Reviewerly/exHarmony huggingface dataset:
Analysing Representations of Memory Impairment in a Clinical Notes Classification Model

Despite recent advances in the application of deep neural networks to various kinds of medical data, extracting information from unstructured textual sources remains a challenging task.The challenges of training and interpreting document classification models are amplified when dealing with small and highly technical datasets, as are common in the clinical domain.Using a dataset of de-identified clinical letters gathered at a memory clinic, we construct several recurrent neural network models for letter classification, and evaluate them on their ability to build meaningful representations of the documents and predict patients' diagnoses.Additionally, we probe sentence embedding models in order to build a humaninterpretable representation of the neural network's features, using a simple and intuitive technique based on perturbative approaches to sentence importance.In addition to showing which sentences in a document are most informative about the patient's condition, this method reveals the types of sentences that lead the model to make incorrect diagnoses.Furthermore, we identify clusters of sentences in the embedding space that correlate strongly with importance scores for each clinical diagnosis class.
LIUM’s Contributions to the WMT2019 News Translation Task: Data and Systems for German-French Language Pairs

This paper describes the neural machine translation (NMT) systems of the LIUM Laboratory developed for the French↔German news translation task of the Fourth Conference onMachine Translation (WMT 2019). The chosen language pair is included for the first time in the WMT news translation task. We de-scribe how the training and the evaluation data was created. We also present our participation in the French↔German translation directions using self-attentional Transformer networks with small and big architectures.
Object and Human Action Recognition From Video Using Deep Learning Models

This paper explores the deep learning models aiming at two tasks, which are classifying objects and recognizing human action from a video. The deep learning models are the convolutional neural networks and long short-term memory network. For the action recognition, the optical flow is employed as the feature representation of movement on each video. The video data simulates one person doing either taking, returning, or browsing items on a shelf. From the experiments, the model achieve accuracy of 56.41% of accuracy for the object classification task and 76.92% for the action recognition.
Bridging the Gap for Tokenizer-Free Language Models

Purely character-based language models (LMs) have been lagging in quality on large scale datasets, and current state-of-the-art LMs rely on word tokenization. It has been assumed that injecting the prior knowledge of a tokenizer into the model is essential to achieving competitive results. In this paper, we show that contrary to this conventional wisdom, tokenizer-free LMs with sufficient capacity can achieve competitive performance on a large scale dataset. We train a vanilla transformer network with 40 self-attention layers on the One Billion Word (lm1b) benchmark and achieve a new state of the art for tokenizer-free LMs, pushing these models to be on par with their word-based counterparts.
Grounding learning of modifier dynamics: An application to color naming

Grounding is crucial for natural language understanding.An important subtask is to understand modified color expressions, such as "dirty blue".We present a model of color modifiers that, compared with previous additive models in RGB space, learns more complex transformations.In addition, we present a model that operates in the HSV color space.We show that certain adjectives are better modeled in that space.To account for all modifiers, we train a hard ensemble model that selects a color space depending on the modifiercolor pair.Experimental results show significant and consistent improvements compared to the state-of-the-art baseline model. 1
Analyzing the Adequacy of Readability Indicators to a Non-English Language

Readability is a linguistic feature that indicates how difficult it is to read a text. Traditional readability formulas were made for the English language. This study evaluates their adequacy to the Portuguese language. We applied the traditional formulas in 10 parallel corpora. We verified that the Portuguese language had higher grade scores (less readability) in the formulas that use the number of syllables per words or number of complex words per sentence. Formulas that use letters by words instead of syllables by words output similar grade scores. Considering this, we evaluated the correlation of the complex words in 65 Portuguese school books of 12 schooling years. We found out that the concept of complex word as a word with 4 or more syllables, instead of 3 or more syllables as originally used in traditional formulas applied to English texts, is more correlated with the grade of Portuguese school books. In the end, for each traditional readability formula, we adapted it to the Portuguese language performing a multiple linear regression in the same dataset of school books.
Overview of CENTRE@CLEF 2019: Sequel in the Systematic Reproducibility Realm

Reproducibility has become increasingly important for many research areas, among those IR is not an exception and has started to be concerned with reproducibility and its impact on research results. This paper describes our second attempt to propose a lab on reproducibility named CENTRE, held during CLEF 2019. The aim of CENTRE is to run both a replicability and reproducibility challenge across all the major IR evaluation campaigns and to provide the IR community with a venue where previous research results can be explored and discussed. This paper reports the participant results and preliminary considerations on the second edition of CENTRE@CLEF 2019.
DCStore: A Deduplication-Based Cloud-of-Clouds Storage Service

