Reviewerly/exHarmony huggingface dataset:
Landscape Boolean functions

In this paper we define a class of generalized Boolean functions defined on $ {\mathbb F}_2^n $ with values in $ {\mathbb Z}_q $ (we consider $ q = 2^k $, $ k\geq 1 $, here), which we call landscape functions (whose class contains generalized bent, semibent, and plateaued) and find their complete characterization in terms of their Boolean components. In particular, we show that the previously published characterizations of generalized plateaued Boolean functions (which includes generalized bent and semibent) are in fact particular cases of this more general setting. Furthermore, we provide an inductive construction of landscape functions, having any number of nonzero Walsh-Hadamard coefficients. We also completely characterize generalized plateaued functions in terms of the second derivatives and fourth moments.
Simultaneous removal of nonrotationally symmetric errors in tilted wave interferometry

The tilted wave interferometer has been developed as a fast and accurate instrument for the measurement of aspheric and freeform surfaces. We present a method for increasing its robustness and flexibility. Tilted wave interferometry crucially depends on accurate calibration and any changes to a calibrated setup require, in general, a recalibration. Therefore, we propose a method for simultaneous topography reconstruction and elimination of errors arising from such changes. An approach to identify trends in systematic errors for the complex non-null setup with a large number of blackbox model parameters is worked out. The procedure allows deriving an error removal scheme for nonrotationally symmetric components based on measurements in different rotational positions. The feasibility and benefit of the error elimination method are shown both by simulation methods and dedicated experiments. A significant reduction of systematic errors even in a miscalibrated state is achieved. Hence, recalibrations are avoided and measurement time and flexibility are improved.
Graph Reasoning Networks for Visual Question Answering.

The interaction between language and visual information has been emphasizedin visual question answering (VQA) with the help of attention mechanism.However, the relationship between words in question has been underestimated,which makes it hard to answer questions that involve the relationship betweenmultiple entities, such as comparison and counting. In this paper, we developthe graph reasoning networks to tackle this problem. Two kinds of graphs areinvestigated, namely inter-graph and intra-graph. The inter-graph transfersfeatures of the detected objects to their related query words, enabling theoutput nodes to have both semantic and factual information. The intra-graphexchanges information between these output nodes from inter-graph to amplifyimplicit yet important relationship between objects. These two kinds of graphscooperate with each other, and thus our resulting model can reason therelationship and dependence between objects, which leads to realization ofmulti-step reasoning. Experimental results on the GQA v1.1 dataset demonstratethe reasoning ability of our method to handle compositional questions aboutreal-world images. We achieve state-of-the-art performance, boosting accuracyto 57.04%. On the VQA 2.0 dataset, we also receive a promising improvement onoverall accuracy, especially on counting problem.
On Poisson approximations for the Ewens sampling formula when the mutation parameter grows with the sample size

The Ewens sampling formula was firstly introduced in the context of population genetics by Warren John Ewens in 1972, and has appeared in a lot of other scientific fields. There are abundant approximation results associated with the Ewens sampling formula especially when one of the parameters, the sample size $n$ or the mutation parameter $\theta$ which denotes the scaled mutation rate, tends to infinity while the other is fixed. By contrast, the case that $\theta$ grows with $n$ has been considered in a relatively small number of works, although this asymptotic setup is also natural. In this paper, when $\theta$ grows with $n$, we advance the study concerning the asymptotic properties of the total number of alleles and of the counts of components in the allelic partition assuming the Ewens sampling formula from the viewpoint of Poisson approximations.
DIGITAL MATURITY MODEL FOR BULGARIAN HIGHER EDUCATION INSTITUTIONS

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 6111-6120ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.1474Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Convergence of Multi-Agent Learning with a Finite Step Size in General-Sum Games

Learning in a multi-agent system is challenging because agents are simultaneously learning and the environment is not stationary, undermining convergence guarantees. To address this challenge, this paper presents a new gradient-based learning algorithm, called Gradient Ascent with Shrinking Policy Prediction (GA-SPP), which augments the basic gradient ascent approach with the concept of shrinking policy prediction. The key idea behind this algorithm is that an agent adjusts its strategy in response to the forecasted strategy of the other agent, instead of its current one. GA-SPP is shown formally to have Nash convergence in larger settings than existing gradient-based multi-agent learning methods. Furthermore, unlike existing gradient-based methods, GA-SPP's theoretical guarantees do not assume the learning rate to be infinitesimal.
THE PREPARATION OF TEACHERS IN THE FIELD OF MULTIMEDIA EDUCATION

