Reviewerly/exHarmony huggingface dataset:
A cost-effective algorithm for inferring the trust between two individuals in social networks

The popularity of social networks has significantly promoted online individual interaction in the society. In online individual interaction, trust plays a critical role. It is very important to infer the trust among individuals, especially for those who have not had direct contact previously in social networks. In this paper, a restricted traversal method is defined to identify the strong trust paths from the truster and the trustee. Then, these paths are aggregated to predict the trust rate between them. During the traversal on a social network, interest topics and topology features are comprehensively considered, where weighted interest topics are used to measure the semantic similarity between users. In addition, trust propagation ability of users is calculated to indicate micro topology information of the social network. In order to find the top-k most trusted neighbors, two combination strategies for the above two factors are proposed in this paper. During trust inference, the traversal depth is constrained according to the heuristic rule based on the “small world” theory. Three versions of the trust rate inference algorithm are presented. The first algorithm merges interest topics and topology features into a hybrid measure for trusted neighbor selection. The other two algorithms consider these two factors in two different orders. For the purpose of performance analysis, experiments are conducted on a public and widely-used data set. The results show that our algorithms outperform the state-of-the-art algorithms in effectiveness. In the meantime, the efficiency of our algorithms is better than or comparable to those algorithms.
Eigenfunction-Based Multitask Learning in a Reproducing Kernel Hilbert Space

Multitask learning aims to improve the performance on related tasks by exploring the interdependence among them. Existing multitask learning methods explore the relatedness among tasks on the basis of the input features and the model parameters. In this paper, we focus on nonparametric multitask learning and propose to measure task relatedness from a novel perspective in a reproducing kernel Hilbert space (RKHS). Past works have shown that the objective function for a given task can be approximated using the top eigenvalues and corresponding eigenfunctions of a predefined integral operator on an RKHS. In our method, we formulate our objective for multitask learning as a linear combination of two sets of eigenfunctions, common eigenfunctions shared by different tasks and unique eigenfunctions in individual tasks, such that the eigenfunctions for one task can provide additional information on another and help to improve its performance. We present both theoretical and empirical validations of our proposed approach. The theoretical analysis demonstrates that our learning algorithm is uniformly argument stable and that the convergence rate of the generalization upper bound can be improved by learning multiple tasks. Experiments on several benchmark multitask learning data sets show that our method yields promising results.
A general moving detection method using dual-target nonparametric background model

Designing a general motion detection method that has self-adaptive parameters remains a challenging issue in video surveillance. To address this problem, in this paper, a dual-target nonparametric background modeling (DTNBM) method is proposed. This model integrates the gray value and gradient to represent each pixel, which enhances the discriminative ability of the background model. We design a simple but effective classification rule for determining whether a pixel belongs to a motionless object or dynamic background. Moreover, DTNBM provides suitable updating strategies for the two categories of pixels. Most importantly, DTNBM utilizes a dual-target updating strategy to preserve the completeness of static objects and prevent false detections that are caused by background initialization or frequent background variations. To improve the updating effectiveness and efficiency, we combine similar and random schemes for background updating. The key features of DTNBM include nonparametric modeling and a controlling threshold adaptation process, which render our method easy to use on various scenarios. Comprehensive experiments have been conducted, and the results demonstrate that DTNBM outperforms the state-of-the-art methods in foreground detection.
A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure

This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation. CIM is a generalized similarity measurement which can maintain a high-noise reduction ability even in a high-dimensional space. In addition, a Growing Neural Gas (GNG)-based topology construction process is integrated into the ART framework to enhance its self-organizing ability. The simulation experiments with synthetic and real-world datasets show that the proposed model has an outstanding stable self-organizing ability for various test environments.
The Effect of Mobile Learning Approach on University Students' Academic Success for Database Management Systems Course

This article investigates the effect of mobile learning support on students' academic success on a database management systems (DBMS) course. The research was carried out with 36 students attending a state university in Turkey. In this study, a mixed method was used, which includes both quantitative and qualitative data collection techniques. For the quantitative data, the study was conducted in accordance with the pre-test and post-test-based two group quasi-experimental model. For the qualitative data, the descriptive analysis technique was used. The independent sample t-test and paired simple t-test were employed in the analysis phase. According to the findings, mobile learning positively affected students' academic achievement on the DBMS course, in that the students supported by mobile learning were more successful than students supported only by face-to-face training method. Finally, it is seen that the students have emphasized themes of motivation, 1-1 access, and an interest in terms of the effect of m-learning approach on academic success.
Automatic Question Classifiers: A Systematic Review

