Miracle-dz/UniFAR-FTUs huggingface dataset:
GWU NLP at SemEval-2019 Task 7: Hybrid Pipeline for Rumour Veracity and Stance Classification on Social Media

Social media plays a crucial role as the main resource news for information seekers online. However, the unmoderated feature of social media platforms lead to the emergence and spread of untrustworthy contents which harm individuals or even societies. Most of the current automated approaches for automatically determining the veracity of a rumor are not generalizable for novel emerging topics. This paper describes our hybrid system comprising rules and a machine learning model which makes use of replied tweets to identify the veracity of the source tweet. The proposed system in this paper achieved 0.435 F-Macro in stance classification, and 0.262 F-macro and 0.801 RMSE in rumor verification tasks in Task7 of SemEval 2019.
Applications of Data Mining in Stock Market

Data mining is being actively applied to stock market since 1980s. The various aspects of stock market to which data mining has been applied include predicting stock indices, predicting stock prices, portfolio management, portfolio risk management, trend detection, designing recommender systems etc. The various algorithms and methods which have been used for the same include neural networks, association rules, decision trees, fuzzy logic, PSO (Particle Swarm Optimization), DE (Differential Evolution), GA (Genetic Algorithm) etc.
YNU-HPCC at SemEval-2019 Task 8: Using A LSTM-Attention Model for Fact-Checking in Community Forums

We propose a system that uses a long short-term memory with attention mechanism (LSTM-Attention) model to complete the task. The LSTM-Attention model uses two LSTM to extract the features of the question and answer pair. Then, each of the features is sequentially composed using the attention mechanism, concatenating the two vectors into one. Finally, the concatenated vector is used as input for the MLP and the MLP’s output layer uses the softmax function to classify the provided answers into three categories. This model is capable of extracting the features of the question and answer pair well. The results show that the proposed system outperforms the baseline algorithm.
Epidemiological Investigation of Toxoplasma gondii Infections in Commercial Sheep Flock in an Endemic Area for Ocular Toxoplasmosis in Southern Brazil

Toxoplasma gondii is a widely distributed parasite and of great importance to human and animal health. The objective of this study was to assess the prevalence of T. gondii antibodies and risk factors associated with the infection in sheep in the Northwest region of the State of Rio Grande do Sul, Brazil; this region has a very high rate of human ocular toxoplasmosis. Ovine sera were tested by the modified agglutination test (cut-off 1:25). T. gondii antibodies were detected in 70.2% (224 of 319). According to the logistic regression, the most significant factors associated were age and cat access to food stock facility. Preventive measures are discussed to reduce the risk of transmission of this zoonosis.
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 behavior 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 have employed neural network models which manipulate attention with Temporal Convolutional Neural Network for the three shared sub-tasks 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.
On intermittence bugs in the battery-less internet of things (WIP paper)

The resource-constrained devices of the battery-less Internet of Things are powered off energy harvesting and compute intermittently, as energy is available. Forward progress of programs is ensured by creating persistent state. Mixed-volatile platforms are thus an asset, as they map slices of the address space onto non-volatile memory. However, these platforms also possibly introduce intermittence bugs, where intermittent and continuous executions differ. Our ongoing work on intermittence bugs includes [label=()] an analysis that demonstrates their presence in settings that current literature overlooks; the design of efficient testing techniques to check their presence in arbitrary code, which would be otherwise prohibitive given the sheer number of different executions to check; the implementation of an offline tool called ScEpTIC that implements these techniques. ScEpTIC finds the same bugs as a brute-force approach, but is six orders of magnitude faster.
GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks

This paper describes the system submitted to the SemEval 2019 shared task 1 ‘Cross-lingual Semantic Parsing with UCCA’. We rely on the semantic dependency parse trees provided in the shared task which are converted from the original UCCA files and model the task as tagging. The aim is to predict the graph structure of the output along with the types of relations among the nodes. Our proposed neural architecture is composed of Graph Convolution and BiLSTM components. The layers of the system share their weights while predicting dependency links and semantic labels. The system is applied to the CONLLU format of the input data and is best suited for semantic dependency parsing.
eventAI at SemEval-2019 Task 7: Rumor Detection on Social Media by Exploiting Content, User Credibility and Propagation Information

This paper describes our system for SemEval 2019 RumorEval: Determining rumor veracity and support for rumors (SemEval 2019 Task 7). This track has two tasks: Task A is to determine a user’s stance towards the source rumor, and Task B is to detect the veracity of the rumor: true, false or unverified. For stance classification, a neural network model with language features is utilized. For rumor verification, our approach exploits information from different dimensions: rumor content, source credibility, user credibility, user stance, event propagation path, etc. We use an ensemble approach in both tasks, which includes neural network models as well as the traditional classification algorithms. Our system is ranked 1st place in the rumor verification task by both the macro F1 measure and the RMSE measure.
University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities

