Reviewerly/exHarmony huggingface dataset:
A unified framework for simultaneous assessment of accuracy, between-, and within-reader variability of image segmentations

Medical imaging is utilized in a wide range of clinical applications. To enable a detailed quantitative analysis, medical images must often be segmented to label (delineate) structures of interest; for example, a tumor. Frequently, manual segmentation is utilized in clinical practice (e.g., in radiation oncology) to define such structures of interest. However, it can be quite time consuming and subject to substantial between-, and within-reader variability. A more reproducible, less variable, and more time efficient segmentation approach is likely to improve medical treatment. This potential has spurred the development of segmentation algorithms which harness computational power. Segmentation algorithms’ widespread use is limited due to difficulty in quantifying their performance relative to manual segmentation, which itself is subject to variation. This paper presents a statistical model which simultaneously estimates segmentation method accuracy, and between- and within-reader variability. The model is simultaneously fit for multiple segmentation methods within a unified Bayesian framework. The Bayesian model is compared to other methods used in literature via a simulation study, and application to head and neck cancer PET/CT data. The modeling framework is flexible and can be employed in numerous comparison applications. Several alternate applications are discussed in the paper.
Trip Recommendation Algorithm Based on Attraction Tags

Many route recommendation algorithms have been presented recently, it' s necessary to consider user`s preference in the recommendation. We propose a travel recommendation algorithm based on visitors preferences. It analyzes the user' s preference for different types of attractions and forms a user-preference matrix. Then It calculate an initial clustering center based on the interest distributed by k-means algorithm, and establishes a neighboring set for the target user to score the historical users' route value for target user by target user`s reference on scenic spot type distribution. The method finds the historical user route with the largest value for the target user, thereby generating the trip recommendation. The experimental results show that the algorithm can quickly calculate the smaller neighboring users and obtain the recommended results. It not only has faster recommendation efficiency, but also has better recommendation accuracy. It provides a good service on personalized route recommendation.
Reengineering ILE: towards a model for adapting the learning path

The main challenge faced by ILE is its ability to offer tools adapted to each learner allowing him to learn effectively by taking into account the different constraints. In this article, we describe an approach to adapting the learner's path adapted to his profile while respecting the time he has for learning and making sure to maximize his mark during the final exam. To achieve this objective, a double-layer adapted learning path graph is generated, the educational resources associated with the path identified, the selection of the most suitable resources for each step carried out and the overall time of the estimated path and the forecast score of the final test calculated. Thereafter, the graph of the generated path is re-evaluated at each learning step taken to best adapt it to the learner.
E-Textile Technology in Electroceutical Devices for Tip Stimulation Therapy

Electrostimulation of the human body is one of the typical rehabilitation treatments. It is used to improve the patient's sensory and motor functions. In the light of developing technologies, it is important to combine electrodes for electrostimulation with textile products. The "tip-stim" glove is such an example. The authors of the article present the results of work on supplementing this product in the form of electroconducting electrodes, made with the physical vacuum deposition method, attached to an existing glove. This supplement is intended for people with degenerations in the nerve systems.
Selective acquisition of Malayalam

n this paper, I will present a curated account of my linguistic reflections on my journeyinto the Malayalam language community of my husband. My reflections can be classifiedroughly into three parts—morpho-syntax, phonology, and literary engagement with thesecond language.
A Genetic Algorithm-Oriented Model of Agent Persuasion for Multi-agent System Negotiation

In recent years, reaching agreements is an important problem in multi-agent systems (MAS), which require different types of dialogues between agents. Persuasion is one of them and it is traditionally based on first order logic. However, this type of technique is not adequate for solving complex problems. This situation requires developing new models for optimizing an agent persuasion process. This work presents a persuasion model for MAS based in genetic algorithms (GA) for reaching agreements in problem solving. The objective of this work is to optimize the negotiation between agents that solve complex problems. First, it was designed the persuasion model. Then, it was implemented and evaluated in experimental scenarios and it was compared its results against traditional models. The experimental results showed that a GA-oriented persuasion model optimizes the negotiation in MAS by improving execution time, which also eventually will optimize the processes carried out by MAS.
Research Progress and Development Trend Evolution of Recommendation System

Based on CNKI and Web of Science, this paper uses the methods of bibliometric, social network analysis, Co-word Analysis and data visualization to analyze the trends in the volume of publications, the organization, high-frequency keywords and their relevance, and explore the research hotspots and development trends in this field. Through the analysis of the development process of foreign research, we can find the change in international influence of domestic research.
PlayMaker

