Reviewerly/exHarmony huggingface dataset:
Semantical Analysis of Contextual Types

Abstract We describe a category-theoretic semantics for a simply typed variant of Cocon, a contextual modal type theory where the box modality mediates between the weak function space that is used to represent higher-order abstract syntax (HOAS) trees and the strong function space that describes (recursive) computations about them. What makes Cocon different from standard type theories is the presence of first-class contexts and contextual objects to describe syntax trees that are closed with respect to a given context of assumptions. Following M. Hofmann’s work, we use a presheaf model to characterise HOAS trees. Surprisingly, this model already provides the necessary structure to also model Cocon. In particular, we can capture the contextual objects of Cocon using a comonad $$\flat $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>♭</mml:mo> </mml:math> that restricts presheaves to their closed elements. This gives a simple semantic characterisation of the invariants of contextual types (e.g. substitution invariance) and identifies Cocon as a type-theoretic syntax of presheaf models. We express our category-theoretic constructions by using a modal internal type theory that is implemented in Agda-Flat.
Diffuse cognitive maps for analysis of vulnerability to climate variability in Andean rural micro-watersheds

Diffuse cognitive map was applied to analyse factors of vulnerability to climate variability in rural Andean micro-watershed. The iModeler software was used as a tool that allows the modelling of these maps. As input information, the results of the socioecological dynamics analysis previously implemented in the case study were taken, and weights were assigned to each input factor in relation to a central factor in a time scale. The model applied in the El Chocho micro-watershed - Department of Valle del Cauca in Colombia, can be validated in similar contexts of the Andean Colombian area. A high degree of water shortage and increased pressure on the associated ecosystems was evidenced, which puts at risk the resilience of the socio-ecosystem to face climate variability. The results of this type of models are tools for decision making in adaptation at rural level.
Survey of Scientific Document Summarization Techniques

The number of scientic or research papers published every year is growing at an exponential rate, which has led to an intensive research in scientic document summarization. The different methods commonly used in automatic text summarization are discussed in this paper with their pros and cons. Commonly used evaluation techniques and datasets in this field are also discussed. Rouge and Pyramid scores of the different methods are tabulated for easy comparison of the results.
Blockchain Innovation and Its Capacity to Enhance the Quality From Accounting Information Systems

Blockchain is a promising technology for real-time accounting records and continuous monitoring. Blockchain innovation offers a technique for sharing data sources among individuals, also if they don't count on each other, and this produces a market for moving possessions based on peer-to-peer networks without a main authority. Blockchain innovation has drawn considerable financial investment from endeavor capitalists, multinational bankers, and the attention of regulators. This paper attempts to talk about the background from blockchain innovation, present use, and capacity applications that may enhance the timeliness, high quality, and precision from bookkeeping info. This paper will likewise assess the efficiency from some useful bookkeeping applications
Enhanced Image Encryption using multiple iterated Arnold Coupled Logistic Map Lattices

The proposed arnold coupled generalized logistic map lattices (ACGLML) image encryption method is based on non-linear coupling of lattices using multiple iterated 2D Arnold cat maps where the number of permutations is also introduced as a key space. The proposed ACGLML method of nth order permutation reduces the mutual information among the lattice values from existing 0% to 0.1%. In addition, generalized logistic map is used, in order to expand the diffusion key space ranging from 0.5 to 4 against 3.57 to 4 in existing works. Various analysis has been presented to prove the chaotic behavior of the proposed method with the aid of metrics, such as Kolmogorov-Sinai entropy, space amplitude diagram, space time diagram, bifurcation diagram and the strength of diffusion using the mutual information. Though encryption and decryption are the major task for the proposal, we have included compression and decompression using conventional wavelet based decompositions in order to ensure its application in common communication channel.
Event Recognition Based on Classification of Generated Image Captions

In this paper, we consider the problem of event recognition on single images. In contrast to conventional fine-tuning of convolutional neural networks (CNN), we proposed to use image captioning, i.e., a generative model that converts images to textual descriptions. The motivation here is the possibility to combine conventional CNNs with a completely different approach in an ensemble with high diversity. As event recognition task has nothing serial or temporal, obtained captions are one-hot encoded and summarized into a sparse feature vector suitable for the learning of an arbitrary classifier. We provide the experimental study of several feature extractors for Photo Event Collection, Web Image Dataset for Event Recognition and Multi-Label Curation of Flickr Events Dataset. It is shown that the image captions trained on the Conceptual Captions dataset can be classified more accurately than the features from an object detector, though they both are obviously not as rich as the CNN-based features. However, an ensemble of CNN and our approach provides state-of-the-art results for several event datasets.
Algorithmic Analysis of Blockchain Efficiency with Communication Delay

