Reviewerly/exHarmony huggingface dataset:
Processing Megapixel Images with Deep Attention-Sampling Models

Existing deep architectures cannot operate on very large signals such as megapixel images due to computational and memory constraints. To tackle this limitation, we propose a fully differentiable end-to-end trainable model that samples and processes only a fraction of the full resolution input image. The locations to process are sampled from an attention distribution computed from a low resolution view of the input. We refer to our method as attention sampling and it can process images of several megapixels with a standard single GPU setup. We show that sampling from the attention distribution results in an unbiased estimator of the full model with minimal variance, and we derive an unbiased estimator of the gradient that we use to train our model end-to-end with a normal SGD procedure. This new method is evaluated on three classification tasks, where we show that it allows to reduce computation and memory footprint by an order of magnitude for the same accuracy as classical architectures. We also show the consistency of the sampling that indeed focuses on informative parts of the input images.
Geometric decoherence in diffusive open quantum systems

Based on a generic quantum open system model, we study the geometric nature of decoherence by defining a complex-valued geometric phase through stochastic pure states describing non-unitary, non-cyclic and non-adiabatic evolutions. The ensemble average of the complex geometric phases for the pure stochastic states yields a conventional geometric phase together with an amplitude factor. We show that the decoherence process described by the decaying amplitude can be a geometric quantity independent of the system's dynamics. It is a remarkable fact that the geometric phase of a quantum system can serve as an ideal realisation of quantum gates due to its robustness against dynamical errors, however, in this paper we show that, for some open quantum systems, a desirable geometric phase may be accompanied by an unwanted robust geometric decoherence factor. Two exactly solvable models are studied to demonstrate that, while the decoherence is a purely dynamical effect for a dephasing two-level model, the decoherence in a dissipative two-level model can be a geometric process. Finally, we show that such a geometric decoherence effect may be eliminated by a non-perturbative control scheme.
Provably Scale-Covariant Networks from Oriented Quasi Quadrature Measures in Cascade

This article presents a continuous model for hierarchical networks based on a combination of mathematically derived models of receptive fields and biologically inspired computations. Based on a functional model of complex cells in terms of an oriented quasi quadrature combination of first- and second-order directional Gaussian derivatives, we couple such primitive computations in cascade over combinatorial expansions over image orientations. Scale-space properties of the computational primitives are analysed and it is shown that the resulting representation allows for provable scale and rotation covariance. A prototype application to texture analysis is developed and it is demonstrated that a simplified mean-reduced representation of the resulting QuasiQuadNet leads to promising experimental results on three texture datasets.
E-learning Automated Essay Scoring System Menggunakan Metode Searching Text Similarity Matching Text

E-learning is an information technology that has been widely applied to the field of education in the form of websites because it can be accessed indefinitely and place. The development of E-learrning is logical because it follows the age of putting forward information and communication technology. Conventional learning is especially an evaluation of essay form learning, where teachers check students' essay answers that require more time and effort. Automated Essay Scoring (AES) is one of the features of Elearning that can facilitate the teacher in matching the answer of student essay with answer key. Text similarity is a method of matching text that is recall word based on specified condition (term). The language used in AES is Indonesian which has a different word morphology with other languages. The results obtained in this study is the Error rate using the term frequency (tf) is lower with high accuracy.
Penentuan Mutu Kelapa Sawit Menggunakan Metode K-Means Clustering

The classification of the quality of palm oil in PT Tasma Puja is still done by laboratory testing and then the data is saved manually in Excel. The method of grouping takes time and allows data to be lost. With the development of knowledge, it can be replaced by a data mining approach that can be used to classify the quality of palm oil based on its standards. The k-Means clustering method can be applied to classify the quality of palm oil based on water, dirt and free fatty acids. The data used is the quality data of palm oil in December 2017 as many as 31 data with criteria of good, very good and not good. The test results contained 3 clusters, namely cluster 0 for good categories amounted to 12 data, cluster 1 for very good category amounted to 13 data and cluster 2 for less good categories amounted to 6 data. The k-Means clustering method can be used for data processing using the concept of data mining in grouping data according to criteria.
modelDown: automated website generator with interpretable documentation for predictive machine learning models

Romaszko et al., (2019). modelDown: automated website generator with interpretable documentation for predictive machine learning models. Journal of Open Source Software, 4(38), 1444, https://doi.org/10.21105/joss.01444
AR Object Manipulation on Depth-Sensing Handheld Devices