We present the Named Entity Recognition (NER) and disambiguation model used by the University of Arizona team (UArizona) for the SemEval 2019 task 12. We achieved fourth place on tasks 1 and 3. We implemented a deep-affix based LSTM-CRF NER model for task 1, which utilizes only character, word, pre- fix and suffix information for the identification of geolocation entities. Despite using just the training data provided by task organizers and not using any lexicon features, we achieved 78.85% strict micro F-score on task 1. We used the unsupervised population heuristics for task 3 and achieved 52.99% strict micro-F1 score in this task.
Fermi at SemEval-2019 Task 4: The sarah-jane-smith Hyperpartisan News Detector

This paper describes our system (Fermi) for Task 4: Hyper-partisan News detection of SemEval-2019. We use simple text classification algorithms by transforming the input features to a reduced feature set. We aim to find the right number of features useful for efficient classification and explore multiple training models to evaluate the performance of these text classification algorithms. Our team - Fermi’s model achieved an accuracy of 59.10% and an F1 score of 69.5% on the official test data set. In this paper, we provide a detailed description of the approach as well as the results obtained in the task.
sthruggle at SemEval-2019 Task 5: An Ensemble Approach to Hate Speech Detection

In this paper, we present our approach to detection of hate speech against women and immigrants in tweets for our participation in the SemEval-2019 Task 5. We trained an SVM and an RF classifier using character bi- and trigram features and a BiLSTM pre-initialized with external word embeddings. We combined the predictions of the SVM, RF and BiLSTM in two different ensemble models. The first was a majority vote of the binary values, and the second used the average of the confidence scores. For development, we got the highest accuracy (75%) by the final ensemble model with majority voting. For testing, all models scored substantially lower and the scores between the classifiers varied more. We believe that these large differences between the higher accuracies in the development phase and the lower accuracies we obtained in the testing phase have partly to do with differences between the training, development and testing data.
Team Kermit-the-frog at SemEval-2019 Task 4: Bias Detection Through Sentiment Analysis and Simple Linguistic Features

In this paper we describe our participation in the SemEval 2019 shared task on hyperpartisan news detection. We present the system that we submitted for final evaluation and the three approaches that we used: sentiment, bias-laden words and filtered n-gram features. Our submitted model is a Linear SVM that solely relies on the negative sentiment of a document. We achieved an accuracy of 0.621 and a f1 score of 0.694 in the competition, revealing the predictive power of negative sentiment for this task. There was no major improvement by adding or substituting the features of the other two approaches that we tried.
Steve Martin at SemEval-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan News

This paper describes our submission to task 4 in SemEval 2019, i.e., hyperpartisan news detection. Our model aims at detecting hyperpartisan news by incorporating the style-based features and the content-based features. We extract a broad number of feature sets and use as our learning algorithms the GBDT and the n-gram CNN model. Finally, we apply the weighted average for effective learning between the two models. Our model achieves an accuracy of 0.745 on the test set in subtask A.
Sensory nutrition: The role of taste in the reviews of commercial food products

Many factors play a role in choosing what to eat or drink. Here we explored the role of sensation to explain these differences, drawing on consumer reviews for commercially available food products sold through an online retailer. We analyzed 393,568 unique food product reviews from Amazon customers with a total of 256,043 reviewers rating 67,553 products. Taste-associated words were mentioned more than words associated with cost, food texture, customer service, nutrition, smell, or those referring to the trigeminal senses, e.g., spicy. We computed the overall number of reviews that mentioned taste qualities: the word taste was mentioned in over 30% of the reviews (N= 142,768), followed by sweet (10.7%, N=42,315), bitter (2.9%, N=11,424), sour (2.1%, N=8,252), and salty (1.4%, N=5,688). We identified 38 phrases used to describe the evaluation of sweetness, finding that ‘too sweet’ was used in nearly 0.8% of the reviews and oversweetness was mentioned over 25 times more often than under-sweetness. We then focused on ‘polarizing’ products, those that elicited a wide difference of opinion (as measured by the ranges of the product ratings). Using the products that had more than 50 reviews, we identified the top 10 most polarizing foods (i.e., those with the largest standard deviation in ratings) and provide representative comments about the polarized taste experience of consumers. Overall, these results support the primacy of taste in real-world food ratings and individualized taste experience, such as whether a product is ‘too sweet’. Analysis of consumer review data sets can provide information about purchasing decisions and customer sensory responses to particular commercially available products and represents a promising methodology for the emerging field of sensory nutrition.
A pattern recognition based esophageal speech enhancement system

