Reviewerly/exHarmony huggingface dataset:
Toward Fail-Safe Speaker Recognition: Trial-Based Calibration With a Reject Option

The output scores of most of the speaker recognition systems are not directly interpretable as stand-alone values. For this reason, a calibration step is usually performed on the scores to convert them into proper likelihood ratios, which have a clear probabilistic interpretation. The standard calibration approach transforms the system scores using a linear function trained using data selected to closely match the evaluation conditions. This selection, though, is not feasible when the evaluation conditions are unknown. In previous work, we proposed a calibration approach for this scenario called trial-based calibration (TBC). TBC trains a separate calibration model for each test trial using data that is dynamically selected from a candidate training set to match the conditions of the trial. In this work, we extend the TBC method, proposing: 1) a new similarity metric for selecting training data that result in significant gains over the one proposed in the original work; 2) a new option that enables the system to reject a trial when not enough matched data are available for training the calibration model; and 3) the use of regularization to improve the robustness of the calibration models trained for each trial. We test the proposed algorithms on a development set composed of several conditions and on the Federal Bureau of Investigation multi-condition speaker recognition dataset, and we demonstrate that the proposed approach reduces calibration loss to values close to 0 for most of the conditions when matched calibration data are available for selection, and that it can reject most of the trials for which relevant calibration data are unavailable.
Compositional coding capsule network with k-means routing for text classification

Text classification is a challenging problem which aims to identify the category of texts. In the process of training, word embeddings occupy a large part of parameters. Under the constraint of limited computing resources, it indirectly restricts the ability of subsequent network design. In order to reduce the number of parameters for constructing word embeddings, this paper explores compositional coding mechanism and proposes a compositional weighted coding method to replace the conventional embedding layer. Furthermore, inspired by the excellent performance of capsule network in image classification, we design a capsule network combined with our compositional weighted coding method for text classification. We also offer a new routing algorithm based on k-means clustering theory to thoroughly mine the relationship between capsules. Experiments conducted on eight challenging text classification datasets show that the proposed method achieves competitive accuracy compared to the state-of-the-art approach with significantly fewer parameters.
A local Zernike moment-based unbiased nonlocal means fuzzy C-Means algorithm for segmentation of brain magnetic resonance images

Clustering based unsupervised segmentation approaches have become important tools to model data and infer knowledge. Such approaches are often used in various biomedical applications to assist experts in analyzing the data. Segmentation of brain magnetic resonance (MR) images has become a very challenging task due to the presence of Rician noise and intensity inhomogeneity. The existing methods dealing with such issues work in the spatial domain. These methods are prone to losing image details while reducing the effect of noise. The proposed method works in the moment domain, and for this purpose, we have selected Zernike moments (ZMs). The ZMs are orthogonal, rotation invariant and robust to image noise. The proposed method is based on our previous work on Zernike moments-based unbiased nonlocal means (ZM-UNLM) approach for denoising MR images affected by the Rician noise. The ZM-UNLM-based approach uses both the local and nonlocal information while eliminating Rician noise from brain MR images which have fine image details along with the noise components. Keeping in view the superior performance of the ZM-UNLM, we extend it to segment brain MR images and propose a method called local ZMs (LZM)-based unbiased nonlocal means fuzzy C-means (LZM-UNLM-FCM) approach which provides much-improved segmentation accuracy as compared to the existing spatial domain-based state-of-the-art techniques. It is shown how the LZMs based technique reduces the effect of the Rician noise while retaining the fine structures, edges, and other image details in the moment domain. The complete framework consists of an intelligent knowledge-driven approach that segments tissues from brain MR images using both the local and nonlocal information in the moment domain. The proposed approach has significance in developing future expert and intelligent systems that do not require any human intervention to manually annotate data and saves time for diagnostic related tasks. Detailed experimental results are provided to demonstrate the superior performance of the proposed method over the existing state-of-the-art methods.
Task-generic semantic convolutional neural network for web text-aided image classification

In this work, we explore how to use external and auxiliary web text to improve image classification. The keystone of web text-aided image classification is the representation learning for these two modalities of data. In the recent decade, convolutional neural networks (CNN) as the core representation methods of images have become a commodity in computer vision community. On the other hand, the long reign of word vectors has the same wide-ranging impact on NLP for representation learning. Based on the pre-trained word vectors, we propose a novel semantic CNN (s-CNN) model for high-level text representation learning using task-generic semantic filters. However, the s-CNN model inevitably brings about surplus semantic filters to achieve better applicability and generalization in universal tasks. Moreover, the surplus filters may lead to semantic overlaps and feature redundancy issue. To address this issue, we develop the so-called s-CNN Clustered (s-CNNC) models that uses filter clusters instead of individual filters. Interacting with the image CNN models, the s-CNNC models can further boost image classification under a multi-modal framework (mm-CNN). In addition, we propose to use the external text information selectively in the mm-CNN network to alleviate the noise problem inherent in web text. We validate the effectiveness of the proposed models on six benchmark datasets, and the results show that our approaches achieve remarkable improvements.
Multi-Switches Fault Diagnosis Based on Small Low-Frequency Data for Voltage-Source Inverters of PMSM Drives

