Reviewerly/exHarmony huggingface dataset:
Provable Data Possession with Outsourced Data Transfer

With the rapid development of cloud computing, more and more enterprises would like to upload and store their data in the public cloud. When the parts of the business of an enterprise are purchased by another enterprise, the corresponding data will be transferred to the acquiring enterprise. For the usual case, how to outsource the computation cost of data transfer to the cloud? How to ensure the remote purchased data integrity? Thus, it is important to study provable data possession with outsourced data transfer (DT-PDP). In this paper, for the first time, we propose the novel concept: DT-PDP. By taking use of DT-PDP, the following three security requirements can be satisfied: (1) the other un-purchased data security of acquired enterprise can be ensured; (2) the purchased data integrity and privacy can be ensured; (3) the data transferability&#x2019;s computation can be outsourced to the public cloud servers. For the security concept of DT-PDP, we give its motivation, system model and security model. Then, we design a concrete DT-PDP scheme based on the bilinear pairings. At last, we analyze the security, efficiency and flexibility of the concrete DT-PDP scheme. It shows that our scheme is provably secure and efficient.
Interpretable regularized class association rules algorithm for classification in a categorical data space

Using association rules in classification is a great success which produces high accuracy classifiers. Even so, the principal advantage of the associative classifiers lies in interpretation. However, pruning the useless rules among the huge set of the mined rules as well as combining them to build a classifier remains a subject for improvement and further research. In this paper, we introduce a new algorithm to build a classifier based on Regularized Class Association Rules in a categorical data space called RCAR. The characteristic of this algorithm is, therefore, threefold: First, mining an exhaustive set of Class Association Rules (CARs) according to a predefined values of support and confidence thresholds. Second, applying a regularized logistic regression algorithm with Lasso penalty on the rules space to build a model that predicts the conditional probability of the existence of the outcome. Useless rules are pruned thanks to the selective nature of Lasso regularization. Third, organizing and visualizing the CARs which survive the first step of pruning by Lasso regularization using metarules. An optional step of pruning could be undertaken on the basis of the metarules and subject knowledge. Likewise, the empirical results indicate that RCAR gives comparable accuracy against Random Forest and GBM.
A survey on projection neural networks and their applications

Constrained optimization problems arise in numerous scientific and engineering applications, and many papers on the online solution of constrained optimization problems using projection neural networks have been published in the literature. The purpose of this paper is to provide a comprehensive review of the research on projection neural networks for solving various constrained optimizations as well as their applications. Since convergence and stability are important for projection neural networks, theoretical results of projection neural networks are reviewed in detail. In addition, various applications of projection neural networks, e.g., the motion generation of redundant robot manipulators, coordination control of multiple robots with limited communications, generation of winner-take-all strategy, model predictive control and WSN localizations, are discussed and compared. Concluding remarks and future directions of projection neural networks as well as their applications are provided.
Visual Servo Tracking of Wheeled Mobile Robots With Unknown Extrinsic Parameters

In most visual servoing methods for wheeled mobile robots, the camera is supposed at the center of the robot, while it will bring much convenience for practical use when putting the camera a little away from the robot center. In this paper, to handle the challenge that is incurred by the uncalibrated translational camera-to-robot parameters, a novel visual servoing scheme is designed for wheeled mobile robots to track a given trajectory, which is defined by a sequence of images recorded previously. The trajectory tracking errors are defined with respect to both current and desired poses of the robot, and the open-loop error system is obtained after analyzing kinematic models of the visual robot system. Subsequently, to compensate both unknown extrinsic parameters and the visual depth, an adaptive visual servo tracking controller is elaborately designed, with disposing the nonholonomic motion constraint of the mobile robot. The tracking errors are strictly proven converging to zero asymptotically by employing Lyapunov techniques and Barbalat's lemma. Both simulation and comparative experiments are conducted to prove that the proposed strategy can drive the robot track the desired trajectory efficiently, even translational camera-to-robot parameters are unknown.
Reversible data hiding based on pairwise embedding and optimal expansion path