Recently released, depth-sensing-capable, and moderately priced handheld devices support the implementation of augmented reality (AR) applications without the requirement of tracking visually distinct markers. This relaxed constraint allows for applications with significantly increased augmentation space dimension, virtual object size, and user movement freedom. Being relatively new, there is currently a lack of study on issues concerning direct virtual object manipulation for AR applications on these devices. This paper presents the results from a survey of the existing object manipulation methods designed for traditional handheld devices and identifies potentially viable ones for newer, depth-sensing-capable devices. The paper then describes the following: a test suite that implements the identified methods, test cases designed specifically for the characteristics offered by the new devices, the user testing process, and the corresponding results. Based on the study, this paper concludes that AR applications on newer, depth-sensing-capable handheld devices should manipulate small-scale virtual objects by mapping directly to device movements and large-scale virtual objects by supporting separate translation and rotation modes. Our work and results are the first step in better understanding the requirements to support direct virtual object manipulation for AR applications running on a new generation of depth-sensing-capable handheld devices.
Design, Development and Comparison of Heuristic Driven Algorithms Based on the Crossed Domain Products’ Reviews for User’s Summarization

Background: In recent time, people love online shopping but before any shopping feedbacks or reviews always required. These feedbacks help customers in decision making for buying any product or availing any service. In the country like India this trend of online shopping is increasing very rapidly because awareness and the use of internet which is increasing day by day. As result numbers of customers and their feedbacks are also increasing. It is creating a problem that how to read all reviews manually. So there should be some computerized mechanism that provides customers a summary without spending time in reading feedbacks. Besides big number of reviews another problem is that reviews are not structured. Objective: In this paper, we try to design, implement and compare two algorithms with manual approach for the crossed domain Product’s reviews. Methods: Lexicon based model is used and different types of reviews are tested and analyzed to check the performance of these algorithms. Results: Algorithm based on opinions and feature based opinions are designed, implemented, applied and compared with the manual results and it is found that algorithm # 2 is performing better than algorithm # 1 and near to manual results. Conclusion: Algorithm # 2 is found better on the different product’s reviews and still to be applied on other product’s reviews to enhance its scope. Finally, it will be helpful to automate existing manual process.
Online Multiple Pedestrians Tracking using Deep Temporal Appearance Matching Association

In online multi-target tracking, modeling of appearance and geometric similarities between pedestrians visual scenes is of great importance. The higher dimension of inherent information in the appearance model compared to the geometric model is problematic in many ways. However, due to the recent success of deep-learning-based methods, handling of high-dimensional appearance information becomes feasible. Among many deep neural networks, Siamese network with triplet loss has been widely adopted as an effective appearance feature extractor. Since the Siamese network can extract the features of each input independently, one can update and maintain target-specific features. However, it is not suitable for multi-target settings that require comparison with other inputs. To address this issue, we propose a novel track appearance model based on the joint-inference network. The proposed method enables a comparison of two inputs to be used for adaptive appearance modeling and contributes to the disambiguation of target-observation matching and to the consolidation of identity consistency. Diverse experimental results support the effectiveness of our method. Our work was recognized as the 3rd-best tracker in BMTT MOTChallenge 2019, held at CVPR2019. The code is available at https://github.com/yyc9268/Deep-TAMA.
Towards the Development of a Universal Testing Environment for Attention Guiding Techniques

Contexts as diverse as control rooms, driving and picking in logistic systems provide the backdrop to the potential of attention guiding to improve human performance. Due to recent technological progress in the field of head-mounted displays for augmented reality, attention guiding in mobile and context-sensitive situations has become widely applicable. Accordingly, the gap in existent literature concerning design, development and in particular evaluation and comparison of attention guiding techniques is surprising. Our literature reviewed showed that most studies were conducted within specific use cases and accordingly were using a specific technique. In order to start building knowledge on the comparison of techniques, we review the literature and run a focus group. From both, we derive pointers for the design and development of a universal testing environment to evaluate attention guiding techniques. We build a prototype with selected evaluation methods and test both the environment and two combined attention guiding techniques.
Formalizing the Solution to the Cap Set Problem

In 2016, Ellenberg and Gijswijt established a new upper bound on the size of subsets of $\mathbb{F}^n_q$ with no three-term arithmetic progression. This problem has received much mathematical attention, particularly in the case $q = 3$, where it is commonly known as the \emph{cap set problem}. Ellenberg and Gijswijt's proof was published in the \emph{Annals of Mathematics} and is noteworthy for its clever use of elementary methods. This paper describes a formalization of this proof in the Lean proof assistant, including both the general result in $\mathbb{F}^n_q$ and concrete values for the case $q = 3$. We faithfully follow the pen and paper argument to construct the bound. Our work shows that (some) modern mathematics is within the range of proof assistants.
A Corpus of Negations and their Underlying Positive Interpretations

