Reviewerly/exHarmony huggingface dataset:
Automating the Detection of Poetic Features: The Limerick as Model Organism

In this paper we take up the problem of "limerick detection" and describe a system to identify five-line poems as limericks or not.This turns out to be a surprisingly difficult challenge with many subtleties.More precisely, we produce an algorithm which focuses on the structural aspects of the limerick -rhyme scheme and rhythm (i.e., stress patterns) -and when tested on a a culled data set of 98,454 publicly available limericks, our "limerick filter" accepts 67% as limericks.The primary failure of our filter is on the detection of "nonstandard" rhymes, which we highlight as an outstanding challenge in computational poetics.Our accent detection algorithm proves to be very robust.Our main contributions are (1) a novel rhyme detection algorithm that works on English words including rare proper nouns and made-up words (and thus, words not in the widely used CMUDict database); (2) a novel rhythm-identifying heuristic that is robust to language noise at moderate levels and comparable in accuracy to state-of-the-art scansion algorithms.As a third significant contribution (3) we make publicly available a large corpus of limericks that includes tags of "limerick" or "not-limerick" as determined by our identification software, thereby providing a benchmark for the community.The poetic tasks that we have identified as challenges for machines suggest that the limerick is a useful "model organism" for the study of machine capabilities in poetry and more broadly literature and lan-guage.We include a list of open challenges as well.Generally, we anticipate that this work will provide useful material and benchmarks for future explorations in the field.
Efficient Protocol for Gender Identification using Machine Learning

Gender identification of names is an important task to identify human beings. Gender identification uses many attributes as voice-based gender prediction, face-based, and many other attributes. The Natural Language Processing (NLP) is a technique which can identify easily and accurately. These identification problems can be classified through various techniques. The binary classification of gender is considered. The proposed model consists of Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM) concepts for identifying the gender. The gender names an important key for identifying the gender based on the last character(s) of consonant/vowel features. The model supports the unigram, bigram, trigram, four-gram, and vowels postfix technique to identify the gender. The simulation result shows a better performance under unigram and bigram model compare to others.
Hapax remains: Regularity of low-frequency words in authorial texts

Abstract This article highlights the usual overlook in the literature of regular occurrences of low-frequency words (hapax legomena) in specific authors’ texts. This overlook arises from a linguistic assumption of non-systematic and context-dependent low-frequency word occurrences in extensive texts, and from the tendency of SVM methods to mark low-frequency words as irrelevant compared to the more frequent lexicon (e.g. Boukhaled, M. A. and Ganascia, J.-G. (2015). Using function words for authorship attribution: bag-of-words vs. sequential rules. In The 11th International Workshop on Natural Language Processing and Cognitive Science, October 2014, Venice, Italy. de Gruyter, Natural Language Processing and Cognitive Science Proceedings 2014, pp. 115–122.). Many approaches to authorship attribution are based on the n most frequent ‘function words’, which (1) are grammatically essential, frequent, and therefore included in each text; (2) are not affected by the topic of the text; and (3) reflect the unintentional linguistic activity of the author (Binongo, J. N. G. (2003). Who wrote the 15th book of Oz? An application of multivariate analysis to authorship attribution. Chance, 16(2): 9–17). Hapax legomena meet these conditions as well, except frequency (Baayen, H., van Halteren, H., and Tweedie, F. (1996). Outside the cave of shadows: using syntactic annotation to enhance authorship attribution. Literary and Linguistic Computing, 11(3): 121–32). We test the hypothesis that hapax legomena may work for purposes of authorship attribution based on selecting only hapaxes from whole texts (or randomly selected tokens of hapaxes) while using a specific pre-processed input (eigendecomposition of a cosine distance matrix) to the SVM classifier. This method evaluated the attribution of texts from fourteen Czech authors (yielding ninety-one pairs in total) and Evert, S., Proisl, T., Jannidis, F. et al. (2017). Understanding and explaining Delta measures for authorship attribution. Digital Scholarship in the Humanities, 32(2): 4–16 data set, and proved itself a suitable tool for identifying authors of previously unknown texts. Our method identifies a sparse network of regular occurrences of low-frequency words in different authors’ texts.
Datasets: A Community Library for Natural Language Processing

The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available at https://github.com/huggingface/datasets.
Towards Green Service Composition Approach in the Cloud

With the popularization of the cloud, an increasing number of users and developers need to combine multiple different services to satisfy their requirements in the cloud. Consequently, the question of how to combine these services that are published by different service providers in the cloud has become an active focus of research in service-oriented cloud systems. Service composition is the core technology of service-oriented cloud systems.
The Student-centered STEM Learning Model Based on Artificial Intelligence Project: A Case Study on Intelligent Car