As an efficient technique for high-dimensional reversible data hiding (RDH), pairwise prediction-error expansion (pairwise PEE) has achieved better performance comparing with the conventional PEE. With pairwise PEE, the correlations among prediction-errors are well utilized by modifying the generated two-dimensional prediction-error histogram (2D-PEH). However, its performance can be further improved since the histogram modification manner (i.e., the employed modification mapping) of pairwise PEE is fixed and independent of image content. To better utilize image redundancy, instead of embedding data based on an empirically designed modification mapping, a content dependent pairwise embedding scheme is proposed in this paper. Based on a specific division of 2D-PEH, the expansion bins selection is formulated as an optimal path determination problem, and the histogram modification mapping is adaptively determined by taking the optimal expansion bins. To reduce the computation cost, a dynamic programming algorithm is proposed to solve the optimization problem with low computational complexity. Moreover, by combining the proposed optimal expansion path with the existing one-dimensional adaptive embedding mechanism, the embedding performance can be further enhanced. The proposed method performs well and its superiority is experimentally verified comparing with pairwise PEE and some other state-of-the-art methods.
A vehicle routing problem arising in unmanned aerial monitoring

Unmanned aerial vehicles (UAVs) are widely used to perform monitoring tasks both in the military and civilian areas, and the planning of their routes is critical. This study investigates a routing problem in which UAVs monitor a set of areas with different accuracy requirements. This problem is a variant of the classical vehicle routing problem (VRP), where one must determine not only the order in which to visit a set of nodes located in the plane, but also the height at which to visit them, which impacts the accuracy level and the service time. An integer programming model is formulated to optimize flight routes and minimize the total time needed to complete the monitoring tasks. A tabu search metaheuristic is developed for the problem. Extensive numerical experiments are conducted to assess the efficiency of the heuristic.
Multi-task learning model based on recurrent convolutional neural networks for citation sentiment and purpose classification

Automated citation analysis is a method of identifying sentiment and purpose of citations in the citing works. Most of the existing approaches use machine learning techniques to boost the performance of citation sentiment classification (CSC) and citation purpose classification (CPC), which are the main tasks of automated citation analysis. However, such approaches address CPC and CSC by learning them separately, which often suffer from inadequate training data and time-consuming for feature engineering. To alleviate these problems, we propose a multitask learning model based on convolutional and recurrent neural networks. The proposed model benefits from jointly learning CSC and CPC by modeling the citation context with task-specific information and shared layers for citation sentiment and purpose classification. The network architecture of the proposed model is useful to represent the citation context and extracts the features automatically. We conduct experiments on two public datasets to evaluate the performance of the proposed model using standard metrics such as precision, recall, and F-score. The results of CSC and CPC tasks show improvements relative to classical machine learning algorithms such as SVM and NB as well as single-task deep learning models.
A novel genetic algorithm for large scale colored balanced traveling salesman problem

The paper gives an applicable model called colored balanced traveling salesman problem (CBTSP), it is utilized to model optimization problems with partially overlapped workspace such as the scheduling and deploying of the resources and goods. CBTSP is NP-hard problem, the traditional nature-inspired algorithms, such as genetic algorithm (GA), hill-climbing GA and simulated annealing GA, are easy to fall into local optimum. In order to improve it, the paper proposes a novel genetic algorithm (NGA) based on ITÖ process to solve CBTSP. First of all, NGA utilizes the dual-chromosome coding to represent solution of this problem, and then updates the solution by the crossover and mutation operator. During the process of crossover operator, the length of crossover can be affected by activity intensity, which is directly proportional to environmental temperature and inversely proportional to particle radius. The experiments verify that NGA can demonstrate better solution quality than the compared algorithms for large scale CBTSP.
A Novel Patch Variance Biased Convolutional Neural Network for No-Reference Image Quality Assessment

Deep convolutional neural networks (CNNs) have been successfully applied on no-reference image quality assessment (NR-IQA) with respect to human perception. Most of these methods deal with small image patches and use the average score of the test patches for predicting the whole image quality. We discovered that image patches from homogenous regions are unreliable for both neural network training and final image quality score estimation. In addition, image patches with complex structures have much higher chances of achieving better image quality prediction. Based on these findings, we enhanced the conventional CNN-based NR-IQA algorithm to avoid homogenous patches for the network training and quality score estimation. Moreover, we also use a variance-based weighting average to bias the final image quality score to the patches with complex structure. The experimental results show that this simple approach can achieve state-of-the-art performance compared with well-known NR-IQA algorithms.
Dynamic Cloud Resource Allocation Considering Demand Uncertainty

