Reviewerly/exHarmony huggingface dataset:
On the nonlinearity of quantum dynamical entropy

Linearity of a dynamical entropy means that the dynamical entropy of the n-fold composition of a dynamical map with itself is equal to n times the dynamical entropy of the map for every positive integer n. We show that the quantum dynamical entropy introduced by Słomczyński and Życzkowski is nonlinear in the time interval between successive measurements of a quantum dynamical system. This is in contrast to Kolmogorov-Sinai dynamical entropy for classical dynamical systems, which is linear in time. We also compute the exact values of quantum dynamical entropy for the Hadamard walk with varying Lüders-von Neumann instruments and partitions.
Micro-Browsing Models for Search Snippets

Click-through rate (CTR) is a key signal of relevance for search engine results, both organic and sponsored. CTR of a result has two core components: (a) the probability of examination of a result by a user, and (b) the perceived relevance of the result given that it has been examined by the user. There has been considerable work on user browsing models, to model and analyze both the examination and the relevance components of CTR. In this paper, we propose a novel formulation: a micro-browsing model for how users read result snippets. The snippet text of a result often plays a critical role in the perceived relevance of the result. We study how particular words within a line of snippet can influence user behavior. We validate this new micro-browsing user model by considering the problem of predicting which snippet will yield higher CTR, and show that classification accuracy is dramatically higher with our micro-browsing user model. The key insight in this paper is that varying relatively few words within a snippet, and even their location within a snippet, can have a significant influence on the clickthrough of a snippet.
Angelic processes for CSP via the UTP

Demonic and angelic nondeterminism play fundamental roles as abstraction mechanisms for formal modelling. In contrast with its demonic counterpart, in an angelic choice failure is avoided whenever possible. Although it has been extensively studied in refinement calculi, in the context of process algebras, and of the Communicating Sequential Processes (CSP) algebra for refinement, in particular, it has been elusive. We show here that a semantics for an extended version of CSP that includes both demonic and angelic choice can be provided using Hoare and He's Unifying Theories of Programming (UTP). Since CSP is given semantics in the UTP via reactive designs (pre and postcondition pairs) we have developed a theory of angelic designs and a conservative extension of the CSP theory using reactive angelic designs. To characterise angelic nondeterminism appropriately in an algebra of processes, however, a notion of divergence that can undo the history of events needs to be considered. Taking this view, we present a model for CSP where angelic choice completely avoids divergence just like in the refinement calculi for sequential programs.
Evaluating the effectiveness of information retrieval systems using effort-based relevance judgment

Purpose The effort in addition to relevance is a major factor for satisfaction and utility of the document to the actual user. The purpose of this paper is to propose a method in generating relevance judgments that incorporate effort without human judges’ involvement. Then the study determines the variation in system rankings due to low effort relevance judgment in evaluating retrieval systems at different depth of evaluation. Design/methodology/approach Effort-based relevance judgments are generated using a proposed boxplot approach for simple document features, HTML features and readability features. The boxplot approach is a simple yet repeatable approach in classifying documents’ effort while ensuring outlier scores do not skew the grading of the entire set of documents. Findings The retrieval systems evaluation using low effort relevance judgments has a stronger influence on shallow depth of evaluation compared to deeper depth. It is proved that difference in the system rankings is due to low effort documents and not the number of relevant documents. Originality/value Hence, it is crucial to evaluate retrieval systems at shallow depth using low effort relevance judgments.
Statically Optimal Binary Search Tree Computation Using Non-Serial Polyadic Dynamic Programming on GPU's

Modern GPUs perform computation at a very high rate when compared to CPUs; as a result, they are increasingly used for general purpose parallel computation. Determining if a statically optimal binary search tree is an optimization problem to find the optimal arrangement of nodes in a binary search tree so that average search time is minimized. Knuth's modification to the dynamic programming algorithm improves the time complexity to O(n2). We develop a multiple GPU-based implementation of this algorithm using different approaches. Using suitable GPU implementation for a given workload provides a speedup of up to four times over other GPU based implementations. We are able to achieve a speedup factor of 409 on older GTX 570 and a speedup factor of 745 is achieved on a more modern GTX 1060 when compared to a conventional single threaded CPU based implementation.
Random subspace oracle (RSO) ensemble to solve small sample-sized classification problems