Este articulo propone un sistema para mejorar la calidad e inteligibilidad de la voz de personas laringetomizadas, el cual se basa en el reemplazo de segmentos vocalizados de voz laringetomizada por segmentos equivalentes de voz normal. Con esta finalidad el sistema identifica los segmentos vocalizados de voz laringetomizada usando tecnicas de reconocimiento de comandos aislados de voz, y las reemplaza por los segmentos equivalentes de voz normal, conservando sin cambio los segmentos y los no‐vocalizados. Resultados obtenidos usando metodos de evaluacion tanto subjetivos como objetivos muestran que el sistema propuesto proporciona una mejoria importante tanto en la calidad como en la inteligibilidad de senales de voz laringetomizada.
An Emotional Response

audiTory hypersensiTiviTy and auTism specTrum disorders One concern parents and professionals may have with children who have been diagnosed with an autism spectrum disorder (ASD) is that some of these children cannot tolerate listening to certain sounds. Often, parents observe that the children put their hands over their ears, run away from sounds, or sometimes lose control of their behaviors in the presence of certain sounds. These children are often identified as having auditory hypersensitivity or hypersensitive hearing. Although concern about auditory hypersensitivity has been especially focused on children on the autism spectrum, many non-autistic children also present with behaviors labeled as auditory hypersensitivity. For many years, experts focusing on auditory hypersensitivity and treatment for problems of hypersensitive hearing looked at this phenomenon as an auditory-system-based problem involving abnormal hearing but not hearing loss. Some defined auditory hypersensitivity as an overreaction to sounds that should (over time) no longer warrant such responses. However, recent research has led us to a better understanding of what may be going on in children who have hypersensitive hearing. An understanding of possible mechanisms can assist us in providing more appropriate interventions to help children overcome auditory sensitivity problems. This article discusses auditory hypersensitivity with a focus on the current state of our understanding of the problem and treatment for such problems. hearing versus percepTion and audiTory processing Generically, auditory hypersensitivity usually refers to a person who is considered overly sensitive to sounds, especially loud sounds. There are many who might think that children with auditory hypersensitivities, therefore, have hearing-related audiTory hyPersensiTiviTy and auTism sPeCTrum disorders: problems. However, the following three definitions clarify that auditory hypersensitivity has to do with our perception of sound rather than our hearing of sound: 1 One definition of auditory hypersensitivity is " abnormally sensitive hearing in which normally tolerable sounds are perceived as excessively loud. " 11 2 Another definition is " an increased sensitivity to sound perception, subjective in nature. " 12 3 in discussing auditory hypersensitivity, Gomes and colleagues 5 cite Kanner, 13 who discusses children on the autism spectrum as having aversions to certain loud sounds rather than having problems hearing or listening to loud sounds. Abstract Many children diagnosed with an autism spectrum disorder are described as having auditory hypersensitivities. This paper describes auditory hypersensitivities, the systems involved in hypersensitive hearing, methods for evaluating auditory hypersensitivity in children, and possible treatments. Auditory hypersensitivity involves the …
Activation of butterfly eyespots by Distal-less is consistent with a reaction-diffusion process

ABSTRACT Eyespots on the wings of nymphalid butterflies represent colorful examples of pattern formation, yet the developmental origins and mechanisms underlying eyespot center differentiation are still poorly understood. Using CRISPR-Cas9 we re-examine the function of Distal-less (Dll) as an activator or repressor of eyespots, a topic that remains controversial. We show that the phenotypic outcome of CRISPR mutations depends upon which specific exon is targeted. In Bicyclus anynana, exon 2 mutations are associated with both missing and ectopic eyespots, and also exon skipping. Exon 3 mutations, which do not lead to exon skipping, produce only null phenotypes, including missing eyespots, lighter wing coloration and loss of scales. Reaction-diffusion modeling of Dll function, using Wnt and Dpp as candidate morphogens, accurately replicates these complex crispant phenotypes. These results provide new insight into the function of Dll as a potential activator of eyespot development, scale growth and melanization, and suggest that the tuning of Dll expression levels can generate a diversity of eyespot phenotypes, including their appearance on the wing. This article has an associated ‘The people behind the papers’ interview. Highlighted Article: CRISPR-generated mutants and theoretical modeling show that Dll is a crucial activator of butterfly wing eyespot center differentiation.
Assessment of spinal cord relative vulnerability in C4–C5 compressive cervical myelopathy using multi-modal spinal cord evoked potentials and neurological findings

