Reviewerly/exHarmony huggingface dataset:
Domain learning joint with semantic adaptation for human action recognition

Action recognition is a challenging task in the field of computer vision. The deficiency in training samples is a bottleneck problem in the current action recognition research. With the explosive growth of Internet data, some researchers try to use prior knowledge learned from various video sources to assist in recognizing the action video of the target domain, which is called knowledge adaptation. Based on this idea, we propose a novel framework for action recognition, called Semantic Adaptation based on the Vector of Locally Max Pooled deep learned Features (SA-VLMPF). The proposed framework consists of three parts: Two-Stream Fusion Network (TSFN), Vector of Locally Max-Pooled deep learned Features (VLMPF) and Semantic Adaptation Model (SAM). TSFN adopts a cascaded convolution fusion strategy to combine the convolutional features extracted from two-stream network. VLMPF retains the long-term information in videos and removes the irrelevant information by capturing multiple local features and extracting the features with the highest response to action category. SAM first maps the data of the auxiliary domain and the target domain into the high-level semantic representation through the deep network. Then the obtained high-level semantic representations from auxiliary domain are adapted into target domain in order to optimize the target classifier. Compared with the existing methods, the proposed methods can utilize the advantages of deep learning methods in obtaining the high-level semantic information to improve the performance of knowledge adaptation. At the same time, SA-VLMPF can make full use of the auxiliary data to make up for the insufficiency of training samples. Multiple experiments are conducted on several couples of datasets to validate the effectiveness of the proposed framework. The results show that the proposed SA-VLMPF outperforms the state-of-the-art knowledge adaptation methods.
Authorship of Pauline epistles revisited

The name Paul appears in 13 epistles, but is he the real author? According to different biblical scholars, the number of letters really attributed to Paul varies from 4 to 13, with a majority agreeing on seven. This article proposes to revisit this authorship attribution problem by considering two effective methods (Burrows' Delta, Labbé's intertextual distance). Based on these results, a hierarchical clustering is then applied showing that four clusters can be derived, namely: { Colossians ‐ Ephesians }, {1 and 2 Thessalonians }, { Titus , 1 and 2 Timothy }, and { Romans , Galatians , 1 and 2 Corinthians }. Moreover, a verification method based on the impostors' strategy indicates clearly that the group { Colossians ‐ Ephesians } is written by the same author who seems not to be Paul. The same conclusion can be found for the cluster { Titus , 1 and 2 Timothy }. The Letter to Philemon stays as a singleton, without any close stylistic relationship with the other epistles. Finally, a group of four letters { Romans , Galatians , 1 and 2 Corinthians } is certainly written by the same author (Paul), but the verification protocol also indicates that 2 Corinthians is related to 1 Thessalonians, rendering a clear and simple interpretation difficult.
Meta Diagram Based Active Social Networks Alignment

Network alignment aims at inferring a set of anchor links matching the shared entities between different information networks, which has become a prerequisite step for effective fusion of multiple information networks. In this paper, we will study the network alignment problem to fuse online social networks specifically. Social network alignment is extremely challenging to address due to several reasons, i.e., lack of training data, network heterogeneity and one-to-one constraint. Existing network alignment works usually require a large number of training data, but such a demand can hardly be met in applications, as manual anchor link labeling is extremely expensive. Significantly different from other homogeneous network alignment works, information in online social networks is usually of heterogeneous categories, the incorporation of which in model building is not an easy task. Furthermore, the one-to-one cardinality constraint on anchor links renders their inference process intertwistingly correlated. To resolve these three challenges, a novel network alignment model, namely ActiveIter, is introduced in this paper. ActiveIter defines a set of inter-network meta diagrams for anchor link feature extraction, adopts active learning for effective label query and uses greedy link selection for anchor link cardinality filtering. Extensive experiments are conducted on real-world aligned networks datasets, and the experimental results have demonstrated the effectiveness of ActiveIter compared with other state-of-the-art baseline methods.
Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients

We present two approximate Bayesian inference methods for parameter estimation in partial differential equation (PDE) models with space-dependent and state-dependent parameters. We demonstrate that these methods provide accurate and cost-effective alternatives to Markov Chain Monte Carlo simulation. We assume a parameterized Gaussian prior on the unknown functions, and approximate the posterior density by a parameterized multivariate Gaussian density. The parameters of the prior and posterior are estimated from sparse observations of the PDE model's states and the unknown functions themselves by maximizing the evidence lower bound (ELBO), a lower bound on the log marginal likelihood of the observations. The first method, Laplace-EM, employs the expectation maximization algorithm to maximize the ELBO, with a Laplace approximation of the posterior on the E-step, and minimization of a Kullback-Leibler divergence on the M-step. The second method, DSVI-EB, employs the doubly stochastic variational inference (DSVI) algorithm, in which the ELBO is maximized via gradient-based stochastic optimization, with noisy gradients computed via simple Monte Carlo sampling and Gaussian backpropagation. We apply these methods to identifying diffusion coefficients in linear and nonlinear diffusion equations, and we find that both methods provide accurate estimates of posterior densities and the hyperparameters of Gaussian priors. While the Laplace-EM method is more accurate, it requires computing Hessians of the physics model. The DSVI-EB method is found to be less accurate but only requires gradients of the physics model.
Channel Pruning for Visual Tracking