The increasing popularity of cloud storage is leading many organizations to move their data into the cloud. However, putting all data in one cloud causes problems such as vendor lock-in, increased service costs, and data availability. In this paper, we introduce DCStore, a Cloud-of-Clouds storage service designed for an organization to outsource their data into the clouds. To achieve the goal of cost-efficient and high-available, we combine three key techniques. First, DCStore eliminates the redundant data at client-side to save storage cost via application-aware chunking method. Second, DCStore uses an inner-chunk based erasure coding scheme to distribute unique chunks across multiple clouds for high availability. Finally, a container-based share management strategy is used for performance optimization. Our experimental evaluations show that DCStore can improve the performance and cost efficiency significantly, compared with existing Cloud-of-Clouds storage systems.
A System for Automatic English Text Expansion

We present an automatic text expansion system to generate English sentences, which performs automatic Natural Language Generation (NLG) by combining linguistic rules with statistical approaches. Here, “automatic” means that the system can generate coherent and correct sentences from a minimum set of words. From its inception, the design is modular and adaptable to other languages. This adaptability is one of its greatest advantages. For English, we have created the highly precise aLexiE lexicon with wide coverage, which represents a contribution on its own. We have evaluated the resulting NLG library in an Augmentative and Alternative Communication (AAC) proof of concept, both directly (by regenerating corpus sentences) and manually (from annotations) using a popular corpus in the NLG field. We performed a second analysis by comparing the quality of text expansion in English to Spanish, using an ad-hoc Spanish-English parallel corpus. The system might also be applied to other domains such as report and news generation.
Keeping the balance—Bridge sampling for marginal likelihood estimation in finite mixture, mixture of experts and Markov mixture models

Finite mixture models and their extensions to Markov mixture and mixture of experts models are very popular in analysing data of various kind. A challenge for these models is choosing the number of components based on marginal likelihoods. The present paper suggests two innovative, generic bridge sampling estimators of the marginal likelihood that are based on constructing balanced importance densities from the conditional densities arising during Gibbs sampling. The full permutation bridge sampling estimator is derived from considering all possible permutations of the mixture labels for a subset of these densities. For the double random permutation bridge sampling estimator, two levels of random permutations are applied, first to permute the labels of the MCMC draws and second to randomly permute the labels of the conditional densities arising during Gibbs sampling. Various applications show very good performance of these estimators in comparison to importance and to reciprocal importance sampling estimators derived from the same importance densities.
"Brute-Force Sentence Pattern Extortion from Harmful Messages for Cyberbullying Detection"

Cyberbullying, or humiliating people using the Internet, has existed almost since the beginning of Internet communication.The relatively recent introduction of smartphones and tablet computers has caused cyberbullying to evolve into a serious social problem.In Japan, members of a parent-teacher association (PTA) attempted to address the problem by scanning the Internet for cyberbullying entries.To help these PTA members and other interested parties confront this difficult task we propose a novel method for automatic detection of malicious Internet content.This method is based on a combinatorial approach resembling brute-force search algorithms, but applied in language classification.The method extracts sophisticated patterns from sentences and uses them in classification.The experiments performed on actual cyberbullying data reveal an advantage of our method vis-à-vis previous methods.Next, we implemented the method into an application for Android smartphones to automatically detect possible harmful content in messages.The method performed well in the Android environment, but still needs to be optimized for time efficiency in order to be used in practice.
Discovering Latent Domains for Unsupervised Domain Adaptation Through Consistency

In recent years, great advances in Domain Adaptation (DA) have been possible through deep neural networks. While this is true even for multi-source scenarios, most of the methods are based on the assumption that the domain to which each sample belongs is known a priori. However, in practice, we might have a source domain composed by a mixture of multiple sub-domains, without any prior about the sub-domain to which each source sample belongs. In this case, while multi-source DA methods are not applicable, restoring to single-source ones may lead to sub-optimal results. In this work, we explore a recent direction in deep domain adaptation: automatically discovering latent domains in visual datasets. Previous works address this problem by using a domain prediction branch, trained with an entropy loss. Here we present a novel formulation for training the domain prediction branch which exploits (i) domain prediction output for various perturbations of the input features and (ii) the min-entropy consensus loss, which forces the predictions of the perturbation to be both consistent and with low entropy. We compare our approach to the previous state-of-the-art on publicly-available datasets, showing the effectiveness of our method both quantitatively and qualitatively.
Out-of-domain Detection for Natural Language Understanding in Dialog Systems