Under certain situations, researchers were forced to work with small sample-sized (SSS) data. With very limited sample size, SSS data have the tendency to undertrain a machine learning algorithm and rendered it ineffective. Some extreme cases in SSS problems will have to deal with large feature-to- instance ratio, where the high number of features compared to small number of instances will overfit the classification algorithm. This paper intends to solve small sample-sized classification problems through hybrid of random subspace method and random linear oracle ensemble by utilizing binary feature subspace splitting and oracle selection scheme. Experimental results on artificial data indicate the proposed algorithm can outperform single decision tree and linear discriminant classifiers in small sample-sized data, but its performance is identical to k-nearest neighbor classifier due to both shared similar selection approach. Results from real-world medical data indicate the proposed method has better classification performance than its corresponding single base classifier especially in the case of decision tree.
Non-redundant array configurations for optical interferometric systems combined with a large telescope

In this paper, we propose a method to design the configuration of the interferometric array working with a large telescope. It limits the array configuration region and minimum aperture distance to make sure baseline length is in a certain interval. And it gives aperture locations by “push” and “pull” operations. The UV-plane coverage obtained from this method has the advantage that there is no overlap between UV-plane coverage from the interferometric array and the large aperture telescope, and the overall UV-plane coverage is non-redundant and symmetric. Specifically, design results with varying diameters of the large telescope are provided.
A Dynamic Task Scheduling Algorithm for Cloud Computing Environment

Background: Cloud computing environment is a novel paradigm in which the services are hosted, delivered and managed over the internet. Tasks scheduling problem in the cloud has become a very interesting research area. However, the problem is more complex and challenging due to the dynamic nature of cloud and users’ needs as well as cloud providers’ requirements including the quality of service, users’ priorities and computing capabilities. Objective: The main objective is to solve the problem of tasks scheduling through an algorithm which can not only improves the client satisfaction, but also allows cloud service provider to gain maximum profit and ensure that the cloud resources are utilized efficiently. Method: (a) Optimization of the waiting time and the queue length. Methods: (a) Optimization of the waiting time and the queue length. (b) Distribution of all requests into a novel queueing system in a dynamic manner based on a decision threshold. (c) Assignment of requests to VMs based on Particle Swarm Optimization and Simulated Annealing algorithms. (d) Incorporation of the priority constraint in the scheduling process by considering three priorities levels including the tasks, queues and VMs. Results: The results comparison of our algorithm with particle swarm optimization and First Come First Serve algorithms demonstrate the effectiveness of our algorithm in terms of waiting time, makespan, resources utilization and degree of imbalance. Conclusion: This study introduces an efficient strategy to schedule users’ tasks by using dynamic dispatch queues and particle swarm optimization with simulated annealing algorithms. Moreover, it incorporates the priority issue in the scheduling process.
Path queries on functions

Let f:[1..n]→[1..n] be a function, and ℓ:[1..n]→[1..σ] indicate a label assigned to each element of the domain. We design several compact data structures that answer various kinds of summary queries on the labels of paths in f. For example, we can find either the minimum label in fk(i) for a given i and any k≥0 in a given range [k1..k2], or the minimum label in f−k(i) for a given i and k>0, using nlg⁡n+nlg⁡σ+o(nlg⁡n) bits and time O(α(n)), the inverse Ackermann function. Within similar space we can count, in time O(lg⁡n/lg⁡lg⁡n), the number of labels within a range, and report each element with such labels in O(lg⁡n/lg⁡lg⁡n) additional time. Several other tradeoffs and possible queries are considered, such as selection, top-r queries and τ-majorities. Finally, we consider queries that allow us navigate on the graph of the function, such as the nearest common successor of two elements, or the nearest successor or predecessor of an element within a range of labels.
Normalized Moment of Inertia-Based Detection Algorithm for Copy-Paste Image Tampering

Copy-paste tampering is one of the most common image content attacking methods. Considering the low accuracy and high feature dimension of the existing algorithms, a normalized moment of inertia method is proposed in this paper to overcome these defects. In the phase of feature extracting, the algorithm first transforms the tested image using wavelet, and then selects the similar subbands to build overlapped blocks, finally uses Perceived Hash Algorithm (PHA) to make binarization processing for the subblocks and carries out the normalize moment of inertia of the subblocks which satisfy the coarse matching conditions between adjacent two lines after performing dictionary sorting. In feature matching phase, the algorithm first counts the similar subblocks whose shift is above the distance threshold, and then obtains the main shift vectors with specific frequencies, finally performs feature matching according to the difference of the normalized moment of inertia in the neighborhood. Experiment results illustrate that the proposed algorithm with lower feature dimension can efficiently improve the matching speed and accuracy. Furthermore, it has better robustness for some post-processing operations, such as compression, Gaussian Blur, adding Gaussian noise, etc.
Design and Implementation of a Real-time Publish/Subscribe Middleware

