Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks. Thus far, efforts mostly focused on generating adversarial samples or defending adversarial attacks, but little is known about the difference between normal and adversarial samples. In this work, we take the first step to leverage the salience-based method to analyze those adversarial samples. We observe that salience tokens have a direct correlation with adversarial perturbations. We further find the adversarial perturbations are either those tokens not existing in the training set or superficial cues associated with relation labels. To some extent, our approach unveils the characters against adversarial samples. We release an open-source testbed, "DiagnoseAdv".

It is widely known that Google Trends have become one of the most popular free tools used by forecasters both in academics and in the private and public sectors. There are many papers, from several different fields, concluding that Google Trends improve forecasts' accuracy. However, what seems to be widely unknown, is that each sample of Google search data is different from the other, even if you set the same search term, data and location. This means that it is possible to find arbitrary conclusions merely by chance. This paper aims to show why and when it can become a problem and how to overcome this obstacle.

The explosion of user-generated content (UGC)--e.g. social media posts, comments, and reviews--has motivated the development of NLP applications tailored to these types of informal texts. Prevalent among these applications have been sentiment analysis and machine translation (MT). Grounded in the observation that UGC features highly idiomatic, sentiment-charged language, we propose a decoder-side approach that incorporates automatic sentiment scoring into the MT candidate selection process. We train separate English and Spanish sentiment classifiers, then, using n-best candidates generated by a baseline MT model with beam search, select the candidate that minimizes the absolute difference between the sentiment score of the source sentence and that of the translation, and perform a human evaluation to assess the produced translations. Unlike previous work, we select this minimally divergent translation by considering the sentiment scores of the source sentence and translation on a continuous interval, rather than using e.g. binary classification, allowing for more fine-grained selection of translation candidates. The results of human evaluations show that, in comparison to the open-source MT baseline model on top of which our sentiment-based pipeline is built, our pipeline produces more accurate translations of colloquial, sentiment-heavy source texts.

Keyword extraction is called identifying words or phrases that express the main concepts of texts in best. There is a huge amount of texts that are created every day and at all times through electronic infrastructure. So, it is practically impossible for humans to study and manage this volume of documents. However, the need for efficient and effective access to these documents is evident in various purposes. Weblogs, News, and technical notes are almost long texts, while the reader seeks to understand the concepts by topics or keywords to decide for reading the full text. To this aim, we use a combined approach that consists of two models of graph centrality features and textural features. In the following, graph centralities, such as degree, betweenness, eigenvector, and closeness centrality, have been used to optimally combine them to extract the best keyword among the candidate keywords extracted by the proposed method. Also, another approach has been introduced to distinguishing keywords among candidate phrases and considering them as a separate keyword. To evaluate the proposed method, seven datasets named, Semeval2010, SemEval2017, Inspec, fao30, Thesis100, pak2018 and WikiNews have been used, and results reported Precision, Recall, and F- measure.

The sophisticated network of nerves connecting our eyes to our brains evolved 100 million years earlier than previously thought – a discovery that'literally changes the textbook.' A team of international scientists found the connection scheme was already present in the ancient gar fish that lived 450 million years ago, which means the eye-brain connection pre-dates animals living on land. The long-held theory suggests the connection first evolved in terrestrial creatures and, from there, carried on into humans where scientists believe it helps with our depth perception and 3D vision. Michigan State University's Ingo Braasch said: 'Modern fish, they don't have this type of eye-brain connection.' 'That's one of the reasons that people thought it was a new thing in tetrapods.' A team of international scientists found the connection scheme was already present in the ancient gar fish that lived 450 million years ago, which means the eye-brain connection pre-dates animals living on land.

Dogs are devoted companions that offer unwavering loyalty to their humans, but new research has exposed the full extent of their inner green-eyed monster. Anecdotal evidence from owners is now backed up by scientists which have found pet pooches get jealous when their human strokes another dog. But research has also found dogs can get jealous just by imagining their owner is fussing another dog, even when they can't see the interaction. 'Research has supported what many dog owners firmly believe -- dogs exhibit jealous behaviour when their human companion interacts with a potential rival,' said study lead author Amalia Bastos from the University of Auckland. 'We wanted to study this behaviour more fully to determine if dogs could, like humans, mentally represent a situation that evoked jealousy.'

THE COFFEESHOP is an engine of social mobility. Barista jobs require soft skills and little experience, making them a first port of call for young people and immigrants looking for work. So it may be worrying that robotic baristas are spreading. RC Coffee, which bills itself "Canada's first robotic café", opened in Toronto last summer. "[T]he barista-to-customer interaction is somewhat risky despite people's best efforts to maintain a safe environment," the firm says.

This paper presents a novel deep learning enabled, video based analysis framework for assessing the Unified Parkinsons Disease Rating Scale (UPDRS) that can be used in the clinic or at home. We report results from comparing the performance of the framework to that of trained clinicians on a population of 32 Parkinsons disease (PD) patients. In-person clinical assessments by trained neurologists are used as the ground truth for training our framework and for comparing the performance. We find that the standard sit-to-stand activity can be used to evaluate the UPDRS sub-scores of bradykinesia (BRADY) and posture instability and gait disorders (PIGD). For BRADY we find F1-scores of 0.75 using our framework compared to 0.50 for the video based rater clinicians, while for PIGD we find 0.78 for the framework and 0.45 for the video based rater clinicians. We believe our proposed framework has potential to provide clinically acceptable end points of PD in greater granularity without imposing burdens on patients and clinicians, which empowers a variety of use cases such as passive tracking of PD progression in spaces such as nursing homes, in-home self-assessment, and enhanced tele-medicine.

This paper deals with an important subject in classification problems addressed by machine learning techniques: the evaluation of the influence of each of the features on the classification of individuals. Specifically, a measure of that influence is introduced using the Shapley value of cooperative games. In addition, an axiomatic characterisation of the proposed measure is provided based on properties of efficiency and balanced contributions. Furthermore, some experiments have been designed in order to validate the appropriate performance of such measure. Finally, the methodology introduced is applied to a sample of COVID-19 patients to study the influence of certain demographic or risk factors on various events of interest related to the evolution of the disease.

Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires the advanced equipment and chemical knowledge of certified laboratories, and has therefore a limited accessibility. In this work a minimalist, portable and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing classification of olive oil in the three mentioned classes with an accuracy of 100$\%$. These results confirm that this minimalist low-cost sensor has the potential of substituting expensive and complex chemical analysis.