Reviewerly/exHarmony huggingface dataset:
Machine learning algorithms for distributed operations in internet of things IoT

Generally, the things that have the great role in facilitating the emergence of internet-connected sensory devices can be embodied in the developments that happen in the sphere of software, hardware, and communication technologies.The internet-connected sensory devices present perceptions and measurements of data from the real world.It is suggested that nearly through 2020, the total use of internet-connected devices may reach to 25 to 50 billion.Actually, the relation between technologies and the volume of data being published is kept in one line.That is, if there is growth in the technologies, the volume of the data will be increased.Such technology, i.e. internetconnected devices, can be called as Internet of Things (IoT).Its role is to connect the real world with the cyber one.Furthermore, generating great data with velocity as its main characteristic will help in increasing the volume of IoT.To develop smart IoT applications, one can use such intelligent processing and analyzing such big data.In this paper, we tend to study the impact of implementing machine learning (ML) algorithms and methods and their efficiency in the IoT domain.As well as explore how these algorithms help in founding efficient backbone solutions to analyze and estimate the huge amounts of data that are expected to arise in the coming few years due to the rapid growth on demands for IoT based applications.
Mobile robot path planning based on Q-learning algorithm

Nowadays, reinforcement learning is gaining more and more attention with AlphaGo’s triumph. Path planning algorithm based on Q-learning, a model free reinforcement learning algorithm, was proposed for mobile robots. The algorithm translated sonar sensor information of the environment around the robot, the robot’s pose, and the location of target points to finite states. Afterwards, a reasonable environment model and state space were constructed, with discrete rewarding functions being established. The experimental results validated the effectiveness of the proposed algorithm, as each action of the robot obtained the corresponding rewarding value, which improved the convergence efficiency of the proposed algorithm.
Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

The multi-robot task allocation problem consists of two distinct sets: a set of tasks (requiring resources) and a set of robots (offering resources); then tasks are allocated to robots; while optimizing a certain objective function, subject to some constraints: e.g. allocate the maximum number of tasks, minimize the distances travelled by robots, etc. In this paper, we propose some objective functions, and our main contribution is the introduction of energetic constraints on multi-robot task allocation problems. In addition, we propose an allocation method (based on parallel distributed guided genetic algorithms) and compare it to two state-of-the-art algorithms. Performed simulations and obtained results show the effectiveness and scalability of our solution, even with a large number of robots and tasks.
Recurrent Hierarchical Topic-Guided Neural Language Models.

To simultaneously capture syntax and global semantics from a text corpus, we propose a new larger-context recurrent neural network (RNN) based language model, which extracts recurrent hierarchical semantic structure via a dynamic deep topic model to guide natural language generation. Moving beyond a conventional RNN-based language model that ignores long-range word dependencies and sentence order, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence topic dependencies. For inference, we develop a hybrid of stochastic-gradient Markov chain Monte Carlo and recurrent autoencoding variational Bayes. Experimental results on a variety of real-world text corpora demonstrate that the proposed model not only outperforms larger-context RNN-based language models, but also learns interpretable recurrent multilayer topics and generates diverse sentences and paragraphs that are syntactically correct and semantically coherent.
Preventing Additive Attacks to Relational Database Watermarking

False ownership claims are carried on through additive and invertibility attacks and, as far as we know, current relational watermarking techniques are not always able to solve the ownership doubts raising from the latter attacks. In this paper, we focus on additive attacks. We extend a conventional image-based relational data watermarking scheme by creating a non-colluded backup of the data owner marks, the so-called secondary marks positions. The technique we propose is able to identify the data owner beyond any doubt.
Content Analysis Made Simple for Students. An Interactive Online Application Solution

This paper aims to present an interactive online solution developed for students to better understand the application of content analysis (as a research method) and the use of a coding scheme (as the research instrument) by using a software platform, an application for analysing an online place brand’s content, in terms of the characteristics of a place brand’s identity communicated through official websites.
Talking about what is not there: Generating indefinite referring expressions in Minecraft

When generating technical instructions, it is often necessary to describe an object that does not exist yet.For example, an NLG system which explains how to build a house needs to generate sentences like "build a wall of height five to your left" and "now build a wall on the other side."Generating (indefinite) referring expressions to objects that do not exist yet is fundamentally different from generating the usual definite referring expressions, because the new object must be distinguished from an infinite set of possible alternatives.We formalize this problem and present an algorithm for generating such expressions, in the context of generating building instructions within the Minecraft video game.
Mode in Arabic-English Translation: with Reference to the Quran