Question classification is a key point in many applications, such as Question Answering (QA, e.g., Yahoo! Answers), Information Retrieval (IR, e.g., Google search engine), and E-learning systems (e.g., Bloom's tax. classifiers). This paper aims to carry out a systematic review of the literature on automatic question classifiers and the technology directly involved. Automatic classifiers are responsible for labeling a certain evaluation item using a type of categorization as a selection criterion. The analysis of 80 primary studies previously selected revealed that SVM is the main algorithm of the Machine Learning used, while BOW and TF-IDF are the main techniques for feature extraction and selection, respectively. According to the analysis, the taxonomies proposed by Li and Roth and Bloom were the most used ones for the classification criteria, and Accuracy/Precision/Recall/F1-score were proven to be the most used metrics. In the future, the objective is to perform a meta-analysis with the studies that authorize the availability of their data.
An Intermediate Data Partition Algorithm for Skew Mitigation in Spark Computing Environment

In the parallel computing framework of Hadoop/Spark, data skew is a common problem resulting in performance degradation, such as prolonging of the entire execution time and idle resources. What lies behind this issue is the partition imbalance, that causes significant differences in the amount of data processed by each reduce task. This paper proposes a key reassigning and splitting partition algorithm (SKRSP) to solve the partition skew from the source codes of Spark-core_2.11 project, which considers both the partition balance of the intermediate data and the partition balance after shuffle operators. First, we propose a step-based algorithm for sampling the input data to estimate the general key distribution of entire intermediate data. According to the types of the specific applications, we design two algorithms: hash based key reassigning algorithm (KRHP) and rang based key splitting algorithm (KSRP), which can generate appropriate strategy and implement the skew mitigation in shuffle phase. KKSRP generates the weighted bounds to split intermediate data for the type of sort-based applications while KRHP records these reassigned keys and the new reducers these keys belong to for other applications. Finally, we implement SKRSP in Spark 2.2.0 and evaluate its performance through four benchmarks exhibiting significant data skew: WordCount, Sort, Join, and PageRank. The experimental results verify that our algorithm not only can achieve a better partition balance but also reduce the execution time of reduce tasks effectively.
A methodological framework for cloud resource provisioning and scheduling of data parallel applications under uncertainty

Data parallel applications are being extensively deployed in cloud environments because of the possibility of dynamically provisioning storage and computation resources. To identify cost-effective solutions that satisfy the desired service levels, resource provisioning and scheduling play a critical role. Nevertheless, the unpredictable behavior of cloud performance makes the estimation of the resources actually needed quite complex. In this paper we propose a provisioning and scheduling framework that explicitly tackles uncertainties and performance variability of the cloud infrastructure and of the workload. This framework allows cloud users to estimate in advance, i.e., prior to the actual execution of the applications, the resource settings that cope with uncertainty. We formulate an optimization problem where the characteristics not perfectly known or affected by uncertain phenomena are represented as random variables modeled by the corresponding probability distributions. Provisioning and scheduling decisions – while optimizing various metrics, such as monetary leasing costs of cloud resources and application execution time – take fully account of uncertainties encountered in cloud environments. To test our framework, we consider data parallel applications characterized by a deadline constraint and we investigate the impact of their characteristics and of the variability of the cloud infrastructure. The experiments show that the resource provisioning and scheduling plans identified by our approach nicely cope with uncertainties and ensure that the application deadline is satisfied.
Late fusion of deep learning and handcrafted visual features for biomedical image modality classification

Much of medical knowledge is stored in the biomedical literature, collected in archives like PubMed Central that continue to grow rapidly. A significant part of this knowledge is contained in images with limited metadata available which makes it difficult to explore the visual knowledge in the biomedical literature. Thus, extraction of metadata from visual content is important. One important piece of metadata is the type of the image, which could be one of the various medical imaging modalities such as X-ray, computed tomography or magnetic resonance images and also of general graphs that are frequent in the literature. This study explores a late, score-based fusion of several deep convolutional neural networks with a traditional hand-crafted bag of visual words classifier to classify images from the biomedical literature into image types or modalities. It achieved a classification accuracy of 85.51% on the ImageCLEF 2013 modality classification task, which is better than the best visual methods in the challenge that the data were produced for, and similar compared to mixed methods that make use of both visual and textual information. It achieved similarly good results of 84.23 and 87.04% classification accuracy before and after augmentation, respectively, on the related ImageCLEF 2016 subfigure classification task.
Collaborative extreme learning machine with a confidence interval for P2P learning in healthcare

