Reviewerly/exHarmony huggingface dataset:
Ensemble Neural Networks (ENN): A gradient-free stochastic method

In this study, an efficient stochastic gradient-free method, the ensemble neural networks (ENN), is developed. In the ENN, the optimization process relies on covariance matrices rather than derivatives. The covariance matrices are calculated by the ensemble randomized maximum likelihood algorithm (EnRML), which is an inverse modeling method. The ENN is able to simultaneously provide estimations and perform uncertainty quantification since it is built under the Bayesian framework. The ENN is also robust to small training data size because the ensemble of stochastic realizations essentially enlarges the training dataset. This constitutes a desirable characteristic, especially for real-world engineering applications. In addition, the ENN does not require the calculation of gradients, which enables the use of complicated neuron models and loss functions in neural networks. We experimentally demonstrate benefits of the proposed model, in particular showing that the ENN performs much better than the traditional Bayesian neural networks (BNN). The EnRML in ENN is a substitution of gradient-based optimization algorithms, which means that it can be directly combined with the feed-forward process in other existing (deep) neural networks, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), broadening future applications of the ENN.
Robotic vacuum cleaners save energy? Raising cleanliness conventions and energy demand in Australian households with smart home technologies

The ‘digital revolution’ of household life is underway, with technologies such as robotic vacuum cleaners (robovacs) increasingly common. Various other automated appliances are emerging and being adopted in pursuit of the ‘smart home’. Current discourses include assumptions and explicit claims that smart homes will be energy efficient and therefore more environmentally sustainable. However, smart home technologies are also presented as affording lifestyle enhancements in the areas of comfort, cleanliness, convenience, entertainment and security. Focusing specifically on one smart device – the robovac – this paper aims to demonstrate how visions of cleanliness in the smart home could be counterproductive to energy reductions and household wellbeing. We draw on a content analysis of smart home marketing and articles, and conversational interviews (involving home tours) with early adopters of robovacs and smart home technologies in Australia. We find that these devices may escalate conventions of cleanliness in the home and invite supplementary energy consumption. The paper concludes by providing suggestions for how energy stakeholders can respond.
Efficient User Involvement in Semiautomatic Ontology Matching

Semiautomatic ontology matching poses a new challenge of how to implement an efficient user interactions. To address this challenge, we answer three questions in this paper: (1) when should we activate the interacting process; (2) which correspondences should be presented for user validation; and (3) how to make use of the validating results. In particular, we present an interactive compact memetic algorithm (ICMA) based semiautomatic ontology matching technique to: (1) determine the timing of getting a user involved; (2) determine the problematic correspondences; and (3) propagate the user validating results. The experimental results show that three proposed strategies can effectively reduce the user's workload and the algorithm's runtime, increase the alignment's quality, and the performance of ICMA outperforms the state-of-the-art semiautomatic ontology matching techniques.
Emotion-Semantic-Enhanced Neural Network

Although sentiment analysis on microblog posts has been studied in depth, sentiment analysis of posts is still challenging because of the limited contextual information that they normally contain. In microblog environments, emoticons are frequently used and they have clear emotional meanings. They are important emotional signals for microblog sentimental analysis. Existing studies typically use emoticons as noisy sentiment labels or similar sentiment indicators to effectively train classifier but overlook their emotional potentiality. We address this issue by constructing an emotional space as a feature representation matrix and projecting emoticons and words into the emotional space based on the semantic composition. To improve the performance of sentimental analysis, we propose a new emotion-semantic-enhanced convolutional neural network (ECNN) model. ECNN can use emoticon embedding as an emotional space projection operator. By projecting emoticons and words into an emoticon space, it can help identify subjectivity, polarity, and emotion in microblog environments. It is more capable of capturing emotion semantic than other models, so it can improve the sentiment analysis performance. The experimental results show that this model consistently outperforms other models on the dataset of several sentiment tasks. This paper provides insights on the design of ECNN for sentimental analysis in other natural language processing tasks.
A three-way decision ensemble method for imbalanced data oversampling