Deep convolutional feature based Correlation Filter trackers have achieved record-breaking accuracy, but the huge computational complexity limits their application. In this paper, we derive the efficient convolution operators (ECO) tracker which obtains the top rank on VOT-2016. Firstly, we introduce a channel pruned VGG16 model to fast extract most representative channels for deep features. Then an Average Feature Energy Ratio method is put forward to select advantageous convolution channels, and an adaptive iterative strategy is designed to optimize object location. Finally, extensive experimental results on four benchmarks OTB-2013, OTB-2015, VOT-2016 and VOT-2017, demonstrate that our tracker performs favorably against the state-of-the-art methods.
Modelling ore-forming processes through a cosine similarity measure: Improved targeting of porphyry copper deposits in the Manzhouli belt, China

Recognizing distribution patterns of ore-related pathfinder elements requires an understanding of the corresponding ore-forming systems and may lead to the definition of more reliable exploration targets. Given that complex ore-forming processes in complex geological terrains can result in either enrichment or depletion of ore elements, simultaneous consideration of both processes in geochemical anomaly modelling would be advantageous for defining exploration targets. Here, we adapted the “cosine similarity measure” method for pattern recognition of ore-related geochemical anomalism to facilitate simultaneous modelling of element enrichment and depletion. We tested the performance of this new method on porphyry copper exploration data from the Manzhouli belt, China, and demonstrated that our method is highly efficient with respect to recognizing ore-related geochemical signatures and, thus, useful for “vectoring” towards porphyry copper deposits and defining more reliable geochemical exploration targets.
Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images

Automated detection and segmentation of nuclei from high-resolution histopathological images is a challenging problem owing to the size and complexity of digitized histopathologic images. In the context of breast cancer, the modified Bloom-Richardson Grading system is highly correlated with the morphological and topological nuclear features are highly correlated with Modified Bloom-Richardson grading. Therefore, to develop a computer-aided prognosis system, automated detection and segmentation of nuclei are critical prerequisite steps. We present a method for automated detection and segmentation of breast cancer nuclei named a convolutional neural network initialized active contour model with adaptive ellipse fitting (CoNNACaeF). The CoNNACaeF model is able to detect and segment nuclei simultaneously, which consist of three different modules: convolutional neural network (CNN) for accurate nuclei detection, (2) region-based active contour (RAC) model for subsequent nuclear segmentation based on the initial CNN-based detection of nuclear patches, and (3) adaptive ellipse fitting for overlapping solution of clumped nuclear regions. The performance of the CoNNACaeF model is evaluated on three different breast histological data sets, comprising a total of 257 H&E-stained images. The model is shown to have improved detection accuracy of F-measure 80.18%, 85.71%, and 80.36% and average area under precision-recall curves (AveP) 77%, 82%, and 74% on a total of 3 million nuclei from 204 whole slide images from three different datasets. Additionally, CoNNACaeF yielded an F-measure at 74.01% and 85.36%, respectively, for two different breast cancer datasets. The CoNNACaeF model also outperformed the three other state-of-the-art nuclear detection and segmentation approaches, which are blue ratio initialized local region active contour, iterative radial voting initialized local region active contour, and maximally stable extremal region initialized local region active contour models.
Measuring Similarity of Educational Items: An Overview

A measure of similarity of educational items has many applications in adaptive learning systems and can be useful also for teachers and content creators. We provide a thorough overview of approaches for measuring item similarity. We document the computation pipeline, explicitly highlighting many choices that have to be made in order to quantify item similarity. We also discuss methods for the analysis of similarity measures. For illustration and evaluation, we consider items from diverse domains, e.g., mathematics, programming, and language learning. Although there is no ultimate, universal approach to measuring item similarity, our overview leads to guidelines that facilitate computation of item similarity in practical applications.
Design and Low-Cost Implementation of an Electric Wheelchair Control

