Reviewerly/exHarmony huggingface dataset:
Cross-Resolution Person Re-identification with Deep Antithetical Learning

Images with different resolutions are ubiquitous in public person re-identification (ReID) datasets and real-world scenes, it is thus crucial for a person ReID model to handle the image resolution variations for improving its generalization ability. However, most existing person ReID methods pay little attention to this resolution discrepancy problem. One paradigm to deal with this problem is to use some complicated methods for mapping all images into an artificial image space, which however will disrupt the natural image distribution and requires heavy image preprocessing. In this paper, we analyze the deficiencies of several widely-used objective functions handling image resolution discrepancies and propose a new framework called deep antithetical learning that directly learns from the natural image space rather than creating an arbitrary one. We first quantify and categorize original training images according to their resolutions. Then we create an antithetical training set and make sure that original training images have counterparts with antithetical resolutions in this new set. At last, a novel Contrastive Center Loss (CCL) is proposed to learn from images with different resolutions without being interfered by their resolution discrepancies. Extensive experimental analyses and evaluations indicate that the proposed framework, even using a vanilla deep ReID network, exhibits remarkable performance improvements. Without bells and whistles, our approach outperforms previous state-of-the-art methods by a large margin.
On the difficulty of a distributional semantics of spoken language

The bulk of research in the area of speech processing concerns itself with supervised approaches to transcribing spoken language into text. In the domain of unsupervised learning most work on speech has focused on discovering relatively low level constructs such as phoneme inventories or word-like units. This is in contrast to research on written language, where there is a large body of work on unsupervised induction of semantic representations of words and whole sentences and longer texts. In this study we examine the challenges of adapting these approaches from written to spoken language. We conjecture that unsupervised learning of spoken language semantics becomes possible if we abstract from the surface variability. We simulate this setting by using a dataset of utterances spoken by a realistic but uniform synthetic voice. We evaluate two simple unsupervised models which, to varying degrees of success, learn semantic representations of speech fragments. Finally we suggest possible routes toward transferring our methods to the domain of unrestricted natural speech.
Decoding High-Order Interleaved Rank-Metric Codes

This paper presents an algorithm for decoding homogeneous interleaved codes of high interleaving order in the rank metric. The new decoder is an adaption of the Hamming-metric decoder by Metzner and Kapturowski (1990) and guarantees to correct all rank errors of weight up to $d-2$ whose rank over the large base field of the code equals the number of errors, where $d$ is the minimum rank distance of the underlying code. In contrast to previously-known decoding algorithms, the new decoder works for any rank-metric code, not only Gabidulin codes. It is purely based on linear-algebraic computations, and has an explicit and easy-to-handle success condition. Furthermore, a lower bound on the decoding success probability for random errors of a given weight is derived. The relation of the new algorithm to existing interleaved decoders in the special case of Gabidulin codes is given.
Scatgan for Reconstruction of Ultrasound Scatterers Using Generative Adversarial Networks

Computational simulation of ultrasound (US) echography is essential for training sonographers. Realistic simulation of US interaction with microscopic tissue structures is often modeled by a tissue representation in the form of point scatterers, convolved with a spatially varying point spread function. This yields a realistic US B-mode speckle texture, given that a scatterer representation for a particular tissue type is readily available. This is often not the case and scatterers are nontrivial to determine. In this work we propose to estimate scatterer maps from sample US B-mode images of a tissue, by formulating this inverse mapping problem as image translation, where we learn the mapping with Generative Adversarial Networks using a US simulation software for training. We demonstrate robust reconstruction results, invariant to US viewing and imaging settings such as imaging direction and center frequency. Our method is shown to generalize beyond the trained imaging settings, demonstrated on in vivo US data. Our inference runs orders of magnitude faster than optimization-based techniques, enabling future extensions for reconstructing 3DB-mode volumes with only linear computational complexity.
An Improved Radial Basis Function Networks Based on Quantum Evolutionary Algorithm for Training Nonlinear Datasets