The public's ever-growing interest in health has led the well-being industry to explosive growth over the years. This propelled activity trackers as one of the trendiest items among the current day wearable devices. Seeking new opportunities for effective data utilization, we present a participatory design method that explores the fusion of activity data with entertainment application. In this method we spur participants to design by mix-and-matching activity tracker data attributes to existing entertainment application features to produce new concepts.
Possible approaches to deciphering Russian ancient Znamenny chant

In this article we provide approaches to translating into modern music notation Znamenny Russian Church chants, written in neumatic (“znamenny” or “hook”) notation. Neumes are sequences of notes of varying lengths, that correspond to singing one syllable of old Slavonic verse. Starting in 17th century, neumes were supplemented with pomjeta facilitating mapping into modern music notation. Manuscripts from earlier periods have no pomjeta and are thus not readable today. This paper introduces computer-based approaches to the reconstruction of znamenny notation without pomjeta into modern music notation. These approaches are trained on dvojyeznamenniks but can be applied to manuscripts without pomjeta.
GENDER DIFFERENCES IN COMPUTER-MEDIATED COMMUNICATION: A CASE STUDY ON MALAYSIAN MILLENNIALS

Purpose of the study: This study aims to analyse which gender language features are used by both genders in computer-mediated communication (CMC) and also investigates if online gender communication reflects normal face-to-face communication.&#x0D; Methodology: A qualitative research design was employed in addressing the objectives of the study. A total of 260 Facebook comments were collected and analysed using more than one methodological approach in accordance with CMDA (Computer-Mediated Discourse Analysis).&#x0D; Main Findings: The findings show that male language features occurred more frequently compared to female language features. However, commenters of both genders do not always follow their respective gender language features. This shows that commenters from both genders do not necessarily follow their own gender stereotypes according to the different contextual situations they face.&#x0D; Applications of this study: The findings from this study can contribute to gender language studies and also benefit those who are interested in millennial research and the usage of CMC in recent times.&#x0D; Novelty/Originality of this study: In this research, common framework in analysing gender differences is improved to fit the needs of current CMC trends.
Application of selected optimization algorithms for determining the shortest path for mobile robot

This paper presents the developed of the test bench to determine the optimal path for a autonomous mobile robot. The results presented in this paper relate to the three optimization algorithms (NNI, FNI, RMI) belonging to the heuristic algorithms group.
Three methods of finding remainder on the operation of modular exponentiation by using calculator

The primary purpose of this paper is that the contents of the paper were to become a learning aid for the learners. Learning aids enhance one's learning abilities and help to increase one's learning potential. They may include books, diagram, computer, recordings, notes, strategies, or any other appropriate items. In this paper we would review the types of modulo operations and represent the knowledge which we have been known with the easiest ways. The modulo operation is finding of the remainder when dividing. The modulus operator abbreviated “mod” or “%” in many programming languages is useful in a variety of circumstances. It is commonly used to take a randomly generated number and reduce that number to a random number on a smaller range, and it can also quickly tell us if one number is a factor of another. If we wanted to know if a number was odd or even, we could use modulus to quickly tell us by asking for the remainder of the number when divided by 2. Modular exponentiation is a type of exponentiation performed over a modulus. The operation of modular exponentiation calculates the remainder c when an integer b rose to the eth power, bᵉ, is divided by a positive integer m such as c = be mod m [1]. It is useful in computer science, especially in the field of public-key cryptography. The modulo operation is important for cryptography because it can act as a one-way function that the output obscures the input. In this paper we discuss the three methods of finding the remainder in the modular exponentiation based on the formula by using a calculator.
Based on the OSGi framework LIS isomers Big data collection practice

The collection and utilization of big data information is changing the concept and method of collecting data. For the new situation map information acquisition, the challenge than the greatest collection of vast amounts of heterogeneous information from the network, extraction, analysis and other operations. Heterogeneous network information such as blogs, news and user behaviors are gradually becoming an important part of the attention and collection in the field of graphics. At the same time, the collection of heterogeneous big data information is a difficult problem for the graphic workers. In this paper, through the heterogeneous big data collection mechanism of OSGi framework, the application of graph-related big data is practiced, and it is hoped that it can provide a heterogeneous graph information collection idea for peers.
Fast High Dynamic Range MRI by Contrast Enhancement Networks

