Reviewerly/exHarmony huggingface dataset:
Automated Segmentation of Abnormal Tissues in Medical Images

Nowadays, medical image modalities are almost available everywhere. These modalities are bases of diagnosis of various diseases sensitive to specific tissue type. Usually physicians look for abnormalities in these modalities in diagnostic procedures. Count and volume of abnormalities are very important for optimal treatment of patients. Segmentation is a preliminary step for these measurements and also further analysis. Manual segmentation of abnormalities is cumbersome, error prone, and subjective. As a result, automated segmentation of abnormal tissue is a need. In this study, representative techniques for segmentation of abnormal tissues are reviewed. Main focus is on the segmentation of multiple sclerosis lesions, breast cancer masses, lung nodules, and skin lesions. As experimental results demonstrate, the methods based on deep learning techniques perform better than other methods that are usually based on handy feature engineering techniques. Finally, the most common measures to evaluate automated abnormal tissue segmentation methods are reported.
Book Recommender System Using Genetic Algorithm and Association Rule Mining

Recommender system aims to provide on something that likely most suitable and attractive for users. Many researches on the book recommender system for library have already been done. One of them used association rule mining. However, the system was not optimal in providing recommendations that appropriate to the user's preferences and achieving the goal of recommender system. This research proposed a book recommender system for the library that optimizes association rule mining using genetic algorithm. Data used in this research has taken from Yogyakarta City Library during 2015 until 2016. The experimental results of the association rule mining study show that 0.01 for the greatest value of minimum support and 0.4359 for the average confidence value due to a lot of data and uneven distribution of data. Furthermore, other results are 0.499471 for the average of Laplace value, 30.7527 for the average of lift value and 1.91534252 for the average of conviction value, which those values indicate that rules have good enough level of confidence, quite interesting and dependent which indicates existing relation between antecedent and consequent. Optimization using genetic algorithm requires longer execution time, but it was able to produce book recommendations better than only using association rule mining. In Addition, the system got 77.5% for achieving the goal of recommender system, namely relevance, novelty, serendipity and increasing recommendation diversity.
Autoencoder-based candidate waypoint generation method for autonomous flight of multi-unmanned aerial vehicles

Unmanned aerial vehicles may collide with obstacles, such as trees or other unmanned aerial vehicles, while flying. A waypoint-based flight path is an approach to avoid such obstacles. To specify waypoints for the safe flight of unmanned aerial vehicles, it is necessary to define a flight path in advance by analyzing the flight records of unmanned aerial vehicles and thereby designate the waypoints automatically. However, there is a problem in that pilots tend to make errors in controlling unmanned aerial vehicles and collecting flight records. This article proposes a method to generate candidate waypoints for a flight path by removing such unintended flight records. In this method, images representing the positions in the collected flight records are generated. The candidate waypoints are generated as positions corresponding to the overlapping pixels of the images generated via image accumulation based on the flight records and the ones generated by accumulating the images reconstructed using an Autoencoder. The unmanned aerial vehicles can be set the waypoints for an autonomous flight using the candidate waypoints. An experiment was conducted in a university to generate candidate waypoints for road monitoring. The results obtained using the proposed method and K-means algorithm were compared. The candidate waypoints generated using the proposed method were reduced by 84.21% compared to those generated using the K-means algorithm.
Secure string pattern query for open data initiative

The open data initiative allows the organizations from different countries to share and integrate their data to produce value added information products and services. However, the trust relationship between any two organizations can hardly be established due to the data sensitivity and their specific responsibilities. In this paper, we propose a secure string pattern query processing framework for multiple data owners. With this framework, we design an efficient and secure indexing structure called Secure String PAttern Search tree (S2PAStree). S2PAStree allows the execution of the secure string pattern query with logarithmic time complexity. We further propose a set of secure index construction protocols under the scenario that multiple data owners share and integrate their sensitive data. Finally, we conduct comprehensive experiments on three public datasets to evaluate the efficiency and the scalability of our solutions.
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction

The task of unsupervised bilingual lexicon induction (UBLI) aims to induce word translations from monolingual corpora in two languages.Previous work has shown that morphological variation is an intractable challenge for the UBLI task, where the induced translation in failure case is usually morphologically related to the correct translation.To tackle this challenge, we propose a morphology-aware alignment model for the UBLI task.The proposed model aims to alleviate the adverse effect of morphological variation by introducing grammatical information learned by the pre-trained denoising language model.Results show that our approach can substantially outperform several state-of-the-art unsupervised systems, and even achieves competitive performance compared to supervised methods.Input: Être adulte, c'est être seul.Noisy translation: Be adult, it's be alone.
Edge N-Level Sparse Visibility Graphs: Fast Optimal Any-Angle Pathfinding Using Hierarchical Taut Paths