In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and spread value. Radial basis function network (RBFN) is a type of feedforward network that capable of perform nonlinear approximation on unknown dataset. It has been widely used in classification, pattern recognition, nonlinear control and image processing. Thus, with the increases in RBFN application, some problems and weakness of RBFN network is identified. Through the combination of quantum computing and RBFN provides a new research idea in design and performance improvement of RBFN system. This paper describes the theory and application of quantum computing and cloning operators, and discusses the superiority of these theories and the feasibility of their optimization algorithms. This proposed improved RBFN (I-RBFN) that combined with cloning operator and quantum computing algorithm demonstrated its ability in global search and local optimization to effectively speed up learning and provides better accuracy in prediction results. Both the algorithms that combined with RBFN optimize the centers and spread value of RBFN. The proposed I-RBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to I-RBFN for root mean square error (RMSE) values with standard RBFN. The proposed I-RBFN yielded better results with an average improvement percentage more than 90 percent in RMSE.
Determinant Equivalence Test over Finite Fields and over Q

The determinant polynomial Det_n(x) of degree n is the determinant of a n x n matrix of formal variables. A polynomial f is equivalent to Det_n(x) over a field F if there exists a A in GL(n^2,F) such that f = Det_n(A * x). Determinant equivalence test over F is the following algorithmic task: Given black-box access to a f in F[x], check if f is equivalent to Det_n(x) over F, and if so then output a transformation matrix A in GL(n^2,F). In (Kayal, STOC 2012), a randomized polynomial time determinant equivalence test was given over F = C. But, to our knowledge, the complexity of the problem over finite fields and over Q was not well understood.In this work, we give a randomized poly(n,log |F|) time determinant equivalence test over finite fields F (under mild restrictions on the characteristic and size of F). Over Q, we give an efficient randomized reduction from factoring square-free integers to determinant equivalence test for quadratic forms (i.e. the n=2 case), assuming GRH. This shows that designing a polynomial-time determinant equivalence test over Q is a challenging task. Nevertheless, we show that determinant equivalence test over Q is decidable: For bounded n, there is a randomized polynomial-time determinant equivalence test over Q with access to an oracle for integer factoring. Moreover, for any n, there is a randomized polynomial-time algorithm that takes input black-box access to a f in Q[x] and if f is equivalent to Det_n over Q then it returns a A in GL(n^2,L) such that f = Det_n(A * x), where L is an extension field of Q and [L : Q] <= n.The above algorithms over finite fields and over Q are obtained by giving a polynomial-time randomized reduction from determinant equivalence test to another problem, namely the full matrix algebra isomorphism problem. We also show a reduction in the converse direction which is efficient if n is bounded. These reductions, which hold over any F (under mild restrictions on the characteristic and size of F), establish a close connection between the complexity of the two problems. This then leads to our results via applications of known results on the full algebra isomorphism problem over finite fields (Ronyai, STOC 1987 and Ronyai, J. Symb. Comput. 1990) and over Q (Ivanyos {et al}., Journal of Algebra 2012 and Babai {et al}., Mathematics of Computation 1990).
Architecture Design of CTC Log Module Based on Web Service

At present, the log module of high-speed rail dispatching system has poor compatibility and high development difficulty due to its component embedding mode; in addition, network quality and security reinforcement seriously affect its performance. With Web Service technology, the loosely coupled and heterogeneous log module architecture is designed and implemented to solve the above problems. It takes task distribution and implementation as the core and provides 3 services: log access service, log query service and log statistics mining service. 1) A large number of cluster service providers makes full use of computing resources, storage resources and network resources to provide services externally; 2) the simplify centralized management service requestor receives unified registration services remotely; 3) the application of log compression method, AOP theory and the complete data flow management scheme further improves module capability. Simulation experiments verify the effectiveness of system optimization.
Pengembangan Bahan Ajar Menulis Esai Dengan Memanfaatkan Kearifan Lokal Melalui Pembelajaran Berbasis  Proyek (PBP) Siswa SMA

The purpose of research for the development of teaching materials to write essays with local wisdom through PBP in grade XII high school students. The scope of this development is limited only to the development of teaching materials, development of learning tools and effectiveness in the learning process. The method used in this research is Research and Development (R&D), using development research design with Brog & Gall model. Source of data that become object in this research is student of class XII in SMA Negeri 9 Balikpapan. Development of teaching materials obtained by the material expert test results 82% declared feasible, 84% media expert test declared feasible, 85% design assessment test declared feasible, field test 72.3% small group declared good, a field test of large group 79.6% expressed well. So as a whole the data obtained from the development of teaching materials declared eligible, the development of learning tools refers to the modified syllabus and RPP adapted to the learning activities, evaluation results provide improvements in students' skills in writing essays. The overall quality of teaching materials that are the product of this development is categorized eligible for use of class XII students even semester.
Gestão do escopo para o projeto Biblioteca de reuso de requisitos de software para diferentes domínios