Natural Language Understanding (NLU) is a vital component of dialogue systems, and its ability to detect Out-of-Domain (OOD) inputs is critical in practical applications, since the acceptance of the OOD input that is unsupported by the current system may lead to catastrophic failure. However, most existing OOD detection methods rely heavily on manually labeled OOD samples and cannot take full advantage of unlabeled data. This limits the feasibility of these models in practical applications. In this paper, we propose a novel model to generate high-quality pseudo OOD samples that are akin to IN-Domain (IND) input utterances, and thereby improves the performance of OOD detection. To this end, an autoencoder is trained to map an input utterance into a latent code. and the codes of IND and OOD samples are trained to be indistinguishable by utilizing a generative adversarial network. To provide more supervision signals, an auxiliary classifier is introduced to regularize the generated OOD samples to have indistinguishable intent labels. Experiments show that these pseudo OOD samples generated by our model can be used to effectively improve OOD detection in NLU. Besides, we also demonstrate that the effectiveness of these pseudo OOD data can be further improved by efficiently utilizing unlabeled data.
On the Need of an Explainable Artificial Intelligence

This plenary talk will explore a fascinating nex research field: the Explainable Artificial Intelligence (or XAI), whose goal if the building of explanatory models, to try and overcome the shortcomings of pure statistical learning by providing justifications, understandable by a human, for decisions or predictions made by them.
Reasoning About Exceptions in Ontologies: An Approximation of the Multipreference Semantics

Starting from the observation that rational closure has the undesirable property of being an “all or nothing” mechanism, we here consider a multipreferential semantics, which enriches the preferential semantics underlying rational closure in order to separately deal with the inheritance of different properties in an ontology with exceptions. We show that the MP-closure of an $$\mathcal {ALC}$$ knowledge base is a construction which is sound with respect to minimal entailment in the multipreference semantics for $$\mathcal {ALC}$$ .
The types and functions of code switching in a thesis defense examination

This study investigated the acts of code switching by lecturers and student in thesis defence examination at a university in Banda Aceh, Indonesia. The study involved five participants (four lectures and one student) of the English Language Department. Data from recordings were used to analyse the types and functions of code switching that occurred during the defence. The types of code switching were analysed based on Poplack, and the functions of code switching were analysed based on the theories by Gumperz, Hoffman and Holmes. The result showed that there were three types of code switching found in 109 examples during the interactions between the lecturers and the student, they were: intra-sentential switching (77.06%), inter-sentential switching (15.59%), and tag switching (7.33%). In term of the functions of code switching, 10 functions were identified from 68 switches, they were: addressee specification at 22.05%, followed by interjections (16.17%), loanwords (16.17%), message qualifications (11.76%), transfer of the subconscious markers (8.82%), proper names (8.82%), quotations (5.88%), message reiteration (4.41%), personalization versus objectification (2.94%) and specific features of Islamic terms (2.94%) as the least. It can be concluded that in this case, code switching allowed the participants achieve a wide range of important and interesting ends in their discourse during the thesis defence examination.
Vision Based Driver Smoking Behavior Detection Using Surveillance Camera Images

Intelligent traffic enforcement has gained immense traction in the computer vision community. Recently, automated violation enforcement methods have been proposed towards seat belt violation, cell phone violation and occupancy violation detection tasks. Smoking while driving is another common violation type that has been prohibited in many countries. Smoking inspections are typically performed manually by the road side officers. In this study, we propose an automated approach towards driver smoking behavior detection using near infrared (NIR) surveillance camera images. During the puff, cigarette tip reaches 800–900 °C creating a hot-spot on the NIR image. Proposed method aims to detect these hot-spots around the drivers’ head region. First, we utilize a deep learning based object detection technique to localize the front windshield and driver head region, sequentially. Next, we perform a dual window (local) anomaly detector on the localized region to determine white hot-spot, hence, the driver smoking behavior. We have collected 1472 real world NIR images to evaluate the performance of the proposed approach. Proposed method achieved an overall accuracy rate of % 84 and sensitivity rate of % 70 on the test set.
Noise amplification at spin-glass bottlenecks of quantum annealing: A solvable model

To gain better insight into the complexity theory of quantum annealing, we propose and solve a class of spin systems which contain bottlenecks of the kind expected to dominate the runtime of quantum annealing as it tries to solve difficult optimization problems. We uncover a noise amplification effect at these bottlenecks, whereby tunneling rates caused by flux-qubit noise scale in proportion to the number of qubits $\mathcal{N}$ in the limit that $\mathcal{N}\ensuremath{\rightarrow}\ensuremath{\infty}$. By solving the incoherent annealing dynamics exactly, we find a wide range of regimes where the probability that a quantum annealer remains in the ground state upon exiting the bottleneck is close to one-half. We corroborate our analysis with detailed simulations of the performance of the D-Wave 2X quantum annealer on our class of computational problems.
ENVIRONMENTAL IMPACT ON DIGITAL ERA BY ARCHITECTURAL PRODUCTION