In the Any-Angle Pathfinding problem, the goal is to find the shortest path between a pair of vertices on a uniform square grid, that is not constrained to any fixed number of possible directions over the grid. Visibility Graphs are a known optimal algorithm for solving the problem with the use of pre-processing. However, Visibility Graphs are known to perform poorly in terms of running time, especially on large, complex maps. In this paper, we introduce two improvements over the Visibility Graph Algorithm to compute optimal paths. Sparse Visibility Graphs (SVGs) are constructed by pruning unnecessary edges from the original Visibility Graph. Edge N-Level Sparse Visibility Graphs (ENLSVGs) is a hierarchical SVG built by iteratively pruning non-taut paths. We also introduce Line-of-Sight Scans, a faster algorithm for building Visibility Graphs over a grid. SVGs run much faster than Visibility Graphs by reducing the average vertex degree. ENLSVGs, a hierarchical algorithm, improves this further, especially on larger maps, with millisecond runtimes even on 6000 x 6000 maps. On large maps, with the use of pre-processing, these algorithms are at least an order of magnitude faster than existing algorithms like Visibility Graphs, Anya and Theta*.
ДОСВІД ВИКОРИСТАННЯ Е-ПІДРУЧНИКІВ І ЕЛЕКТРОННИХ ЗАСОБІВ НАВЧАЛЬНОГО ПРИЗНАЧЕННЯ В УМОВАХ ЦИФРОВІЗАЦІЇ ЗАГАЛЬНОЇ СЕРЕДНЬОЇ ОСВІТИ УКРАЇНИ

The article describes the possibilities of e-textbooks introduction to the school curriculum as an instrument of digital pedagogy for the implementation of quality education. In the research, theoretical methods for systematizing and analyzing various approaches to the definition of the concept "e-textbook" and its classifications are used, the requirements for a modern e-textbook and the conditions for its use at school are generalized, as well as analyzed the proposals for electronic educational means for Ukrainian schools. The needs of teachers, students and their parents in the use of e-textbooks and electronic educational means were identified through empirical methods (observation, survey). Experience of using e-textbooks, electronic educational means is described on the basis of research and experimental work of 25 secondary education institutions. The results are based on sociological surveys, interviews and questionnaires of the participants in the educational process. A total of 1005 respondents took part in the study: 352 students, 296 teachers, 357 parents. The research has shown that most teachers are ready to use e-textbooks, e-tutorials, electronic educational means. It is more convenient for students and teachers to work with multimedia, interactive e-learning tools, e-textbooks. The Ukrainian state educational policy contributes to the development of digital pedagogy, digital education and the creation of high-quality electronic educational resources. International experience determines the priorities of the development of e-textbooks and the transition from static scanned versions of printed textbooks to interactive, multimedia, universal editions in which teachers can transform educational material, and, subsequently, to the development of adaptive versions that will provide students with the opportunity to form individual educational routes. In our future research we intend to concentrate on organizational, psychological and pedagogical conditions for the introduction of e-textbooks to secondary education and teacher training for their use as a guarantee of the development of digital pedagogy.
Colorectal Cancer Outcome Prediction from H&amp;E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

Digital pathology (DP) is a new research area which falls under the broad umbrella of health informatics. Owing to its potential for major public health impact, in recent years DP has been attracting much research attention. Nevertheless, a wide breadth of significant conceptual and technical challenges remain, few of them greater than those encountered in digital oncology. The automatic analysis of digital pathology slides of cancerous tissues is particularly problematic due to the inherent heterogeneity of the disease, extremely large images, and numerous others. In this paper we introduce a novel machine learning based framework for the prediction of colorectal cancer outcome from whole haematoxylin &amp;amp; eosin (H&amp;amp;E) stained histopathology slides. Using a real-world data set we demonstrate the effectiveness of the method and present a detailed analysis of its different elements which corroborate its ability to extract and learn salient, discriminative, and clinically meaningful content.
Innovative Model of Communicative Practices