Synthetic Minority Over-sampling Technique (SMOTE) is an effective method for imbalanced data classification. Many variants of SMOTE have been proposed in the past decade. These methods mainly focused on how to select the crucial minority samples which implicitly assume the selection of key minority samples is binary. Thus, the cost of key sample selection is seldom considered. To this end, this paper proposes a three-way decision model (CTD) by considering the differences in the cost of selecting key samples. CTD first uses Constructive Covering Algorithm (CCA) to divide the minority samples into several covers. Then, a three-way decision model for key sample selection is constructed according to the density of the cover on minority samples. Finally, the corresponding threshold α and β of CTD are obtained based on the pattern of cover distribution on minority samples, after that key samples can be selected for SMOTE oversampling. Moreover, to overcome the shortage of CCA which may contain non-optimal by randomly selecting the cover center, an ensemble model based on CTD (CTDE) is further proposed to improve the performance of CTD. Numerical experiments on 10 imbalanced datasets show that our method is superior to the comparison methods. By constructing the ensemble of the three-way decision based key sample selection, performance of the model can be effectively improved compared with several state-of-the-art methods.
A semi-quantitative modelling application for assessing energy efficiency strategies

Given the international efforts in tackling climate change as well as the potential dependence on conventional energy imports and the adverse economic environment, countries in the European Union face significant challenges in the critical task of enhancing energy efficiency. Approaches exclusively oriented on detailed quantitative modelling tools like energy system and climate-economy models, however, tend to exclude certain policy instruments and risks, and be too formalised or complex for policymakers to participate, understand and trust. Several decision support frameworks have been proposed for bridging the policy-model gap and helping policymakers confidently select among a number of alternative strategies. This paper employs the expert-driven method of fuzzy cognitive mapping, a semi-quantitative modelling technique in which system dynamics are captured and simulated against different scenarios. To this end, an innovative decision support tool for building and simulating complex fuzzy cognitive maps for assessing policy strategies with the help of experts, ESQAPE, is introduced and presented. An application in Greece shows that long-term energy efficiency measures focusing mainly on behavioural change in the residential sector – as opposed to services in the private and public sectors – are perceived to be more sustainable in a socio-economically optimistic future; this is not the case when challenges across the mitigation and adaptation axes are expected to be higher.
Language-Adversarial Transfer Learning for Low-Resource Speech Recognition

The acoustic model trained using the knowledge from the shared hidden layer (SHL) model outperforms the model trained only by using the target language, especially under low resource conditions. However, the shared features may contain some unnecessary language dependent information. It will degrade the performance of the target model. Therefore, this paper proposes language-adversarial transfer learning to alleviate this problem. Adversarial learning is used to ensure that the shared layers of the SHL-model can learn more language invariant features. Experiments are conducted on IARPA Babel datasets. The results show that the target model trained using the knowledge transferred from the adversarial SHL-model achieves up to 10.1% relative word error rate reduction when compared with the target model trained using the knowledge transferred from the SHL-model.
Polysemy and co-predication

Many word forms in natural language are polysemous, but only some of them allow for co-predication, that is, they allow for simultaneous predications selecting for two different meanings or senses of a nominal in a sentence. In this paper, we try to explain (i) why some groups of senses allow co-predication and others do not, and (ii) how we interpret co-predicative sentences. The paper focuses on those groups of senses that allow co-predication in an especially robust and stable way. We argue, using these cases, but focusing particularly on the multiply polysemous word school, that the senses involved in co-predication form especially robust activation packages, which allow hearers and readers to access all the different senses in interpretation.
An android based course attendance system using face recognition

Student attendance system is needed to measure student participation in a course. Several automated attendance systems have been proposed based on biometric recognition, barcode, QR code, and near field communication mobile device. However, the previous systems are inefficient in term of processing time and low in accuracy. This paper aims to propose an Android based course attendance system using face recognition. To ensure the student attend in the course, QR code contained the course information was generated and displayed at the front of classroom. The student only needed to capture his/her face image and displayed QR code using his/her smartphone. The image was then sent to server for attendance process. The experimental result shows that the proposed attendance system achieved face recognition accuracy of 97.29 by using linear discriminant analysis and only needed 0.000096s to recognize a face image in the server.
A new effective and powerful medical image segmentation algorithm based on optimum path snakes

Novel segmentation methods based on models of deformable active contours are constantly proposed and validated in different fields of knowledge, with the aim to make the detection of the regions of interest standard. This paper propose a new method called Optimum Path Snakes (OPS), a new adaptive algorithm and free of parameters to define the total energy of a active contour model with automatic initialization and stop criteria. In the experimental assessment, the OPS is compared against some approaches commonly used in the following fields, such as vector field convolution, gradient vector flow, and other specialists methods for lung segmentation using thorax computed tomography images. The segmentation of regions with stroke was carried out with methods based on region growing, watershed and a specialist level set approach. Statistical validations metrics using Dice coefficient (DC) and Hausdorff distance (HD) were also evaluated, as well as the processing time. The results showed that the OPS is a promising tool for image segmentation, presenting satisfactory results for DC and HD, and, many times, superior to the other algorithms it was compared with, including those generated by specialists. Another advantage of the OPS is that it is not restricted to specific types of images, neither applications.
ESTHER: Joint Camera Self-Calibration and Automatic Radial Distortion Correction From Tracking of Walking Humans