For the inverter fault diagnosis of permanent magnet synchronous machine drives, it is difficult to simply and conveniently use the small low-frequency data without needing additional hardware and affecting the accuracy of the diagnosis results. This problem often leads to unnecessary computation and wastes a lot of corresponding system memory, increases the hardware requirements and realization difficulty, and affects practicability. In order to improve this problem, to make the design, debugging, and implementation of fault diagnosis more convenient and at low cost, and further to improve the practicability of the algorithm, a novel multi-switches fault diagnosis algorithm is proposed. First, a second low frequency processing method is used to obtain the small low-frequency data simply from the feedback signals of the controller. These small low-frequency data contain the main feature information of different switch states. Second, these small low-frequency data are effectively normalized by the single extremum normalization method, which is based on the symmetry features of different state data. These processed data can keep the asymmetry feature of the fault state of an inverter very well. Third, the main fault components and features are extracted from the distortion part and envelope change of the processed small low-frequency data. Fourth, echo state network is used by combining it with the extracted features to implement the intelligent classification. Different from the traditional neural network, the design of structure of hidden layer network is simplified, and the network training speed is very fast. Therefore, the debugging of this network is very convenient. Compared with the existing fault diagnosis algorithms, the proposed algorithm is convenient and at low cost to realize the multi-switches fault diagnosis. The effectiveness of fault diagnosis algorithm is verified by the experiment.
What We Don’t Know. The Effect of Realism in Virtual Reality on Experience and Behaviour

This study addresses the question how realistic Virtual Worlds should be designed in order to create engaging experiences and stimulate ‘natural’ behaviour. Creating high realistic worlds is time consuming and expensive and it is unclear whether it is always needed. With the aim of gaining insights about questions related to presence and realism, an experiment involving 72 participants was set up upon a Virtual Reality cycling apparatus in which different levels of realism were created. Users were observed and evaluated regarding their experience (engagement, presence, naturalness and negative effects), awareness of realism and behaviour. The results indicate that, despite that differences in realism were observed, differences in realism do not have an effect on experience and behaviour. There seem other variables involved that can affect the whole experience in an enough intensity to obliterate the effects of a better sense of presence and realism. In addition, an increase in a perceived higher level of realism seems to be depended on a congruent increase of different elements within the virtual world. Merely increasing the level of realism of one element does not alter levels of perceived realism of users.
Cost-sensitive feature selection via the ℓ2,1-norm

An essential step in data mining and machine learning is selecting a useful feature subset from the high-dimensional feature space. Many existing feature selection algorithms only consider precision, but do not consider error types and test cost. In this paper, we use the ℓ 2 , 1 -norm to propose a cost-sensitive embedded feature selection algorithm that minimizes the total cost rather than maximizing accuracy. The algorithm is a cost-sensitive feature selection algorithm with joint ℓ 2 , 1 -norm minimization of the loss function with misclassification costs. The ℓ 2 , 1 -norm based loss function with misclassification costs is robust to outliers. We also add an orthogonal constraint term to guarantee that each selected feature is independent. The proposed algorithm simultaneously takes into account both test costs and misclassification costs. Finally, an iterative updating algorithm is provided using the objective function that makes cost-sensitive feature selection more efficient. The cost-sensitive feature selection algorithm is more realistic than existing feature selection algorithms. Extensive experimental results on publicly available datasets demonstrate that the proposed algorithm is effective, can select a low-cost subset and achieve better performance than other feature selection algorithms in real-world applications.
The effects of moral disengagement and organizational ethical climate on insiders’ information security policy violation behavior