Artificial intelligence has become a crucial technology for the development of the times. In the practice of emerging engineering education about artificial intelligence, how to enhance students' understanding of artificial intelligence technology, strengthen students' enthusiasm for learning, and improve their research and application innovation ability is a key issue. This study constructs a student-centered STEM learning model that emphasizes innovative applications, so as to promote project-based learning effects for students, reduce learning burnout as well as improve their research and application capabilities. We take the design improvement and experimental result analysis of the intelligent car target tracking system as an example and verifies the effectiveness of the learning model through multi-dimensional evaluation and comparative experiments. Under the same experimental equipment and necessary project functions, we divide students into STEM learning groups and traditional project learning groups to carry out learning activities. This study focused on the learning process of the STEM learning group. The empirical analysis shows that the project completion time and quantitative evaluation of the effects are better than the general learning model.
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing

While neural networks are ubiquitous in state-of-the-art semantic parsers, it has been shown that most standard models suffer from dramatic performance losses when faced with compositionally out-of-distribution (OOD) data.Recently several methods have been proposed to improve compositional generalization in semantic parsing.In this work we instead focus on the problem of detecting compositionally OOD examples with neural semantic parsers, which, to the best of our knowledge, has not been investigated before.We investigate several strong yet simple methods for OOD detection based on predictive uncertainty.The experimental results demonstrate that these techniques perform well on the standard SCAN and CFQ datasets.Moreover, we show that OOD detection can be further improved by using a heterogeneous ensemble.
Accessible Tangible User Interfaces in eXtended Reality Experiences for Cultural Heritage

This paper describes three Tangible User Interface (TUI) systems for smartphone-based eXtended Reality (XR) experiences: the Tangible VR Book, the Spectare device, and the Virtual Guia Fortress Playset. These examples, and the interactions they implement, demonstrate how accessible materials and technologies can be used in favor of Cultural Heritage (CH). They show that cheap, easy to find, easy to program materials can be used to create interesting and flexible TUI solutions. We argue that this approach can foster creativity around, and raise interest in CH.
“I’ll be there for you”: The One with Understanding Indirect Answers

Indirect answers are replies to polar questions without the direct use of word cues such as 'yes' and 'no'.Humans are very good at understanding indirect answers, such as 'I gotta go home sometime', when asked 'You wanna crash on the couch?'.Understanding indirect answers is a challenging problem for dialogue systems.In this paper, we introduce a new English corpus to study the problem of understanding indirect answers.Instead of crowdsourcing both polar questions and answers, we collect questions and indirect answers from transcripts of a prominent TV series and manually annotate them for answer type.The resulting dataset contains 5,930 question-answer pairs.We release both aggregated and raw human annotations.We present a set of experiments in which we evaluate Convolutional Neural Networks (CNNs) for this task, including a cross-dataset evaluation and experiments with learning from disagreements in annotation.Our results show that the task of interpreting indirect answers remains challenging, yet we obtain encouraging improvements when explicitly modeling human disagreement.
Named Entity Recognition of liver cancer data based on Damped Pointer Network and Dynamic Fusion

The electronic medical record (EMR) of liver cancer covers a large amount of key information, including the pathological stage of the tumor, the location of the tumor, and the size of the tumor, which helps doctors quickly understand the patient’s condition and make diagnosis. However, conceptually complex and multi-terminological EMR makes doctors hard to retrieve useful information, which not only leads to low work efficiency, but also may miss the key information of pathological understanding. Named entity recognition (NER) technology can help doctors quickly screen out key entities and improve the efficiency of clinicians. In this study, we proposed two model structures (Damped Pointer Network and Dynamic Fusion) to improve the accuracy and recall rate of entity recognition. The model structures were well fitted the EMR with small size of samples and many technical terms in the form of uncommon words. Experiments showed that the F1-score (harmonic average of accuracy and recall rate) of the model structures proposed in this study was 98.56%, which was 2% higher than other frequently-used recognition technologies like BERT and BERT-CRF.
Identification of Hate Speech using Natural Language Processing and Machine Learning

From the past decade, social media has gained a lot of momentum both in a positive way as well as in a negative way. With this rapid increase of networking through social platforms and websites, people are able to communicate with each other directly with no cultural or economic gap. While there have been many benefits of social media but there are no less negative impacts on the society. One such problem that has arised since the past few years is the hate speech. Hate speech is basically the use of offensive and hostile language happening on the social media. It may refer to any individual or a certain group of people with the same interests. In this paper, we have introduced our way of dealing with this hate speech and minimizing it to a large extent. People convey their hatred and anger straightaway on social media which would hurt the feelings of other people. It would affect their caste, creed, religion, race and would have a very negative impact on them. Some comments might not be intentional to anyone but would be counted as hate speech due the foul language used. We have dived deep into natural language processing to eliminate hate speech and used various machine learning models to decide which one to use as per its accuracy.
A Voice Controlled and Vision based Smart Wheel Chair for Paralyzed People