In modern e-healthcare systems, medical institutions can provide more reliable diagnoses by introducing Machine-Learning (ML)-based classifiers. These ML classifiers are frequently trained with huge numbers of patients’ data to keep updated with new diseases and changes in current disease patterns. To increase the accuracy in prediction process, Peer-to-Peer (P2P) learning systems have been explored by many studies by which medical institutions can share their data with others: the more data are available, the more accurate the predictions. However, the traditional P2P network system requires much time in which the training data are shared among the nodes in the network. The system also spends much time on learning from samples where the data labels are unknown. Moreover, some nodes may perform certain computations which had already been computed by other nodes, resulting in redundant computations. In this paper, in order to deal with samples having unknown data labels, we propose a Collaborative Extreme Learning Machine (CELM) with a Confidence Interval (CI), which is an enhanced version of the traditional Extreme Learning Machine (ELM). Our proposed model eliminates redundant calculations of the network nodes (the e-healthcare institutions) to improve the learning efficiency, and improves the prediction accuracy by considering where plausible predictions lie. The extensive experimental analysis shows that the proposed model is efficient and achieves high accuracy (up to 98%) in diagnosing clinical events by analyzing patients’ medical records.
Differentially Private Maximum Consensus: Design, Analysis and Impossibility Result

In network systems, calculating maximum element plays a fundamental role for distributed data analysis. Maximum consensus algorithm can accomplish the calculation in a fully distributed way. However, the data for communication are privacy-sensitive in many scenarios, making nodes unwilling to participate in the distributed calculation process. How to ensure privacy and accuracy for distributed calculation of maximum element is essential while challenging. In this paper, we first prove that exact maximum consensus and differential privacy cannot be guaranteed simultaneously. Then, to provide sufficient privacy preservation for nodes, we propose a novel differentially private maximum consensus (DPMC) algorithm where nodes perturb their initial states with Laplacian noises. We prove that DPMC algorithm preserves differential privacy and give the analytical expression of privacy preserving degree. Furthermore, the proposed algorithm also guarantees finite-time convergence and achieves high convergence accuracy. The performance tradeoff is finally analyzed and evaluated by extensive numerical simulations.
Performance evaluation of a SaaS cloud under different levels of workload computational demand variability and tardiness bounds

As the paradigm shift toward Software as a Service (SaaS) continues to gain momentum, there is a growing focus on the performance of SaaS clouds from both academia and industry. In this paper, we evaluate the performance of a SaaS cloud under various tardiness bounds and different levels of workload computational demand variability. The workload consists of bag-of-tasks jobs, which are scheduled on the underlying virtualized host environment. The jobs have soft deadlines and different levels of variability in their computational demands. A multi-tier SLA is employed, which imposes a soft and a hard tardiness bound on each job. Furthermore, the employed pricing scheme is based on the provided level of Quality of Service (QoS). The performance of the SaaS cloud is evaluated by simulation, in an attempt to shed light on how it is affected by the tardiness bound and the computational demand variability of the workload.
Parallel Cyber-Physical-Social Systems Based Smart Energy Robotic Dispatcher and Knowledge Automation: Concepts, Architectures, and Challenges

We propose a novel concept of the robot of energy control based on parallel cyber-physical-social systems for the next generation of energy and electric power systems. We thoroughly investigate its knowledge automation technologies and discuss its main challenges in preparation of promising evolution from Energy 4.0 to Energy 5.0 in China.
Semantic image segmentation via guidance of image classification

This paper describes a joint segmentation and classification approach that exploits global image features to validate the predictions from local appearance descriptors and to ensure their consistent labeling. The in-between interplay is encoded by a parameter-learning process of a unified deep learning model embedding a fully convolution network portion. Although FCN has a relatively large recept field, the integration of the image content as a whole makes the prediction more reasonable and logical, since coincidences in local neighborhoods are more likely to be depressed given global structures. We also propose a content-sensitive co-occurrence priori for label compatibility, which provides additional constraints for CRF based segmentation.
Privacy-Preserving iVector-Based Speaker Verification