Background: Middleware has been widely adopted as an efficient approach to facilitate the development process of large-scale distributed systems. Although this approach can achieve simplified system design and reduced time consumption, it has a strong demand for real-time task processing and data transmitting. Traditional middleware, which lacks the real-time performance guarantee mechanism, cannot be adopted by the distributed systems with strict time restrictions. Methods: In order to meet the real-time requirements of distributed systems, the functional requirements of middleware are studied, and a real-time publish/subscribe middleware is designed and implemented in this paper. A hierarchical design is adopted to build this middleware from a model layer, a communication layer and a support layer. The middleware has the data synchronization on distributed objects, Ethernet-based publish/subscribe mechanism and the real-time performance guarantee mechanism. Results: Experiments show that the proposed middleware satisfies the needs of distributed applications, and ensures that the end-to-end data communication delay is less than 1 millisecond, which meets the real-time requirements of data transmission in distributed systems. Conclusion: In this paper, we design and implement a real-time middleware based on the publish/ subscribe mechanism.
Reversible Watermarking on Stereo Audio Signals by Exploring Inter-Channel Correlation

A new reversible watermarking algorithm on stereo audio signals is proposed in this article. By utilizing correlations between two channels of audio signal, the authors segment one channel based on another one according to the smoothness. For each segmented sub-host sequence, they estimate its capacity and the corresponding embedding distortion firstly, and then select the optimal combinations of sub-host sequences for embedding. Experimental results indicate that the proposed algorithm can improve SNR (signal to noise ratio) for various kinds of capacity.
Relative Degrees and Implicit Function-Based Control of Discrete-Time Noncanonical Form Neural Network Systems

This paper studies the relative degrees of discrete-time neural network systems in a general noncanonical form, and develops a new feedback control scheme for such systems, based on implicit function theory and feedback linearization. After time-advance operation on output of such systems, the output dynamics nonlinearly depends on the control input. To address this issue, we use implicit function theory to define the relative degrees, and to establish a normal form. Then, an implicit function equation solution-based control scheme and an iterative solution-based control scheme are proposed, which ensure not only the closed-loop stability but also the output tracking for the controlled plant. An adaptive control framework for the controlled plant with uncertainties is also presented to illustrate the basic design procedure. The simulation results are given to demonstrate the desired system performance.
Generating contextual help for user interfaces from software requirements

Software requirements are an essential stepping stone for engineering any software system that meets the needs of its stakeholders. User interface (UI) contextual help provides end users with much-needed support for understanding how to use a software system. This article presents an approach for generating contextual help from software requirements, while maintaining the ability to provide human input on the generated help. Domain classes, use cases, and UI flow models embody information that is valuable for help generation and are thereby used by the proposed approach. Templates are also used to define how requirements are associated with their contextual help counterpart elements. The generated help is represented using an existing contextual help definition language called CHAINXML. The latter was extended to support templates and alternatives in order to make the help generation process more seamless and to reduce the repetition in the generated help. The usefulness (usability and utility) of the generated help was evaluated by conducting a study with end users. The results showed that the participants found the help to be highly useful and most of them chose to use this help when given the opportunity to do so in a real-life case.
General model for linear information extraction based on the shear transformation

Most images have lots of linear information, which plays an important role in the tasks of image processing and pattern recognition. However, due to the interference of complex background in the images, the multiple directional characteristics of the linear information, and the problems of directional limitations in traditional methods, all these lead to the incomplete and discontinuities existed in the extracted linear information. To solve these problems, this paper puts forward a general model for improving the performance of the linear information extraction methods (LIEM) by utilizing the shear transformation. In this model, the shear transformation can transform an object (a filter or an image) in multiple directions, which directly or indirectly increases the directional characteristics of the traditional LIEM, and improves their performances to extract directional linear information and weak linear information. This paper elaborates on the basic principles of the model and its two implementations for the specific tasks. Furthermore, a variety of experiments are made to verify the versatility and effectiveness of the proposed model.
A High-Throughput Architecture for the SHA-256/224 Compliant With the DSRC Standard

This article presents a word serial retimed architecture for the SHA-256/224 algorithm. The architecture is compliant with the dedicated-short range communication for safety message authentications. We elaborate three-operand adder architectures suitable for field programmable gate array implementation. Several transformation techniques at the data-flow-graph level have been used to derive the architecture. Synthesis results show that the architecture has high throughput/ slice value compared with state-of-the-art SHA-256 implementations. The article also promulgates a comparison between high-level synthesis and RTL design.
Analyzing Data Using Discrete Transforms