Purpose The purpose of this paper is to develop a model that integrates moral disengagement (MD) and organizational ethical climate (OEC) to understand information security policy (ISP) violation behavior in the workplace. This study extends prior work by identifying the moderating mechanisms of the ethical culture of OECs in the relationship between employees’ MD and ISP violation behavior intention. Design/methodology/approach By using scenario-based survey data from 433 employees in Chinese enterprises and by applying PLS-based structural equation modeling, the authors test a series of hypotheses. Findings Our empirical results highlight that the concept of MD has a significant effect on employees’ intention to violate ISPs. The authors also find that the OEC has a moderating role in the relationship between MD and ISP violation intention: the moderating role of law-and-rule-oriented OEC is significantly negative, but instrumentalism-oriented OEC positively moderates this relationship. Originality/value This study contributes to the literature on information security behavior by integrating two ethical theory frameworks MD and OECs into one theoretical model, and it calls attention to how ethical factors at the individual cognition level and organizational climate level work together to influence personal information security behavior. This study provides a new perspective of OEC from which to understand policy violation caused by moral self-regulation failure, and empirically explores its moderating role.
Managing energy, performance and cost in large scale heterogeneous datacenters using migrations

Improving datacenter energy efficiency becomes increasingly important due to energy supply problems, fuel costs and global warming. Virtualisation can help to improve datacenter energy efficiency through server consolidation which involves migrations that can be expensive in terms of extra energy consumption and performance loss. This is because, in clouds, Virtual Machines (VMs) of the same instance class running on different hosts may perform quite differently due to resource heterogeneity. As a result of variations in performance, different runtimes will exist for a given workload, with longer runtimes potentially leading to higher energy consumption. For a large datacenter, this would both reduce the overall throughput, and increase overall energy consumption and costs. In this paper, we demonstrate how the performance of workloads across different CPU models leads to variability in energy efficiencies, and therefore costs. We investigate through a number of experiments, using the Google workload traces for 12,583 hosts and 492,309 tasks, the impact of migration decisions on energy efficiency when performance variations of workloads are taken into account. We discuss several findings, including (i) the existence of a trade-off between overall energy consumption and performance (hence cost), (ii) that higher utilization decreases the energy efficiency as it offers fewer chances to CPU management tools for energy savings, and (iii) how our migration approach could save up to 3.66% energy, and could improve VMs performance up to 1.87% compared with no migration. Similarly, compared with migrate all, the proposed migration approach could save up to 2.69% energy, and improve VMs performance up to 1.01%. We discuss these results for different combinations of VM allocation, migration policies and different benchmark workloads.1
Bitrate-Based No-Reference Video Quality Assessment Combining the Visual Perception of Video Contents

In video communication, the quality of video is mainly determined by bitrate in general. Moreover, the effect of video contents and their visual perception on video quality assessment (VQA) is often overlooked. However, in fact, for different videos, although the bitrates are the same, their VQA scores are still significantly different. Hence, it is assumed that the bitrate, video contents, and human visual characteristics mainly affect the VQA. Based on the above three aspects, in this paper, we designed a bitrate-based no-reference (NR) VQA metric combining the visual perception of video contents, namely, BRVPVC. In this metric, first an initial VQA model was proposed by only considering the bitrate alone. Then, the visual perception model for video contents was designed based on the texture complexity and local contrast of image, temporal information of video, and their visual perception features. Finally, two models were synthesized by adding certain weight coefficients into an overall VQA metric, namely, BRVPVC. Furthermore, ten reference videos and 150 distorted videos in the LIVE video database were used to test the metric. Moreover, based on the results of evaluating the videos in LIVE, VQEG, IRCCyN, EPFL-PoliMI, IVP, CSIQ, and Lisbon databases, the performance of BRVPVC is respectively compared with that of six full-reference (FR) metrics and ten NR VQA metrics. The results show that our VQA metric has a higher accuracy than six common FR VQA metrics and eight NR VQA metrics, and it is close to other two NR VQA metrics in accuracy. The corresponding values of Pearson linear correlation coefficient and Spearman rank order correlation coefficient reached 0.8547 and 0.8260, respectively. In addition, the computational complexity of proposed VQA metric is lower than video signal-to-noise ratio, video quality model, motion-based video integrity evaluation, spatiotemporal most apparent distortion, V-BLINDS, and V-CORNIA metrics. Moreover, the proposed metric has a better generalization property than these metrics.
A verifiable threshold secret sharing scheme based on lattices

The generalized compact knapsack function is defined as fa(x)=∑iai.xi, where a=(a1,…,am)∈R for some ring R and x=(x1,…,xm)∈S for a specified subset S ⊂ R. It is known that, for appropriate choice of R and S, inverting this function is at least as hard as solving certain worst-case problems on cyclic lattices. In this paper, we exploit collision-resistance as well as homomorphic properties of this function to propose a threshold verifiable secret sharing scheme with two distinguished features: first, the security of the scheme is completely based on lattice problems and second, upon receiving shares, participants can verify consistency of their shares with the secret without any communication.
Multi-model fusion metric learning for image set classification