There are 144 million disabled people who use a wheel chair in 32 developed countries, accounting for 1.82 percent of the global population. Furthermore, lots of people suffer from diseases that cause them to be unable to produce coordinated movement in any of their limbs or even their heads. So using the Raspberry Pi, we devised a new way to provide paralyzed people with an eyeball-based and voice-controlled wheel chair access. The movement of the eye ball is tracked using the camera module, and the motor driver controls the direction of the wheel chair based on pupil location. The voice module receives user feedback through a cell phone. When an ultrasonic sensor senses an obstacle, the wheel chair will come to a halt. In the event of an emergency, people may send a message to their guardian through voice command via the message module.
Haptic Feedback System for Differently Abled Using Their Inputs

Haptic advancement is frequently alluded to as deepesestic correspondence or 3D touch; implies any improvement that can make a feeling of touch by utilizing a couple of powers, vibrations, or upgrades to the client. These enhancements can be utilized to make special visualizations for PC age, visual control gadgets, and update hardware and contraptions (telerobotics). Haptic gadgets might join the visual feelings of the work power utilized by the client on the interface. The word haptic, got from the Greek: signifies “property, related with the feeling of touch.” Straightforward haptic contraptions are significant as game controls, fun sticks, and associating wheels
Natural Scene Text Recognition Using Convolutional Recurrent Neural Network

In this article, we explore the scene text recognition problem, which is one of the challenging sub-fields of computer vision. Recently, deep learning has achived state-of-the-art performance for recognition task. The convolutional recurrent neural network (CRNN) architecture is explored for this task, which consists of feature extraction, sequence modeling. Moreover, an attention mechanism is introduced in our study. Unlike many of previous scene text recognition systems, the proposed architecture has several advantages: the model can be trained using the end-to-end manner and the CRNN can deal with the sequences of arbitrary length. Comparing the detection results of several mainstream CNN network structures, the experimental results show that the accuracy of the detection results is improved, and false positives are reduced, which clearly demonstrate its effectiveness.
Robotic Coverage Path Planning for Ultrasonic Inspection

Automatic robotic inspection of arbitrary free-form shapes is relevant for many quality control applications in different industries. We propose a method for planning the motion of an industrial robot to perform ultrasonic inspection of varying 3D shapes. Our method starts with the calculation of a set of sub-paths. These sub-paths are derived from streamlines. The underlying vector field is deduced from local curvature of the inspected geometry. Intermediate robot motions are planned to connect individual sub-paths to obtain a single complete inspection path. Coverage is calculated via ray tracing to simulate the propagation of ultrasound signals. This simulation enables the algorithm to proceed adaptively and to find a good trade-off between path length and coverage. We report experiments for four different geometries. The results indicate that shorter paths are achieved by using ray tracing for adaptive adjustment of streamline density. Our algorithm is tailored to ultrasonic inspection. However, the main concept of exploiting local surface curvature and streamlines for coverage path planning generalizes to other robotic inspection problems.
Lexicon-based sentiment analysis on movie review in the Gujarati language

Film assessment helps watchers with choosing if the film merits their time. A rundown of all surveys for a film can help watchers to make a suitable selection without the wastage of users' time by ...
Evaluation of Quality of Service in VoIP Traffic Using the E Model

Real-time applications such as Voice over IP (VoIP) are very sensitive to variables such as jitter, delay and packet loss. The Quality of Service (QoS) together with the available bandwidth allows to have a quality service, which added to the Quality of User Experience (QoE) provides a way to evaluate the communication. In this work we evaluate the variables delay, jitter and packet loss in VoIP application, together with the QoE (R factor and MOS) of the user using the E model. The variables of delay, jitter and packet loss were measured in three scenarios where the channel was saturated and the bandwidth was limited, to determine the relationship between two QoS variables: delay and jitter with respect to the R factor. Finally, the QoS and QoE that allows early detection of quality degradation in a VoIP call was evaluated, taking into account factors such as bandwidth and codec.
UniteD-SRL: A Unified Dataset for Span- and Dependency-Based Multilingual and Cross-Lingual Semantic Role Labeling