A blockchain is a distributed hierarchical data structure. Widely-used applications of blockchain include digital currencies such as Bitcoin and Ethereum. This paper proposes an algorithmic approach to analyze the efficiency of a blockchain as a function of the number of blocks and the average synchronization delay. The proposed algorithms consider a random network model that characterizes the growth of a tree of blocks by adhering to a standard protocol. The model is parametric on two probability distribution functions governing block production and communication delay. Both distributions determine the synchronization efficiency of the distributed copies of the blockchain among the so- called workers and, therefore, are key for capturing the overall stochastic growth. Moreover, the algorithms consider scenarios with a fixed or an unbounded number of workers in the network. The main result illustrates how the algorithms can be used to evaluate different types of blockchain designs, e.g., systems in which the average time of block production can match the average time of message broadcasting required for synchronization. In particular, this algorithmic approach provides insight into efficiency criteria for identifying conditions under which increasing block production has a negative impact on the stability of a blockchain. The model and algorithms are agnostic of the blockchain’s final use, and they serve as a formal framework for specifying and analyzing a variety of non-functional properties of current and future blockchains.
Artificial intelligence-based smart engineering education

Artificial intelligence has the capacity to open new opportunities and potentials for engineering education. Artificial intelligence in education has undergone several paradigmatic shifts in its brief history. This study investigates the advent, development and future trends of artificial intelligence-based smart engineering education (AIED-Eng). Particular focus is placed on major paradigms in AIED-Eng including leaner-receiver, learner-partner and learner-center. The artificial intelligence techniques applied to these paradigms are evaluated. Computer-based tools enabling the engineering education paradigms are summarized. Further discussion is presented about the key role of artificial intelligence in improving smart engineering education as a guidance for future learning, teaching and design processes
Oblique Decision Tree Algorithm with Minority Condensation for Class Imbalanced Problem

In recent years, a significant issue in classification is to handle a dataset containing imbalanced number of instances in each class.Classifier modification is one of the well-known techniques to deal with this particular issue.In this paper, the effective classification model based on an oblique decision tree is enhanced to work with an imbalanced dataset that is called oblique minority condensed decision tree (OMCT).Initially, it selects the best axis-parallel hyperplane based on the decision tree algorithm using the minority entropy of instances within the minority inner fence selection.Then it perturbs this hyperplane along each axis to improve its minority entropy.Finally, it stochastically perturbs this hyperplane to escape the local solution.From the experimental results, OMCT significantly outperforms six state-of-the-art decision tree algorithms that are CART, C4.5, OC1, AE, DCSM and ME on 18 real-world datasets from UCI in term of precision, recall and F1 score.Moreover, the size of a decision tree from OMCT is significantly smaller than others.
Pengembangan E-Book Berbasis Android dengan Soal HOTS untuk Membantu Menganalisis Besaran Pada Materi Gerak Lurus

The integration between interactive media and HOTS question could give a big impact on learning process in class room, especially on learning interest and student concept understanding. Android is a learning media that could save more information and questions like ebook. This study was conducted to determine the feasibility of an Android-based electronic book with High Order Thinking Skills to Help Students Analyze Amounts on Straight Motion Material. This research method was used in the ADDIE model (analysis, design, development, implementation, and evaluation). The electronic book was tested for validation by the validator with an average value of 3.94 and the module readability test with respondents of 25 students who obtained an average value of 96%. Based on the data obtained, it can be conclude that the electronic book has decent criteria.
Maneuverability hazard detection and localization in low-altitude UAS imagery

Object detection and localization is an important problem in computer vision and remote sensing. While there have been several techniques presented and used in recent years, the You Only Look Once (YOLO) and derivative architectures have gained popularity due to their ability to perform real-time object localization as well as achieve remarkable detection scores in ground-based applications. Here, we present methods and results for performing maneuverability hazard detection and localization in low-altitude unmanned aerial systems (UAS) imagery. Imagery is captured over a variety of flight routes and altitudes, and then analyzed with modern deep learning techniques to discover objects such as civilian and military vehicles, barriers, and related hindrances to navigating cluttered semi-urban environments. We present our findings for the deep learning architectures under a variety of training and validation parameters that include pre-trained weights from benchmark public datasets, as well as training with a custom, mission-relevant dataset provided by U.S. Army ERDC.
Generator of a Toy Dataset of Multi-Polygon Monochrome Images for Rapidly Testing and Prototyping Semantic Image Segmentation Networks

