current submitted queries and predict results for newly entered queries from the public.
Resolution-Aware Network for Image Super-Resolution

In existing deep network-based image super-resolution (SR) methods, each network is only trained for a fixed upscaling factor and can hardly generalize to unseen factors at test time, which is non-scalable in real applications. To mitigate this issue, this paper proposes a resolution-aware network (RAN) for simultaneous SR of multiple factors. The key insight is that SR of multiple factors is essentially different but also shares common operations. To attain stronger generalization across factors, we design an upsampling network (U-Net) consisting of several sub-modules, in which each sub-module implements an intermediate step of the overall image SR and can be shared by SR of different factors. A decision network (D-Net) is further adopted to identify the quality of the input low-resolution image and adaptively select suitable sub-modules to perform SR. U-Net and D-Net together constitute the proposed RAN model, and are jointly trained using a new hierarchical loss function on SR tasks of multiple factors. Experimental evaluations demonstrate that the proposed RAN compares favorably against the state-of-the-art methods and its performance can well generalize across different upscaling factors.
Unsupervised Anomaly Detection Based on Minimum Spanning Tree Approximated Distance Measures and its Application to Hydropower Turbines

Anomalies are data points or a cluster of data points that lie away from the neighboring points or clusters and are inconsistent with the overall pattern of the data. Anomaly detection techniques help distinguish the anomalous observations from the regular ones, and thus provide the basis for developing a standard performance guideline for process control. The process of identifying anomalies becomes complicated in the absence of labeled training data as in supervised learning. Moreover, Euclidean distance between two points is less likely able to reflect the intrinsic structural distance imposed by the underlying manifold structure. In this paper, the authors propose a minimum spanning tree (MST)-based anomaly detection method. The merit of the method is that an MST provides a new distance measure, capable of capturing the relative connectedness of data points/clusters in a complicated manifold, and could be a better (dis)similarity metric, than the simple Euclidean distance, to identify anomalies in unsupervised learning settings. The proposed method is compared with 13 popular anomaly detection methods on 20 benchmark data sets, demonstrating a considerable improvement in its ability of identifying anomalies. Furthermore, the MST-based anomaly detection is applied to the data set from a hydropower turbine and demonstrates remarkable detection competence.
Gradient Prior-Aided CNN Denoiser With Separable Convolution-Based Optimization of Feature Dimension

We propose a novel image denoising method based on a convolutional neural network (CNN), which uses the separable convolution and the gradient prior to reduce the computational complexity while enhancing the denoising performance. The proposed method converts the existing convolution filter in the conventional CNN denoiser to cascaded vertical and horizontal separable convolutions and reduces the number of feature channels between these convolutions by analyzing the distribution of convolution weights. The proposed separable convolution with feature dimension shrinking can greatly reduce the number of multiplications for CNN while minimizing the degradation of denoising quality. In addition, gradients of a given image are used as input for the proposed CNN denoiser by exploiting the relation between an anisotropic diffusion-based denoiser and a residual CNN denoiser to improve the quality of the image denoising. The simulation results showed that the proposed method provided comparable denoising quality while reducing the number of multiplications to 41% compared to the existing state-of-the-art CNN denoiser.
Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps

Hand gesture recognition (HGR) from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of the human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of keyframes of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatiotemporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient and local binary pattern are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D HGR. We also achieve competitive results on NTU action dataset.
Modelling the global solar radiation climate of Mauritius using regression techniques

The tropical island of Mauritius (20.3°S, 57.6°E), situated in the southwestern Indian Ocean, is blessed with abundant sunshine throughout the year. In this study, eleven regression models grouped into three main categories: sunshine-based, temperature-based and hybrid-parameter-based are investigated using twenty-nine years meteorological data which include sunshine hours, temperature and relative humidity for fifteen stations on the island. Calibration of these models in the varying climatic regimes of the island is performed using global solar irradiation measurements recorded at the fifteen stations. An additional model, Sayigh Universal Formula, is modified through the implementation of a relative humidity factor trend specific to Mauritius and applicable to all regions on the island. The prediction capabilities of all twelve models are determined using statistical evaluation indicators and the better performance of the Sayigh Universal Formula acclimatized to Mauritius is revealed. Spatially clustered cloud cover zones are found to influence significantly the spatial distribution of global solar irradiation on a horizontal surface on the island, which varies from a maximum value of 22.5 MJ/m2day to a minimum of 9.5 MJ/m2day throughout the year giving an average of about 16 MJ/m2day.
Robustness analysis of multi-criteria collaborative filtering algorithms against shilling attacks

