Reviewerly/exHarmony huggingface dataset:
Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net

Medical image processing plays an increasingly important role in clinical diagnosis and treatment. Using the results of kidney CT image segmentation for three-dimensional reconstruction is an intuitive and accurate method for diagnosis. In this paper, we propose a three-step automatic segmentation method for kidney, tumors and cysts, including roughly segmenting the kidney and tumor from low-resolution CT, locating each kidney and fine segmenting the kidney, and finally extracting the tumor and cyst from the segmented kidney. The results show that the average dice of our method for kidney, tumor and cysts is about 0.93, 0.57, 0.73.
Quantum–Classical Entropy Analysis for Nonlinearly-Coupled Continuous-Variable Bipartite Systems

The correspondence principle plays a fundamental role in quantum mechanics, which naturally leads us to inquire whether it is possible to find or determine close classical analogs of quantum states in phase space-a common meeting point to both classical and quantum density statistical descriptors. Here, this issue is tackled by investigating the behavior of classical analogs arising upon the removal of all interference traits displayed by the Wigner distribution functions associated with a given pure quantum state. Accordingly, the dynamical evolution of the linear and von Neumann entropies is numerically computed for a continuous-variable bipartite system, and compared with the corresponding classical counterparts, in the case of two quartic oscillators nonlinearly coupled under regular and chaos conditions. Three quantum states for the full system are considered: a Gaussian state, a cat state, and a Bell-type state. By comparing the quantum and classical entropy values, and particularly their trends, it is shown that, instead of entanglement production, such entropies rather provide us with information on the system (either quantum or classical) delocalization. This gradual loss of information translates into an increase in both the quantum and the classical realms, directly connected to the increase in the correlations between both parties' degrees of freedom which, in the quantum case, is commonly related to the production of entanglement.
The Value Orientation of Implementing the Autonomy of Running Schools in Ordinary High Schools

Practice has proved that the implementation of the autonomy of personnel, finance and teaching in ordinary high schools will help to improve the school's decision-making level, stimulate the school's vitality, and improve students' academic performance and school efficiency.However, we must clearly implement the value orientation of school running autonomy of ordinary high schools, that is, to promote the all-round development of students and the sustainable development of economy and society.Otherwise, it is very easy to cause the alienation of school running autonomy.
Oral English Teaching System Based on Speech Recognition Technology

Oral expression is an important purpose and the most important skill of learning English.Computer online teaching and oral online test scores have become a part of College English teaching andfinal test. Many studies have proved that the teaching and automatic scoring performance of computersystem is very close to that of teachers, and can basically replace the batch teaching and grading of teachersto complete the direct oral test teaching task. It can test students' oral ability fairly and effectively, and hasgood test validity and reliability. According to the actual situation of school oral test, this paper analyzes theeffectiveness of online oral English teaching and testing, and puts forward some suggestions forimprovement, hoping to provide some reference for foreign language teaching researchers.
Intelligent Learning Environments

The educational environment is an intelligent ternary integration where the physical space, social space, and information space infiltrate each other. Educational environments are transformed by the technology and interwoven with reality and seamless flow. Intelligence has become an essential attribute of an educational environment.
Testing Boolean Functions Properties

The goal in the area of functions property testing is to determine whether a given black-box Boolean function has a particular given property or is ɛ-far from having that property. We investigate here several types of properties testing for Boolean functions (identity, correlations and balancedness) using the Deutsch-Jozsa algorithm (for the Deutsch-Jozsa (D-J) problem) and also the amplitude amplification technique. At first, we study here a particular testing problem: namely whether a given Boolean function f, of n variables, is identical with a given function g or is ɛ-far from g, where ɛ is the parameter. We present a one-sided error quantum algorithm to deal with this problem that has the query complexity O(1ε). Moreover, we show that our quantum algorithm is optimal. Afterwards we show that the classical randomized query complexity of this problem is Θ(1ε). Secondly, we consider the D-J problem from the perspective of functional correlations and let C(f, g) denote the correlation of f and g. We propose an exact quantum algorithm for making distinction between |C(f, g)| = ɛ and |C(f, g)| = 1 using six queries, while the classical deterministic query complexity for this problem is Θ(2n) queries. Finally, we propose a one-sided error quantum query algorithm for testing whether one Boolean function is balanced versus ɛ-far balanced using O(1ε) queries. We also prove here that our quantum algorithm for balancedness testing is optimal. At the same time, for this balancedness testing problem we present a classical randomized algorithm with query complexity of O(1/ɛ2). Also this randomized algorithm is optimal. Besides, we link the problems considered here together and generalize them to the general case.
Hyperembodiment

