Reviewerly/exHarmony huggingface dataset:
Influence of non-uniform airflow on optical deformation measurement in subsonic conditions

Non-uniform airflow over a wing causes distortion in real time optically recorded images, which can lead to the erroneous displacement or strain measurement. This situation is more severe when an aeronautical object is tested in high-speed airflow for wind-tunnel or in-flight deformation measurement. In this paper, the principle of the deformation measurement error caused by non-uniform airflow is presented firstly. Then, wind-tunnel experiments were conducted on a two-dimensional OA309 airfoil over the Mach number range from 0.3 to 0.7 and the angle of attack varied from -7.02° to 12.18°. After this, the effect of non-uniform airflow was validated using virtual displacement and strain obtained by image correlation technique. The results showed that the virtual strain varies linearly with the square of Mach number and reciprocal of the cosine of the angle of attack. The beam propagation of the camera through the upstream region rather than the downstream region behind the wing might cause the virtual strain to fall by approximately 50%. To mitigate the effects of non-uniform airflow on measurements, an optimization of the experimental setup is necessary to enable appropriate placement of the camera.
CN-DBpedia2: An Extraction and Verification Framework for Enriching Chinese Encyclopedia Knowledge Base

Knowledge base plays an important role in machine understanding and has been widely used in various applications, such as search engine, recommendation system and question answering. However, most knowledge bases are incomplete, which can cause many downstream applications to perform poorly because they cannot find the corresponding facts in the knowledge bases. In this paper, we propose an extraction and verification framework to enrich the knowledge bases. Specifically, based on the existing knowledge base, we first extract new facts from the description texts of entities. But not all newly-formed facts can be added directly to the knowledge base because the errors might be involved by the extraction. Then we propose a novel crowd-sourcing based verification step to verify the candidate facts. Finally, we apply this framework to the existing knowledge base CN-DBpedia and construct a new version of knowledge base CN-DBpedia2, which additionally contains the high confidence facts extracted from the description texts of entities.
IOT Based Weather Monitoring and Reporting System Project

The IOT based Weather Monitoring and Reporting System project is used to get Live reporting of weather conditions.It will Monitor temperature, humidity, moisture and rain level.Suppose Scientists/nature analysts want to monitor changes in a particular environment like volcano or a rain-forest.And these people are from different places in the world.In this case, SMS based weather monitoring system has some limitations.Since it sends SMS to few numbers.And time for sending SMS increases as the number of mobile numbers increases.In order to know the information about weather of a perticular place then they have to visit that particular sites.Where everyone can see it.
Application of Big Data in Libraries

In the dynamic environment, records and management systems are independently maintained by education institutions, libraries and books whilst data are not readily accessible in a centralized position.Big data is being created due to digitalization of libraries and this has imposed limitations to researchers, educationists, scholars and policy maker's efforts in improving the quality and efficiency.As a result, serving the users with books and articles that are in line with their interests is a great challenge.This paper addresses the issues of bringing various sources of information from different sources and institutions into one place in real time which can be time saving.The primary objective is to decrease the time that lapses between searching the reading material and the actual reading.Thus, a mechanism by which this bridge can be gapped is of paramount importance as access to information is costly especially to those with limited internet access.The research focuses on the development of a strategy that reduces time of finding reading material and this is in line with current recommendation system.Through this system there is great analyses of book descriptions to identify books that are in line with users' interests.Within time huge amount of data is collected from the researchers, educationists, scholars and policy makers and this big data will be used to train machines to automate the tasks to some extent.As a result, the valuable information gained from analyzing massive amounts of aggregated libraries data can provide key insights in improving information accessibility.This makes researchers, educationists, scholars and policy maker's reach out for research solutions easily and cheaply and also makes information more accessible to the underprivileged and marginalized.
Blind Certificate Authorities

We explore how to build a blind certificate authority (CA).Unlike conventional CAs, which learn the exact identity of those registering a public key, a blind CA can simultaneously validate an identity and provide a certificate binding a public key to it, without ever learning the identity.Blind CAs would therefore allow bootstrapping truly anonymous systems in which no party ever learns who participates.In this work we focus on constructing blind CAs that can bind an email address to a public key.To do so, we first introduce secure channel injection (SCI) protocols.These allow one party (in our setting, the blind CA) to insert a private message into another party's encrypted communications.We construct an efficient SCI protocol for communications delivered over TLS, and use it to realize anonymous proofs of account ownership for SMTP servers.Combined with a zero-knowledge certificate signing protocol, we build the first blind CA that allows Alice to obtain a X.509 certificate binding her email address alice@domain.comto a public key of her choosing without ever revealing "alice" to the CA.We show experimentally that our system works with standard email server implementations as well as Gmail.
A Cognitive Assistant that Uses Small Talk in Tutoring Conversation

