Reviewerly/exHarmony huggingface dataset:
Deriving the sentiment polarity of term senses using dual-step context-aware in-gloss matching

Vital to the task of Sentiment Analysis (SA), or automatically mining sentiment expression from text, is a sentiment lexicon. This fundamental lexical resource comprises the smallest sentiment-carrying units of text, words, annotated for their sentiment properties, and aids in SA tasks on larger pieces of text. Unfortunately, digital dictionaries do not readily include information on the sentiment properties of their entries, and manually compiling sentiment lexicons is tedious in terms of annotator time and effort. This has resulted in the emergence of a large number of research works concentrated on automated sentiment lexicon generation. The dictionary-based approach involves leveraging digital dictionaries, while the corpus-based approach involves exploiting co-occurrence statistics embedded in text corpora. Although the former approach has been exhaustively investigated, the majority of works focus on terms. The few state-of-the-art models concentrated on the finer-grained term sense level remain to exhibit several prominent limitations, e.g., the proposed semantic relations algorithm retrieves only senses that are at a close proximity to the seed senses in the semantic network, thus prohibiting the retrieval of remote sentiment-carrying senses beyond the reach of the ‘radius’ defined by number of iterations of semantic relations expansion. The proposed model aims to overcome the issues inherent in dictionary-based sense-level sentiment lexicon generation models using: (1) null seed sets, and a morphological approach inspired by the Marking Theory in Linguistics to populate them automatically; (2) a dual-step context-aware gloss expansion algorithm that ‘mines’ human defined gloss information from a digital dictionary, ensuring senses overlooked by the semantic relations expansion algorithm are identified; and (3) a fully-unsupervised sentiment categorization algorithm on the basis of the Network Theory. The results demonstrate that context-aware in-gloss matching successfully retrieves senses beyond the reach of the semantic relations expansion algorithm used by prominent, well-known models. Evaluation of the proposed model to accurately assign senses with polarity demonstrates that it is on par with state-of-the-art models against the same gold standard benchmarks. The model has theoretical implications in future work to effectively exploit the readily-available human-defined gloss information in a digital dictionary, in the task of assigning polarity to term senses. Extrinsic evaluation in a real-world sentiment classification task on multiple publically-available varying-domain datasets demonstrates its practical implication and application in sentiment analysis, as well as in other related fields such as information science, opinion retrieval and computational linguistics.
Seismic Vulnerability Assessment Using Rapid Visual Screening: Case Study of Educational Facility Buildings of Jenderal Soedirman University, Indonesia

Earthquakes are natural phenomena occurring in various parts of the globe. Severe earthquakes caused substantial loss of life and property when nearly populated districts. Although some progress has been made in the area of seismic prediction, earthquakes in time, magnitude or location can not be estimated correctly. The primary method of reducing casualties is therefore to build seismic resistant structures. Current earthquakes show that the old houses, which are not intended to withstand earthquakes, have been harmed rather than the structures intended according to seismic regulations. Many current structures in Indonesia were intended only without seismic provisions to withstand the gravity loads. There is a need to study these buildings' vulnerability in order to prevent a severe danger. A Rapid Visual Screening (RVS) technique is conducted in this study to determine a Final Level 1 Score, SL1, for Jenderal Soedirman University, Indonesia’s educational facility buildings. In nine constructions situated in Purwokerto and Purbalingga, the method was implemented. Moreover, the final SL1 score is an estimate of the collapse probability if an earthquake occurs with ground motions called the maximum considered earthquake targeted risk, MCER. These score estimates are based on restricted observed and analytical information, thus the probability of collapse is therefore an approximation.
An Improved Framework for Sentiment Analysis for College Reviews

This is the data era and large amount of data is processed every second for various purposes and deep learning is used to abstract the key aspects of data.Lack of core knowledge on deep learning makes this a Black Box machine which remains dark to the real world.In the recent years many researches are going in the field of Machine learning in which Deep Learning has gained more attention.The aim of the paper is to provide survey of trending deep learning algorithms.The algorithms such as Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are discussed in detail.Then, performance of RNN-LSTM algorithm is compared by training and predicting college reviews feedback of data set size 500 with epochs 1 to 7. It is identified that accuracy of RNN-LSTM algorithm was around 83%.This paper provides better understanding of the deep learning algorithms and their implementation in various research areas.
Image style transfer with collection representation space and semantic-guided reconstruction

