Reviewerly/exHarmony huggingface dataset:
NeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing

We present a novel method called NeTO, for capturing the 3D geometry of solid transparent objects from 2D images via volume rendering. Reconstructing transparent objects is a very challenging task, which is ill-suited for general-purpose reconstruction techniques due to the specular light transport phenomena. Although existing refraction-tracing-based methods, designed especially for this task, achieve impressive results, they still suffer from unstable optimization and loss of fine details since the explicit surface representation they adopted is difficult to be optimized, and the self-occlusion problem is ignored for refraction-tracing. In this paper, we propose to leverage implicit Signed Distance Function (SDF) as surface representation and optimize the SDF field via volume rendering with a self-occlusion aware refractive ray tracing. The implicit representation enables our method to be capable of reconstructing high-quality reconstruction even with a limited set of views, and the self-occlusion aware strategy makes it possible for our method to accurately reconstruct the self-occluded regions. Experiments show that our method achieves faithful reconstruction results and outperforms prior works by a large margin. Visit our project page at https://www.xxlong.site/NeTO/.
Multi-threshold signature

The work presents a new signature scheme, called the multi-threshold signature, which generalizes the concept of multisignature and threshold signature. This scheme protects the anonymity of signers in a way the group signature does – in exceptional circumstances the identities of signers may be revealed. Due to the new party – completer, in our scheme the threshold size may vary together with the message to be signed. The presented scheme is based on the RSA signature standard, however other signature standards might be applied to it as well.
Neurodynamical classifiers with low model complexity

The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane classifier by minimizing an upper bound on the Vapnik–Chervonenkis (VC) dimension. The VC dimension measures the capacity or model complexity of a learning machine. Vapnik’s risk formula indicates that models with smaller VC dimension are expected to show improved generalization. On many benchmark datasets, the MCM generalizes better than SVMs and uses far fewer support vectors than the number used by SVMs. In this paper, we describe a neural network that converges to the MCM solution. We employ the MCM neurodynamical system as the final layer of a neural network architecture. Our approach also optimizes the weights of all layers in order to minimize the objective, which is a combination of a bound on the VC dimension and the classification error. We illustrate the use of this model for robust binary and multi-class classification. Numerical experiments on benchmark datasets from the UCI repository show that the proposed approach is scalable and accurate, and learns models with improved accuracies and fewer support vectors.
CCE: A Coupled Framework of Clustering Ensembles

Clustering ensemble mainly relies on the pairwise similarity to capture the consensus function. However, it usually considers each base clustering independently, and treats the similarity measure roughly with either 0 or 1. To address these two issues, we propose a coupled framework of clustering ensembles CCE, and exemplify it with the coupled version CCSPA for CSPA. Experiments demonstrate the superiority of CCSPA over baseline approaches in terms of the clustering accuracy.
Help - Trasser.pl

Helpdesk describes all the main options in the portal trasser.pl, it can learn how to use them.
Teachers, Learners, and Oracles

We exhibit a family of computably enumerable sets which can be learned within polynomial resource bounds given access only to a teacher, but which requires exponential resources to be learned given access only to a membership oracle. In general, we compare the families that can be learned with and without teachers and oracles for four measures of efficient learning.
Computational Intelligence Course in Undergraduate Computer Science and Engineering Curricula