HDR-MRI is a sophisticated non-linear image fusion technique for MRI which enhances image quality by fusing multiple contrast types into a single composite image. It offers improved outcomes in automatic segmentation and potentially in diagnostic power, but the existing technique is slow and requires accurate image co-registration in order to function reliably. In this work, a lightweight fully convolutional neural network architecture is developed with the goal of approximating HDR-MRI images in real-time. The resulting Contrast Enhancement Network (CEN) is capable of performing near-perfect ( SSIM = 0.98) 2D approximations of HDR-MRI in 10ms and full 3D approximations in 1s, running two orders of magnitude faster than the original implementation. It is also able to perform the approximation ( SSIM = 0.93) with only two of the three contrasts required to generate the original HDR-MRI image, while requiring no image co-registration.
Chapter 2. “Gracias pana”

Compliments and compliment responses are a frequent topic of study in face-to-face interactions (see Chen 2010 for an overview), and are gaining traction in online interactions. This study builds on works on compliment responses online; particularly work by Placencia, Lower, and Powell (2016), by examining compliment responses given by Ecuadorian males on Facebook (FB). The sole focus here is male behavior: how males respond to compliments from other males. Largely ignored in compliment research as a whole, this study demonstrates that Ecuadorian males frequently engage in compliment response behavior on FB, and, appear to be guided by face needs and constraints when responding. Despite the ubiquitous nature of emoticons in online communications, their use in compliment responses by Ecuadorian males on FB was totally absent.
JUSTIFYING BLASPHEMY LAWS: FREEDOM OF EXPRESSION, PUBLIC MORALS, AND INTERNATIONAL HUMAN RIGHTS LAW

Abstract In its General Comment No. 34 dealing with freedom of expression, the United Nations Human Rights Committee (UNHRC) rejected the idea that a blasphemy law could ever be human-rights compliant, unless its function was to prevent incitement to religious or racial hatred. This is a widely shared view that is consistently endorsed when any international blasphemy controversy (such as that involving the Danish Cartoons in 2005) arises. This article assesses the legitimacy of this view. The International Covenant on Civil and Political Rights (ICCPR) permits freedom of expression to be limited inter alia in the name of public morality, provided that the law in question is also necessary to achieve this end. This article argues that because a blasphemy law can be a response to a public moral vision; therefore a blasphemy law can serve a legitimate purpose insofar as human rights law is concerned. It is further submitted that whereas some blasphemy laws are unacceptably draconian, it is not inherently impossible for such a law to represent a proportionate response to a public morals concern. Thus, the conclusion from the UNHRC is not warranted by the text of the ICCPR. Moreover, there is a risk that, in reaching this conclusion the committee is evincing an exclusively secularist worldview in its interpretation of the ICCPR that undermines its claim to universality.
New Solutions for the Support Region Calculation of Logarithmic Quantizers for the Laplacian Source

The main aim of the paper is to provide effective and accurate solutions for the calculation of the support region of the logarithmic companding quantizers. A new solution for the starting point of iterative methods will be proposed, that provide very accurate value of the support region only after one iteration of the iterative method. Based on this new starting point, an accurate closed-form approximate expression for the calculation of the support region will be derived, as a main contribution of the paper. Due to their numerous advantages (robustness, adjustability to the statistical distribution of the input signal), logarithmic quantizers are considered to be used in many topical applications, such as in receivers of 5G wireless systems or in neural networks for quantization of weights and activations.
Braided Networks for Scan-Aware MRI Brain Tissue Segmentation

Recent advances in supervised deep learning, mainly using convolutional neural networks, enabled the fast acquisition of high-quality brain tissue segmentation from structural magnetic resonance brain images (MRI). However, the robustness of such deep learning models is limited by the existing training datasets acquired with a homogeneous MRI acquisition protocol. Moreover, current models fail to utilize commonly available relevant non-imaging information (i.e., meta-data). In this paper, the notion of a braided block is introduced as a generalization of convolutional or fully connected layers for learning from paired data (meta-data, images). For robust MRI tissue segmentation, a braided 3D U-Net architecture is implemented as a combination of such braided blocks with scanner information, MRI sequence parameters, geometrical information, and task-specific prior information used as meta-data. When applied to a large (> 16,000 scans) and highly heterogeneous (wide range of MRI protocols) dataset, our method generates highly accurate segmentation results (Dice scores > 0.9) within seconds****The concepts and information presented in this paper are based on research results that are not commercially available..
Item Concept Network: Towards Concept-Based Item Representation Learning