This paper presents a cognitive conversational agent for use in teaching and learning processes named THOTH (Training by Highly Ontology-oriented Tutoring Host) that is capable of formulating and enunciating a well-defined set of small talk segments in a Q&amp;A (Question and Answer) interaction. The small talk structures are placed within the tutoring conversation by an agent designed as a cognitive assistant, in order to make communication smoother and less formal, presenting a more “concerned” behavior. Twelve small talk segments are suggested, included in conversation stages such as opening and closing the conversation, maintaining the rhythm and managing learning. We also explore some branches of the theoretical assumptions and concepts grounding THOTH, such as Dennett’s intentional stance, Bloom’s taxonomy and microlearning theory. In order to measure the perception and effects of using THOTH, we performed a quantitative and qualitative study with a group of students from a course in Applied Artificial Intelligence over one semester. The outcomes are classified into two main categories of analysis – interactivity and intentionality – informing the discussion on the potential uses of a small talk agent as a valuable resource in tutoring interaction, and also raising some points for improvement. In addition to this study, we also drew a small talk profile for this group of students revealing what structures and topics they use the most, as well as a partial performance analysis that allows identifying some effects on learning.
Similarity corpus on microbial transcriptional regulation

The ability to express the same meaning in different ways is a well-known property of natural language. This amazing property is the source of major difficulties in natural language processing. Given the constant increase in published literature, its curation and information extraction would strongly benefit from efficient automatic processes, for which corpora of sentences evaluated by experts are a valuable resource. Given our interest in applying such approaches to the benefit of curation of the biomedical literature, specifically that about gene regulation in microbial organisms, we decided to build a corpus with graded textual similarity evaluated by curators and that was designed specifically oriented to our purposes. Based on the predefined statistical power of future analyses, we defined features of the design, including sampling, selection criteria, balance, and size, among others. A non-fully crossed study design was applied. Each pair of sentences was evaluated by 3 annotators from a total of 7; the scale used in the semantic similarity assessment task within the Semantic Evaluation workshop (SEMEVAL) was adapted to our goals in four successive iterative sessions with clear improvements in the agreed guidelines and interrater reliability results. Alternatives for such a corpus evaluation have been widely discussed. To the best of our knowledge, this is the first similarity corpus—a dataset of pairs of sentences for which human experts rate the semantic similarity of each pair—in this domain of knowledge. We have initiated its incorporation in our research towards high-throughput curation strategies based on natural language processing.
Mobile Technology

The advancement of mobile communication technology has contributed to an increasingly interconnected world; however, these devices are not being used as effectively as they could be to improve global challenges. One challenging issue is the lack of preparation college graduates receive to positively contribute to the needs of an interdependent global society. Organizations such as UNESCO, NAFSA, and the OECD have recently declared the critical need for the rising generation to strengthen their global competence, the capacity to examine societal issues and work alongside those of various backgrounds to make a change. School instructors are crucial to preparing students to thrive in multicultural societies and address present day issues. With a staggeringly high rate of cellular device ownership among college students, mobile devices could be optimally positioned as a multi-functional tool ready to assist students in gaining these skills. This paper proposes that, while mobile devices may have contributed to a growing need for globally competent individuals, they can also be used to expand these capacities within university students. The PISA global competency framework developed by the OECD is used to propose how instructors can use mobile technology and research grounded practices to strengthen global competence in students.&#x0D;
Practical Person‐Specific Eye Rigging

Abstract We present a novel parametric eye rig for eye animation, including a new multi‐view imaging system that can reconstruct eye poses at submillimeter accuracy to which we fit our new rig. This allows us to accurately estimate person‐specific eyeball shape, rotation center, interocular distance, visual axis, and other rig parameters resulting in an animation‐ready eye rig. We demonstrate the importance of several aspects of eye modeling that are often overlooked, for example that the visual axis is not identical to the optical axis, that it is important to model rotation about the optical axis, and that the rotation center of the eye should be measured accurately for each person. Since accurate rig fitting requires hand annotation of multi‐view imagery for several eye gazes, we additionally propose a more user‐friendly “lightweight” fitting approach, which leverages an average rig created from several pre‐captured accurate rigs. Our lightweight rig fitting method allows for the estimation of eyeball shape and eyeball position given only a single pose with a known look‐at point (e.g. looking into a camera) and few manual annotations.
Wetin dey with these comments? Modeling Sociolinguistic Factors Affecting Code-switching Behavior in Nigerian Online Discussions