Abstract The article introduces hyperembodiment as a general feature of artefacts for perception and representation and as a research agenda for cognitive semiotics and cognitive science at large. At the heart of the article, I offer analyses of two different selected examples: Hyperembodiment in a Facetime conversation and in a Snapchat message. These digital productions of appearances of social interactants are analysed with particular attention to their intercorporeal qualities and it is argued that social perception is facilitated which takes the users of visual, interpersonal communication devices beyond the corporeal limits of ordinary ways of being present for each other. Broadening the scope, I then discuss how a general focus on hyperembodiment opens new, productive avenues of inquiry. First, I relate hyperembodiment to the heterogenous field of social presence research and suggest how it may contribute to it. Thereafter I discuss hyperembodiment as a topic of cognitive semiotics.
Procedural Generation of Isometric Racetracks Using Chain Code for Racing Games

In this work, we propose a procedural generation method for railroad circuits based on the chain code algorithm. The process creates a list with directions in a two-dimensional space from a series of operations using the chain code and images of predefined real tracks. This list was used in the Unity engine to provide the positions, starting from a predefined starting point, which will insert the elements of the circuit during its creation. The race track format still goes through a preprocessing method to filter the ladder effect generated by the chain code and fine-tune details before being simulated in a game. This allows each track to have a unique pattern and good gameplay. Tests carried out using our generation method in a 2D (two dimensions) isometric kart game showed that in all runs, the tracks had an average of 98.5% difference among them and maintained the gameplay standards.
Simultaneous Augmentation of Textures and Deformation Based on Dynamic Projection Mapping

In this paper, we exploit human perception characteristics and dynamic projection mapping techniques and realize overwriting of both textures and deformation of a real object. To keep the projection following a moving object and induce deformation illusion, we developed a 1000 fps projector-camera system and demonstrated augmentation of the real world. In the demonstration, the audience will see a plaster figure turning into a colorful and flabby object.
Model-Based Verification of Security and Non-Functional Behavior using AADL

As computer-based systems have become more complex and we continue to exploit the benefits of code generation for those systems' components or subsystems, we're finding significant integration problems due to unanticipated behavior. It isn't enough to have correct code for software components and subsystems, they must be properly integrated and correctly executed to create a fully functional system that meets its nonfunctional requirements, such as real-time performance, reliability, security, or safety. A single-source system model annotated with analysis-specific information can reflect architectural changes with little additional effort. The Architecture Analysis and Design Language (AADL) provides a platform for multidimensional model analysis and verification.
Sampling and reconstruction of sparse signals on circulant graphs – an introduction to graph-FRI

With the objective of employing graphs toward a more generalized theory of signal processing, we present a novel sampling framework for (wavelet-)sparse signals defined on circulant graphs which extends basic properties of Finite Rate of Innovation (FRI) theory to the graph domain, and can be applied to arbitrary graphs via suitable approximation schemes. At its core, the introduced Graph-FRI-framework states that any K-sparse signal on the vertices of a circulant graph can be perfectly reconstructed from its dimensionality-reduced representation in the graph spectral domain, the Graph Fourier Transform (GFT), of minimum size 2K. By leveraging the recently developed theory of e-splines and e-spline wavelets on graphs, one can decompose this graph spectral transformation into the multiresolution low-pass filtering operation with a graph e-spline filter, and subsequent transformation to the spectral graph domain; this allows to infer a distinct sampling pattern, and, ultimately, the structure of an associated coarsened graph, which preserves essential properties of the original, including circularity and, where applicable, the graph generating set.
Secure Data Exchange