Multilingual and cross-lingual Semantic Role Labeling (SRL) have recently garnered increasing attention as multilingual text representation techniques have become more effective and widely available.While recent work has attained growing success, results on gold multilingual benchmarks are still not easily comparable across languages, making it difficult to grasp where we stand.For example, in CoNLL-2009, the standard benchmark for multilingual SRL, language-to-language comparisons are affected by the fact that each language has its own dataset which differs from the others in size, domains, sets of labels and annotation guidelines.In this paper, we address this issue and propose UNITED-SRL, a new benchmark for multilingual and crosslingual, span-and dependency-based SRL.UNITED-SRL provides expert-curated parallel annotations using a common predicateargument structure inventory, allowing direct comparisons across languages and encouraging studies on cross-lingual transfer in SRL.We release UNITED-SRL v1.
Ranking Online Reviews Based on Their Helpfulness: An Unsupervised Approach

Online reviews are an essential aspect of online shopping for both customers and retailers.However, many reviews found on the Internet lack in quality, informativeness or helpfulness.In many cases, they lead the customers towards positive or negative opinions without providing any concrete details (e.g., very poor product, I would not recommend it).In this work, we propose a novel unsupervised method for quantifying helpfulness leveraging the availability of a corpus of reviews.In particular, our method exploits three characteristics of the reviews, viz., relevance, emotional intensity and specificity, towards quantifying helpfulness.We perform three rankings (one for each feature above), which are then combined to obtain a final helpfulness ranking.For the purpose of empirically evaluating our method, we use review of four product categories from Amazon review 1 .The experimental evaluation demonstrates the effectiveness of our method in comparison to a recent and state-of-the-art baseline.
BPoMP: The Benchmark of Poetic Minimal Pairs – Limericks, Rhyme, and Narrative Coherence

We adapt BLiMP (Benchmark of Linguistic Minimal Pairs) language model evaluation framework to the context of poetry, introducing the first of a series of tasks titled Benchmark of Poetic Minimal Pairs (BPoMP).The tasks presented herein use one genre of English-language poetry, the limerick (fivelines, rhyme scheme AABBA).Following the BLiMP schema, the BPoMP tasks use 10,000 minimal pairs of limerick/corrupted limerick.The latter is created by (1) shuffling two rhyming end-of-the-line words, (2) shuffling two rhyming lines, (3) replacing end-of-theline word by a non-rhyming synonym.Our general task is detection of the original limerick, which we believe tests a language model's capacity to utilize "end rhymes", a common feature of poetry.We evaluate Transformerbased models by checking if they assign a higher probability to the non-corrupted limerick in each minimal pair.We find that the models identify the original limerick at rates better than chance, but with a nontrivial gap relative to human accuracy (average of 98.3% across tasks).The publicly available curated set of limericks accompanying this paper is an additional contribution.In general, we see this as a first step to create a community of NLP activity around the rigorous computational study of poetry.
A Multi-task Deep Learning System for Face Detection and Age Group Classification for Masked Faces

COVID-19 is an ongoing pandemic and according to the experts, using a face mask can reduce the spread of the disease. On the other hand, masks cause occlusion in faces and can create safety problems such as the recognition of the face and the estimation of its age. To prevent the spread of COVID-19, some countries have restrictions according to age groups. Also in different countries, people in some age groups have safety restrictions such as driving and consuming alcohol, etc. But these rules are difficult to follow due to occlusion in faces. Automated systems can assist to monitor these rules. In this study, a deep learning-based automated multi-task face detection and age group classification system is proposed for masked faces. The system first detects masked/no-masked-faces. Then, it classifies them according to age-groups. It works for multi-person regardless of indoor/outdoor environment. The system achieved 79.0% precision score for masked face detection using Faster R-CNN with resnet50 network. Also, 83.87% accuracy for classifying age groups with masked faces and 84.48% accuracy for no-masked faces using densenet201 network have been observed. It produced better results compared to the literature. The results are significant because they show that a reliable age classification for masked faces is possible.
A New Method for Image Superpixel Segmentation

Considering the problem that the existing superpixel methods are usually unable to set an appropriate number of generated superpixels automatically and unable to adhere to image boundaries effectively, a new superpixel method is proposed in this paper, which utilizes local information to perform multi-level simple linear iterative clustering to generate superpixels. First, original image is initially segmented by Simple Liner Iterative Clustering based on Local Information (LI-SLIC). Then, each superpixel is segmented iteratively until its color standard deviation is lower than a predefined threshold. Finally, the over-segmented superpixels are merged based on the color differences between adjacent superpixels. Experiments on Berkeley, Pascal VOC and 3Dircadb databases, as well as comparison with other methods indicate that the proposed method can adhere to image boundaries more accurately, and can prevent over- and under- segmentations more effectively.