Multilingual individuals code switch between languages as a part of a complex communication process.However, most computational studies have examined only one or a handful of contextual factors predictive of switching.Here, we examine Naijá-English code switching in a rich contextual environment to understand the social and topical factors eliciting a switch.We introduce a new corpus of 330K articles and accompanying 389K comments labeled for code switching behavior.In modeling whether a comment will switch, we show that topic-driven variation, tribal affiliation, emotional valence, and audience design all play complementary roles in behavior.
Mapping Bug Reports to Relevant Source Code Files Based on the Vector Space Model and Word Embedding

Although software bug localization in software maintenance and evolution is cumbersome and time-consuming, it is also very important, especially for large-scale software projects.To lighten the workload of developers, researchers have developed various information retrieval (IR)-based bug localization models for automated software support.In this paper, we propose a new method that reduces the time required for bug localization.First, the surface lexical similarity between a bug report and source code file is calculated based on the vector space model.Second, to address the lexical gap between the programming language and natural language, the word vector is used to calculate the semantic similarity between the bug report and source code file.Then, we use surface lexical and semantic similarity to calculate the total similarity for detecting buggy source code files.Our experimental word vectors are derived from Skip-gram and GloVe model training.We select an optimal 100 dimensional word vector for bug localization by evaluating it on four open source software examples.Finally, our experimental results show that our method outperforms classical IR-based methods in locating relevant source code files based on several indicators.
Amrita School of Engineering - CSE at SemEval-2019 Task 6: Manipulating Attention with Temporal Convolutional Neural Network for Offense Identification and Classification

With the proliferation and ubiquity of smart gadgets and smart devices, across the world, data generated by them has been growing at exponential rates, in particular social media platforms like Facebook, Twitter and Instagram have been generating voluminous data on a daily basis.According to Twitter's usage statistics, about 500 million tweets are generated each day.While the tweets reflect the users' opinions on several events across the world, there are tweets which are offensive in nature that need to be tagged under the hateful conduct policy of Twitter.Offensive tweets have to be identified, captured and processed further, for a variety of reasons, which include i) identifying offensive tweets in order to prevent violent/abusive behaviour in Twitter (or any social media for that matter), ii) creating and maintaining a history of offensive tweets for individual users (would be helpful in creating meta-data for user profile), iii) inferring the sentiment of the users on particular event/issue/topic .We (CodaLab Team/User Name: murali sr) have employed neural network models which manipulate attention with Temporal Convolutional Neural Network for the three shared subtasks i) ATT-TCN (ATTention based Temporal Convolutional Neural Network) employed for shared sub-task A that yielded a best macro-F1 score of 0.46, ii) SAE-ATT-TCN(Self Attentive Embedding-ATTention based Temporal Convolutional Neural Network) employed for shared sub-task B and sub-task C that yielded best macro-F1 score of 0.61 and 0.51 respectively.Among the two variants ATT-TCN and SAE-ATT-TCN, the latter performed better.
Learning to Rank Reviewers for Pull Requests

In pull-based software development, anyone who wants to contribute to a project can request integration of the code changes to the public repository by sending a pull request to the development team. Upon receiving a pull request, a team member will review the changes and decide on merging it or not in the repository. Finding the appropriate reviewer for a pull request is a crucial step. To support reviewer recommendation, this paper introduces an adaptive ranking model to rank all the reviewer candidates for a pull request. The ranking model leverages 14 features to measure the relationships between a pull request and the reviewer candidates. The weight parameters of the ranking model are trained automatically based on previously resolved pull requests by using a learning-to-rank technique. The experimental evaluations on 12 open-source projects show that the proposed approach outperforms the baseline and a state-of-the-art approach. It can recommend the suitable reviewers within top-1 recommendation for over 80% of the pull requests in the opencv and jekyll projects. The feature selection experiments show that the most important feature is the feature that counts the number of previous pull requests sent by the requester and reviewed by a developer. The feature that measures the file path similarity between files changed in the pull request and files previously modified by a developer is another important feature.
A Balanced Perspective on Prediction and Inference for Data Science in Industry