Objective: The correlation between the progression of spinal cord lesions using spinal cord evoked potentials (SCEPs) and neurological findings are unclear. The purpose is to electrophysiologically evaluate relative vulnerability of spinal cord in patients with compressive cervical myelopathy (CCM) at C4–C5 intervertebral level using SCEPs and correlate the progression of spinal cord lesions with neurological findings. Design: Retrospective study. Setting: Yamaguchi University Hospital. Participants: 36 patients. Methods: SCEPs following median nerve stimulation (MN-SCEPs), ulnar nerve stimulation (UN-SCEPs), transcranial electric stimulation (TCE-SCEPs), and spinal cord stimulation (SC-SCEPs) were intraoperatively recorded. MN-SCEPs are mediated by posterior horns (4, 5 layers), UN-SCEPs by the Burdach tract, TCE-SCEPs by the lateral corticospinal tract, and SC-SCEPs by the Goll tract. We evaluated the neurological findings (numbness, tactile sense and pain sense in the C6 area, tactile sense in the lower extremities, and triceps tendon reflex [TTR]). Results: The incidence of electrophysiological and clinical abnormalities decreased in the order of UN-SCEPs (100%), TCE-SCEPs (94.4%), MN-SCEPs (77.8%), and SC-SCEPs (69.4%), and in the order of numbness (100%), pain sense (97.2%), TTR (91.7%), tactile sense in the C6 area (83.3%), and tactile sense in the lower extremities (70.0%), respectively. Conclusions: The relative vulnerability of spinal cord occurred in the order of the Burdach tract, the lateral corticospinal tract, posterior horns (4, 5 layers), and the Goll tract in most patients with CCM at the C4–C5 intervertebral level.
Narrowband Internet of Things (NB-IoT): From Physical (PHY) and Media Access Control (MAC) Layers Perspectives

Narrowband internet of things (NB-IoT) is a recent cellular radio access technology based on Long-Term Evolution (LTE) introduced by Third-Generation Partnership Project (3GPP) for Low-Power Wide-Area Networks (LPWAN). The main aim of NB-IoT is to support massive machine-type communication (mMTC) and enable low-power, low-cost, and low-data-rate communication. NB-IoT is based on LTE design with some changes to meet the mMTC requirements. For example, in the physical (PHY) layer only single-antenna and low-order modulations are supported, and in the Medium Access Control (MAC) layers only one physical resource block is allocated for resource scheduling. The aim of this survey is to provide a comprehensive overview of the design changes brought in the NB-IoT standardization along with the detailed research developments from the perspectives of Physical and MAC layers. The survey also includes an overview of Evolved Packet Core (EPC) changes to support the Service Capability Exposure Function (SCEF) to manage both IP and non-IP data packets through Control Plane (CP) and User Plane (UP), the possible deployment scenarios of NB-IoT in future Heterogeneous Wireless Networks (HetNet). Finally, existing and emerging research challenges in this direction are presented to motivate future research activities.
Prospective Multicenter Study on the Prognostic and Predictive Impact of Tumor Budding in Stage II Colon Cancer: Results From the SACURA Trial

PURPOSE The International Union Against Cancer highlighted tumor budding as a tumor-related prognostic factor. International assessment criteria for tumor budding were recently defined by the 2016 International Tumor Budding Consensus Conference (ITBCC2016). This study aimed to clarify the prognostic and predictive values of tumor budding in a randomized controlled trial evaluating the superiority of adjuvant chemotherapy with oral tegafur-uracil over surgery alone for stage II colon cancer (SACURA trial; ClinicalTrials.gov identifier: NCT00392899). PATIENTS AND METHODS Between 2006 and 2010, we enrolled 991 patients from 123 institutions with stage II colon cancer. Tumor budding was diagnosed by central review on the basis of the criteria adopted in the ITBCC2016. We prospectively recorded all clinical and pathologic data, including the budding grade, and performed prognostic analyses after 5 years of completing the patients’ registration. RESULTS Of 991 tumors, 376, 331, and 284 were classified as BD1, BD2, and BD3, respectively; the 5-year relapse-free survival (RFS) rate was 90.9%, 85.1%, and 74.4%, respectively (P < .001), and ranged widely in T4 tumors (86.6% to 53.3%). The budding grade significantly correlated with recurrence in the liver, lungs, lymph nodes, and peritoneum (P < .001 to .01). Multivariable analysis revealed that budding and T stage exerted an independent impact on RFS, and on the basis of the Harrell concordance index, these two factors substantially contributed to the improvement of the Cox model for predicting RFS. Both the BD2 and BD3 groups demonstrated greater improvement in the 5-year recurrence rate in the adjuvant chemotherapy group than the surgery-alone group by approximately 5%, but the difference was statistically nonsignificant. CONCLUSION Tumor budding grade on the basis of the ITBCC2016 criteria should be routinely evaluated in pathologic practice and could improve the benefit of adjuvant chemotherapy for stage II colon cancer.