Image style transfer renders the content of an image into different styles. Current methods made decent progress with transferring the style of single image, however, visual statistics from one image cannot reflect the full scope of an artist. Also, previous work did not put content preservation in the important position, which would result in poor structure integrity, thus deteriorating the comprehensibility of generated image. These two problems would limit the visual quality improvement of style transfer results. Targeting at style resemblance and content preservation problems, we propose a style transfer system composed of collection representation space and semantic-guided reconstruction. We train an encoder–decoder network with art collections to construct a representation space that can reflect the style of the artist. Then, we use semantic information as guidance to reconstruct the target representation of the input image for better content preservation. We conduct both quantitative analysis and qualitative evaluation to assess the proposed method. Experiment results demonstrate that our approach well balanced the trade-off between capturing artistic characteristics and preserving content information in style transfer tasks.
Drivers Contributing to the Implementation of Generalized Audit Software (GAS) within Audit Firms in Malaysia

Many businesses are moving towards the usage of information technology to be in line with their daily operations. Traditional audit methods are no longer relevant these days. Generalized Audit Software (GAS) is a type of software that is suitable for auditors to perform audit tasks. GAS can assist auditors to improve the effectiveness and efficiency of audit. To date, the development of GAS in audit firms is still low, although many practitioners had argued that auditors can receive greater benefits by using GAS. This research is motivated by an interest in understanding drivers contributing to the implementation of GAS within audit firms located in the central region of Malaysia. Prior studies had placed more focus on internal auditors in large accounting firms, or auditors in other countries. However, there were not many researches being conducted on small and medium-sized audit firms in Malaysia. This study is therefore intended to fill in this gap. Data were collected using questionnaire survey on small and medium-sized audit firms registered with the Malaysian Institute of Accountants (MIA) in the central region of Malaysia. A total of 96 responses were analysed using multiple regression analysis. It was found that performance expectation, effort expectation and facilitating conditions had significantly influenced the implementation of GAS among small and medium-sized audit firms. However, social influence was found to have no significant relationship. The outcome of this study might help audit firms, software developers and academia to increase the usage of GAS to perform audit tasks. Future research should further explore on the influence of gender, age, voluntariness, experience, and other drivers contributing to the implementation of GAS within audit firms.
Sustainable Technology Integration in Underserved Area Schools: The Impact of Perceived Student Change on Teacher Continuance Intention

This study aims to examine the determining factors of teachers’ continuance intention to integrate technology in a smart classroom of schools in underserved areas. Smart classrooms provide a supportive learning environment for students by equipping them with advanced multi-functional and mobile technologies. A smart classroom can provide opportunities for teaching and learning by facilitating curriculum implementation and encouraging student success. The sustainable integration of technology in a smart classroom depends on the teacher’s ability to effectively utilize digital technology in the classroom. We assessed teachers’ perceptions of their technology integration by building a research model for sustainable technology integration in an underserved area in South Korea. For this, we included four aspects of teachers’ perceptions: the frequency of technology integration, the effort toward instructional practices, student change, and continuance intention. Data were gathered via a self-administered online survey with a sample of teachers who are participating in a smart school program and were analyzed using partial least squares structural equation modeling. The findings of the study show that teachers in smart classrooms are motivated to continue technology integration when they experience positive changes among students after employing smart classroom technologies. The research findings can contribute to the efforts of educators, scholars, and policy-makers to pursue sustainable development in underserved area schools.
Decorrelated Clustering with Data Selection Bias

Most of existing clustering algorithms are proposed without considering the selection bias in data. In many real applications, however, one cannot guarantee the data is unbiased. Selection bias might bring the unexpected correlation between features and ignoring those unexpected correlations will hurt the performance of clustering algorithms. Therefore, how to remove those unexpected correlations induced by selection bias is extremely important yet largely unexplored for clustering. In this paper, we propose a novel Decorrelation regularized K-Means algorithm (DCKM) for clustering with data selection bias. Specifically, the decorrelation regularizer aims to learn the global sample weights which are capable of balancing the sample distribution, so as to remove unexpected correlations among features. Meanwhile, the learned weights are combined with k-means, which makes the reweighted k-means cluster on the inherent data distribution without unexpected correlation influence. Moreover, we derive the updating rules to effectively infer the parameters in DCKM. Extensive experiments results on real world datasets well demonstrate that our DCKM algorithm achieves significant performance gains, indicating the necessity of removing unexpected feature correlations induced by selection bias when clustering.
Efficient Context-Aware Neural Machine Translation with Layer-Wise Weighting and Input-Aware Gating

Existing Neural Machine Translation (NMT) systems are generally trained on a large amount of sentence-level parallel data, and during prediction sentences are independently translated, ignoring cross-sentence contextual information. This leads to inconsistency between translated sentences. In order to address this issue, context-aware models have been proposed. However, document-level parallel data constitutes only a small part of the parallel data available, and many approaches build context-aware models based on a pre-trained frozen sentence-level translation model in a two-step training manner. The computational cost of these approaches is usually high. In this paper, we propose to make the most of layers pre-trained on sentence-level data in contextual representation learning, reusing representations from the sentence-level Transformer and significantly reducing the cost of incorporating contexts in translation. We find that representations from shallow layers of a pre-trained sentence-level encoder play a vital role in source context encoding, and propose to perform source context encoding upon weighted combinations of pre-trained encoder layers' outputs. Instead of separately performing source context and input encoding, we propose to iteratively and jointly encode the source input and its contexts and to generate input-aware context representations with a cross-attention layer and a gating mechanism, which resets irrelevant information in context encoding. Our context-aware Transformer model outperforms the recent CADec [Voita et al., 2019c] on the English-Russian subtitle data and is about twice as fast in training and decoding.
Self-attention for Twitter sentiment analysis in Spanish