This paper discusses the design and low-cost implementation of direction and speed controller for an electric wheelchair actuated using a permanent magnet direct current (PMDC) motor. Most of the works are performed either with the simulation of PMDC motor control with various techniques or studied the performance of a single type of controller in real-time with the wheelchair. In this work, the authors simulated the control of PMDC motor with three different controllers and tuned the parameters for real-time implementation of electric wheelchair control using PMDC motor. The authors attempted the simple solution using push button-based interface as well as a graphical user interface for the direction and speed control of electric wheelchair. The signal processing path of the controller, decode the user command input for the direction and desired speed. The controller generates necessary pulse width modulated signals to the H-bridge driver for directions forward, backward, left, and right direction as well as speed control of wheelchair. The user speed control commands low, slightly medium, medium, high, and very high speed are sensed to limit the speed of wheelchair. The performance capability of the intelligent neural network and fuzzy logic controller is studied and compared with proportional-integral-derivative (PID) controller with variations in speed. The PID, neural network and fuzzy logic controller are designed using Matlab-Simulink for PMDC motor and controller parameters are tuned for driving the wheelchair in the real-time implementation using the ATmega 328P microcontroller. The fuzzy logic controller in the electric wheelchair demonstrated the improved performance with less peak overshoot and faster settling with less oscillation compared to PID and neural network controller.
Information Security Policy Compliance: Leadership and Trust

The purpose of this study was to examine the influence of trust variables (trust: competence, trust: benevolence, trust: integrity) on leadership regarding the organization’s information security policy (ISP) compliance. An instrument with four constructs was used to collect data from 474 non-management subjects from various organizations in the USA. Collected data were analyzed through multiple regression procedure. Results revealed that all trust variables (trust: competence, trust: benevolence, trust: integrity) were influential in predicting the leadership regarding the organization’s ISP compliance. The findings are discussed and implications for practice are outlined. Conclusion, limitations, and recommendations for future research are drawn.
Nonlinear Information Processing Algorithm for Navigation Complex with Increased Degree of Parametric Identifiability

The aircraft navigation system with the error compensation algorithm of the basic inertial navigation system is considered. A nonlinear correction algorithm has been developed using an SDC representation of the navigation system’s error model matrix. To improve the accuracy of the model, a method is proposed for increasing the degree of identifiability of the parameters in the model matrix. The problem of identification of nonlinear systems is investigated. A numerical criterion for the degree of identifiability of the parameters of a non-linear model of one class, based on the SDC representation of the non-linear model, has been developed.
“Are You Playing a Shooter Again?!” Deep Representation Learning for Audio-Based Video Game Genre Recognition

In this paper, we present a novel computer audition task: audio-based video game genre classification. The aim of this study is threefold: 1) to check the feasibility of the proposed task; 2) to introduce a new corpus: The Game Genre by Audio + Multimodal Extracts (G2 AME), collected entirely from social multimedia; and 3) to compare the efficacy of various acoustic feature spaces to classify the G2 AME corpus into six game genres using a linear support vector machine classifier. For the classification we extract three different feature representations from the game audio files: 1) Knowledge-based acoustic features; 2) DEEP SPECTRUM features; and 3) quantized DEEP SPECTRUM features using Bag-of-Audio-Words. The DEEP SPECTRUM features are a deep-learning-based representation derived from forwarding the visual representations of the audio instances, in particular spectrograms, mel-spectrograms, chromagrams, and their deltas through deep task-independent pretrained CNNs. Specifically, activations of fully connected layers from three common image classification CNNs, GoogLeNet, AlexNet, and VGG16 are used as feature vectors. Results for the six-genre classification problem indicate the suitability of our deep learning approach for this task. Our best method achieves an accuracy of up to 66.9% unweighted average recall using tenfold cross-validation.
Modelling resource heterogeneities in cloud simulations and quantifying their accuracy

Simulations are often used to evaluate the performance of various scheduling and migration techniques in the context of large computing systems such as clouds and datacenters. To ensure that simulations match the real platform as close as possible, plausible assumptions and accurate statistical models are used in designing simulations; and that could also offer accurate results. However, it is not always possible that similar numerical results would also be achievable in a real cloud test-bed. The reason is that a simulator only abstracts a model and, hence, a system; but does not always reflect the real world scenarios. Therefore, the solution of any research problem using numerical simulation (experimentation) is not just to find a result, but also to ensure the quality and accuracy of the estimated results. CloudSim is largely used in the cloud research community to evaluate the performance of various resource allocation and migration policies. However, resources such as CPU, memory and application heterogeneities are not modelled yet. Moreover, its accuracy is rarely addressed. In this paper, we: (i) describe an extension to CloudSim that offers support for resource (CPU) and application heterogeneities; and (ii) demonstrate several techniques that could be used to measure the accuracy of results obtained in simulations, particularly, in the extended CloudSim. Based on our evaluation, we suggest that the accuracy and precision of the extended version of the CloudSim simulator may be as high as ∼ 98.63% for certain energy and performance efficient resource allocation and consolidation with migration policies in heterogeneous datacenters.
Enhancements of rule-based models through refinements of Fuzzy C-Means

