Reviewerly/exHarmony huggingface dataset:
Image Deblurring Based on Fuzzy Kernel Estimation in HSV Color Space

Image deblurring is intended to restore the clear images from the damaged images. The main factor of the image deblurring is to precisely estimate the fuzzy kernel in the unknown blurring process. For color images, the fuzzy kernel is estimated by converting them into the gray domain with the most existing effective image deblurring approaches. In fact, the influence of the fuzzy kernel function on each channel in the HSV color space is different. In this paper, a more accurate image deblurring algorithm is proposed. In HSV color space, the Radon transform based on the spectral edge detection is used to estimate the fuzzy kernel. For the fuzzy kernel obtained from the different channels independently, the deblurring restoration based on Richardson-Lucy guided filter is carried out in each channel. The Simulation results demonstrate that the proposed method is effective.
Agent–based monitoring of the task scheduling in computational clouds

The monitoring of the computational processes in highly distributed environments remains challenging in today’s High Performance Computing. In this paper, we define the agent-based cloud monitoring system for supporting the computational tasks scheduling and resource allocation. The system consists of two types of agents, which may decide about the initialization of the schedule execution and monitor the work of the cloud computational nodes. The decision about running the new scheduling process is based on the expected number of available computational units in the specified time window. The efficiency of the proposed MAS-based model was justified through 40 empirical tests, where clouds without and within the MAS support were compared. The multiagent system (MAS) effectiveness has been expressed in the average number of floating point operations completed at the cloud resources in one second. The obtained results show the importance of setting the optimal initial time for execution of the new schedule. Our experiments show that for running the new schedule, at least 25% of the computing units in the clouds should be in the idle mode. Also the batches of tasks should not be too large, cause the waiting time for new schedule for execution should be short and not greater than 10% of expected batch execution time.
National-Scale Inspection of Flood Defence Infrastructure with Remotely Sensed Data

Monitoring flood defence condition is a critical part of the effective management of flood defence infrastructure.In this paper, the use of data on flood defences and floodplains gathered from remote sensing is explored to identify indicators of weaknesses, deterioration and damage.Such indicators are often tell-tale signs of processes that can lead to flood defence failure and breaching.Sources of data and the techniques to analyse those data are discussed.A range of examples are presented that highlight both the value and limitations of bringing together multiple sources of data such as LIDAR and historical mapping to supplement visual-based condition assessments.The paper then, describes the results or a national mapping exercise that has brought these datasets together, and made them available to flood defence inspectors working in the field.
The entanglement of logic and set theory, constructively

Theories of sets such as Zermelo Fraenkel set theory are usually presented as the combination of two distinct kinds of principles: logical and set-theoretic principles. The set-theoretic principles are imposed ‘on top’ of first-order logic. This is in agreement with a traditional view of logic as universally applicable and topic neutral. Such a view of logic has been rejected by the intuitionists, on the ground that quantification over infinite domains requires the use of intuitionistic rather than classical logic. In the following, I consider constructive set theories, which use intuitionistic rather than classical logic, and argue that they manifest a distinctive interdependence or an entanglement between sets and logic. In fact, Martin-Löf type theory identifies fundamental logical and set-theoretic notions. Remarkably, one of the motivations for this identification is the thought that classical quantification over infinite domains is problematic, while intuitionistic quantification is not. The approach to quantification adopted in Martin-Löf’s type theory is subtly interconnected with its predicativity. I conclude by recalling key aspects of an approach to predicativity inspired by Poincaré, which focuses on the issue of correct quantification over infinite domains and relate it back to Martin-Löf type theory.
Dressing of superconducting qubits by their interaction with a low frequency photon reservoir

Abstract Dressing of states of superconducting qubits by their interaction with low frequency photons is investigated. We show that the derivative of the self-energy function of a qubits state at its eigenenergy is equal to the total probability to find the system in the virtual states where the reservoir degrees of freedom manifest themselves explicitly. This parameter determines the strongness of the qubit-reservoir interaction. The results of calculations of the self-energy function show that contributions to the dressed states of qubits can be very significant.
Design of Improved Deep Convolution Network Model

Convolution neural network is a feed-forward neural network which contains convolution calculation and has depth structure. In this paper, the basic structure and operation principle of convolution neural network are analyzed. CNN has the best effect in handwritten recognition, and the application of CNN in face-based gender recognition is also very good. In this paper, the factors that influence the convolution neural network model are analyzed. In this paper, the improved deep convolution network model is proposed, and the results show that the improved method can effectively improve the classification effect and classification accuracy of the convolution neural network.
Quantification of Contributing Degree to Braking Operation of Driver based on the Hidden Markov Model