Negation often conveys implicit positive meaning.In this paper, we present a corpus of negations and their underlying positive interpretations.We work with negations from Simple Wikipedia, automatically generate potential positive interpretations, and then collect manual annotations that effectively rewrite the negation in positive terms.This procedure yields positive interpretations for approximately 77% of negations, and the final corpus includes over 5,700 negations and over 5,900 positive interpretations.We also present baseline results using seq2seq neural models.
DANGNT@UIT.VNU-HCM at SemEval 2019 Task 1: Graph Transformation System from Stanford Basic Dependencies to Universal Conceptual Cognitive Annotation (UCCA)

This paper describes the graph transformation system (GT System) for SemEval 2019 Task 1: Cross-lingual Semantic Parsing with Universal Conceptual Cognitive Annotation (UCCA) 1 .The input of GT System is a pair of text and its unannotated xml, which is a layer 0 part of UCCA form.The output of GT System is the corresponding full UCCA xml.Based on the idea of graph illustration and transformation, we perform four main tasks when building GT System.At the first task, we illustrate the graph form of stanford dependencies 2 of input text.We then transform into an intermediate graph in the second task.At the third task, we continue to transform into ouput graph form.Finally, we create the output UCCA xml.The evaluation results show that our method generates good-quality UCCA xml and has a meaningful contribution to the semantic representation sub-field in Natural Language Processing.
Approximate Causal Abstraction

Scientific models describe natural phenomena at different levels of abstraction. Abstract descriptions can provide the basis for interventions on the system and explanation of observed phenomena at a level of granularity that is coarser than the most fundamental account of the system. Beckers and Halpern (2019), building on work of Rubenstein et al. (2017), developed an account of abstraction for causal models that is exact. Here we extend this account to the more realistic case where an abstract causal model offers only an approximation of the underlying system. We show how the resulting account handles the discrepancy that can arise between low- and high-level causal models of the same system, and in the process provide an account of how one causal model approximates another, a topic of independent interest. Finally, we extend the account of approximate abstractions to probabilistic causal models, indicating how and where uncertainty can enter into an approximate abstraction.
Fundamentos de diseño de un entorno tecnológico para el estudio de las habilidades en resolución de problemas en primeras edades escolares

In this paper we present the fundamentals of software design capable of monitoring and recording data related to the management of mathematical problem solving processes. The software allows a systematic collection of the variables of the resolution process from which it is possible to obtain different student learning analytics. By not focusing exclusively on the final outcome of the resolution process, it is possible to identify cognitive tendencies of the students. In this manuscript we describe the characteristics of this technological environment together with the didactic basis that justifies the interest of research at school levels in which poor knowledge limits the formalization of problem solving situations. The first results of a pilot study that has allowed the evaluation of strengths and weaknesses of the developing version are attached. Finally, future directions for this technological environment are discussed as one more step in both innovation and research in Mathematics Education.
Peculiarities of professional motivation of private and public schools teachers

The establishment of the water corporations in Israel, which are financial companies that took the place of the Water Departments in the local governments, constitutes a turning point in the relation between the local governments and the citizens.If in the past the local government saw itself as a nonprofit organization that served the community, the reform in the water industry converted the community activity of the municipal sector into a totally capitalist business operation.In the first years of the establishment of the State of Israel, it was clear that the State had to provide water to its residents as a source of livelihood and a basis for agricultural development and the conquest of the wilderness, but today in the era of privatization the precious resource, water, has become a "product" from which economic benefit can be derived and which can even be traded.The local governments acquire water "to the city gate" and then they sell it to the city resident at a high mark-up, with the addition of VAT, when the difference goes to the Water Corporations.The neoliberal approach has reached a basic product such as water, without distinction between the socioeconomic situations of people, while placing the burden on the resident.Many Investigation Committees on the topic of the water sector have noted the improper management of the government, which does not prioritize alternative solutions such as desalination, use of treated wastewater, and re-use of water for industry and agriculture.Instead, dozens of water corporations have been established to be used as collection agents and to place the economic burden on the citizen's back.
Towards the Detection of Partial Feature Interactions