Cloud computing provisions scalable resources for high performance industrial applications. Cloud providers usually offer two types of usage plans: reserved and on-demand. Reserved plans offer cheaper resources for long-term contracts while on-demand plans are available for short or long periods but are more expensive. To satisfy incoming user demands with reasonable costs, cloud resources should be allocated efficiently. Most existing works focus on either cheaper solutions with reserved resources that may lead to under-provisioning or over-provisioning, or costly solutions with on-demand resources. Since inefficiency of allocating cloud resources can cause huge provisioning costs and fluctuation in cloud demand, resource allocation becomes a highly challenging problem. In this paper, we propose a hybrid method to allocate cloud resources according to the dynamic user demands. This method is developed as a two-phase algorithm that consists of reservation and dynamic provision phases. In this way, we minimize the total deployment cost by formulating each phase as an optimization problem while satisfying quality of service. Due to the uncertain nature of cloud demands, we develop a stochastic optimization approach by modeling user demands as random variables. Our algorithm is evaluated using different experiments and the results show its efficiency in dynamically allocating cloud resources.
A comprehensive survey of error measures for evaluating binary decision making in data science

Binary decision making is a topic of great interest for many fields, including biomedical science, economics, management, politics, medicine, natural science and social science, and much effort has been spent for developing novel computational methods to address problems arising in the aforementioned fields. However, in order to evaluate the effectiveness of any prediction method for binary decision making, the choice of the most appropriate error measures is of paramount importance. Due to the variety of error measures available, the evaluation process of binary decision making can be a complex task. The main objective of this study is to provide a comprehensive survey of error measures for evaluating the outcome of binary decision making applicable to many data-driven fields. This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data MiningTechnologies > PredictionAlgorithmic Development > Statistics.
Vision-Based Target-Following Guider for Mobile Robot

A vision-based target-following guider for mobile robot is presented in this paper. It consists of three parts: the visual tracking part; the target redetection part; and the visual servo part. In the visual tracking part, the target contour band is proposed for irregular sampling, which can improve the performance of the correlation filter-based methods. In the target redetection part, potential targets are searched by an offline trained detector. Then an online training module is used to determine the real target and achieve accurate positioning. The interaction matrix of point features is used in the visual servo part for motion control to maintain the relative pose between the target and the robot. The visual tracking part and the target redetection part are tested respectively on many videos, which proved to work well. To show the effectiveness of our method, some state-of-the-art methods are also tested. The target-following guider is evaluated on mobile robots. In our experiments, the robot can robustly follow the human target over a long distance, which strongly proved the validity of our target-following guider.
Learning Dual Retrieval Module for Semi-supervised Relation Extraction

Relation extraction is an important task in structuring content of text data, and becomes especially challenging when learning with weak supervision-where only a limited number of labeled sentences are given and a large number of unlabeled sentences are available. Most existing work exploits unlabeled data based on the ideas of self-training (i.e., bootstrapping a model) and self-ensembling (e.g., ensembling multiple model variants). However, these methods either suffer from the issue of semantic drift, or do not fully capture the problem characteristics of relation extraction. In this paper, we leverage a key insight that retrieving sentences expressing a relation is a dual task of predicting the relation label for a given sentence-two tasks are complementary to each other and can be optimized jointly for mutual enhancement. To model this intuition, we propose DualRE, a principled framework that introduces a retrieval module which is jointly trained with the original relation prediction module. In this way, high-quality samples selected by the retrieval module from unlabeled data can be used to improve the prediction module, and vice versa. Experimental results1 on two public datasets as well as case studies demonstrate the effectiveness of the DualRE approach.
Spatiotemporal Task Scheduling for Heterogeneous Delay-Tolerant Applications in Distributed Green Data Centers

A growing number of organizations deploy multiple heterogeneous applications in infrastructures of distributed green data centers (DGDCs) to flexibly provide services to users around the world in a low-cost and high-quality way. The skyrocketing growth in types and number of heterogeneous applications dramatically increases the amount of energy consumed by DGDCs. The spatial and temporal variations in prices of power grid and availability of renewable energy make it highly challenging to minimize the energy cost of DGDC providers by intelligently scheduling arriving tasks of heterogeneous applications among GDCs while meeting their expected delay bound constraints. Unlike existing studies, this paper proposes a spatiotemporal task scheduling (STTS) algorithm to minimize energy cost by cost-effectively scheduling all arriving tasks to meet their delay bound constraints. STTS well investigates spatial and temporal variations in DGDCs. In each time slot, the energy cost minimization problem is formulated as a nonlinear constrained optimization one and addressed with the proposed genetic simulated-annealing-based particle swarm optimization. Trace-driven experiments show that STTS achieves larger throughput and lower energy cost than several typical task scheduling approaches while strictly meeting all tasks' delay bound constraints. Note to Practitioners-This paper investigates the energy cost minimization problem for a DGDC provider while meeting delay bound constraints for all arriving tasks. Previous scheduling methods do not jointly consider spatial and temporal variations in prices of power grid and availability of renewable energy in DGDCs. Therefore, they fail to adopt such variations to minimize the energy cost of a DGDC provider. In this paper, a new method that avoids disadvantages of previous methods is proposed. It is realized by adopting a hybrid metaheuristic algorithm named GSP to solve a nonlinear constrained optimization problem. Experimental results demonstrate that compared with several typical methods, it reduces energy cost and increases throughput. It can be readily integrated into realistic industrial DGDCs. The future work requires engineers to consider the effect of indeterminacy and uncertainty of green energy on scheduling methods.
SAFETY: Secure gwAs in Federated Environment through a hYbrid Solution

