Reviewerly/exHarmony huggingface dataset:
Short-term Prediction of Hyperchaotic Flow Using Echo State Network

An echo state network with a reservoir consisting of 200 tanh neurons is applied to the short-term prediction of a chaotic time series generated using the augmented Lorenz equations as a hyperchaotic flow model. The predictive performance is examined in terms of the Kolmogorov−Sinai entropy and the Kaplan − Yorke dimension of a chaotic attractor in comparison with those for chaotic flow models having a single positive Lyapunov exponent. We discuss the predictive performance of the reservoir in terms of a universal simulator of chaotic attractors on the basis of Ueda’s view of chaos, i.e., random transitions between unstable periodic orbits in a chaotic attractor.
Higher-Order Tarski Grothendieck as a Foundation for Formal Proof.

We formally introduce a foundation for computer verified proofs based on higher-order Tarski-Grothendieck set theory. We show that this theory has a model if a 2-inaccessible cardinal exists. This assumption is the same as the one needed for a model of plain Tarski-Grothendieck set theory. The foundation allows the co-existence of proofs based on two major competing foundations for formal proofs: higher-order logic and TG set theory. We align two co-existing Isabelle libraries, Isabelle/HOL and Isabelle/Mizar, in a single foundation in the Isabelle logical framework. We do this by defining isomorphisms between the basic concepts, including integers, functions, lists, and algebraic structures that preserve the important operations. With this we can transfer theorems proved in higher-order logic to TG set theory and vice versa. We practically show this by formally transferring Lagrange's four-square theorem, Fermat 3-4, and other theorems between the foundations in the Isabelle framework.
Improvement of Container Placement Using Multi-Objective Ant Colony Optimization

High resource requirements on software containers lead to the need for cloud users to find an optimal placement for each container to maximize the resource utilization in the cloud environment. Pre ...
Declarative Proof Translation (Short Paper)

Declarative proof styles of different proof assistants include a number of incompatible features. In this paper we discuss and classify the differences between them and propose efficient algorithms for declarative proof outline translation. We demonstrate the practicality of our algorithms by automatically translating the proof outlines in 200 articles from the Mizar Mathematical Library to the Isabelle/Isar proof style. This generates the corresponding theories with 15301 proof outlines accepted by the Isabelle proof checker. The goal of our translation is to produce a declarative proof in the target system that is both accepted and short and therefore readable. For this three kinds of adaptations are required. First, the proof structure often needs to be rebuilt to capture the extensions of the natural deduction rules supported by the systems. Second, the references to previous items and their labels need to be matched and aligned. Finally, adaptations in the annotations of individual proof step may be necessary.
Auditing Deep Learning processes through Kernel-based Explanatory Models.

While NLP systems become more pervasive, their accountability gains value as a focal point of effort. Epistemological opaqueness of nonlinear learning methods, such as deep learning models, can be a major drawback for their adoptions. In this paper, we discuss the application of Layerwise Relevance Propagation over a linguistically motivated neural architecture, the Kernel-based Deep Architecture, in order to trace back connections between linguistic properties of input instances and system decisions. Such connections then guide the construction of argumentations on network’s inferences, i.e., explanations based on real examples, semantically related to the input. We propose here a methodology to evaluate the transparency and coherence of analogy-based explanations modeling an audit stage for the system. Quantitative analysis on two semantic tasks, i.e., question classification and semantic role labeling, show that the explanatory capabilities (native in KDAs) are effective and they pave the way to more complex argumentation methods.
The Role of Teachers in Utilizing Learning Media as A Learning Source for Millenial Students

In this writing, the author does a study about the role of teachers in utilizing learning media as a source of learning for millennial students.This study departs from the anxiety of the author about the condition of the teachers in the digital era.The author sees that at this time some teachers have difficulties in dealing with millennial students.Based on this problem, the authors do a study about the role of teachers in utilizing learning media empirically.Based on the study which did by the authors in this paper, three important point must be done by teachers in dealing with millennial students, that are: first, teachers can take advantage of all forms of technological development.Technological progress is something that cannot be avoided in this life, so the teacher should not be allergic to all forms of development that exist.Second, teachers can understand the power of the media as a source of learning.The benefit of learning media is determining the quality of learning delivered by a teacher.Therefore, teachers need to prepare learning media before teaching in class.After that, the teacher has to understand the needs of the millennial students.The needs of millennial students are not the same, but at least the presence of teachers provides solutions both cognitive, affective and psychomotor.The uniqueness of the teacher, in this case, is a teacher can act as anything as long as it can influence student behavior towards positive actions, even those students can become actors of the progress of the media.This is evident from the student activities that have been narrated in this
Concept-Centric Visual Turing Tests for Method Validation