Rule-based modeling has been one of the key directions in fuzzy modeling since its inception. Nowadays it exhibits a number of conceptual development and algorithmic pursuits. Fuzzy clustering, especially Fuzzy C-Means (FCM), is a commonly used algorithmic tool to construct rules, in particular building fuzzy sets forming conditions of the rules. While being efficient with this regard and transforming data into a collection of fuzzy sets, one should note that the agenda of fuzzy clustering (and clustering, in general) does not fully align with the agenda of system modeling and because of this, it requires some attention and calls for further refinements. Clustering is a direction-free (relational) process and developed constructs (clusters) are optimized in light of the direction-free criterion (say, a commonly used objective function). In contrast, in fuzzy models the rules are direction-sensitive artifacts, which implies that the clusters themselves need to be reflective of this directionality requirement. This paper contributes to this direction of studies by bringing a collection of augmentations of the generic FCM algorithm along this line. There are three original enhancements considered in the study: (i) an accommodation of extreme (minimal and maximal) values encountered in the output variable, (ii) a reduction of spurious impact of rules being the result of variable overlap existing among fuzzy sets forming the condition parts of the rules, and (iii) a development of the core (granular) structure of rules and analysis of their features. The motivation behind these augmentations is presented followed by the detailed algorithms along with a series of illustrative examples. In the sequel, a number of numeric studies are conducted demonstrating in a quantitative manner the contributions delivered by the refinements.
Write-Aware Replica Placement for Cloud Computing

Cloud applications such as virtualized network functions usually rely on static or dynamic replication mechanisms to achieve high service reliability and satisfy the requirements of service level agreement. Because replicas are allocated in geographically distributed datacenters, their updates increase the difficulty of maintaining consistency among replicas in different datacenters. The consistency overheads among replicas increase bandwidth consumption and transmission latencies to degrade the quality of service (QoS) performance and service reliability. Although previous mechanisms attempt to shorten response time, reduce bandwidth consumption, or maintain high service reliability with minimized storage cost, they do not consider the overhead of data consistency. In this paper, we study the problem of replica placement with consistency overheads by analyzing the latency and reliability for different types of operations. A replication algorithm, write-aware replica placement (WARP), is then presented based on the analysis. The simulation results show that WARP can shorten response time and improve QoS without degrading reliability.
Bug Localization for Version Issues With Defect Patterns

Version issues are becoming more and more prominent with the continuous development of software. Bug localization for version issues is time-consuming and labor-intensive. Although some bug localization techniques, such as those based on information retrieval (IR), have been proposed, they cannot handle these bugs very well as the version-related bugs have their own defect patterns. However, few existing works have focused on revealing these defect patterns and utilizing them for localization of bugs. To fill this gap, we propose a new approach by leveraging the version-related defect patterns to localize the version-related issues integrated with the IR technique. First, we extract version-related bugs from bug repositories and build a version-related bug repository. Given a new version-related bug report, we identify the defect patterns of corresponding similar historical bug reports from the version-related bug repository. Then, we combine these defect patterns with the IR technique to rank the candidate code snippets as suspicious code for developers to fix. The evaluation demonstrates that our approach is more effective to identify the faulty code related to version issues than the existing IR-based bug localization technique.
A Fuzzy-Based Brokering Service for Cloud Plan Selection

The current cloud market features a multitude of cloud services that differ from one another in terms of functionality or of security/performance guarantees. Users wishing to use a cloud service for storing, processing, or sharing their data must be able to select the service that best matches their desiderata. In this paper, we propose a novel, user centric, brokering service for supporting users in the specification of requirements and enabling their evaluation against available cloud plans, assessing how much the different plans can satisfy the user's desiderata. Our brokering service allows users to specify their desiderata in an easy and intuitive way by using natural language expressions and high-level concepts. Fuzzy logic and fuzzy inference systems are adopted to quantitatively assess the compliance of cloud services with the users' desiderata, and hence to help users in the cloud service selection process.
Iterative Approximation of Analytic Eigenvalues of a Parahermitian Matrix EVD

We present an algorithm that extracts analytic eigenvalues from a parahermitian matrix.Operating in the discrete Fourier transform domain, an inner iteration re-establishes the lost association between bins via a maximum likelihood sequence detection driven by a smoothness criterion.An outer iteration continues until a desired accuracy for the approximation of the extracted eigenvalues has been achieved.The approach is compared to existing algorithms.