Recent studies demonstrate that effective healthcare can benefit from using the human genomic information. Consequently, many institutions are using statistical analysis of genomic data, which are mostly based on genome-wide association studies (GWAS). GWAS analyze genome sequence variations in order to identify genetic risk factors for diseases. These studies often require pooling data from different sources together in order to unravel statistical patterns, and relationships between genetic variants and diseases. Here, the primary challenge is to fulfill one major objective: accessing multiple genomic data repositories for collaborative research in a privacy-preserving manner. Due to the privacy concerns regarding the genomic data, multi-jurisdictional laws and policies of cross-border genomic data sharing are enforced among different countries. In this article, we present SAFETY, a hybrid framework, which can securely perform GWAS on federated genomic datasets using homomorphic encryption and recently introduced secure hardware component of Intel Software Guard Extensions to ensure high efficiency and privacy at the same time. Different experimental settings show the efficacy and applicability of such hybrid framework in secure conduction of GWAS. To the best of our knowledge, this hybrid use of homomorphic encryption along with Intel SGX is not proposed to this date. SAFETY is up to 4.82 times faster than the best existing secure computation technique.
How influencer ‘mumpreneur’ bloggers and ‘everyday’ mums frame presenting their children online

The rise of mum/mom/mommy bloggers has been a worldwide trend, with some mum bloggers now earning a significant income from their personal ‘brand’ and role as social media influencers. As ‘prosumers’ (i.e. consumers turned producers), successful mum bloggers are often courted by international brands and organisations. While mum bloggers were early adopters in the digital landscape, everyday (non-blogging) mums have also embraced technology and have become avid users of social media, in particular Facebook, and are sometimes influenced by the bloggers they follow. Invariably, as pro-am mothers, mumpreneur bloggers use their families (in particular, their young children) as characters in their story posts and also co-opt them in sponsorship and other money-making opportunities. These posts help the audience identify with the bloggers, increasing their social capital and influence. Everyday mothers may also follow the example set by the mumpreneurs, sharing images and stories related to their offspring, sometimes from the first ultrasound scan. This article investigates and compares the motivations, hesitations and justifications of both influencer mumpreneur bloggers and everyday mums related to sharing their children’s images and stories.
Zero-Shot Cross-Media Embedding Learning With Dual Adversarial Distribution Network

Existing cross-media retrieval methods are mainly based on the condition where the training set covers all the categories in the testing set, which lack extensibility to retrieve data of new categories. Thus, zero-shot cross-media retrieval has been a promising direction in practical application, aiming to retrieve data of new categories (unseen categories), only with data of limited known categories (seen categories) for training. It is challenging for not only the heterogeneous distributions across different media types, but also the inconsistent semantics across seen and unseen categories need to be handled. To address the above issues, we propose dual adversarial distribution network (DADN), to learn common embeddings and explore the knowledge from word-embeddings of different categories. The main contributions are as follows. First, zero-shot cross-media dual generative adversarial networks architecture is proposed, in which two kinds of generative adversarial networks (GANs) for common embedding generation and representation reconstruction form dual processes. The dual GANs mutually promote to model semantic and underlying structure information, which generalizes across different categories on heterogeneous distributions and boosts correlation learning. Second, distribution matching with maximum mean discrepancy criterion is proposed to combine with dual GANs, which enhances distribution matching between common embeddings and category word-embeddings. Finally, adversarial inter-media metric constraint is proposed with an inter-media loss and a quadruplet loss, which further model the inter-media correlation information and improve semantic ranking ability. The experiments on four widely used cross-media datasets demonstrate the effectiveness of our DADN approach.