Discrete feature interactions occur when the presence of overlapping, yet conflicting, features impact the functionality of an entire system such that one or more features are unsatisfied. However, adaptive systems may mitigate uncertainty by composing features from requirements that may be partially satisfied or partially unsatisfied (i.e., satisficed). In contrast to discrete interactions, these satisficed features that are composed of satisficed requirements may be partially unsatisfied due to a feature interaction. Feature interactions, in the case of satisficement, result in a proportional trade-off between conflicting objectives rather than a complete, or discrete, failure of features or the system. In this paper, we propose a new concept, partial feature interactions, to represent the class of feature interactions that allow for satisficement trade-offs between conflicting features. We also describe a method to reduce the dimensionality of the satisficement trade-offs to be reviewed when assessing the severity of the n-way interactions. Finally, we present a method for identifying Pareto optimal trade-offs among the conflicting features using the reduced dimensionality in order to detect partial feature interactions and multiple candidate adaptation points. We apply our method to an exemplar feature interaction from the automotive domain.
ON UNDERSTANDING A SCIENTIFIC TEXT AND ITS PERCEPTION

The article is devoted to the problem relevant for modern linguistics -the issue of understanding a scientific text and its perception in the large-scale space of informatization and communication.The study is of analytical nature making an attempt to consider the features of a scientific text and the related problems of perceiving and understanding scientific information.We identify possible ways of overcoming the emerging communication difficulties.The importance of this research is its study of a scientific text as a complex semantic-syntactic entity with a number of characteristic features (informative richness and conclusiveness, hierarchical construction, generalized and abstract character of presentation, the focus on a uniform understanding).The article identifies the main reasons for the lack of understanding of the scientific text: complexity of the coded meaning, complexity of the language code and limited capabilities of the subject of speech.According to the researchers, these reasons reflect the essence of the problem of perceiving and understanding the scientific text -the discrepancy between the cognitive-psychological capabilities of the subject and the specific object of the study.The article draws attention to the fact that perception of the text content image is possible due to the actualization of personal meanings, reflecting practical, mental, and emotional experiences of the subject.Thus, we conclude that the choice of a strategy for overcoming communicative difficulties in perceiving a scientific text should be determined by cognitive-psychological capabilities of the recipient person, his/her speech and communicative experience.
PROJECT EDUCATION IS THE KEY TO THE TRAINING OF A SUCCESSFUL SPECIALIST

The article has been devoted to the project approach as the basis of the modern educational process. The importance of changing the educational process has been shown in order to ensure the high quality of Russian education in accordance with the changing demands of both the population and the long-term objectives of the development of Russian society and economy, which will lead to an increase in the demand for graduates of Russian higher education institutions by not only domestic corporations, but also foreign. Advantages and disadvantages of the project-education, as well as the complexity of the transition from traditional learning, have been considered. An example of successful implementation of the model has been analyzed.
CLASSIFICATION OF ENGINEERING STUDENTS' SELF-EFFICACY TOWARDS VISUAL-VERBAL PREFERENCES USING DATA MINING METHODS

The purpose of this research was to build a classification model and to measure the correlation of self-efficacy with visual-verbal preferences using data mining methods. This research used the J48 classifier and linear projection method as an approach to see patterns of data distribution between self-efficacy and visual-verbal preferences. The measurement of the correlation of engineering students' self-efficacy with visual-verbal preferences using the data mining method approach gets the result that self-efficacy does not correlate with visual-verbal preferences. However, engineering students' self-efficacy influences the achievement of initial learning outcomes. Visual-verbal preference is more influenced by students' interest in images so it can be concluded that self-efficacy affects the initial results of learning but does not have a correlation with visual-verbal preferences. The results of the decision tree provide the results that are easily understood and present a correlation between self-efficacy and visual-verbal preferences in a visual form. Keywords: self-efficacy, visual-verbal preferences, data mining.
Solving problems relating to ICT security management systems including various characteristics of the environment and system

The work presents the essence of problems appearing in the ICT security management process in the context of systems characterized by significant dynamics of configuration and heterogeneity of resources both in the hardware and software layer. Basic differences in security management in systems with traditional centralized and monolithic architecture as well as in systems with service-oriented architecture have been presented. A layered reference model for service-oriented systems taking account of the basic goals of ICT security for dynamic information systems has been discussed. The basic assumptions of the multi-agent ICT security analysis system in service-oriented systems as well as the results of the safety analysis, including the correlation between events observed in low and high layers of the reference model have been discussed.