In order to assist cognitive of driver, it is effective for driver to teach the objects that he should recognize in a visual way. However, it is expected that too much information is provided for a driver from the system and it cause him distraction. Therefore, information presentation without exaggeration and without omission is demanded for assistant system. In this paper, it is assumed that the objects which contribute to driver's braking operation should be presented to him. However, these objects changes according to the facing driving situation. Therefore, a selection method of these objects in the appeared objects model is desired. In this paper, a quantification method of contributing degree to braking operation is proposed to select object that system should present. In this method, the degree of contributing to braking operation is calculated based on a driving behavior model. This model express the relationship between the braking operation and time series features of the object that contributes to braking operation. Therefore, in this paper, the modeling method is also proposed.In the experiment, the modeled time series features of the objects that contribute to braking operation was evaluated based on the propose modeling method. Moreover, the calculated degree of contributing to braking operation based on the proposed method was evaluated by using driving simulator. As the results, it was confirmed that high detection accuracy of object that contributes to braking operation was confirmed.
Control of 3-DOF Robotic Manipulator by Neural Network Based Fuzzy-PD

A new control scheme of Back-Propagation Neural Network Based Fuzzy-PD (BPNNBF-PD) has been proposed for control of a 3-DOF robotic manipulator in this paper. Combined with Back-Propagation Neural Network (BPNN), the performance of traditional Fuzzy-PD (F-PD) has been optimized by online calibration of the overlapping ratio of membership function. The overlapping ratio is firstly defined as the value of the intersection point between two adjacent membership functions. And then BPNN is utilized to optimize the overlapping ratio online. Simulations and real experiments with a 3-DOF robotic manipulator have been carried out to demonstrate the effectiveness and superiority of the proposed scheme.
Research on the Attenuation Law of User Experience Satisfaction of Mobile Phone

Abstract With the continuous development and widespread popularity of smart phones, people have been deeply studying mobile phones, and pay more attention to user experience. Based on literature research, this paper establishes a hierarchical chart of influencing factors of user experience satisfaction of mobile phone. A calculation method of user experience satisfaction is proposed. User interviews are used to determine the key factors, and user satisfaction model is constructed. Using questionnaire survey method to collect data and explore the process and rules of satisfaction changes in the process of purchasing, using and replacing mobile phones. The results show that user experience satisfaction of mobile phone decreases with the increase of mobile phone usage time, and battery loss, running stuck, damage and insufficient storage space are all key factors affecting the satisfaction of mobile phone users. Through data analysis, the change rule of user experience satisfaction of each key factor is obtained, and then guide relevant enterprises to upgrade and improve products pertinently.
Guards in action: First-order SCA secure implementations of KETJE without additional randomness

Recently the CAESAR competition has announced several finalists among the submitted authenticated encryption algorithms, after an open selection process during the last five years. Applications using these algorithms are rapidly increasing today. Devices implementing these applications are enormously susceptible to physical attacks, which are able to retrieve secret data through side-channel information such as power consumption or the electromagnetic radiations. In this work, we present a Side-Channel Analysis resistant hardware implementation of the whole family of authenticated encryption schemes Ketje. By changing just one parameter, any of the Ketje designs can be obtained, and tailored for different applications, either lightweight or high throughput. We introduce a new protected Keccak implementation, as well as unprotected and protected Ketje implementations, which allow both encryption and decryption modes in the same module. In order to secure these implementations we make use of the masking scheme known as Threshold Implementations and complement it with the technique of “Changing of the Guards”, achieving a first-order Side-Channel Analysis protected implementation with zero extra randomness needed. This way, no dedicated PRNG needs to be additionally implemented, avoiding issues such as the security of the PRNG itself or the quality of the randomness. We elaborate on the importance of the input dependencies and how the addition of linear blocks could thwart the security provided by a correctly protected module. This is of special importance when extending Keccakto implement Ketje. Furthermore, we provide an empirical evaluation of both Keccakand Ketjeimplementations, demonstrating how with our methodology we can construct a secure Ketjebased on a previously secured Keccak.
Improving Efficiency of Quality Correlation in Query Using Spark