A rhetorical question has the form of a question but does not perform its function, i.e. does not seek any information but rather, is used to give a specific or rhetoric function such as denial, assertion, testing, equalization and negation. The present study investigates the two English translations that were used in the translation of the Quranic rhetorical questions. In a nutshell, this is a comparative study that aims to discover if the grammatical shifts that had occurred in the two English translations would have an effect on the denial, assertion, testing, and equalization and negation modes of the Quranic rhetorical questions. For this purpose, we had adopted the register theory of Halliday and Hassan (1985) as well as the translation shifts of Catford (1965) in the comparison of the two English translations, namely the Koran Interpreted that was authored by Arberry (1955) and the Noble Quran: English translation of the meanings and commentary as transcribed by al-Hilali and Khan (1996). According to the analyses, the occurrence of grammatical shifts between the two translations had in fact affected the mode of the ST rhetorical questions, their rhetorical meanings and consequently, issues on mode sustenance. Therefore, it can be said that the register theory of Halliday and Hassan (1985) had been a beneficial tool used in the analysis of the translation process.
Calibration of phase liquid crystal spatial light modulators using high-order Taiji radial-shearing interferometry

The modulation characteristics of liquid crystal spatial light modulators (LC-SLMs) are related to the incident wavelength. In order to load the grayscale image correctly, LC-SLMs must be calibrated before using. We propose high-order Chinese Taiji radial-shearing interferometry to calibrate the phase modulation curve of an LC-SLM, which is different from the conventional calibration method. The new method is proposed to establish the mapping relationship between LC-SLM grayscale and phase modulation by rotating multiple Airy spots, which are derived from radial-shearing interference through a high-order Chinese Taiji lens. Compared with the traditional interference fringes, one of the greatest advantages is that the function between the grayscale and rotation angle of the two Airy spots is established more easily. Moreover, it eliminates the complex data processing steps and has a high enough signal-to-noise ratio. The experimental results show that high-order Chinese Taiji radial-shearing interferometry is a very effective calibration method for LC-SLM.
Smart Medicine Drawers Using IOS Application and Arduino Board

As people age and engage with life, many people drop the commitments in things which become like a routine that they go on with There are many patients forget to take their treatment on time, while some of them take the treatment but in irregular schedules, which reduces the effectiveness of the treatment. The method of reminding patients their schedules by the nurse for the elderly and sitter kids is not considered a successful solution in some cases, because it relies on another person, who may probably omit or forgets the exact time. With the proliferation of smartphones, it requires a technology solution to help patients in determining medication schedules accurately and make them more controlling on taking their medication.&#x0D; This paper proposed an application running on the iPhone connecting with smart drawers through the Arduino Board. The primary purpose of this application is to organize and remind patients to take their medicines on the accurate time. Drawers can be opened and closed through the application.
Investigating the relationship between entrepreneurial activity and economic growth: a case of Middle East

The current research is aimed at investigating the relationship between entrepreneurial activity and economic growth, more specifically, in the context of Middle East countries.Entrepreneurial activities are professed to improve the economic development of the country through employment generation, increase in the rate of gross domestic product (GDP), increased productivity levels, poverty alleviation, and so on.Based on the possibility of these positive aspects, economic growth is considered to be driven through increased entrepreneurial activities, in the context of developing countries.To investigate this relationship between research variables, quantitative data was collected using the World Bank database of 8 Middle Eastern countries for the period 2009-2016.Based on the correlation and regression results, the study finds a significant positive impact of entrepreneurial activity (measured as new businesses registered) on economic growth (GDP growth) in the Middle East.Therefore, it is important for states in the Middle East to focus on promoting entrepreneurial activity, which influences economic growth.
Effects of Filter Numbers and Sampling Frequencies on the Performance of MFCC and PLP based Bangla Isolated Word Recognition System

In this work, a 5 state left to right HMM-based Bangla Isolated word speech recognizer has been developed.To train and test the recognizer, a small corpus of various sampling frequencies have been developed in noisy as well as the noiseless environment.The number of filter banks is varied during the feature extraction phase for both MFCC and PLP.The effects of 2nd and 3rd differential coefficients have also been observed.Experimental results exhibit that MFCC based feature extraction technique is better in CLASSROOM environment on the contrary PLP based technique performs better not only in a noiseless environment but also in when AC or FAN noise is present.We have also noticed that higher sampling frequency and higher filter order don't always help to improve the performance.
Multi-criteria based Item Recommendation Methods