Abstract In the paper, the problem of building semantic image segmentation networks in a more efficient way is considered. Building a network capable of successfully segmenting real-world images does not require a real semantic image segmentation task. At this stage, called prototyping, a toy dataset can be used. Such a dataset can be artificial and thus may not need augmentation for training. Besides, its entries are images of much smaller size, which allows training and testing the network a way faster. Objects to be segmented are one or few convex polygons in one image. Thus, a toy dataset generator is created whose complexity is regulated by the number of edges in a polygon, the maximal number of polygons in one image, the set of scale factors, and the set of probabilities determining how many polygons in a current image are generated. The dataset capacity and image size are concurrently adjustable, although they are much less influential.
Artistic will as the ontological basis of art

The subject of this research is the artistic will as the ontological basis of art. In the process of comprehension of the nature of art as on object of research, emerges the problem of identification and determination of the ontological will with regards to arts as a fundamental factor in &amp;ldquo;conception&amp;rdquo; of an artful work. The will is an ontological link in the chain &amp;ldquo;author &amp;ndash; willful acts &amp;ndash; creative process &amp;ndash; artwork&amp;rdquo;, which allows the latter to manifest from nothingness. This leads to understanding of the &amp;ldquo;relative mechanism of conception&amp;rdquo; of an artwork, which comprises the goal of the research. The scientific novelty consists in the thesis that two types of will coexist at the current stage of the development of art: will of a modern artist and artistic will (will to beauty). Due to the fact that the will of a modern artist doesn&amp;rsquo;t always shift him towards aesthetization of the environment, whole the artistic will is particularly aimed at aesthetization of the phenomena of surrounding reality, the author underlines the crucial difference between the two wills. The conclusion is made that the &amp;ldquo;artistic will&amp;rdquo; as an aestheticizing will or the will to beauty is the ontological basis of art.
Towards Content Sensitivity Analysis

With the availability of user-generated content in the Web, malicious users dispose of huge repositories of private (and often sensitive) information regarding a large part of the world’s population. The self-disclosure of personal information, in the form of text, pictures and videos, exposes the authors of such contents (and not only them) to many criminal acts such as identity thefts, stalking, burglary, frauds, and so on. In this paper, we propose a way to evaluate the harmfulness of any form of content by defining a new data mining task called content sensitivity analysis. According to our definition, a score can be assigned to any object (text, picture, video...) according to its degree of sensitivity. Even though the task is similar to sentiment analysis, we show that it has its own peculiarities and may lead to a new branch of research. Thanks to some preliminary experiments, we show that content sensitivity analysis can not be addressed as a simple binary classification task.
Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams

Methods that learn the structure of Probabilistic Sentential Decision Diagrams (PSDD) from data have achieved state-of-the-art performance in tractable learning tasks. These methods learn PSDDs incrementally by optimizing the likelihood of the induced probability distribution given available data and are thus robust against missing values, a relevant trait to address the challenges of embedded applications, such as failing sensors and resource constraints. However PSDDs are outperformed by discriminatively trained models in classification tasks. In this work, we introduce D-LearnPSDD, a learner that improves the classification performance of the LearnPSDD algorithm by introducing a discriminative bias that encodes the conditional relation between the class and feature variables.
MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization

Recently, large-scale datasets have vastly facilitated the development in nearly all domains of Natural Language Processing. However, there is currently no cross-task dataset in NLP, which hinders the development of multi-task learning. We propose MATINF, the first jointly labeled large-scale dataset for classification, question answering and summarization. MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the merits held by MATINF.
Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language

To better tackle the named entity recognition (NER) problem on languages with little/no labeled data, cross-lingual NER must effectively leverage knowledge learned from source languages with rich labeled data. Previous works on cross-lingual NER are mostly based on label projection with pairwise texts or direct model transfer. However, such methods either are not applicable if the labeled data in the source languages is unavailable, or do not leverage information contained in unlabeled data in the target language. In this paper, we propose a teacher-student learning method to address such limitations, where NER models in the source languages are used as teachers to train a student model on unlabeled data in the target language. The proposed method works for both single-source and multi-source cross-lingual NER. For the latter, we further propose a similarity measuring method to better weight the supervision from different teacher models. Extensive experiments for 3 target languages on benchmark datasets well demonstrate that our method outperforms existing state-of-the-art methods for both single-source and multi-source cross-lingual NER.
Making Learners (More) Monotone