Abstract Artificial Neural Network Course in Undergraduate Computer Science and Engineering Curriculums Abstract: An artificial neural network is a system based on the operation of biological neural networks, in other words, is an emulation of biological neural system. Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements (neurons) working in parallel to solve a specific problem. The study of neural and adaptive systems is a unique and growing interdisciplinary field that considers adaptive, distributed, and mostly nonlinear systems. Neural and adaptive systems are used in many important engineering applications, such as signal filtering, data segmentation, data mining, text mining, classification of input patterns, system identification, prediction, and control. They are used in many commercial products such as modems, image‐processing systems, speech recognition, front‐end signal processors, and biomedical instrumentation. Nowadays, neural networks offer improved performance over conventional technologies in areas such as machine vision, robust pattern detection, signal filtering, virtual reality, data segmentation, data compression, data mining, artificial life, adaptive control, and optimization and many more. The principal characteristic of neural systems is their adaptivity, which brings a totally new system design technique. It is believed that neural and adaptive systems should be considered as another tool in the scientists and engineer’s toolbox. Educational excellence requires exposing students to the current edge of research. To ensure that student projects are along the same trajectory that the industry is moving, educators must continually introduce emerging techniques, practices, and applications into the curriculum. The field of artificial neural networks is growing rapidly. It is crucial that the emerging field of neural networks be integrated into the computer science and engineering curriculums. This paper will study different approaches that are used by different institutions of higher education around the world to integrate neural networks in their curriculum.
The Applications of Blockchain Technology on Electronic Bill of Lading

본 연구는 블록체인기술을 기반으로 한 전자선하증권의 활용을 위한 법적 제도적 문제를 확 인하고 사례연구를 이행하는 것을 목적으로 한다. 연구목적을 달성하기 위해 본 연구는 선행연구와 사례분석을 실시하였다. 분석결과는 다음과 같다. 첫째, 블록체인기술을 기반으로 한 전자 선하증권은 그 법적 성질에서 종이선하증권과 동일한 것으로 확인되었다. 둘째, 볼레로 전자선하증권과 uTradeHub의 e-B/L은 중앙등록기관에 등록해야 하는 반면에 블록체인기술을 기반으로 한 전자선하증권은 분산형 방식으로 보안성과 신뢰성이 높다. 또한 상법 제862조에 따르면 전자선하증권은 중앙등록기관에 등록해야 한다. 셋째, 블록체인기술을 기반으로 한 사례에서 나타난 바와 같이 국제무역의 프로세스에서 전자선하증권은 수출상의 화물인도에 대해 운송인이 발행하고 이를 분산된 원장에 기록함으로서 화물의 소유자를 확인할 수 있어 수입지에서 운송인이 화물을 인도하는데 문제가 없는 것으로 확인되었다. 따라서 블록체인기술을 기반으로 한 전자선하증권은 국제무역의 프로세스에서 발생되는 시간과 비용을 절감할 수 있고 암호화기술을 통해 효율적인 거래를 가능하게 한다.Purpose : The aim of this study is to analyze legal and system atic problem s and a case study for the application of electronic bill of lading based on blockchain technology. Research design, data, methodology : To achieve the above aim, this study uses two methodology such as prior research and the case study. The former is concerned with a literature review and the latter is connected with a blockchain case in the international logistics process. Results : First, the legal nature of electronic bill of lading based on blockchain technology is equal to paper based bill of lading. Second, on the basis of the case study, electronic bill of lading is issued by carriers after delivering goods of shippers, and consignees are verified because bona-fide holders of electronic bill of lading is registered in the ledgers. As a result, carriers can deliver the goods in destinations. Conclusions : Electronic bill of lading based on blockchain technology can save cost and time in the global supply chain processes, and it is possible to make efficient transactions based on cryptographic technology.
Strategies: Top 8 List of How to Support Tier 3 Publishers

Learn by teaching. Help a third tier publisher understand the library market. Support a “NASIG/SSP Advisor Matchmaker Service.” These small publishers may not have any staff devoted exclusively to marketing; they cannot support library advisory groups or conduct focus groups, but they would love to have contact with library customers to help them understand changes in the market and new developments. We think SSP would approve a program like this. Will you help us persuade the NASIG Board?
Geodynamic Evolution of the Taimyr and Yenisey Ridge Foldbelts and Particularities of Gold Deposit Formation