Current developments in architecture and construction demand innovation in design.Increasing demand and rapid production time require mass production.Digital fabrication with the combination of computed design and robotic manufacturing is a development in architectural design.Digital fabrication is also a product of modernization, which affects various components of the environment as well as social, economic, and cultural components.This study aims to provide an overview of the technology upgrade in the architectural and construction sectors that affect the environment, starting from the process of architectural design and construction products in the digital era up to the impact of these activities on the environment.The method used is descriptive research-by conducting observations from scientific evidence (literature studies) that support this research.First, the concept is described followed by the discussion of the production process of architectural design products in the current digital era and the influence of these activities on the environment, such as degradation on the aspects of the environment, economy, society, and culture.The results show that architectural production in the digital era has a negative impact on the environment due to architectural products created using digital fabrication systems as well as exploitation of natural resources, encouraging finance, and transportation of these products across borders.Social and cultural changes are degraded by local culture caused by cross-cultural, as well as a new phenomenon of universalization of culture (world culture) or what is now called the digital culture.
Forgetting in Answer Set Programming with Anonymous Cycles

It is now widely accepted that the operation of forgetting in the context of Answer Set Programming [10, 18] is best characterized by the so-called strong persistence, a property that requires that all existing relations between the atoms not to be forgotten be preserved. However, it has been shown that strong persistence cannot always be satisfied. What happens if we must nevertheless forget? One possibility that has been explored before is to consider weaker versions of strong persistence, although not without a cost: some relations between the atoms not to be forgotten are broken in the process. A different alternative is to enhance the logical language so that all such relations can be maintained after the forgetting operation. In this paper, we borrow from the recently introduced notion of fork [1] – a conservative extension of Equilibrium Logic and its monotonic basis, the logic of Here-and-There – which has been shown to be sufficient to overcome the problems related to satisfying strong persistence. We map this notion into the language of logic programs, enhancing it with so-called anonymous cycles, and we introduce a concrete syntactical forgetting operator over this enhanced language that we show to always obey strong persistence.
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Iterative Delexicalization for Improved Spoken Language Understanding

Recurrent neural network (RNN) based joint intent classification and slot tagging models have achieved tremendous success in recent years for building spoken language understanding and dialog systems. However, these models suffer from poor performance for slots which often encounter large semantic variability in slot values after deployment (e.g. message texts, partial movie/artist names). While greedy delexicalization of slots in the input utterance via substring matching can partly improve performance, it often produces incorrect input. Moreover, such techniques cannot delexicalize slots with out-of-vocabulary slot values not seen at training. In this paper, we propose a novel iterative delexicalization algorithm, which can accurately delexicalize the input, even with out-of-vocabulary slot values. Based on model confidence of the current delexicalized input, our algorithm improves delexicalization in every iteration to converge to the best input having the highest confidence. We show on benchmark and in-house datasets that our algorithm can greatly improve parsing performance for RNN based models, especially for out-of-distribution slot values.
Variational Quantum Linear Solver: A Hybrid Algorithm for Linear Systems

Solving linear systems of equations is central to many engineering andscientific fields. Several quantum algorithms have been proposed for linearsystems, where the goal is to prepare $|x\rangle$ such that $A|x\rangle \propto|b\rangle$. While these algorithms are promising, the time horizon for theirimplementation is long due to the required quantum circuit depth. In this work,we propose a variational hybrid quantum-classical algorithm for solving linearsystems, with the aim of reducing the circuit depth and doing much of thecomputation classically. We propose a cost function based on the overlapbetween $|b\rangle$ and $A|x\rangle$, and we derive an operational meaning forthis cost in terms of the solution precision $\epsilon$. We also introduce aquantum circuit to estimate this cost, while showing that this cost cannot beefficiently estimated classically. Using Rigetti's quantum computer, wesuccessfully implement our algorithm up to a problem size of $32 \times 32$.Furthermore, we numerically find that the complexity of our algorithm scalesefficiently in both $1/\epsilon$ and $\kappa$, with $\kappa$ the conditionnumber of $A$. Our algorithm provides a heuristic for quantum linear systemsthat could make this application more near term.