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 1477-1483ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.0446Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Component-based face sketch recognition using an enhanced evolutionary optimizer

The main aim of this work is to develop a component-based face sketch recognition model. The proposed model adopts an enhanced evolutionary optimizer (EEO) to perform the task of face sketched components localization. EEO is applied to an unknown input sketch to make an automatic localization for its components i.e. eyes, nose, and mouth. After that, HOG features are extracted, and cosine similarity measure is computed to find the best components location. EEO integrates Q-learning algorithm with the simulated annealing (SA) algorithm as a single mode. The Q-learning algorithm is used to control the execution of SA parameters i.e. temperature and the mutation rate at run time. The proposed approach was evaluated on three face sketch recognition benchmark problem which are LFW, AR, and CUHK. The experimental results show that EEO significantly outperform SA as well as other well-known meta-heuristic optimization algorithms such as PSO, Harmony, and MVO.
Towards Testing Monotonicity of Distributions Over General Posets

In this work, we consider the sample complexity required for testing the monotonicity of distributions over partial orders. A distribution $p$ over a poset is monotone if, for any pair of domain elements $x$ and $y$ such that $x \preceq y$, $p(x) \leq p(y)$. To understand the sample complexity of this problem, we introduce a new property called bigness over a finite domain, where the distribution is $T$-big if the minimum probability for any domain element is at least $T$. We establish a lower bound of $\Omega(n/\log n)$ for testing bigness of distributions on domains of size $n$. We then build on these lower bounds to give $\Omega(n/\log{n})$ lower bounds for testing monotonicity over a matching poset of size $n$ and significantly improved lower bounds over the hypercube poset. We give sublinear sample complexity bounds for testing bigness and for testing monotonicity over the matching poset. We then give a number of tools for analyzing upper bounds on the sample complexity of the monotonicity testing problem.
Aggregation rules based on stochastic gradient descent in Byzantine consensus

With the development of the data, block chain technology has gradually become the focus of the world. In block chain’s consensus algorithm, the typical Byzantine problem is often mentioned. In this paper, the Trimmed mean aggregation rule is proposed based on the stochastic gradient descent method to solve the Byzantine problem. We introduce the concept of Byzantine resilience and proves that the aggregation rule satisfies the Byzantine resilience condition, and the aggregation rule has O(dnlogn) complexity. Finally, in our experiment, we compare various aggregation rules and demonstrate that the Trimmed mean aggregation rules are robust.
INDONESIANS’ TENDENCY TO REFER ABBREVIATION AS ACRONYM: TYPES OF ABBREVIATION AS WORD FORMATION PROCESS

The purpose of this research is to analyze the tendency of Indonesians to assume all types of abbreviation is acronym. This study examines the types of word formation process of abbreviation, and whether abbreviation or acronym is the correct umbrella term for this particular morphological process. Using library research method, this paper refers to two key morphology researchers from Britain and Indonesia, Laurie Bauer and Harimurti Krisdalaksana respectively, as well as one Indonesian Doctorate Arie Ansrasyah Isa whom referred the former two researchers in her study that proposes 16 types of abbreviations using data from top Indonesian newspapers, Jakarta Post and Kompas. This paper concludes that common people in Indonesia refer all abbreviations as acronyms because their high preference in abbreviating long phrases stems from the desire to increase pronounceability and familiarity of words, usually in the context of humor, but is highly prevalent in all situations, formal or non-formal.
Illegally Parked Vehicle Detection Based on Haar-Cascade Classifier

Detection of illegally parked vehicles in the urban traffic scene is required for handling unwanted incidents in the roads such as vehicle collision, traffic jam, accidents etc. Due to occlusion, lightning change and other factors the task becomes more laborious. A video-based proposal is presented in this paper to eliminate these problems. Initially, foreground objects are separated using Gaussian Mixture based background subtraction model. Meanwhile, median filtering is used to remove the salt-pepper noise. A simple shadow removal technique based on HSI and YCbCr color model improves the precision of the vehicle detection. Then, edge detection was performed using Canny method for precise tracking of temporarily static vehicles checking centroid co-ordinate values. Finally, for verification of the vehicles a machine learning mechanism known as ‘Haar-Cascade’ classifier is used. However, experimental data shows that proposed system performed well under different conditions with an accuracy of 92%.
A Comprehensive Quality of Service Evaluation in Mimic System