Recent advances in machine learning for medical imaging have led to impressive increases in model complexity and overall capabilities. However, the ability to discern the precise information a machine learning method is using to make decisions has lagged behind and it is often unclear how these performances are in fact achieved. Conventional evaluation metrics that reduce method performance to a single number or a curve only provide limited insights. Yet, systems used in clinical practice demand thorough validation that such crude characterizations miss. To this end, we present a framework to evaluate classification methods based on a number of interpretable concepts that are crucial for a clinical task. Our approach is inspired by the Turing Test concept and how to devise a test that adaptively questions a method for its ability to interpret medical images. To do this, we make use of a Twenty Questions paradigm whereby we use a probabilistic model to characterize the method's capacity to grasp task-specific concepts, and we introduce a strategy to sequentially query the method according to its previous answers. The results show that the probabilistic model is able to expose both the dataset's and the method's biases, and can be used to reduced the number of queries needed for confident performance evaluation.
Algorithms for Reduction of Input Space Dimensionality in Regression-Based Classification Models

The authors consider algorithms for reducing the dimensionality of the input space of linear and logistic regression models of pattern recognition with a large number of input attributes. The proposed algorithms are based on the ideas of a non-smooth regularization of the non-informative attributes suppression, and one of the special cases is the Tibshirani lasso method. A comparative analysis of the efficiency of the logistic and linear models indicates their practical equivalence on the studied data sets. Based on the features of linear models, the authors propose a new, rapidly converging algorithm that implements the search for a solution under non-smooth regularization conditions without the participation of complex and resource-intensive algorithms of non-differentiable optimization. A comparative analysis of the proposed algorithm confirms its efficiency in comparison with methods based on nonsmooth optimization. The presented method allows us to solve the problem of processing noisy data with a large number of redundant attributes in a reasonable time period. Taking into account the high rate of convergence of the proposed method, the research proposes using it for the purpose of preliminary reduction of the dimension of the input attribute space with a large number of attributes. The article presents some examples of data processing of practically important classification problems.
Runtime Compiled C++ for Rapid AI Development

Scripting languages have always been a foundation of rapid artificial intelligence (AI) development but with the increasing demands of AI, their performance drawbacks are becoming ever more problematic. On the other hand, traditional C++ development approaches generally lead to lengthy compile and link times, which limit the amount of iteration and testing that programmers can undertake. A variety of alternative approaches can be used to achieve fast iteration times in games, each with their own pros and cons. Overall we believe that none offer the main benefits of Runtime Compiled C++, and many techniques can be enhanced by using RCC++ alongside them. Scripting languages provide perhaps the most common solution to achieving fast iteration. A games engine survey showed Lua to be the foremost scripting language in game development, though UnrealScript likely represents a substantial proportion of the market, given the size of the Unreal Engine community.
Application of Brzozowski Derivatives to JSON Schema Validation

Brzozowski derivatives are a technique for computing whether a string of symbols is in the language defined by an extended regular expression. They have been applied to content model validation in XML Schema, following the observation that a content model is an extended regular expression over symbols in the vocabulary described by the schema. This paper explores using an extension of Brzozowski derivatives to the problem of model validation for JSON Schema. It turns out that this application requires extending to "type-tagged" regular expressions, which provide an interesting way of understanding certain matching problems outside of the problem of JSON Schema validation.
One Book One Teacher Program for Upgrading Teachers’ Writing Competence (A Descriptive Survey)

Writing competence is one of significant and basic demands for teachers in delivering their profession as educators. Many teachers feel that writing a book is a pride but it is too difficult for them to do. Many factors influence them not to start to write, such as inner motivation, institution supports, ideas, materials, and opportunities. For examples, many of them are lack of motivation to be confident course book writers, their institutions do not support their efforts and facilitate them well, they have no idea what they are going to write, they are not accustomed to provide their own lesson materials for their daily instruction—only copying from books or other resources, and most of them do not have any opportunity at all to write a course book. Therefore to create teacher-writers and produce course books in one school, One Book One Teacher (OBOT) program comes as a solution. OBOT is a program designed for teachers to be a course book writer at schools. Recently we revealed that OBOT has potential activities as an integrated program in preparing teachers to be professional course book writers. In this study, to dig up their responses on course book writing activity and OBOT implementation in their school, we conducted a descriptive survey and observation on 13 teachers in Senior High School Bina Amal Semarang. We distributed questionnaires, tabulated, analyzed and interpreted the data, then drew the conclusion. The research results show that significantly OBOT encouraged teachers to write course book actively and led them to be productive course book writers.
On Quantum Operations of Photon Subtraction and Photon Addition