In the western Siberian platform (Taimyr and Yenisey Ridge fold and thrust belts), the formation of most gold-quartz and gold-sulfide deposits in Neoproterozoic flysch sediments are connected with Neoproterozoic and Late Paleozoic collision events (Central Angara terranes and Siberian platform, Yenisey Ridge and Kara continental block and Siberian platform, Taimyr). Flysch sediments bearing gold-quartz and gold-sulfide deposits are typical for both regions. During collision, these sediments covered paleo-island-arc and ophiolite complexes that were accreted earlier. Increased gold content, including gold beresite-type metasomatites, occur in these complexes. Collision resulted in metamorphism, granitoid magmatism, hydrothermal activity, and redistribution of gold into ore deposits.
A Theoretical Framework of the Graph Shift Algorithm

Since no theoretical foundations for proving the convergence of Graph Shift Algorithm have been reported, we provide a generic framework consisting of three key GS components to fit the Zangwill’s convergence theorem. We show that the sequence set generated by the GS procedures always terminates at a local maximum, or at worst, contains a subsequence which converges to a local maximum of the similarity measure function. What is more, a theoretical framework is proposed to apply our proof to a more general case.
Logical Approach: Consistency Rules between Activity Diagram and Class Diagram

Requirements engineering (RE) is a fundamental in software development process.Requirements engineering encompasses activities ranging from requirements elicitation and analysis to specification, verification and validation.Poor requirements have been proved to be a major cause of software problems such as cost overruns, delivery delays, failure to meet expectation and degradation.Requirements validation especially models validation has gained quite an interest from a lot of researchers.In recent times, several researchers have expressed a great deal of interest in requirements validation, specifically models validation.The field of research related to consistency checking has undergone a considerable boom from time to time.Numerous methods, approaches and techniques have been recommended to address the requirements inconsistency issues, particularly in models validation.In the software development industry, UML modelling has been extensively used.The different forms of the UML model that characterise the system from various perspectives somehow establish a relation among the models to keep them inseparable from one another.This is the reason why the inconsistency becomes unavoidable.The inconsistency in the models arises when there is an overlap of the elements of the various models representing the different parts of the system and an absence of cooperation.In this paper, the emphasis is given on the consistency rules that exist between the two models.The focus is also on the class diagrams and activity, and the conversion of the rules into logical predicates, where the logical predicates are assessed with a sample case study that constitutes of the two models.
Preface for the special issue of Proof, Structure, and Computation 2014

This special issue contains selected papers from the International Workshop on Proof, Structure, and Computation (PSC) held in Vienna on 17–18 July 2014, within the Vienna Summer of Logic (VSL). PSC was a CSL–LICS-affiliated workshop on the extraction of computational content from proofs. The focus is on the computational aspects of proofs and the specification of the structures involved; the topics of interest are proof theory, program extraction, constructive mathematics, topology and computation, realizability semantics, coalgebra and computation, categorical models and domain theory. The extraction of computational content from proofs has a long tradition in logic, but usually depends on a concrete encoding that allows us to turn proofs into algorithms. A recent trend in this field is the departure from such encoding, which not only makes it simpler to represent the mathematical content, but also makes the extracted computational content encoding independent. This shift in focus allows us to focus on what is relevant: the computational aspects of proofs and the specification (not representation) of the structures involved. We now have growing evidence that this move from representations (e.g. the signed-digit representation of the reals) to axioms (e.g. of the real numbers) is possible. This development largely parallels the step from assembler to high-level languages in programming. As a by-product, this move has already opened up the possibility to gain computational information from axiomatic proofs in more abstract and genuinely structural areas of mathematics such as algebra and topology.
Diffusion methods for classification with pairwise relationships

We define two algorithms for propagating information in classification problems with pairwise relationships. The algorithms are based on contraction maps and are related to non-linear diffusion and random walks on graphs. The approach is also related to message passing algorithms, including belief propagation and mean field methods. The algorithms we describe are guaranteed to converge on graphs with arbitrary topology. Moreover they always converge to a unique fixed point, independent of initialization. We prove that the fixed points of the algorithms under consideration define lower bounds on the energy function and the max-marginals of a Markov random field. The theoretical results also illustrate a relationship between message passing algorithms and value iteration for an infinite horizon Markov decision process. We illustrate the practical application of the algorithms under study with numerical experiments in image restoration and stereo depth estimation.
Enhancing Writing Ability Through Idea Listing Technique