Collaborative filtering is an emerging recommender system technique that aims guiding users based on other customers preferences with behavioral similarities. Such correspondences are located based on preference history of users. A relatively new extension of traditional collaborative filtering schemes takes into account not only how much a user likes an item, but also why she likes the item by collecting multi-criteria preferences focusing on distinctive features of the items. These multi-criteria collaborative filtering systems have the potential to improve recommender system accuracy since they reveal multiple views of users on products. However, due to providing more insightful recommendations, such systems might be subjected to malicious attacks more substantially than the traditional ones. Attackers attempt to insert fake profiles to bias outputs of these systems in favor of a particular product or disrepute the system itself. Since outputs of expert systems directly dependent on input signals; interventions to the inputs coherently cause failures on productions of such systems. In this study, we examine shilling attack strategies against multi-criteria preference collections, how to extend well-known attack scenarios against these systems, and propose an alternative attacking scheme. We analyze the robustness of baseline multi-criteria recommendation algorithms regarding various similarity aggregation procedures against proposed attacking schemes by the extensive experimental investigation. Empirical results on real-world data demonstrate that these systems are highly vulnerable to manipulations and proper attack detection practices are needed to ensure recommendation quality. According to our findings, manipulative attempts at such expert systems mislead decision-making process.
A flexible transfer learning framework for Bayesian optimization with convergence guarantee

Experimental optimization is prevalent in many areas of artificial intelligence including machine learning. Conventional methods like grid search and random search can be computationally demanding. Over the recent years, Bayesian optimization has emerged as an efficient technique for global optimization of black-box functions. However, a generic Bayesian optimization algorithm suffers from a "cold start" problem. It may struggle to find promising locations in the initial stages. We propose a novel transfer learning method for Bayesian optimization where we leverage the knowledge from an already completed source optimization task for the optimization of a target task. Assuming both the source and target functions lie in some proximity to each other, we model source data as noisy observations of the target function. The level of noise models the proximity or relatedness between the tasks. We provide a mechanism to compute the noise level from the data to automatically adjust for different relatedness between the source and target tasks. We then analyse the convergence properties of the proposed method using two popular acquisition functions. Our theoretical results show that the proposed method converges faster than a generic no-transfer Bayesian optimization. We demonstrate the effectiveness of our method empirically on the tasks of tuning the hyperparameters of three different machine learning algorithms. In all the experiments, our method outperforms state-of-the-art transfer learning and no-transfer Bayesian optimization methods.
Flexible Affinity Matrix Learning for Unsupervised and Semisupervised Classification

In this paper, we propose a unified model called flexible affinity matrix learning (FAML) for unsupervised and semisupervised classification by exploiting both the relationship among data and the clustering structure simultaneously. To capture the relationship among data, we exploit the self-expressiveness property of data to learn a structured matrix in which the structures are induced by different norms. A rank constraint is imposed on the Laplacian matrix of the desired affinity matrix, so that the connected components of data are exactly equal to the cluster number. Thus, the clustering structure is explicit in the learned affinity matrix. By making the estimated affinity matrix approximate the structured matrix during the learning procedure, FAML allows the affinity matrix itself to be adaptively adjusted such that the learned affinity matrix can well capture both the relationship among data and the clustering structure. Thus, FAML has the potential to perform better than other related methods. We derive optimization algorithms to solve the corresponding problems. Extensive unsupervised and semisupervised classification experiments on both synthetic data and real-world benchmark data sets show that the proposed FAML consistently outperforms the state-of-the-art methods.
Generative Zero-Shot Learning via Low-Rank Embedded Semantic Dictionary

Zero-shot learning for visual recognition, which approaches identifying unseen categories through a shared visual-semantic function learned on the seen categories and is expected to well adapt to unseen categories, has received considerable research attention most recently. However, the semantic gap between discriminant visual features and their underlying semantics is still the biggest obstacle, because there usually exists domain disparity across the seen and unseen classes. To deal with this challenge, we design two-stage generative adversarial networks to enhance the generalizability of semantic dictionary through low-rank embedding for zero-shot learning. In detail, we formulate a novel framework to simultaneously seek a two-stage generative model and a semantic dictionary to connect visual features with their semantics under a low-rank embedding. Our first-stage generative model is able to augment more semantic features for the unseen classes, which are then used to generate more discriminant visual features in the second stage, to expand the seen visual feature space. Therefore, we will be able to seek a better semantic dictionary to constitute the latent basis for the unseen classes based on the augmented semantic and visual data. Finally, our approach could capture a variety of visual characteristics from seen classes that are “ready-to-use” for new classes. Extensive experiments on four zero-shot benchmarks demonstrate that our proposed algorithm outperforms the state-of-the-art zero-shot algorithms.
Information extraction from historical handwritten document images with a context-aware neural model

Many historical manuscripts that hold trustworthy memories of the past societies contain information organized in a structured layout (e.g. census, birth or marriage records). The precious information stored in these documents cannot be effectively used nor accessed without costly annotation efforts. The transcription driven by the semantic categories of words is crucial for the subsequent access. In this paper we describe an approach to extract information from structured historical handwritten text images and build a knowledge representation for the extraction of meaning out of historical data. The method extracts information, such as named entities, without the need of an intermediate transcription step, thanks to the incorporation of context information through language models. Our system has two variants, the first one is based on bigrams, whereas the second one is based on recurrent neural networks. Concretely, our second architecture integrates a Convolutional Neural Network to model visual information from word images together with a Bidirecitonal Long Short Term Memory network to model the relation among the words. This integrated sequential approach is able to extract more information than just the semantic category (e.g. a semantic category can be associated to a person in a record). Our system is generic, it deals with out-of-vocabulary words by design, and it can be applied to structured handwritten texts from different domains. The method has been validated with the ICDAR IEHHR competition protocol, outperforming the existing approaches.
Answering the “why” in answer set programming – A survey of explanation approaches