Camera calibration and radial distortion correction are the crucial prerequisites for many applications in image big data and computer vision. Many existing works rely on the Manhattan world assumption to estimate the camera parameters automatically; however, they may perform poorly when there was lack of man-made structure in the scene. As walking humans are the common objects in video surveillance, they have also been used for camera self-calibration, but the main challenges include the noise reduction for the estimation of vanishing points, the relaxation of assumptions on unknown camera parameters, and the radial distortion correction. In this paper, we present a novel framework for camera self-calibration and automatic radial distortion correction. Our approach starts with the reliable human body segmentation that is facilitated by robust object tracking. Mean shift clustering and Laplace linear regression are, respectively, introduced in the estimation of the vertical vanishing point and the horizon line. The estimation of distribution algorithm, an evolutionary optimization scheme, is then utilized to optimize the camera parameters and the distortion coefficients, in which all the unknowns in camera projection can be fine-tuned simultaneously. Experiments on the three public benchmarks and our own captured dataset demonstrate the robustness of the proposed method. The superiority of this algorithm is also verified by the capability of reliably converting 2D object tracking into 3D space.
Dual concepts in fuzzy theory: Entropy and knowledge measure

Entropy in a fuzzy set measures the amount of ambiguity/imprecision present in the fuzzy set. In this paper, we introduce a knowledge measure of a fuzzy set and its generalization (accuracy measure). The knowledge measure may be considered as a dual measure of fuzzy entropy. We also investigate the effectiveness and application of the proposed knowledge measure in multiple attribute decision making. Furthermore, we provide a result for obtaining a class of knowledge measure. We also prove a general method for obtaining a knowledge measure using accuracy measure. Among some suggested implications of the proposed fuzzy accuracy measure, we demonstrate the application of fuzzy accuracy measure in image thresholding.
Diversity-Based Cascade Filters for JPEG Steganalysis

Steganalysis is a technique for detecting the existence of secret information hidden in digital media. In this paper, we propose a novel scheme for JPEG steganalysis. In this scheme, we first design the diverse base filters which are able to obtain the image residuals from various directions. Then, we propose a cascade filter generation strategy to construct a set of high order cascade filters from the base filters. We further select the cascade filters with the maximum diversity. The selected filters are convolved with the decompressed JPEG image to obtain residuals which capture the subtle embedding traces. The residuals, termed as the maximum diversity cascade filter residual, are eventually used to extract features to train an ensemble classifier for classification. The experiments are carried out on the detection of stego-images generated using common JPEG steganographic schemes, the results of which demonstrate the effectiveness of the proposed scheme for JPEG steganalysis.
Domain Adaption in One-Shot Learning

Recent advances in deep learning lead to breakthroughs in many machine learning tasks. Due to the data-dri ven nature of deep learning, the training procedure often requires large amounts of manually annotated data, which is often unavailable. One-shot learning aims to categorize the new classes unseen in the training set, given only one example of each new class. Can we transfer knowledge learned by one-shot learning from one domain to another? In this paper, we formulate the problem of domain adaption in one-shot image classification, where the training data and test data come from similar but different distributions. We propose a domain adaption framework based on adversarial networks. This framework is generalized for situations where the source and target domain have different labels. We use a policy network, inspired by human learning behaviors, to effectively select samples from the source domain in the training process. This sampling strategy can further improve the domain adaption performance. We investigate our approach in one-shot image classification tasks on different settings and achieve better results than previous methods. Code related to this paper is available at: https://github.com/NanqingD/DAOSL .
NewMCOS: Towards a Practical Multi-Cloud Oblivious Storage Scheme