The article deals with the philosophical and pedagogical understanding of communicative practices in the educational process of a higher school. A typology of dialogue interaction in pedagogical search space has been proposed. Its usage creates conditions for the analysis, reflection and self-assessment of students in the organization of their training activity. The experience of organization of communicative educational clusters in higher artistic education, which provides for the transition from the primacy of formalized to innovative models of interaction between all participants of the process, has been presented. The conceptual values-semantic aspects of the implementation of interdisciplinary approach have been analyzed, and it has been shown that this approach opens qualitatively new possibilities for the reflection of the investigated object. The contours of a new view on the problem of dialogue, in particular, through the discursive activity of students, have been outlined: Problem group method, group therapy, group “buzz”, “aquarium”, and lecture-forum. The transition to interdisciplinary integration with project design forms is a prerequisite for creating an innovative model for the development of post-non-classical artistic education. In the organization of the dialogue, we take into account: the students’ method to perceive different points of view, the search for the underlying motives, that is, those problem situations through which the own style of the studied material is formed, the development of tasks-conflicts associated with higher spiritual values, ideological and semantic sphere, designing various variants of interaction ways for dialogue participants.
Component-Based 3D Modeling of Dynamic Systems

The objective is to model and simulate larger and more complex 3-dimensional systems as it is possible with a pure equation-based modeling system such as Modelica.The approach shall combine component-based 3D modeling, as used in modern game engines, with equation-based modeling.The proposed methodology has been evaluated and tested in the experimental modeling environment Modia3D that is implemented with the Julia programming language.
PSPACE-Completeness of a Thread Criterion for Circular Proofs in Linear Logic with Least and Greatest Fixed Points

In the context of logics with least and greatest fixed points, circular (i.e. non-wellfounded but regular) proofs have been proposed as an alternative to induction and coinduction with explicit invariants. However, those proofs are not wellfounded and to recover logical consistency, it is necessary to consider a validity criterion which differentiates valid proofs among all preproofs (i.e. infinite derivation trees). The paper focuses on circular proofs for MALL with fixed points. It is known that given a finite circular representation of a non-wellfounded preproof, one can decide in $$\mathrm {PSPACE}$$ whether this preproof is valid with respect to the thread criterion. We prove that the problem of deciding thread-validity for $$\mu \mathrm {MALL}$$ is in fact $$\mathrm {PSPACE}$$ -complete. Our proof is based on a deeper exploration of the connection between thread-validity and the size-change termination principle, which is usually used to ensure program termination.
Piano Strings with Reduced Inharmonicity

On modern straight pianos, the inharmonicity of the lower strings is rather large especially for the first octave. Consequently, the timber of these strings can sometimes sound awful and chords on the first octave be highly dissonant. The idea of the present study is to show how this defect can be rectified using an inhomogeneous winding on the whole string in order to minimize inharmonicity. The problem is solved using an optimisation procedure considering a non uniform linear density. Results show that the inharmonicity of the first partials could be highly reduced by a non uniform winding limited to a quarter of the string.
Localized or Architectural: An Empirical Study of Performance Issues Dichotomy

Performance is an important quality attribute of software systems. This paper categorizes and investigates 225 real-life performance issues in two separate groups: 1) localized issues (60%) that can be fixed by simple, localized code fixes and 2) architectural issues (40%) that require changes to a group of architecturally related files. This paper improves the understanding of causes and solutions to performance issues, specially for architectural performance issues. First, we summarized six typical root causes to performance problems that apply to both localized and architectural issues. In particular, 19% of architectural issues are associated with multiple causes. Second, we summarized four general types of architectural solutions to performance issues: 1) introducing new design elements; 2) changing design dependencies; 3) updating connected files; 4) updating independent files.
Generalized Net Model of Cluster Analysis Using CLIQUE: Clustering in Quest

The purpose of the cluster analysis is to find groups of objects with similar characteristics.Different methods are already developed.In the current research work a CLIQUE: Clustering in quest algorithm is investigated.The presented method combines subspace grid-based and density-based techniques to determine clusters of objects.Generalized net of cluster analysis using CLIQUE: Clustering in quest algorithm is constructed.The presented Generalized net can be used for description and monitoring the parallel processes in the cluster analysis.
Penerapan Model Pembelajaran Project Based Learning Untuk Meningkatkan Keterampilan Menulis Puisi Di Sekolah Dasar

The background of this research is the low writing ability of students especially on poetry writing ability. This research was conducted in grade V, SDIT Cendikia. Learning model applied is model of Project Based Learning. The research method used in this research is Classroom Action Research. The research consisted of three cycles each. The purpose of this research is to know the activity and result of student learning on poetry writing ability, after applied of Project Based Learning model. Research data obtained through observation and test. From the results of this research can be concluded that the implementation of the of Project Based Learning model can improve the ability of writing poetry in class V. The results showed that the ability to write poetry can be increased, can be seen from the results of student learning completeness on the initial test students who meet the KKM as much as 2 people, then the second is 6 people, and the third cycle is 9 people, and 14 people on the third cycle of the number of students as many as 15 people. Therefore, students learning outcomes on writing poetry ability at the end of each cycle I to III have improved considerably. Based on the invention, it is suggested that the teacher can apply the learning of Project Based Learningamodel to improve poetry writing ability, as well as on other language ability.
Selected Papers From the 2018 USENIX Security Symposium