A vast amount of data belonging to companies and individuals is currently stored in the cloud in encrypted form by trustworthy service providers such as Microsoft, Amazon, and Google. Unfortunately, the only way for the cloud to use the data in computations is to first decrypt it, then compute on it, and finally re-encrypt it, resulting in a problematic trade-off between value/utility and security. At a high level, our goal in this paper is to present a general and practical cryptographic solution to this dilemma. More precisely, we describe a scenario that we call Secure Data Exchange (SDE), where several data owners are storing private encrypted data in a semi-honest non-colluding cloud, and an evaluator (a third party) wishes to engage in a secure function evaluation on the data belonging to some subset of the data owners. We require that none of the parties involved learns anything beyond what they already know and what is revealed by the function, even when the parties (except the cloud) are active malicious. We also recognize the ubiquity of scenarios where the lack of an efficient SDE protocol prevents for example business transactions, research collaborations, or mutually beneficial computations on aggregated private data from taking place, and discuss several such scenarios in detail. Our main result is an efficient and practical protocol for enabling SDE using Secure Multi-Party Computation (MPC) in a novel adaptation of the server-aided setting. We also present the details of an implementation along with performance numbers.
QoS-Aware Web Service Selection with Internal Complementarity

Service composition is a key enabling technology in service-oriented computing for developing versatile applications by integrating various existing interoperable services. Although actively studied, most existing works on service composition neglect the existence of complementarity among candidate services within a service class, so-called internal complementarity. In fact, complementary candidate services within a service class can be composed to form a composite candidate service which may yield better service utility than that provided by any existing candidate service within that service class. This work focuses on web service composition where internal complementarity is more likely to happen. Specifically, we aim at addressing the problem of QoS-aware web service selection with internal complementarity (WSS-IC). We first transform this problem into a multi-choice multi-dimensional knapsack problem (MMKP) and prove such a transformation per se has non-polynomial time complexity in the worse case.Then, we perform complexity analysis to demonstrate that existing approaches to MMKPs are not computationally feasible to resolve QoS-aware WSS-IC. This fact motivates us to propose an iteratively improving framework for deriving the solution iteration by iteration while taking into account both solution structure and QoS constraints. At each iteration, the current solution gets improved by solving a disjunctively constrained knapsack problem. To verify the effectiveness of the proposed framework, two heuristic approaches are implemented under this framework. Experimental results demonstrate that our approaches outperform the compared methods in terms of both solution quality and computation time.
DAG: A General Model for Privacy-Preserving Data Mining

Secure multi-party computation (SMC) allows parties to jointly compute a function over their inputs, while keeping every input confidential. It has been extensively applied in tasks with privacy requirements, such as privacy-preserving data mining (PPDM), to learn task output and at the same time protect input data privacy. However, existing SMC-based solutions are ad-hoc - they are proposed for specific applications, and thus cannot be applied to other applications directly. To address this issue, we propose a privacy model DAG (Directed Acyclic Graph) that consists of a set of fundamental secure operators (e.g., +, -, ×, /, and power). Our model is general - its operators, if pipelined together, can implement various functions, even complicated ones like Naı̈ve Bayes classifier. It is also extendable - new secure operators can be defined to expand the functions that the model supports. For case study, we have applied our DAG model to two data mining tasks: kernel regression and Naı̈ve Bayes. Experimental results show that DAG generates outputs that are almost the same as those by non-private setting, where multiple parties simply disclose their data. The experimental results also show that our DAG model runs in acceptable time, e.g., in kernel regression, when training data size is 683,093, one prediction in non-private setting takes 5.93 sec, and that by our DAG model takes 12.38 sec.
A Statistically and Numerically Efficient Independence Test Based on Random Projections and Distance Covariance

Testing for independence plays a fundamental role in many statistical techniques. Among the nonparametric approaches, the distance-based methods (such as the distance correlation-based hypotheses testing for independence) have many advantages, compared with many other alternatives. A known limitation of the distance-based method is that its computational complexity can be high. In general, when the sample size is n , the order of computational complexity of a distance-based method, which typically requires computing of all pairwise distances, can be O ( n 2 ). Recent advances have discovered that in the univariate cases, a fast method with O ( n log n ) computational complexity and O ( n ) memory requirement exists. In this paper, we introduce a test of independence method based on random projection and distance correlation, which achieves nearly the same power as the state-of-the-art distance-based approach, works in the multivariate cases, and enjoys the O ( nK log n ) computational complexity and O ( max{ n , K }) memory requirement, where K is the number of random projections. Note that saving is achieved when K &amp;lt; n / log n . We name our method a Randomly Projected Distance Covariance (RPDC). The statistical theoretical analysis takes advantage of some techniques on the random projection which are rooted in contemporary machine learning. Numerical experiments demonstrate the efficiency of the proposed method, relative to numerous competitors.
Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models

Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generating a good scientific poster, however, is a complex and time-consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, which utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including attributes of each panel and arrangements of graphical elements, are learned and inferred from data. During the inference stage, the maximum a posterior (MAP) estimation framework is employed to incorporate some design principles. In order to bridge the gap between panel attributes and the composition within each panel, we also propose a recursive page splitting algorithm to generate the panel layout for a poster. To learn and validate our model, we collect and release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.
Availability-Aware Virtual Cluster Allocation in Bandwidth-Constrained Datacenters

As greater numbers of data-intensive applications are required to process big data in bandwidth-constrained datacenters with heterogeneous physical machines (PMs) and virtual machines (VMs), network core traffic is experiencing rapid growth. The VMs of a virtual cluster (VC) must be allocated as compactly as possible to avoid bandwidth-related bottlenecks. Since each PM/switch has a certain failure probability, a VC may not be executed when it meets with any PM/switch fault. Although the VMs of a VC can be spread out across different fault domains to minimize the risk of violating the availability requirement of the VC, this increases the network core traffic. Therefore, avoiding the decrease in availability caused by the heterogeneous PM/switch failure probabilities and bandwidth-related bottlenecks has been a constant challenge. In this paper, we first introduce a joint optimization function to measure the overall risk cost and overall bandwidth usage in the network core to allocate the same set of data-intensive applications. We then introduce an approach to maximize the value of the joint optimization function. Finally, we performed a side-by-side comparison with prior algorithms, and the experimental results show that our approach outperforms the other existing algorithms.
Constructing spatial discretizations for sparse multivariate trigonometric polynomials that allow for a fast discrete Fourier transform

The paper discusses the construction of high dimensional spatial discretizations for arbitrary multivariate trigonometric polynomials, where the frequency support of the trigonometric polynomial is known. We suggest a construction based on the union of several rank-1 lattices as sampling scheme. We call such schemes multiple rank-1 lattices. This approach automatically makes available a fast discrete Fourier transform (FFT) on the data. The key objective of the construction of spatial discretizations is the unique reconstruction of the trigonometric polynomial using the sampling values at the sampling nodes. We develop different construction methods for multiple rank-1 lattices that allow for this unique reconstruction. The symbol M denotes the total number of sampling nodes within the multiple rank-1 lattice. In addition, we assume that the multivariate trigonometric polynomial is a linear combination of T trigonometric monomials. The ratio of the number M of sampling points that are sufficient for the unique reconstruction to the number T of distinct monomials is called oversampling factor in this context. The presented construction methods for multiple rank-1 lattices allow for estimates of this number M. Roughly speaking, the oversampling factor M/T is independent of the spatial dimension and, with high probability, only logarithmic in T, which is much better than the oversampling factor that is expected for a sampling method that uses one single rank-1 lattice. The newly developed approaches for the construction of spatial discretizations are probabilistic methods. The arithmetic complexity of these algorithms depend only linearly on the spatial dimension and, with high probability, only linearly on T up to some logarithmic factors. Furthermore, we analyze the computational complexities of the resulting FFT algorithms, that exploits the structure of the suggested multiple rank-1 lattice spatial discretizations, in detail and obtain upper bounds in O(Mlog⁡M), where the constants depend only linearly on the spatial dimension. With high probability, we construct spatial discretizations where M/T≤Clog⁡T holds, which implies that the complexity of the corresponding FFT converts to O(Tlog2⁡T).
A Carlitz type result for linearized polynomials

For an arbitrary $q$-polynomial $f$ over $\mathbb{F}_{q^n}$ we study the problem of finding those $q$-polynomials $g$ over $\mathbb{F}_{q^n}$ for which the image sets of $f(x)/x$ and $g(x)/x$ coincide. For $n\leq 5$ we provide sufficient and necessary conditions and then apply our result to study maximum scattered linear sets of $\mathrm{PG}(1,q^5)$.
Analysis of Flight Variability: a Systematic Approach

In movement data analysis, there exists a problem of comparing multiple trajectories of moving objects to common or distinct reference trajectories. We introduce a general conceptual framework for comparative analysis of trajectories and an analytical procedure, which consists of (1) finding corresponding points in pairs of trajectories, (2) computation of pairwise difference measures, and (3) interactive visual analysis of the distributions of the differences with respect to space, time, set of moving objects, trajectory structures, and spatio-temporal context. We propose a combination of visualisation, interaction, and data transformation techniques supporting the analysis and demonstrate the use of our approach for solving a challenging problem from the aviation domain.