The quality of service has become a central issue for cyber-attacks. Users are concerned more about security than function when sieving from similar products. Inconceivably, the mimic system built with the purpose of security does not regard security as one of quantitative indicators of service quality. Because the original performance evaluation ignores in representing accurately the advantages of the mimic system, comprehensive quality of service evaluation system makes a more comprehensible quantitative assessment in the mimic Web server. With the performance indicators and the security considered, our quantitative model evaluates the mimic service quality more accurately and reflect the positive effect of high security of mimic system on service quality. This evaluation will provide more reasonable evaluation criteria and lay the foundation for the commercial application of the mimic system.
Information and communication technologies and their relationship with academic performance in higher education

The objective of the research was to analyze the incidence of the pedagogical use of ICT with the academic performance of the students of a Higher Education Institution. The type-related methodology is used in order to determine the interaction between the use of the collaborative work tool called Padlet and student performance. For this purpose, nonparametric tests are applied and interpreted for the analysis of correlations and variances of the main variables. The selected population was the students enrolled in the classroom modality of the: Accounting and Audit, Business Administration and Chemical Engineering degrees of the Universidad Técnica Particular de Loja (UTPL), corresponding to the academic period April - August 2018. The results showed that, the Padlet has been qualified as a good tool, regardless of the grade obtained by the students and the amount of resources they use in their learning; highlighting the importance of the use of ICT.
Temperature effects on atmospheric continuous-variable quantum key distribution*

Compared with the fiber channel, the atmospheric channel offers the possibility of a broader geographical coverage and more flexible transmission for continuous-variable quantum key distribution (CVQKD). However, the fluctuation of atmospheric conditions will lead to the loss of performance in atmospheric quantum communication. In this paper, we study how temperature affects atmospheric CVQKD. We mainly consider the temperature effects on the transmittance and interruption probability. From the numerical simulation analysis, it can be shown that the performance of atmospheric CVQKD is improved as temperature increases, with the other factors fixed. Moreover, the results in this work can be used to evaluate the feasibility of the experimental implementation of the atmospheric CVQKD protocols.
Sistem Pelacakan Lokasi Pelaporan Petugas Lapangan Irigasi Provinsi Sumatera Barat Berbasis GPS Smartphone dan WebGIS

Tracking the location from the side of the report and the position of the reporter in real-time is very much needed in validating the performance of irrigation field officers. The location tracking system represented in smartphone applications, and digital maps are a practical solution for supervisors in monitoring the performance of field officers. The Global Positioning System (GPS) module on Android smartphones and the Web Geographic Information System (WebGIS) are supporting technologies for the system. Each location coordinate on the report will be sent automatically to the MySQL database server, then each coordinate location of the field officer will be sent to the Firebase Realtime Database, coupled with a database synchronization system to handle reports offline when officers are in areas that do not have internet access. Database synchronization plays a role in handling report data stored in the SQLite database on an Android smartphone with a MySQL database server. This system is useful for supervisors in controlling and monitoring the performance of irrigation field officers and can be used as material for decision making. After being implemented in several Irrigation Areas in West Sumatra Province by 113 users consisting of 13 supervisors and 100 field officers, the supervisors can track the location of the report, follow the position of field officers in real-time and send reports offline.
Knowledge Engineering for Competence Assessment on Serious Games Based on Semantic Web

Recognizing the inadequacy of traditional knowledge-based assessment systems in higher education to discover Learner competencies, we rethink the design of traditional assessment, using approaches that can both respond to the corporate requirements of the twenty-first century and reflect learners' competencies. The serious game sector has gained prominence in the last decade and the need to assess players' competencies in terms of knowledge, skills, and attitudes has grown. The assessment typically involves analyzing the player's performance in the game environment. At the same time, ontologies have emerged as one of the most popular and widely accepted tools in technology-enhanced learning and assessment. This paper proposes a framework for Competence Assessment based on ontological models for the generation of Personalized Assessment Scenarios, Assessment Portfolios and Personalized Assessment Paths on Serious games. Assessment Portfolios are used to track and store learners' interactions with the games, which can then be used as proof of competence during the assessment process. The personalized assessment scenarios on serious games are formalized with First Order Logic rules that are able to reason over resources annotated with Semantic Web metadata, thus allowing the reusability and interoperability of data.
Unifying Research to Address Motion Sickness

Be it discussed as cybersickness, immersive sickness, simulator sickness, or virtual reality sickness, the ill effects of visuo-vestibular mismatch in immersive environments are of great concern for the wider adoption of virtual reality and related technologies. In this position paper, we discuss a unified research approach that may address motion sickness and identify critical research topics.
Stand-alone, Wearable System for Full Body VR Avatars: Towards Physics-based 3D Interaction