The articles presented in this special section were presented at the 27th USENIX Security Symposium, that was held 15–17 August 2018 in Baltimore, Maryland.
Robotic Path Planning Based on Improved Ant Colony Algorithm

Ant colony algorithm is an intelligent bionic optimization algorithm. Its self-organization and intelligence provide guiding for studying the global path planning problem. Based on this, an improved ant colony algorithm is proposed to solve the problem of robotic path planning and improved the convergence speed. The environment model is established by grid method and the traditional ant colony algorithm is improved. The heuristic factor and pheromone updating strategy of the algorithm are improved to enhance the precision of the algorithm and the ability of later convergence. Simulation experiments show that the improved algorithm has a faster convergence speed to achieve the optimal path compared with other algorithms. It shows that the improved algorithm is effective and reliable.
A Video Deblurring Algorithm Based on Motion Vector and An Encorder-Decoder Network

Camera shakes cause video motion blur.Video deblurring has been studied for years, and however, there are still unresolved problems, such as video frame alignment, frame selection, and frame ambiguity evaluation.We propose a video deblurring algorithm based on the motion vector and an encoder-decoder network.Our algorithm consists of four steps: first, the blurry image blocks in a video frame are located using a blurred image quality evaluation algorithm based on a response function of singular values.Second, the corresponding candidates of the blurry image block in the consecutive frames are searched using the motion vector, and the optimal candidate blocks are obtained using an objective function.Third, the blurry image block and the optimal candidate blocks are served as samples, which are inputted to an encoder-decoder network, so that the blurry image block is repaired.Finally, all blurry image blocks are replaced with the repaired ones, the boundary artifacts are eliminated, and the entire video frame is repaired.The experiments show that our algorithm yields sharper repair results, and the overall performance of our algorithm is better than other related algorithms.
A Type-coherent, Expressive Representation as an Initial Step to Language Understanding

A growing interest in tasks involving language understanding by the NLP community has led to the need for effective semantic parsing and inference.Modern NLP systems use semantic representations that do not quite fulfill the nuanced needs for language understanding: adequately modeling language semantics, enabling general inferences, and being accurately recoverable.This document describes underspecified logical forms (ULF) for Episodic Logic (EL), which is an initial form for a semantic representation that balances these needs.ULFs fully resolve the semantic type structure while leaving issues such as quantifier scope, word sense, and anaphora unresolved; they provide a starting point for further resolution into EL, and enable certain structural inferences without further resolution.This document also presents preliminary results of creating a hand-annotated corpus of ULFs for the purpose of training a precise ULF parser, showing a three-person pairwise interannotator agreement of 0.88 on confident annotations.We hypothesize that a divide-and-conquer approach to semantic parsing starting with derivation of ULFs will lead to semantic analyses that do justice to subtle aspects of linguistic meaning, and will enable construction of more accurate semantic parsers.( |E|.sk at-about.p|Now17| ), ((the.dx (x cake.n)(she.pro(want.v(to (eat.vx))))) ** |E|.sk ) x Ñ |Cake3|, she.proÑ |Chell| Anaphora want.vÑ want1.v,eat.vÑ eat1.v,cake.nÑ cake1.nWSD ELF (Episodic) (|E|.skat-about.p|Now17|), (( |Chell| ( want1 .v(to ( eat1 .v|Cake3| )))) ** |E|.sk) structure flow information flow
Statistical learning from biased training samples

With the deluge of digitized information in the Big Data era, massive datasets are becoming increasingly available for learning predictive models. However, in many practical situations, the poor control of the data acquisition processes may naturally jeopardize the outputs of machine learning algorithms, and selection bias issues are now the subject of much attention in the literature. The present article investigates how to extend Empirical Risk Minimization, the principal paradigm in statistical learning, when training observations are generated from biased models, i.e., from distributions that are different from that in the test/prediction stage, and absolutely continuous with respect to the latter. Precisely, we show how to build a “nearly debiased” training statistical population from biased samples and the related biasing functions, following in the footsteps of the approach originally proposed in [46]. Furthermore, we study from a nonasymptotic perspective the performance of minimizers of an empirical version of the risk computed from the statistical population thus created. Remarkably, the learning rate achieved by this procedure is of the same order as that attained in absence of selection bias. Beyond the theoretical guarantees, we also present experimental results supporting the relevance of the algorithmic approach promoted in this paper.