This paper comprehensively investigates and compares the performance of various multi-criteria based item recommendation methods. The development of the methods consists of three main phases: predicting rating per criterion; aggregating rating prediction of all criteria; and generating the top- item recommendations. The multi-criteria based item recommendation methods are varied and labelled based on what approach is implemented to predict the rating per criterion, i.e., Collaborative Filtering (CF), Content-based (CB), and Hybrid. For the experiments, we generate two variations of datasets to represent the normal and cold-start conditions on the multi-criteria item recommendation system. The empirical analysis suggests that Hybrid and CF are best implemented on the normal and cold-start item conditions, respectively. On the other hand, CB should never be (solely) implemented in a multi-criteria based item recommendation system on any conditions.
A Novel Hierarchical Topic Model for Horizontal Topic Expansion With Observed Label Information

Hierarchical topic models, such as hierarchical Latent Dirichlet Allocation (hLDA)and its variations, can organize topics into a hierarchy automatically. On the other hand, there are lots of documents associated with hierarchical label information. Incorporating these information into the topic modeling process can help users to obtain a more reasonable hierarchical structure. However, after analyzing various real-world datasets, we find that these hierarchical labels are ambiguous and conflicting in some levels, which introduces error and restriction to the latent topic and the hierarchical structure exploration process. We call it the horizontal topic expansion problem. To address this problem, in this paper, we propose a novel hierarchical topic model named horizontal and vertical hierarchical topic model (HV-HTM), which aims to incorporate the observed hierarchical label information into the topic generation process, while keeping the flexibility of horizontal and vertical expansion of the hierarchical structure in the modeling process. We conduct experiments on BBC news and Yahoo! Answers datasets and evaluate the effectiveness of HV-HTM on three evaluation metrics. The experimental results show that HV-HTM has a significant improvement on topic modeling, compared to the state-of-the-art models, and it can also obtain a more interpretable hierarchical structure.
3D Medical image compression using the quincunx wavelet coupled with SPIHT

&lt;p&gt;Medical imaging is a growing field due to the development of digital technologies that produce 3D and even 4D data. The counterpart to the resolution offered by these voluminal images resides in the amount of gigantic data, hence the need for compression. This article presents a new coding scheme dedicated to 3D medical images. The originality of our approach lies in the application of the Quinqunx wavelet transform coupled with the SPIHT encoder on a database of medical images. This approach achieves much higher compression rates, while maintaining a very acceptable visual quality.&lt;/p&gt;
Measurement of the wavefunction for a biphoton state with homodyne detection using least squares estimation

We report the measurement of wave function for a biphoton state with continuous variables (CV) by homodyne detection. The biphoton interference occurs on an optical parametric oscillator, which is seeded with a coherent state. A set of second-order correlation function values for the output field are recorded when the intensity coherent state is varied. The wave function, which includes both magnitude and argument for a complex parameter of the biphoton state, is reconstructed using the least square estimation method. This may introduce another approach to characterizing a biphoton state in the regime of CV.
Data-Driven Morphological Analysis for Uralic Languages

This paper describes an initial set of experiments in data-driven morphological analysis of Uralic languages.The paper differs from previous work in that our work covers both lemmatization and generating ambiguous analyses.While hand-crafted finite-state transducers represent the state of the art in morphological analysis for most Uralic languages, we believe that there is a place for datadriven approaches, especially with respect to making up for lack of completeness in the шlexicon.We present results for nine Uralic languages that show that, at least for basic nominal morphology for six out of the nine languages, data-driven methods can achieve an F-score of over 90%, providing results that approach those of finite-state techniques.We also compare our system to an earlier approach to Finnish data-driven morphological analysis (Silfverberg and Hulden, 2018) and show that our system outperforms this baseline.
Pre-Calibrated Visuo-Haptic Co-Location Improves Execution in Virtual Environments

A common approach to visio-haptic human-machine interfaces adopts a simpler design by shifting grounded force feedback away from the virtual scene. The alternative design favors intuitiveness by displaying visual and grounded force feedback at the same location (i.e. visuo-haptic co-location) but requires a sensibly more complex implementation and a tedious kinematic conception. The benefits of one approach over the other had not been fully investigated. Notably, (i) while users seem to better operate under co-located condition, it's not always the case. (ii) In the case of a desktop interface, the cost of a complex implementation to achieve co-location is challenged. We aim here to resolve (i) by conducting a user-centered experiment in which participants performed two generic tasks in co-located and delocated configurations, and comparing their performances. Additionally, we intend to fill the gap (ii) by testing a design without continuous head tracking, i.e., with static co-location. Participants' performances are assessed in terms of execution time, accuracy and force variation, while their subjective experiences are collected via a survey. Findings indicate that co-located configurations lead to shorter execution times, more accurate motions, better management of forces and are largely preferred by users, even when the co-location is pre-calibrated statically.
Automating Loading and Locking of New Generation Air-cargo Containers