We introduce a stand-alone, wearable system with full body and finger tracking for first-person virtual reality (VR) avatars. The system does not rely on any external trackers or components. It comprises of a head-mounted display, inertial motion capture suit, VR gloves, and VR backpack PC. Making use of the wearable system and RUIS toolkit [1], we present an example implementation of our vision for physics-based full body avatar interaction. This envisioned interaction involves three elements from the reality-based interaction framework of Jacob et al. [2]: naïve physics, body awareness, and environment awareness. These elements lend common sense affordances within the virtual world and allow users to employ their everyday knowledge of the real world. We argue that when it comes to full body avatar interfaces, it is not only users, but also developers who benefit from utilizing physics simulation as the basis upon which different interaction techniques are built on. This physics-based approach provides intuitive manipulation and locomotion interactions without requiring individually crafted scripts. Our example implementation presents several such interactions. Furthermore, the many interaction techniques emerging from physical simulation are congruous with each other, which promotes user interface consistency. We also introduce the idea of using physics components (colliders, joints, materials, etc.) as 3D user interface building blocks, as opposed to scripting or visual programming.
Friend Recommendation Mechanism for Social Media Based on Content Matching

This paper introduces a friend recommendation mechanism for social media based on content similarity matching. It can extract information from user's homepage and then analyze the information to generate a group of keywords. Afterwards, it calculates the occurrence frequency of each keyword to form a ranking list. Finally it compares the lists from variety of users and recommends the user with the highest matching result. The paper is organized into five parts: introduction, business discussion, system functional description, methodology and conclusion.
Library Metadata Standards and Linked Data Services: An Introduction to Arab and International Organizations

This article aims to give an overview of Arab and international organizations that promulgate library metadata standards and linked data services—ranging from those whose sole purpose is the setting of standards and linked data technology, to those that develop standards and provide linked data as subsidiary services.
Correction to: A novel adaptable approach for sentiment analysis on big social data

The authors note a correction to the article [1]. Table 5 of the original article is incomplete. Few percentage values are missing. This article presents the corrected version of Table 5.
Correcting crowdsourced annotations to improve detection of outcome types in evidence based medicine

© 2019 for this paper by its authors. The validity and authenticity of annotations in datasets massively influences the performance of Natural Language Processing (NLP) systems. In other words, poorly annotated datasets are likely to produce fatal results in at-least most NLP problems hence misinforming consumers of these models, systems or applications. This is a bottleneck in most domains, especially in healthcare where crowdsourcing is a popular strategy in obtaining annotations. In this paper, we present a framework that automatically corrects incorrectly captured annotations of outcomes, thereby improving the quality of the crowdsourced annotations. We investigate a publicly available dataset called EBM-NLP, built to power NLP tasks in support of Evidence based Medicine (EBM) primarily focusing on health outcomes.
Mask R-CNN Algoritması ile Hangar Tespiti Hangar Detection with Mask R-CNN Algorithm

In this study, the detection of hangars in high resolution airport (civil and military) satellite images was performed using Mask R-CNN algorithm. Although the detection of buildings in the satellite images is a common practice, being some of the hangars camouflaged in different sizes cause difficulty for the detection algorithms. In Mask R-CNN, an instance object segmentation algorithm, objects in the images are detected, bounding box of each object as well as their pixel information with in the box are marked separately. In this study, high resolution hangar data set with 300 samples, collected from various air bases, was prepared, and an 85% average precision is achieved using Mask R-CNN.
Video Representation via Fusion of Static and Motion Features Applied to Human Activity Recognition

In human activity recognition system both static and motion information play crucial role for efficient and competitive results.Most of the existing methods are insufficient to extract video features and unable to investigate the level of contribution of both (Static and Motion) components.Our work highlights this problem and proposes Static-Motion fused features descriptor (SMFD), which intelligently leverages both static and motion features in the form of descriptor.First, static features are learned by two-stream 3D convolutional neural network.Second, trajectories are extracted by tracking key points and only those trajectories have been selected which are located in central region of the original video frame in order to to reduce irrelevant background trajectories as well computational complexity.Then, shape and motion descriptors are obtained along with key points by using SIFT flow.Next, cholesky transformation is introduced to fuse static and motion feature vectors to guarantee the equal contribution of all descriptors.Finally, Long Short-Term Memory (LSTM) network is utilized to discover long-term temporal dependencies and final prediction.To confirm the effectiveness of the proposed approach, extensive experiments have been conducted on three well-known datasets i.e.UCF101, HMDB51 and YouTube.Findings shows that the resulting recognition system is on par with state-of-the-art methods.