Item concept modeling is commonly achieved by leveraging textual information. However, many existing models do not leverage the inferential property of concepts to capture word meanings, which therefore ignores the relatedness between correlated concepts, a phenomenon which we term conceptual “correlation sparsity.” In this paper, we distinguish between word modeling and concept modeling and propose an item concept modeling framework centering around the item concept network (ICN). ICN models and further enriches item concepts by leveraging the inferential property of concepts and thus addresses the correlation sparsity issue. Specifically, there are two stages in the proposed framework: ICN construction and embedding learning. In the first stage, we propose a generalized network construction method to build ICN, a structured network which infers expanded concepts for items via matrix operations. The second stage leverages neighborhood proximity to learn item and concept embeddings. With the proposed ICN, the resulting embedding facilitates both homogeneous and heterogeneous tasks, such as item-to-item and concept-to-item retrieval, and delivers related results which are more diverse than traditional keyword-matching-based approaches. As our experiments on two real-world datasets show, the framework encodes useful conceptual information and thus outperforms traditional methods in various item classification and retrieval tasks.
Supervised Learning for Segmenting Open Boundaries in Medical Images

Image segmentation is one of the most important building blocks in many medical imaging applications. Often, it is the first step in any artificial intelligence (AI) assisted diagnosis system. Most convolutional neural network image-to-image segmentation algorithms compute a binary mask segmentation and extract the object boundary as the edge of the binary mask always leading to a closed boundary. In this paper, we present a novel image-to-image segmentation algorithm that learns open boundaries. The object delineation is directly learnt by training a U-Net like network on a distance map representation of the boundary without any constraints on its shape or topology. We validate the proposed approach on the segmentation of the left atrium in intra-cardiac echocardiography images. For this application, it is important to produce segmentations only where a strong evidence of the anatomy exists. To our knowledge, this is the first work to train a U-Net on a distance map ground truth representation for open boundary segmentation.
A Solar-Powered Irrigation System for Conserving Water and Energy Consumption in Egypt

Africa, with its highly growing population, is in need for environmentally-friendly solutions to meet its food and energy needs.Solar-operated irrigation systems can be used in Egypt where there is no source of electricity and limited access to water.The aim of this paper is to present energy and cost analysis for a simulation of a solar-powered pumping system in Egypt using HOMER software.Experimental work is also carried out, in the GUC campus, for a solar-powered irrigation system which conserves water and energy through the use of soil moisture sensor to regulate the pump flow.The excess energy from the PV is used to power electric loads.Results of the simulation show that the maximum energy generation and consumption occur during the summer season.The annual net energy purchased from the grid is 408 kWh after applying the net metering system.The inverter has the highest initial capital and replacement costs.For the experiments, the irradiance values range from 270 W/m2 to 1450 W/m2 and the panel's temperature from 15 °C to 60 °C.The power output from the PV panel ranges from 3W to 30W depending on the solar irradiance and ambient temperature.The proposed water pumping system proved to be feasible and successful in conserving water and energy in Egypt.
The Analysis of User Tags Recommended Model Based on Selection Association Rule

Social networking allows users to personalize the vocabulary of network resources. Therefore, social networks have become a representative scenario of social tagging systems. In micro-blogging networks, users can personalize their own labels to show the individual social attributes. This paper analyzes the micro-blog user data, summarizes the characteristics of the micro-blog user tags, and for the lack of personalized labels for existing models used in microblogging scene, we propose an improved association rule model based on the recommendation of the selection. On this basis, the microblogging users selected tags is analyzed, the correlation of user labels is calculated, and finally associated personalized labels are recommended to users. The model can be improved determined while ensuring the diversity of recommendations by comparing the experimental results.
Graphical and Collaborative Annotation Support for Semantic Web Services

Web services have become increasingly important for software development. In order to facilitate the search, composition, and reuse of web services, their descriptions can be semantically annotated using definitions provided by an ontology, thus creating the so-called semantic web services. A semantic web service is developed according to different approaches and standards recommended by W3C, such as OWL-S, SAWSDL, and WSMO-Lite. A limited number of tools are available to support the development of semantic annotations using SAWSDL, such as Radiant, Iridescent, and EasyWSDL. However, these tools support the annotation process at a low abstraction level, therefore requiring from users an extensive technical knowledge on XML/WSDL, among other technologies. The semantic annotation task could be facilitated if the semantic annotation process were carried at a higher abstraction level using graphical notations. Additionally, the semantic annotation could also benefit from a collaborative approach. Different people from different backgrounds could individually contribute with the semantic web services creation, regardless of their geographic locations. In that sense, this paper presents a visual notation to represent the main elements of a WSDL 2.0 specification focused on the semantic annotation using SAWSDL. The paper also describes a graphical collaborative semantic annotation support tool.