This paper introduces an efficient algorithm to develop a privacy-preserving voice verification based on iVector and linear discriminant analysis techniques. This research considers a scenario in which users enrol their voice biometric to access different services (i.e., banking). Once enrolment is completed, users can verify themselves using their voice print instead of alphanumeric passwords. Since a voice print is unique for everyone, storing it with a third-party server raises several privacy concerns. To address this challenge, this paper proposes a novel technique based on randomization to carry out voice authentication, which allows the user to enrol and verify their voice in the randomized domain. To achieve this, the iVector-based voice verification technique has been redesigned to work on the randomized domain. The proposed algorithm is validated using a well-known speech dataset. The proposed algorithm neither compromises the authentication accuracy nor adds additional complexity due to the randomization operations.
Application of local invariant symmetry features to detect and localize image copy move forgeries

Today whatever is seen in digital images could be unrealistic due to the advent of sophisticated systems and image editing software's. It is easy to edit digital images without leaving the traces of manipulation, therefore tampering is hard to discern visually. Among the types of digital image forgeries, realizing the copy move forgery is very challenging. Hence, this paper proposes a novel scheme to detect copy-move forgery by means of symmetry based local features. The proposed scheme can also detect the multiple copy move forgeries and localize the detected regions. The experiments are carried out by using the various datasets, and comparative study also made with the existing methods.
Real-Time Vehicle Detection Using an Effective Region Proposal-Based Depth and 3-Channel Pattern

Traditional deep learning-based vehicle detection methods are often designed using a pyramid of filters with multiple scales and sizes; therefore, the processing time is slow due to the large number of scales used and because the classifier runs at all scales. Recently, a deep learning-based region proposal network was introduced to detect vehicles that only employ the network one time regardless of the size of the input image. In object detection, deep learning-based region proposal networks have achieved state-of-the-art performance in terms of accuracy. These systems achieve a very high accuracy under normal driving conditions; however, their performance decreases under difficult driving conditions such as in snow, rain, or fog. In addition, the current state-of-the-art system-based region proposal networks still fail to satisfy the real-time requirements of the driving assistant systems. More recently, the identification of local patterns has been shown to improve the performance of the traditional deep-learning systems; hence, this paper investigates local patterns in region proposal networks to improve their accuracy. Depth information is also investigated to improve the processing time of current region proposal networks. Our experimental results show that the proposed system obtains better performance than the state-of-the-art object region detection systems in terms of both accuracy and processing time.
MTRA: Multi-Tier randomized remote attestation in IoT networks

Large numbers of Internet of Things (IoT) devices are increasingly deployed in many aspects of modern life. Given their limited resources and computational power, verifying program integrity in such devices is a challenging issue. In this paper, we design MTRA, a Multiple-Tier Remote Attestation protocol, by exploiting differences in resources and computational power among various types of networked IoT devices. More powerful devices equipped with a Trusted Platform Module (TPM) are verified through trusted hardware while others are verified through software-based attestation. In addition, a randomized memory region for attestation is used in MTRA to increase the entropy of the attestation responses. MTRA is a flexible means of program integrity verification for heterogeneous IoT devices.
Recognition of radar signals based on AF grids and geometric shape constraint

Low signal-to-noise ratio (SNR) in wideband radar reconnaissance degrades performance of existing signal type recognition methods. To achieve higher correct recognition rate in low SNR levels, this paper proposes a recognition approach containing feature extraction and automatic estimation for signal type number. First, feature vectors of radar pulses are constructed from maximum energy angle (MEA) and maximum energy slice (MES) of ambiguity function (AF). To improve applicability in practical implementing, AF grids method is developed to obtain MEA and MES. A novel algorithm for denoising MES with geometric shape constraint is also proposed to improve validity of features with low SNR. Second, for realistic reconnaissance lacking prior knowledge, a type number estimation method is developed that costs little extra processing burden. Simulation results indicate that the proposed denoising algorithm brings high stability and robustness to extracted features in low SNR levels. The recognition approach in this paper is applicable to more realistic scenarios and achieves higher correct recognition rate with respect to low SNR compared with existing methods.