Multi-model image set classification is an important research topic in pattern recognition, because multiple modeling of image sets can represent different characteristics. This paper focuses on the problem of two-model image set classification. We use the mean vector, subspace and covariance matrix to jointly represent an image set, due to they contain different discriminative information. Since the above three representation methods lie on different spaces, the classical multi-view learning methods cannot be directly utilized. In order to reduce the dissimilarity between the heterogeneous spaces, a new multi-model fusion metric learning (MMFML) framework is developed, which includes three two-model fusion Modes. Specifically, by exploiting corresponding kernel function, the original represent data is first embedded into the high dimensional Hilbert spaces to reduce the gaps between the heterogeneous spaces. The final objective function is then learned from the Hilbert space to a common Euclidean subspace, and in the final subspace the classical Euclidean distance is used to classify image sets. Experimental results on Honda/UCSD, Youtube Celebrities and ETH-80 three databases demonstrate the effectiveness of our proposed method.
Trajectory planning and tracking control for towed carrier aircraft system

As an efficient and safe dispatching scheme is of great importance to ensure the effectiveness and safety of personnel on the Aircraft Carrier, and the towed aircraft system without bar is a common means of dispatching, the dispatching of the system is a significance and interesting question. In this paper, the towed carrier aircraft system without bar is transformed into a tractor–trailer system, and the nonlinear motion constraint is proposed. In order to improve the efficiency and safety of dispatching, the trajectory-planning and tracking of the system are studied, respectively. Obstacles in the environment and the nonlinear motion constraints are taken into consideration, the trajectory-planning is transformed into the optimal control problem, and combined with the symplecticity and pseudospectral method, an offline optimal control algorithm with high efficiency is proposed firstly. In order to accurately track the obtained trajectory, an online tracking method is proposed based on the receding horizon control (RHC) theory and the offline optimal control algorithm. Finally, the offline trajectory-planning algorithm has been proved by simulation experiment, it shows that the algorithm has high computational efficiency and good applicability. And the system can track the standard trajectory, even though there are disturbances in the environment, with high precision using the online tracking method, the calculation efficiency is also verified, and real-time tracking can be achieved.
Complexity of fundamental problems in probabilistic abstract argumentation: Beyond independence

The complexity of the probabilistic counterparts of the classical verification and acceptance problems is investigated over probabilistic Abstract Argumentation Frameworks (prAAFs), in a setting more general than that considered in the current literature, where the complexity has been characterized only under the assumption of independence between arguments/defeats. The complexity of the problems is shown to range from FP to F P # P -complete, with F P ‖ N P -complete cases, depending on the semantics of the extensions, the representation paradigm used for encoding the prAAF, and the imposed correlations between arguments/defeats, thus providing a thorough analysis of the sensitivity to several aspects. In this regard, in order to allow the study of the impact of different forms of correlations between arguments/defeats on the complexity, a new form of prAAF is introduced, called gen . It is based on the well-known paradigm of world-set descriptors and world-set sets for representing probabilities, and it allows the correlations to be easily and explicitly expressed. Interestingly, the introduction of gen is shown to be also a standalone contribution as a powerful representation paradigm for prAAFs, owing to its high expressiveness, compactness, and the possibility to support user-friendly mechanisms for defining correlations.
Achieving Efficient and Privacy-Preserving Multi-Domain Big Data Deduplication in Cloud

Secure data deduplication, as it can eliminate redundancies over encrypted data, has been widely developed in cloud storage to reduce storage space and communication overheads. Among them, the convergent encryption has been extensively adopted. However, it is vulnerable to brute-force attacks that can determine which plaintext in a message space corresponds to a given ciphertext. Many existing schemes have to sacrifice efficiency to resist brute-force attacks, especially for cross-domain deduplication, which is inevitably contrary to practical applications. Moreover, few existing schemes consider protecting the message equality information (i.e., whether two different ciphertexts correspond to an identical plaintext). To address the above challenges, in this paper, we propose an efficient and privacy-preserving big data deduplication scheme for a two-level multi-domain architecture. Specifically, by generating a random tag and a constant number of random ciphertexts for each data, our scheme not only ensures data confidentiality under multi-domain deduplication but also resists brute-force attacks. By allowing only the agent and cloud service provider to perform intra-deduplication and inter-deduplication, respectively, our scheme can protect the message equality information from disclosure as much as possible. Detailed security analysis shows that our scheme achieves privacy-preservation for both data content and the message equality information and data integrity while resisting brute-force attacks. Furthermore, extensive simulations demonstrate that our scheme significantly outperforms the existing competing schemes, especially the computational cost and the time complexity of the duplicate search.