In this paper, an outline of NTUA’s work in the framework of project INTELLICONT is presented. We describe the current state of the air-cargo handling procedures and how the autonomous system that is under development is going to simplify these and increase the overall efficiency. Important issues and challenges regarding the system's development are discussed and a preliminary design of the novel robotic platform is given. The main tasks of this platform include the autonomous motion and locking of containers with mass exceeding one tone, avoiding at the same time obstacles and surpassing terrain discontinuities. Information regarding the selected actuators and other key electrical components, such as motor drivers and sensors are provided also. The architecture of the embedded system and the specifications of the selected Central Control Unit are described, as well as the integration of the motor drivers, sensors and other peripherals with the Robot Operating System (ROS). Further details on the development of a high accuracy localization system, which is mandatory to lock the container safely to the corresponding positions are provided also. In addition, we give details regarding the locking mechanism with integrated monitoring functionalities, an important part of the system. Simulation experiments validate the selected position controller and key system specifications are highlighted based on results. Finally, recent prototype experiments conducted to verify the localization system are presented.
Synthetic topology in Homotopy Type Theory for probabilistic programming

Abstract The ALEA Coq library formalizes measure theory based on a variant of the Giry monad on the category of sets. This enables the interpretation of a probabilistic programming language with primitives for sampling from discrete distributions. However, continuous distributions have to be discretized because the corresponding measures cannot be defined on all subsets of their carriers. This paper proposes the use of synthetic topology to model continuous distributions for probabilistic computations in type theory. We study the initial σ -frame and the corresponding induced topology on arbitrary sets. Based on these intrinsic topologies, we define valuations and lower integrals on sets and prove versions of the Riesz and Fubini theorems. We then show how the Lebesgue valuation, and hence continuous distributions, can be constructed.
Restricted Boltzmann machine as an aggregation technique for binary descriptors

The article presents a novel approach to the challenge of real-time image classification with deep neural networks. The proposed architecture of the neural network exploits computationally efficient local binary descriptors and uses a restricted Boltzmann machine (RBM) as a feature space projection step so that the resulting depth of the deep neural network can be reduced. A contrastive divergence procedure is used both for RBM training and for feature projection. The resulting neural networks exhibit performance close to the current state-of-the-art but are characterized by a small model memory footprint (i.e., number of parameters) and extremely efficient computational complexity (i.e., response time). The low number of parameters makes these architectures applicable in embedded systems with limited memory or reduced computational capabilities.
Exploring the Capacity of Sequential-free Box Discretization Network for Omnidirectional Scene Text Detection.

Omnidirectional scene text detection has received increasing researchattention. Previous methods directly predict words or text lines ofquadrilateral shapes. However, most methods neglect the significance ofconsistent labeling, which is important to maintain a stable training process,especially when a large amount of data are included. For the first time, wesolve the problem in this paper by proposing a novel method termedSequential-free Box Discretization (SBD). The proposed SBD first discretizesthe quadrilateral box into several key edges, which contains all potentialhorizontal and vertical positions. In order to decode accurate vertexpositions, a simple yet effective matching procedure is proposed to reconstructthe quadrilateral bounding boxes. It departs from the learning ambiguity whichhas a significant influence during the learning process. Exhaustive ablationstudies have been conducted to quantitatively validate the effectiveness of ourproposed method. More importantly, built upon SBD, we provide a detailedanalysis of the impact of a collection of refinements, in the hope to inspireothers to build state-of-the-art networks. Combining both SBD and these usefulrefinements, we achieve state-of-the-art performance on various benchmarks,including ICDAR 2015, and MLT. Our method also wins the first place in textdetection task of the recent ICDAR2019 Robust Reading Challenge on ReadingChinese Text on Signboard, further demonstrating its powerful generalizationability. Code is available at https://tinyurl.com/sbdnet.
Examining MDD and MHD as Syntactic Complexity Measures with Intermediate Japanese Learner Corpus Data

The purpose of this study is to examine methods of measuring syntactic complexity by analyzing an original corpus of written Japanese data from native speakers and learners of Japanese.We compared two measures, mean dependency distance (MDD) and mean hierarchical distance (MHD), which have been examined using in English in previous studies.Our research question is to compare the two methods and evaluate them in order to develop an index for measuring Japanese learner's syntactic complexity.