Service oriented models to easily development of the service-based systems, many services has been developed. Based on the service composition it creates the enterprise services. The main aim of the service-based system is to fulfil the specific quality constraints. Quality aware service composition cannot consider the quality correlations among services, to solve this problem develop an approach efficient query of quality correlations(Q2C). To consider these type of quality correlations further increasing search space, to reducing search space and provide efficient correlations develop quality correlation index graph(QCIG). In the QCIG quality constraints in the quality dimensions increased of pre-processing time and search time by the quality correlation. When raising this issue which affect the efficiency of Q2C. To improve efficiency of Q2C and get fast, better results use spark distributed technique.
Geotechnical classification and Bayesian network for real time risk assessment in mining

Mining involves the extraction of finite resources for their use in vast number of applications. Depletion of resources over time has required mining to be carried out underground and unprecedented depths. It is therefore important to conduct geotechnical risk assessments in advance to prevent accidents and sustain economic mining operation. Extent of available geotechnical information varies for a mine as the mine progresses from feasibility to operational stage. Geotechnical risk assessment (GRA) can be incorporated into the mine planning process from as early as the pre-feasibility stage. A formal risk assessment can be planned using appropriate scope definition which can help chose from a number of risk assessment tools and parameters. The goals of the research were: design a geotechnical risk classification system, which can be used from preliminary stages of mine planning and to motivate a detailed risk assessment; develop guidelines to prepare the scope of a detailed GRA; define selection criteria to choose the appropriate hazard identification tool and risk assessment parameters; carry out risk assessment in presence and absence of historical incident data; develop a framework to carry out geotechnical risk assessment in real time; represent and communicate the final risk to the work force for mitigation planning. The proposed geotechnical risk classification system (GRC) can be used to identify, rank and communicate the hazardous sections of a mine to the work force. The guidelines developed for defining the scope of the risk assessment and the numerical ranking system for risk assessment parameter selection can be used to define the risk assessment process and choose between deterministic, probabilistic and empirical method of risk assessment. The demonstrated methodology of fault tree and event tree can be used to break down a hazard into its elemental causes and to plan against all possible outcomes following an incident. Bayesian network (BN) based risk assessment can be used to model complex causal relationship of accidents and carry out incident investigation using the same model. It was shown using parameter learning that normal distribution of mine incidents was a better fit for incident forecasting compared to Poisson distribution for the cases studied in the thesis. A new method to combine multiple probability distributions to forecast future incidents has been proposed. It was demonstrated that BN based risk assessment can incorporate expert opinion in absence of data to forecast incidents. Finally, the measured risk can be communicated and monitored graphically using the F-N diagram.
Prediction of Chemical Oxygen Demand in Sewage Based on Support Vector Machine and Neural Network

Aiming at the problem that the detection accuracy of effluent COD (chemical oxygen demand) in sewage treatment needs to be further improved, a combined model based on support vector machine and neural network is proposed to predict effluent COD. It can reduce the influence of local optimum on the global scope so as to improve the accuracy of prediction. Firstly, the sample data are divided into two categories by support vector machine. Then the BP neural network model and the Echo State Network (ESN) model are established on two sub-samples respectively. Compared with single neural network model, the mean absolute error and root mean square error of combined model are both reduced. Besides, the proposed model has better comprehensive prediction performance and can meet the actual demand of effluent COD prediction in sewage treatment.
Application-centered Lightweight Cloud Simulation Platform Support Technology

In order to meet the requirements of large-scale simulation application for high-performance, highly reliable and efficient coordination of the simulation platform, an application-centered lightweight cloud simulation platform support technology is studied and implemented. The technology centers on the simulation application and servitizes the simulation processes it contains so as to support the simultaneous operation of different users and realize the efficient collaboration among multiple users. In addition, the non-interactive simulation service is deployed to run in the container and the interactive simulation service is deployed to run in the virtual machine, which effectively improves the simulation operation efficiency while taking into account the reliability of the simulation platform. Experimental results show that the performance of the cloud simulation platform based on this technology is higher than that of the traditional cloud simulation platform based on virtual machine.
Spatio-Temporal Multi-Factor Discriminant Analysis for Individual Identification

Individual identification from skeleton sequence is a challenging task because various covariate factors may produce drastic changes in human poses, thus causing large intra-class variability. In this paper, we present a novel spatio-temporal multi-factor discriminant analysis (ST-MFDA) to reduce the impact of covariate factors on identification performance. In our approach, a set of paired factor-specific spatio-temporal projections are learned to project motion features from different factors into a common discriminant subspace. In the common subspace, features from the same individual are united and those from different individuals are separated by Fisher discriminant criterion. We show that spatio-temporal projections of various factors can be jointly learned by solving an iterative generalized eigenvalue problem. According to the experimental results from three public databases, we confirm that spatio-temporal projections trained in this way lead to significant improvements in identification accuracy under the different covariate factors.
Altijd leuk, zo’n ironische constructie!