The conventional photon subtraction and photon addition transformations, ϱ → taϱa† and ϱ → ta†ϱa, are not valid quantum operations for any constant t > 0 since these transformations are not trace nonincreasing. For a fixed density operator ϱ there exist fair quantum operations, $\mathcal{N}_{-}$ and $\mathcal{N}_{+}$ , whose conditional output states approximate the normalized outputs of former transformations with an arbitrary accuracy. However, the uniform convergence for some classes of density operators ϱ has remained essentially unknown. Here we show that, in the case of photon addition operation, the uniform convergence takes place for the energy-second-moment-constrained states such that tr[ϱH2] ≤ E2 < ∞, H = a†a. In the case of photon subtraction, the uniform convergence takes place for the energy-second-moment-constrained states with nonvanishing energy, i.e., the states ϱ such that tr[ϱH] ≥ E1 > 0 and tr[ϱH2] ≥ E2 < ∞. We prove that these conditions cannot be relaxed and generalize the results to the cases of multiple photon subtraction and addition.
Integration of global and local features based on hybrid similarity matching scheme for medical image retrieval system

Similarity measure is a challenging task in Content-Based Medical Image Retrieval (CBMIR) systems and the matching scheme is designed to improve the retrieval performance. However, there are several major shortcomings with conventional approaches for a matching scheme which can extensively affect their application of Medical Image Retrieval (MIR). To overcome the issues, in this paper a Multi-Level Matching (MLM) method for MIR using hybrid feature similarity is proposed. Here, images are represented by multi-level features including local level and global level. The Colour and Edge Directivity Descriptor (CEDD) is used as a colour and edge-based descriptor. Speeded-Up Robust Features (SURF) and Local Binary Pattern (LBP) are used as a local descriptor. The hybrid of both global and local features yields enhanced retrieval accuracy, which is analysed over collected image databases. From the experiment, the proposed method achieves better accuracy value about 92%, which is higher than other methods.
Replacing Linguists with Dummies: A Serious Need for Trivial Baselines in Multi-Task Neural Machine Translation

Abstract Recent developments in machine translation experiment with the idea that a model can improve the translation quality by performing multiple tasks, e.g., translating from source to target and also labeling each source word with syntactic information. The intuition is that the network would generalize knowledge over the multiple tasks, improving the translation performance, especially in low resource conditions. We devised an experiment that casts doubt on this intuition. We perform similar experiments in both multi-decoder and interleaving setups that label each target word either with a syntactic tag or a completely random tag. Surprisingly, we show that the model performs nearly as well on uncorrelated random tags as on true syntactic tags. We hint some possible explanations of this behavior. The main message from our article is that experimental results with deep neural networks should always be complemented with trivial baselines to document that the observed gain is not due to some unrelated properties of the system or training effects. True confidence in where the gains come from will probably remain problematic anyway.
Retraction Note: Can Two-Way Direct Communication Protocols Be Considered Secure?

The editors have retracted this article [1] because after publication concerns were raised regarding the validity of the conclusions drawn. Post-publication peer review has revealed a flaw in the application of the key rate equation r = IAB-IAE. For calculation of the term IAE, the effect of disturbance (D) on both the message mode (MM) and control mode (CM) was not taken into account. The main claim of the paper cannot be reliably reached. The author does not agree to this retraction.
ChaLearn Looking at People: Inpainting and Denoising Challenges

Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the problem has only been studied in specific scenarios (e.g., certain types of occlusions in specific types of images), although it is common to have incomplete information in visual data. This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018. The ChaLearn Looking at People Inpainting Challenge aimed at advancing the state of the art on visual inpainting by promoting the development of methods for recovering missing and occluded information from images and video. Three tracks were proposed in which visual inpainting might be helpful but still challenging: human body pose estimation, text overlays removal and fingerprint denoising. This chapter describes the design of the challenge, which includes the release of three novel datasets, and the description of evaluation metrics, baselines and evaluation protocol. The results of the challenge are analyzed and discussed in detail and conclusions derived from this event are outlined.
Cryptographic Functions and Bit-Error-Rate Analysis with Almost $p$-ary Sequences