The study is aimed at developing the Idea Listing Technique (ILT) to enhance the students‟ writing ability. The Classroom Action Research was applied in this study. The subjects of the study were 31 students, the students taking the course of Writing II, of the third semester of English Department of one State Islamic College at Palangka Raya, Indonesia, in the 2012/2013 academic year. The findings show that the implementation of ILT can enhance the students' ability in writing expository paragraph. It is indicated by the enhancements of the percentage of the students achieving the score greater than or equal to C (60-69), and of the percentage of their involvement in the writing activities during the implementation of ILT in Cycle I and II. Thus, the enhancement of the students' ability in writing expository paragraph can be reached but it should follow the appropriate procedures of the implementation of ILT having been developed.
On a class of random walks in simplexes

We study the limit behaviour of a class of random walk models taking values in the $d$-dimensional unit standard simplex, $d\ge 1$, defined as follows. From an interior point $z$, the process chooses one of the $d+1$ vertices of the simplex, with probabilities depending on $z$, and then the particle randomly jumps to a new location $z'$ on the segment connecting $z$ to the chosen vertex. In some specific cases, using properties of the Beta distribution, we prove that the limiting distributions of the Markov chain are, in fact, Dirichlet. We also consider a related history-dependent random walk model in $[0,1]$ based on an urn-type scheme. We show that this random walk converges in distribution to the arcsine law.
The Logic of Turing Progressions

Turing progressions arise by iteratedly adding consistency statements to a base theory. Different notions of consistency give rise to different Turing progressions. In this paper we present a logic that generates exactly all relations that hold between these different Turing progressions given a particular set of natural consistency notions. Thus, the presented logic is proven to arithmetically sound and complete for a natural interpretation, named the \emph{Formalized Turing progressions} (FTP) interpretation.
Increasing Interpretation of Web Topic Detection via Prototype Learning From Sparse Poisson Deconvolution

Organizing webpages into interesting topics is one of the key steps to understand the trends from multimodal Web data. The sparse, noisy, and less-constrained user-generated content results in inefficient feature representations. These descriptors unavoidably cause that a detected topic still contains a certain number of the false detected webpages, which further make a topic be less coherent, less interpretable, and less useful. In this paper, we address this problem from a viewpoint interpreting a topic by its prototypes, and present a two-step approach to achieve this goal. Following the detection-by-ranking approach, a sparse Poisson deconvolution is proposed to learn the intratopic similarities between webpages. To find the prototypes, leveraging the intratopic similarities, top-k diverse yet representative prototype webpages are identified from a submodularity function. Experimental results not only show the improved accuracies for the Web topic detection task, but also increase the interpretation of a topic by its prototypes on two public datasets.
Generating subgroups of ray class groups with small prime ideals

Explicit bounds are given on the norms of prime ideals generating arbitrary subgroups of ray class groups of number fields, assuming the Extended Riemann Hypothesis. These are the first explicit bounds for this problem, and are significantly better than previously known asymptotic bounds. Applied to the integers, they express that any subgroup of index $i$ of the multiplicative group of integers modulo $m$ is generated by prime numbers smaller than $16(i\log m)^2$, subject to the Riemann Hypothesis. Two particular consequences relate to mathematical cryptology. Applied to cyclotomic fields, they provide explicit bounds on generators of the relative class group, needed in some previous work on the shortest vector problem on ideal lattices. Applied to Jacobians of hyperelliptic curves, they allow one to derive bounds on the degrees of isogenies required to make their horizontal isogeny graphs connected. Such isogeny graphs are used to study the discrete logarithm problem on said Jacobians.
Application of the “descent with mutations” metaheuristic to a clique partitioning problem