Encryption alone is not enough to protect data privacy, because access pattern leaks some sensitive information. Oblivious RAM (ORAM), the solution to this problem, is still far from practical deployment for heavy storage and communication/computation overhead. To reduce them, an insightful idea was proposed to utilize non-colluding clouds to shift client computation and client-cloud communication to the clouds. The proposed multi-cloud ORAM achieved O(1) client-cloud bandwidth cost and removed most of client computation. In this paper, we exploit “disconnected ORAMoperation” and design “two-layerencryption” to further reduce these overheads. Experiments show that our proposed scheme, NewMCOS, significantly reduces evict cache size from GB/MB to KB level with about 2-3 times lower response time and 20 percent savings in bandwidth for clouds, compared to other schemes. Theoretically speaking, we reduce evict cache size from O(√N) to O(ZK), where N is the number of real data blocks, K is the number of clouds (2 <; K <; <; √N ), and Z is the number of real blocks uploaded from the client for eviction. By employing “lazy eviction operation”, the write frequency is reduced by O(Z), the shuffling bandwidth cost is reduced by Ω(Z log Z). Meanwhile, NewMCOS is proved to be secure.
Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks

We analyze algorithms for approximating a function f(x)=Φx mapping ℜd to ℜd using deep linear neural networks, that is, that learn a function h parameterized by matrices Θ1,…,ΘL and defined by h(x)=ΘLΘL-1…Θ1x . We focus on algorithms that learn through gradient descent on the population quadratic loss in the case that the distribution over the inputs is isotropic. We provide polynomial bounds on the number of iterations for gradient descent to approximate the least-squares matrix Φ , in the case where the initial hypothesis Θ1=…=ΘL=I has excess loss bounded by a small enough constant. We also show that gradient descent fails to converge for Φ whose distance from the identity is a larger constant, and we show that some forms of regularization toward the identity in each layer do not help. If Φ is symmetric positive definite, we show that an algorithm that initializes Θi=I learns an ε -approximation of f using a number of updates polynomial in L , the condition number of Φ , and log(d/ε) . In contrast, we show that if the least-squares matrix Φ is symmetric and has a negative eigenvalue, then all members of a class of algorithms that perform gradient descent with identity initialization, and optionally regularize toward the identity in each layer, fail to converge. We analyze an algorithm for the case that Φ satisfies u⊤Φu>0 for all u but may not be symmetric. This algorithm uses two regularizers: one that maintains the invariant u⊤ΘLΘL-1…Θ1u>0 for all u and the other that "balances" Θ1,…,ΘL so that they have the same singular values.
Recompression of JPEG Crypto-Compressed Images Without a Key

The rising popularity of social networks and cloud computing has greatly increased a number of JPEG compressed image exchanges. In this context, the security of the transmission channel and/or the cloud storage can be susceptible to privacy leaks. The selective encryption is an efficient tool to mask the image content and to protect confidentiality while remaining format-compliant. However, image processing in the encrypted domain is not a trivial task. In this paper, we present a JPEG crypto-compression method which allows us to recompress a JPEG crypto-compressed image several times, without any information about the secret key or the original image content. Indeed, using the proposed method in this paper, each recompression can be done directly on the JPEG bitstream by removing the last bit of the code representation of each non-zero coefficient, adapting the entropic code part, and slightly modifying the quantization table. This method is efficient to recompress JPEG crypto-compressed images in terms of compression ratio. Moreover, the decryption of the recompressed image produces an image with a very similar visual quality when compared to the original image, according to the obtained results.
Realtime in-plane displacements tracking of the precision positioning stage based on computer micro-vision

This study presents a micro-vision-based measurement method for realtime and high accuracy in-plane displacements tracking of the precision positioning stage. In this method, to realize high magnification ratio and high video rate imaging of the measured stage’s surface, a micro-vision imaging system is established. In addition, an accelerated inverse optimized searching (AIOS) algorithm is developed in which an optimal template chosen (OTC) strategy and a piecewise update (PU) scheme are proposed and integrated with the IOS method to achieve template matching with high accuracy and high frame rate. Simulation studies are presented to demonstrate the validity of the OTC strategy, PU scheme, and AIOS algorithm, respectively. Experimental results demonstrate that the proposed method can effectively realize displacement measurement of the precision positioning stage at a frequency of hundreds Hertz with accuracy of nanometer degree.
Open data exploration in virtual reality: a comparative study of input technology

In this article, we compare three different input technologies (gamepad, vision-based motion controls, room-scale) for an interactive virtual reality (VR) environment. The overall system is able to visualize (open) data from multiple online sources in a unified interface, enabling the user to browse and explore displayed information in an immersive VR setting. We conducted a user interaction study ($$n=24$$; $$n=8$$ per input technology, between-group design) to investigate experienced workload and perceived flow of interaction. Log files and observations allowed further insights and comparison of each condition. We have identified trends that indicate user preference of a visual (virtual) representation, but no clear trends regarding the application of physical controllers (over vision-based controls), in a scenario that encouraged exploration with no time limitations.