In this chapter we consider a variety of transformations for the analysis of data. A description of Fourier analysis for discrete data is provided along with data analysis using the Hilbert and Walsh transforms, concluding with a short introduction to wavelet analysis.
Accurate π/2-phase shifting setup for a stabilized interference pattern of light fringes

We report on a technique for accurately π/2-phase shifting a stabilized interference pattern of light fringes used for building up a map of phase for 2D deformation measurement. This technique is based on the use of low amplitude phase modulation on the setup in order to generate first and second harmonics temporal terms in the pattern of light to operate an actively stabilized setup. Particular features of this stabilization setup allow one to use them for accurate π/2-phase shifting with the practical advantage of operating on an already stabilized pattern of fringes.
Testing Strategies For Bridging Time-To-Content In Spoken Dialogue Systems

What should dialogue systems do while looking for information or planning their next utterance? We conducted a study in which participants listened to (constructed) conversations between a user and an information system. In one condition, the system remained silent while preparingGambino, Soledad López a reply, whereas in the other, it “bought time” conversationally, using strategies from previously recorded human interactions. Participants perceived the second system as better at responding within an appropriate amount of time. Zarrieß, Sina Additionally, we varied between mid- and high-quality voices, and found that the high-quality voice time-buying system was also seen as more willing to help, better at understanding and more human-like than the silent system. We speculate that participants may have perceived this voice as a better match for the more human-like behavior of the second system. Schlangen, David
Improving the statistical quality of random number generators by applying a simple ratio transformation

It is well-known that the quality of random number generators can often be improved by combining several generators, e.g. by summing or subtracting their results. In this paper we investigate the ratio of two random number generators as an alternative approach: the smaller of two input random numbers is divided by the larger, resulting in a rational number from [0,1]. We investigate some basic theoretical properties of this approach and show that it yields a good approximation to the ideal uniform distribution. The focus of this paper, however, is on the empirical performance of the new transformation when applied to different generators. For a thorough statistical evaluation, we use the well-known test suite TestU01 (see L’Ecuyer and Simard, 2007). We apply the ratio transformation to moderately bad generators, i.e. those that failed up to 40% of the tests from the test battery Crush of TestU01. We show that more than half of them turn into empirically very good generators that pass all tests of Crush and BigCrush from TestU01 when the ratio transformation is applied. In particular, generators based on linear operations seem to benefit from the ratio, as this breaks up some of the unwanted regularities in the input sequences. Thus the additional effort to produce a second random number and to calculate the ratio allows to increase the quality of available random number generators, at least in a statistical sense.
Investigations on quantum-memory-assisted entropic uncertainty relation between coupled qubits in squeezed vacuum reservoir

We investigate the quantum-memory-assisted entropic uncertainty relation between coupled qubits which are initially in entangled mixed states in squeezed vacuum reservoir. We find that the uncertainty of quantum system is dependent on the squeezing effect of optical field. In the squeezed vacuum reservoir, the smaller the squeezed amplitude parameter is, the smaller the entropic uncertainties and its lower bound are, the more favorable to improve the accuracy of quantum measurement. Through optimizing the parameters of the initial quantum state, the measurement precision of observable mechanical quantity can be improved. In particular, when the relative phase of the initial quantum state is an integer multiple of ⁡π, the lower bound of the entropic uncertainty reaches a minimum.
Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence

A unified metric is given for the evaluation of object tracking systems. The metric is inspired by KL-divergence or relative entropy, which is commonly used to evaluate clustering techniques. Since tracking problems are fundamentally different from clustering, the components of KL-divergence are recast to handle various types of tracking errors (i.e., false alarms, missed detections, merges, splits). Scoring results are given on a standard tracking dataset (Oxford Town Centre Dataset), as well as several simulated scenarios. Also, this new metric is compared with several other metrics including the commonly used Multiple Object Tracking Accuracy metric. In the final section, advantages of this metric are given including the fact that it is continuous, parameter-less and comprehensive.
Sparsity Regularization and Total Variation Techniques

Sparsity regularization selects solutions with the minimum number of nonzero model parameters. In compressive sensing, sparsity regularization is applied in association with a change of basis, where the basis is chosen so that the desired model will be sparse, in the sense of having only a few nonzero coefficients in the model expansion. The Iterative Soft Threshholding (ISTA) and Fast Iterative Soft Threshholding (FISTA) algorithms are introduced. Total variation regularization uses a regularization term based on the 1-norm of the model gradient. Resulting models allow for discontinuous jumps so that solutions are biased towards piecewise-constant functions. An Iteratively Reweighted Least Squares (IRLS) algorithm and the Alternating Direction Method of Multipliers (ADMM) algorithm for solving the total variation problem are introduced.