We study here the application of the “descent with mutations” metaheuristic to a problem arising from the field of classification and cluster analysis (dealing more precisely with the aggregation of symmetric relations) and which can be represented as a clique partitioning of a weighted graph. In this problem, we deai with a complete undirected graphe G ; the edges of G have weights which can be positive, negative or equal to 0; the aim is to partition the vertices of G into disjoint cliques (whose number depends on G in order to minimize the sum of the weights of the edges with their two extremities in a same clique; this problem is NP-hard. The “descent with mutations” is a local search metaheuristic, of which the design is very simple and is based on local transformation. It consists in randomly performing random elementary transformations, irrespective improvement or worsening with respect to the objective function. We compare it with another very efficient metaheuristic, which is a simulated annealing method improved by the addition of some ingredients coming from the noising methods. Experiments show that the descent with mutations is at least as efficient for the studied problem as this improved simulated annealing, usually a little better, while it is much easier to design and to tune.
A Simple Approach to the Reconstruction of a Set of Points from the Multiset of Pairwise Distances in <i>n</i><sup>2</sup> Steps for the Sequencing Problem: III. Noise Inputs for the Beltway Case

The approach based on the removal of redundancy in inputs when reconstructing a set of points X from the set of their pairwise distances is generalized for the beltway case by using integral transformations. It is shown that the generalized approach can be successfully used not only for complete and error-free sets of pairwise distances , but also for sets containing a large number of noise and missing data . The proposed approach allows to reconstruct X in n2 steps, where n is the cardinality of noise input sets.
Ultrafilters, finite coproducts and locally connected classifying toposes

We prove a single category-theoretic result encapsulating the notions of ultrafilters, ultrapower, ultraproduct, tensor product of ultrafilters, the Rudin–Kiesler partial ordering on ultrafilters, and Blass's category of ultrafilters UF. The result in its most basic form states that the category FC(Set,Set) of finite-coproduct-preserving endofunctors of Set is equivalent to the presheaf category [UF,Set]. Using this result, and some of its evident generalisations, we re-find in a natural manner the important model-theoretic realisation relation between n-types and n-tuples of model elements; and draw connections with Makkai and Lurie's work on conceptual completeness for first-order logic via ultracategories. As a further application of our main result, we use it to describe a first-order analogue of Jónsson and Tarski's canonical extension. Canonical extension is an algebraic formulation of the link between Lindenbaum–Tarski and Kripke semantics for intuitionistic and modal logic, and extending it to first-order logic has precedent in the topos of types construction studied by Joyal, Reyes, Makkai, Pitts, Coumans and others. Here, we study the closely related, but distinct, construction of the locally connected classifying topos of a first-order theory. The existence of this is known from work of Funk, but the description is inexplicit; ours, by contrast, is quite concrete.
Combinatorial designs for deep learning

Deep learning is a machine learning methodology using multi-layer neural network. A multi-layer neural network can be regarded as a chain of complete bipartite graphs. The nodes of the first partita is the input layer and the last is the output layer. The edges of a bipartite graph function as weights which are represented as a matrix. The values of i-th partita are computed by multiplication of the weight matrix and values of (i-1)-th partita. Using mass training and teacher data, the weight parameters are estimated little by little. Overfitting (or Overlearning) refers to a model that models the `training data` too well. It then becomes difficult for the model to generalize to new data which were not in the training set. The most popular method to avoid overfitting is called dropout. Dropout deletes a random sample of activations (nodes) to zero during the training process. A random sample of nodes causes more irregular frequency of dropout edges. There is a similar sampling concept in the area of design of experiments. We propose a combinatorial design on dropout nodes from each partita which balances the frequency of edges. We analyze and construct such designs in this paper.
Segmentation-free compositional

Geewook Kim, Kazuki Fukui, Hidetoshi Shimodaira. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.