Some linguistic constructions can be considered default ironic construc­tions, as those combinations of words almost always trigger an ironic reading (Giora, Drucker &amp; Fein 2014, Giora et al 2015). According to La Roi (2014), the Dutch altijd-X-construction can be considered such a default ironic construction. However, a corpus analysis reveals that most of the instances are not ironic. The assumption that the altijd-X-construction is ironic by default no longer holds. Nevertheless, the corpus analysis indeed shows that the combination of altijd with a positive evaluative word is significantly more interpreted as ironic than the usage of altijd or the positive evaluative words in isolation.
Dimensions Effect in Word Embeddings of Knowledge Graph

Word embedding is one of the basic of knowledge graph. It is designed to represent the entities and relations with vectors or matrix to make knowledge graph model. Recently many related models and methods were proposed, such as translational methods, deep learning based methods, multiplicative approaches. The TransE models take a relation as transition from head entity to tail entity in principle. The further researches noticed that relations and entities might be able to have different representation to be casted into real world relations. Thus it could improve the embedding accuracy of embeddings in some scenarios. To improve model accuracy, the variant algorithms based on TransE adopt strategies like adjusting the loss function, freeing word embedding dimension freedom limitations or increasing other parameters size etc. After carefully investigate these algorithms, we motivated by researches on the effect of embedding dimension size factor. In this paper, we carefully analyzed the factor impact of dimensions on the accuracy and algorithm complexity of word embedding. By comparing some typical word embedding algorithms and methods, we found there are tradeoff problem to deal with between algorithm's simplicity and expressiveness. We carefully designed an experiment to test such kind of effect and give some description and possible measure to adopt.
2D-3D Heterogeneous Face Recognition Based on Deep Coupled Spectral Regression

As one of the major branches in Face Recognition (FR), 2D-3D Heterogeneous FR (HFR), where face comparison is achieved across the texture and shape modalities, has become more important. This paper proposes a novel deep learning based end-to-end approach, namely Deep Coupled Spectral Regression (DCSR), for such an issue. It jointly makes use of both the advantages of CNN based deep features and CSR based common subspace. Specifically, from 2D texture and 3D depth face maps, DCSR extracts more powerful features by a deep network with the cross-modality triplet loss, which show much better uniqueness and robustness than the hand-crafted ones. Further, DCSR learns the shared space between different modalities with the constraints of sample labels, and is thereby more discriminative than the widely used unsupervised methods. More importantly, the two steps above are integrated through a couple layer to explicitly optimize the weights of deep features and projection directions rather than a simple combination. Experiments are carried out on the FRGC v2.0 database, and the results reported clearly demonstrate the competency of our proposed method. Its generalization ability is also validated by additional experiments conducted on the CASIA NIR-VIS 2.0 database.
The biggest failure of my career

In 2017 the Research Computing team at University of Colorado Boulder began the process of refreshing the PetaLibrary, a campus-wide research and academic data storage service. Updates were motivated by aging hardware, the impending end of upstream support, and a hope to improve the service with lessons learned.
Sound and the Feeling of Presence

As I have written elsewhere, immersion is the holy grail of first-person perspective computer game design (Grimshaw, Lindley, Nacke 2008; Grimshaw 2012). Whether this should be the case is a question I leave to others. Here, what concerns me are other questions such as the relationship of immersion to presence (the term I now prefer to use), what precisely presence is and how is it formed, and what is the role of sound in the formation of presence.
Towards Converging Fully Homomorphic Batch Encryption with Data Integrity Assurance in Cloud Setting

In encryption scheme setting, users who possess the right key can authorize to do computation with the data. In particular, cloud storage users lose their rights in terms of integrity on data, even after they ends up metered service from a CSP(Cloud Storage service provider), There is a possibility of dishonest computation over an old data by CSP also. Fully homomorphic encryption scheme address the problem of possessing the data in untrusted servers. In this proposed work, authors bring in a fully Homomorphic Batch Encryption Scheme using Integers with Shorter public key (HBEIS) converged with integrity verification process in a cloud setting. Any third-party auditor can do random audit on the encrypted data for assuring the correctness of data without possessing the original data. In our experimental set up, HBEIS scheme ensures optimal space and linear computational complexity.