Perfect sequences are very important for achieving non-linearity in a cryptosystem, and they are important in CDMA to ensure a proper communication. It is recently shown by authors that almost $p$-ary nearly perfect sequences (NPS) of type $(\gamma_1,\gamma_2)$ exists. In this study, we show a method for obtaining cryptographic functions from these sequences. In fact, most of the cases we obtain functions with the highest non-linearity, i.e.~generalized bent functions. In the second part of this study, we use almost $p$-ary NPS of type $(\gamma_1,\gamma_2)$ in Code Division Multiple Access (CDMA) communication. We simulate the bit-error-rate (BER) performance of CDMA with these sequences.
Practice-Oriented Approach in Teaching Entrepreneurship

The study of economics at school age helps, regardless of the future specialty, to develop economic thinking, to master the conceptual apparatus, to acquire the elementary skills necessary for orientation and existence in the modern market world, to create the basis for further deeper study of economics in universities.New socio-economic conditions for the development of Russia require the search for other ways of formation and formation of the modern personality of a professional. The Russian state is interested in a modern competitive individual and professional specialist, possessing high professional competence, independence, creative activity, possessing high professional qualities.The education reform, which includes core and pre-profile training in general education institutions, assumes that students have a high level of general intellectual skills and habits, as well as practical, ideological, behavioural personality traits.Therefore, an important component of the transformation of Russian society into a society with a market economy is the process of formation the domestic system of economic and entrepreneurial training of students. At the same time, a change in the nature of social and economic relations in society implies the search for new ways of preparing students for life and work. Analysis of the needs of children, adolescents and young people in the field of professional self-determination indicates a growing interest in economic specialties, including those requiring entrepreneurial training.
A Cross-Lingual Effort Towards Managing English-Chinese Cancer Education Resources.

As translated education resource plays an important role in healthcare providers' training and medical knowledge dissemination, we proposed a method to manage cross-lingual education resources with the goal of facilitating the medical education and physician training. We created an English-Chinese cancer knowledge base including bilingual description on cancer diagnosis, prevention, screening, treatments, etc. We developed a workflow to create the bilingual corpus, and applied it to six cancer monographs in PDQ (Physician Data Query).
Tambov public library as an element of provincial cultural environment

The relevance of the study is due to the need to fill the historiographical gap in the study of public libraries activities of pre-revolutionary Russia as an element of the provincial cultural environment. We consider formation and activities history of Tambov public library (opened in the city of Tambov in 1833). Novelty of the work is the introduction of previously unused historical sources in the characteristic of public libraries. The subject of the study are staffing table features, use pattern of the fund, ways of functioning of the stock company in public library, specifics of shares issue and disposition, social composition of readers. An important element of the study is the research of Tambov public library decline problems in post-reform time, reasons for the lack of material resources and attempts to transfer institution to the balance of the Tambov city budget. We note the role of private initiative in the arrangement of library at different stages of its development. We assess the books catalogues published at the institution which are one of the first printed bibliographies of public libraries in Russia. In the framework of literary preferences of library users study, we consider the assessments of the gymnasium director and Tambov Seminary rector, whose tasks included supervision of students’ reading and librarians’ implementation of circulars and decrees of public education minister. Theoretical and concrete historical analysis of Tambov public library allowed to draw conclusions about the significant impact of the library institution on the local cultural environment. We emphasize that the formation of library brought together the structure of provincial society, contributed to the qualitative social and cultural transformations of local population.
Transfer Learning Based Object Detection and Effect of Majority Voting on Classification Performance

The use of traditional machine learning techniques in the classification tasks of image-based automatic object species requires primarily extracting the feature set. This requires deciding which set of features to use, and is a toilsome process. In this paper, we present a transfer learning based deep learning approach to overcome object classification problems. Various well-known CNN models are used during the experimental study. We also presented the majority voting scheme to improve the performance of the proposed method. According to the obtained results, the highest performance was achieved with the VGG-19 architecture with 98.85% accuracy among the fine-tuned models. Moreover, the majority voting approach improved performance by about 0.2%, achieving 99.03% accuracy.
Necessary and sufficient condition for the equivalence of two pure multipartite states under stochastic local incoherent operations and classical communications

The resource theory of quantum coherence originated like entanglement in quantum information theory. However, until now proper classification of quantum states is missing under coherence. In this work we have provided a classification of states under local incoherent operations. We have succeeded in deriving the necessary and sufficient condition for which two pure multipartite states are equivalent under stochastic local incoherent operations and classical communications (SLICC) and local incoherent operations and classical communications (LICC). In particular, we have succeeded in characterizing three-qubit pure states under SLICC. Our result reveals the existence of an infinite number of SLICC inequivalent classes for three-qubit systems.