The software development process requires the fulfillment of several steps, where the management of requirements proves to be one of the most complex and important. A platform that allows the access of software development teams to an online library containing a set of requirements for the domain that one wishes to implement, and that provides the reuse of these requirements can bring many benefits in terms of quality, time and costs. In this context, a project of structuring a library of reuse of software requirements for different domains is of great interest to the software development community (developers and / or organizations). The purpose of this article is to propose the project "Library of reuse of software requirements for different domains" through the elaboration of two essential documents of scope management, the Work Breakdown Structure - WBS (project scope) and the List of Requirements (product scope). In order to elaborate this work, the following steps were performed: (i) study on scope management and requirements engineering with emphasis on requirements elicitation and libraries of reuse; (ii) elaboration and validation of the WBS through interviews with senior researchers in the Computing area; (iii) preparation of the List of Requirements; (iv) validation of the List of Requirements through field research, using questionnaires, with professionals in the area of Information Technology (TI). The research led to the conclusion that the proposed Library, represented by the documents of scope management (project and product), proved to be important and capable of generating benefits for the TI area.
Building infrastructures for university-community knowledge exchange: The Role of Information Professionals and Literacy Educators

This panel argues that information professionals and literacy educators play an important bridging role between universities and community groups and stakeholders. We describe the context of universitycommunity knowledge exchange, which is increasingly expected, but remains under-supported and under-theorised, and consider new opportunities that exist to support and build capacity in researchers and communities to co-create, share, and use information. We focus on the training and professional development needed to position information professionals and literacy educators as knowledge brokers, and innovative projects that demonstrate their value and potential in this role.
Effectiveness evaluation method of Naval Equipment Support System for task

To solve the main problems existing in the effectiveness evaluation of naval equipment support system at present, this paper studies the hierarchical structure and main contents of task-oriented naval equipment support system based on hierarchical thinking, and puts forward corresponding evaluation indexes and aggregation methods. The research results can provide reference for the enhancement of naval equipment capability.
Perspective Illusions Based On Convergence Vectors

When pictures containing perspective cues are viewed simultaneously, the visual system combines the perspective cues using convergence vectors creating perspective illusions.
Tools of Digital Economy in Russian Realities

The formation of the prerequisites for the development of the economy revises the technological process in the country. The ineffectiveness of the activities of the digital economy has led to the formation of new instruments. The new tools regulate the economic system. Tools of the digital economy don’t take account the development of territories in Russia. In accordance with this premise, it is planned to conduct a study comparing the tools of the digital economy to the Russian realities of Russia’s development. The thesis serves the purpose of scientific research. The tasks of scientific research involve the following activities: to the consideration of tools for the development of the digital economy; to the allocation of directions of “gravitation” of tools of the digital economy; to the presentation of the characteristics of digital economy tools; to the formation of the life cycle of the development of the digital economy. The theoretical significance of the study is the regulation of the tools of the digital economy in the Russian realities. The practical significance is based on the importance of the instrumentality in the Russian economy.
Problems of Globalization and Pedagogical Work with “Digital Natives” at Technical Universities

The article deals with the problems of globalization of modern society, associated with its informatization, the emergence of a new cultural and historical type of personality, the so-called “digital generation”. Artificially created digital and virtual worlds come into conflict with values of human existence, they present moral and spiritual challenge for a person. The process of evolution of an information and knowledge society requires the educational community to participate actively in identifying appropriate responses to modern challenges in education and upbringing. The authors analyze approaches to the organization of pedagogical work and extra-curricular activities at a modern university. They present the experience of implementing traditional and innovative activities of the pedagogical work at Volgograd State Technical University (VSTU) aimed at the development of students’ universal moral values.
Light Weight Stereo Matching via Deep Extraction and Integration of Low and High Level Information

Deep convolutional neural networks (CNN) have demonstrated remarkable progress in stereo matching recently. However, disparity estimation in the ill-posed regions is still difficult. In addition, CNN based stereo matching methods often have impractical computational complexity and memory consumption. To address these problems we propose an end-to-end light weight CNN architecture to effectively learn and integrate low and high level information. To achieve this, a novel enhancement block built upon group convolution and dilated-convolution is proposed. Compared with state-of-the-art methods, the proposed method achieved competitive performance with the least number of network parameters on the Flyingthings3d and KITTI datasets.
Competitive Online Search Trees on Trees