The strategic role of data science teams in industry is fundamentally to help businesses to make smarter decisions.This includes decisions on minuscule scales, such as what fraction of a cent to bid on an ad placement displayed in a web browser, whose importance is only manifest when scaled by orders of magnitude through machine automation.But it also extends to singular, monumental decisions made by businesses, such as how to position a new entrant within a competitive market.In both regimes, the potential impact of data science is only realized when both humans and machine actors are learning from data and when data scientists communicate effectively to decision makers throughout the business.I examine this dynamic through the instructive lens of the duality between inference and prediction.I define these concepts, which have varied use across many fields, in practical terms for the industrial data scientist.Through a series of descriptions, illustrations, contrasting concepts, and examples from the entertainment industry (box office prediction and advertising attribution), I offer perspectives on how the concepts of inference and prediction manifest in the business setting.From a balanced perspective, prediction and inference are integral components of the process by which models are compared to data.However, through a textual analysis of research abstracts from the literature, I demonstrate that an imbalanced, prediction-oriented perspective prevails in industry and has likewise become increasingly dominant among quantitative academic disciplines.I argue that, despite these trends, data scientists in industry must not overlook the valuable, generalizable insights that can be extracted through statistical inference.I conclude by exploring the implications of this strategic choice for how data science teams are integrated in businesses.
Exploration of Noise Strategies in Semi-supervised Named Entity Classification

Noise is inherent in real world datasets and modeling noise is critical during training as it is effective in regularization.Recently, novel semi-supervised deep learning techniques have demonstrated tremendous potential when learning with very limited labeled training data in image processing tasks.A critical aspect of these semi-supervised learning techniques is augmenting the input or the network with noise to be able to learn robust models.While modeling noise is relatively straightforward in continuous domains such as image classification, it is not immediately apparent how noise can be modeled in discrete domains such as language.Our work aims to address this gap by exploring different noise strategies for the semi-supervised named entity classification task, including statistical methods such as adding Gaussian noise to input embeddings, and linguistically-inspired ones such as dropping words and replacing words with their synonyms.We compare their performance on two benchmark datasets (OntoNotes and CoNLL) for named entity classification.Our results indicate that noise strategies that are linguistically informed perform at least as well as statistical approaches, while being simpler and requiring minimal tuning.
Petrologic and mineral physics database for use with the U.S. Geological Survey National Crustal Model

We present a petrologic and mineral physics database as part of the U.S. Geological Survey National Crustal Model (NCM).Each of 209 geologic units, 134 of which are currently part of the geologic framework within the NCM, was assigned a mineralogical composition according to generalized classifications with some refinement for specific geologic formations.This report is concerned with the petrology and mineral physics of each geologic unit within the NCM, which control the physical behavior of the solid mineral matrix within the rock.This mineral physics database builds on the work of Abers and Hacker to include 13 minerals specific to continental rock types.We explored the effect of this database on zero-porosity anharmonic P-and S-wave rock velocities and density relative to a well-used empirical study of relations between wavespeeds and density by Brocher.We found that empirical relations between P-wave velocity and S-wave velocity or density do well on average but can differ from mineral physics calculations by up to 15 percent in S-wave velocity and almost 40 percent in density.This is consistent with Brocher's study where he obtained similar results for in situ measurements and laboratory rock specimens.Additionally, the substantial presence of quartz in many rocks plays a major role in crustal seismic velocities and density due to quartz's α-β phase transition, which can interfere with these empirical relationships.With increasing depth, quartz P-wave velocity can suddenly jump by 15 percent accompanied by little change in S-wave velocity and a modest decrease in density.Empirical relations based on observed P-wave velocity where P-wave velocity is positively correlated with S-wave velocity and density would then significantly overestimate both S-wave velocity and density.
A Novel Approach for Web Service Recommendation Based on Advanced Trust Relationships

Service recommendation is one of the important means of service selection. Aiming at the problems of ignoring the influence of typical data sources such as service information and interaction logs on the similarity calculation of user preferences and insufficient consideration of dynamic trust relationship in traditional trust-based Web service recommendation methods, a novel approach for Web service recommendation based on advanced trust relationships is presented. After considering the influence of indirect trust paths, the improved calculation about indirect trust degree is proposed. By quantifying the popularity of service, the method of calculating user preference similarity is investigated. Furthermore, the dynamic adjustment mechanism of trust is designed by differentiating the effect of each service recommendation. Integrating these efforts, a service recommendation mechanism is introduced, in which a new service recommendation algorithm is described. Experimental results show that, compared with existing methods, the proposed approach not only has higher accuracy of service recommendation, but also can resist attacks from malicious users more effectively.