Abstract Artificial intelligence (AI) approaches to problem-solving and decision-making are becoming more and more complex, leading to a decrease in the understandability of solutions. The European Union’s new General Data Protection Regulation tries to tackle this problem by stipulating a “right to explanation” for decisions made by AI systems. One of the AI paradigms that may be affected by this new regulation is answer set programming (ASP). Thanks to the emergence of efficient solvers, ASP has recently been used for problem-solving in a variety of domains, including medicine, cryptography, and biology. To ensure the successful application of ASP as a problem-solving paradigm in the future, explanations of ASP solutions are crucial. In this survey, we give an overview of approaches that provide an answer to the question of why an answer set is a solution to a given problem, notably off-line justifications, causal graphs, argumentative explanations, and why-not provenance, and highlight their similarities and differences. Moreover, we review methods explaining why a set of literals is not an answer set or why no solution exists at all.
A privacy preserving location service for cloud-of-things system

The natural characteristics of Location Based Services (LBS) cause potential threats to location privacy. Users need to send their current locations to get the service, which may lead to the leakage of their location privacy. An effective way to protect user's location privacy is to use an imprecise location and send this region to the Cloud-of-things system to replace his real location. When user moves and sends continuous queries, the user needs to transmit the region to the server continuously and an attacker is able to infer the real location from the overlapping regions. In this paper, we propose a novel location privacy pre-protection method for cloud-of things system to preserve user's trajectory privacy. User's moving behaviors are analyzed through Mobility Markov chain. The proposed location cloaking algorithm enlarges the small area to satisfy the user's privacy requirements, so that the location trajectory privacy in the environment of cloud-of-things system is addressed efficiently. Experimental results show the performance of our method in terms of the number of moving steps, the cloaking threshold value, in addition to the user chosen anonymity value.
User preferences modeling using dirichlet process mixture model for a content-based recommender system

Abstract Recommender systems have been developed to assist users in retrieving relevant resources. Collaborative and content-based filtering are two basic approaches that are used in recommender systems. The former employs the feedback of users with similar interests, while the latter is based on the feature of the selected resources by each user. Recommender systems can consider users’ behavior to more accurately estimate their preferences via a list of recommendations. However, the existing approaches rarely consider both interests and preferences of the users. Also, the dynamic nature of user behavior poses an additional challenge for recommender systems. In this paper, we consider the interactions of each individual user, and analyze them to propose a user model and capture user’s interests. We construct the user model based on a Bayesian nonparametric framework, called the Dirichlet Process Mixture Model. The proposed model evolves following the dynamic nature of user behavior to adapt both the user interests and preferences. We implemented the proposed model and evaluated it using both the MovieLens dataset, and a real-world dataset that contains news tweets from five news channels (New York Times, BBC, CNN, Reuters and Associated Press). The experimental results and comparisons with several recently developed approaches show the superiority in accuracy of the proposed approach, and its ability to adapt with user behavior over time.
3D object detection: Learning 3D bounding boxes from scaled down 2D bounding boxes in RGB-D images

3D object detection in RGB-D images is a vast growing research area in computer vision. In this paper, we study the problems of amodal 3D object detection in RGB-D images and present an efficient 3D object detection system that can predict object location, size, and orientation. Unlike existing methods that either uses multistage point cloud processing or pre-computed segmentation mask to generate the 3D bounding boxes, we only leverage 2D region proposals for this task. Given a pair of color and depth image as input, we first predict 2D region proposals from the designed multimodal fusion region proposal networks and then we propose an efficient method to generate 3D bounding boxes from those region proposals by scaling down the 2D bounding boxes with a scale factor and project it to 3D space. We evaluate our system on challenging NYUv2 and SUN RGB-D dataset and compare with the state-of-the-art detection methods. The experimental results show that our method outperforms the state-of-the-art by a remarkable margin with faster detection time. We achieve the best results on the NYUv2 dataset on a 19-class object detection task while performing comparably faster detection performances on the SUN RGB-D dataset on a 10-class object detection task.
A bibliometric assessment of software engineering scholars and institutions (2010–2017)

This paper presents the findings of a bibliometric study, targeting an eight-year period (2010–2017), with the aim of identifying: (a) emerging research directions, (b) the top-20 institutions, and (c) top-20 early stage, consolidated, and experienced scholars in the field of software engineering. To perform this goal, we performed a bibliometric study, by applying the mapping study technique on top-quality software engineering venues, and developed a dataset of 14,456 primary studies. As the ranking metric for institutions, we used the count of papers in which authors affiliated with this institute have been identified in the obtained dataset, whereas regarding scholars we computed the corresponding rankings based on the number of published papers and the average number of citations. Finally, we identified the top-20 rising scholars in the SE research community, based on their recent publication record (between 2015 and 2017) and their research age.