We consider the design of adaptive data structures for searching elements of a tree-structured space. We use a natural generalization of the rotation-based online binary search tree model in which the underlying search space is the set of vertices of a tree. This model is based on a simple structure for decomposing graphs, previously known under several names including elimination trees, vertex rankings, and tubings. The model is equivalent to the classical binary search tree model exactly when the underlying tree is a path. We describe an online O (log log n )-competitive search tree data structure in this model, where n is the number of vertices. This matches the best-known competitive ratio of binary search trees. Our method is inspired by Tango trees, an online binary search tree algorithm, but critically needs several new notions including one that we call Steiner-closed search trees, which may be of independent interest. Moreover, our technique is based on a novel use of two levels of decomposition, first from search space to a set of Steiner-closed trees and, second, from these trees into paths.
Jointly Multiple Hash Learning

Hashing can compress heterogeneous high-dimensional data into compact binary codes while preserving the similarity to facilitate efficient retrieval and storage, and thus hashing has recently received much attention from information retrieval researchers. Most of the existing hashing methods first predefine a fixed length (e.g., 32, 64, or 128 bit) for the hash codes before learning them with this fixed length. However, one sample can be represented by various hash codes with different lengths, and thus there must be some associations and relationships among these different hash codes because they represent the same sample. Therefore, harnessing these relationships will boost the performance of hashing methods. Inspired by this possibility, in this study, we propose a new model jointly multiple hash learning (JMH), which can learn hash codes with multiple lengths simultaneously. In the proposed JMH method, three types of information are used for hash learning, which come from hash codes with different lengths, the original features of the samples and label. In contrast to the existing hashing methods, JMH can learn hash codes with different lengths in one step. Users can select appropriate hash codes for their retrieval tasks according to the requirements in terms of accuracy and complexity. To the best of our knowledge, JMH is one of the first attempts to learn multi-length hash codes simultaneously. In addition, in the proposed model, discrete and closed-form solutions for variables can be obtained by cyclic coordinate descent, thereby making the proposed model much faster during training. Extensive experiments were performed based on three benchmark datasets and the results demonstrated the superior performance of the proposed method.
A Light-Weighted Network for Facial Landmark Detection via Combined Heatmap and Coordinate Regression

3D facial landmark, which offers more expressive and occlusive information than its 2D counterpart, receives more and more attention from researchers in recent years. The top performing algorithms for 3D facial landmark detection are mainly divided into two categories: the two-step approach and the volume representation method. However, the former lacks the relation of depth with plat and the latter leads to large computation. In this paper, we propose the Combined Heatmap and Coordinate Regression (CHCR), which is an end-to-end method for 3D facial landmark detection from a single 2D image. To achieve that, we innovatively present the combined heatmap of three channels, and each channel of heatmap records the likelihoods of landmarks location of any two different axes. Such representation maintains the relation of various view while decreases either the channels for encoding or dimension for decoding. Then we retrieve the 3D coordinate vectors from corresponding combined heatmap by coordinate regression. Hence, an encoder-decoder network with a simple CNN attached to an hourglass module is designed to cope with the whole process. Experiments show our model is extremely light-weighted and runs faster than any other methods while on high performance of accuracy.
Conditional Expectation Propagation

Expectation propagation (EP) is a powerful approximate inference algorithm. However, a critical barrier in applying EP is that the moment matching in message updates can be intractable. Handcrafting approximations is usually tricky, and lacks generalizability. Importance sampling is very expensive. While Laplace propagation provides a good solution, it has to run numerical optimizations to find Laplace approximations in every update, which is still quite inefficient. To overcome these practical barriers, we propose conditional expectation propagation (CEP) that performs conditional moment matching given the variables outside each message, and then takes expectation w.r.t the approximate posterior of these variables. The conditional moments are often analytical and much easier to derive. In the most general case, we can use (fully) factorized messages to represent the conditional moments by quadrature formulas. We then compute the expectation of the conditional moments via Taylor approximations when necessary. In this way, our algorithm can always conduct efficient, analytical fixed point iterations. Experiments on several popular models for which standard EP is available or unavailable demonstrate the advantages of CEP in both inference quality and computational efficiency.