Learning performance can show non-monotonic behavior. That is, more data does not necessarily lead to better models, even on average. We propose three algorithms that take a supervised learning model and make it perform more monotone. We prove consistency and monotonicity with high probability, and evaluate the algorithms on scenarios where non-monotone behaviour occurs. Our proposed algorithm $$\text {MT}_{\text {HT}}$$ makes less than $$1\%$$ non-monotone decisions on MNIST while staying competitive in terms of error rate compared to several baselines. Our code is available at https://github.com/tomviering/monotone .
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="sans-serif">NP</mml:mi></mml:math>-completeness of the game Kingdomino<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mtext mathvariant="italic">TM</mml:mtext></mml:mrow></mml:msup></mml:math>

KingdominoTM is a board game designed by Bruno Cathala and edited by Blue Orange since 2016. The goal is to place 2×1 dominoes on a grid layout, and get a better score than other players. Each 1×1 domino cell has a color that must match at least one adjacent cell, and an integer number of crowns (possibly none) used to compute the score. We prove that even with full knowledge of the future of the game, it is NP-complete to decide whether a given score is achievable at KingdominoTM.
The security risks created by cloud migration and how to overcome them

More than half of UK businesses are expected to migrate to the cloud by the end of the year, but concerns over security remain among some contemplating the move. While there are tremendous upsides to using the cloud over traditional data storage methods, there are some risks that arise during the migration process.
Accelerating Hyperparameter Optimization of Deep Neural Network via Progressive Multi-Fidelity Evaluation

Deep neural networks usually require careful tuning of hyperparameters to show their best performance. However, with the size of state-of-the-art neural networks growing larger, the evaluation cost of the traditional Bayesian optimization has become unacceptable in most cases. Moreover, most practical problems usually require good hyperparameter configurations within a limited time budget. To speed up the hyperparameter optimization, the successive halving technique is used to stop poorly-performed configurations as early as possible. In this paper, we propose a novel hyperparameter optimization method FastHO, which combines the progressive multi-fidelity technique with successive halving under a multi-armed bandit framework. Furthermore, we employ Bayesian optimization to guide the selection of initial configurations and an efficient data subsampling based method to warm start the surrogate model of Bayesian optimization. Extensive empirical evaluation on a broad range of neural networks and datasets shows that FastHO is not only effective to speed up hyperparameter optimization but also can achieve better anytime performance and final performance than the state-of-the-art hyperparameter optimization methods.
Kripto Para Cüzdanlarının Ortak Kriterler’de Güvenlik Problemi Tanımı ve Güvenlik Hedefleri

Bitcoin paper gave birth to a new era; cryptocurrencies aiming distributed trust model. Almost all the cryptocurrencies require their users individually manage their own cryptographic keys, provide or recommend use of cryptocurrency wallets. A wallet, which at least stores public-private keys and addresses, is one of the key points for end-users' security. Since the authentication of a transaction strictly depends on private keys, any adversary who gains access to a wallet may seize all the coins within. Hence, cryptocurrency wallet solutions should be carefully analyzed and better to be certified if possible. In this study, we aim to define the security problems and objectives necessary for the development of a certified product that can stand against the known attacks within the Framework of Common Criteria (CC). We believe this would be a brief source for cryptocurrency wallet Protection Profile (PP) and Security Target (ST) documents.
Digital Transformation of the World Economy: Evaluation of the Global and Russian Internet of Things Markets

The Internet of things is discussed in the article, the basic aspects of this concept are analyzed, comprehensive analysis in the field of understanding the Internet of things is attempted and the main segments that are within its scope are identified. Based on the study, an analysis of the global Internet of things market was carried out, its main directions of development were identified, and the prospects for use in different directions were assessed. The main market parameters determining its development dynamics, and financial indicators were investigated. The main leaders of the global Internet of things market are identified in the study. The Russian Internet of things market is examined in this article, its prospects and development directions are examined. A comparative analysis of the global and Russian markets of the Internet of things is carried out.