This paper describes our proposal for Sentiment Analysis in Twitter for the Spanish language. The main characteristics of the system are the use of word embedding specifically trained from tweets in Spanish and the use of self-attention mechanisms that allow to consider sequences without using convolutional nor recurrent layers. These self-attention mechanisms are based on the encoders of the Transformer model. The results obtained on the Task 1 of the TASS 2019 workshop, for all the Spanish variants proposed, support the correctness and adequacy of our proposal.
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error

Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving cars, robotics and financial services. We propose an unsupervised method for detecting adversarial attacks in inner layers of autoencoder (AE) networks by maximizing a non-parametric measure of anomalous node activations. Previous work in this space has shown AE networks can detect anomalous images by thresholding the reconstruction error produced by the final layer. Furthermore, other detection methods rely on data augmentation or specialized training techniques which must be asserted before training time. In contrast, we use subset scanning methods from the anomalous pattern detection domain to enhance detection power without labeled examples of the noise, retraining or data augmentation methods. In addition to an anomalous “score” our proposed method also returns the subset of nodes within the AE network that contributed to that score. This will allow future work to pivot from detection to visualisation and explainability. Our scanning approach shows consistently higher detection power than existing detection methods across several adversarial noise models and a wide range of perturbation strengths.
Efficient Constituency Parsing by Pointing

We propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks.Specifically, our model estimates the likelihood of a span being a legitimate tree constituent via the pointing score corresponding to the boundary words of the span.Our parsing model supports efficient top-down decoding and our learning objective is able to enforce structural consistency without resorting to the expensive CKY inference.The experiments on the standard English Penn Treebank parsing task show that our method achieves 92.78 F1 without using pre-trained models, which is higher than all the existing methods with similar time complexity.Using pre-trained BERT, our model achieves 95.48 F1, which is competitive with the state-of-theart while being faster.Our approach also establishes new state-of-the-art in Basque and Swedish in the SPMRL shared tasks on multilingual constituency parsing.
Apriori Algorithm for Association Rules Mining in Aircraft Runway Excursions

Aviation Organization's (ICAO) strategic objectives to foster a global civil aviation system.Statistically, almost 40% of aviation accidents occur at airports, the most frequent type is runway excursion.The assessment of accident severity is an essential part of safety assessment methods.In this study, a set of influencing factors which would affect the severity of different types of runway excursions were investigated in order to determine those factors that typically occur together in runway excursion accidents.In order to achieve this aim, a large database was created, which contains information on all the conditions surrounding the runway excursion event, in a period between 2006 and 2016, for a total of 434 runway excursions.Associate Rules method with Apriori algorithm was used.The Apriori algorithm was used separately for each type of runway excursion.The results of this study show that different variables are associated with different types of runway excursions and different categories of severity.The most significant variable for all types of runway excursion is the class of the aircraft.Events with Major and Hazardous severity are associated with small aircraft, while events with catastrophic severity are associated with aircraft of medium-large dimensions.The least significant variable for runway excursion accidents is "Potential causes".The knowledge of the runway excursions severity based on analysis of their causes is essential to priorities safety budgets and safety risk mitigation measures, as required by ICAO regulations.
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation

We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.
Learning Dialog Policies from Weak Demonstrations

Deep reinforcement learning is a promising approach to training a dialog manager, but current methods struggle with the large state and action spaces of multi-domain dialog systems.Building upon Deep Q-learning from Demonstrations (DQfD), an algorithm that scores highly in difficult Atari games, we leverage dialog data to guide the agent to successfully respond to a user's requests.We make progressively fewer assumptions about the data needed, using labeled, reduced-labeled, and even unlabeled data to train expert demonstrators.We introduce Reinforced Fine-tune Learning, an extension to DQfD, enabling us to overcome the domain gap between the datasets and the environment.Experiments in a challenging multi-domain dialog system framework validate our approaches, and get high success rates even when trained on outof-domain data.
A recommendation system for car insurance

We construct a recommendation system for car insurance, to allow agents to optimize up-selling performances, by selecting customers who are most likely to subscribe an additional cover. The originality of our recommendation system is to be suited for the insurance context. While traditional recommendation systems, designed for online platforms (e.g. e-commerce, videos), are constructed on huge datasets and aim to suggest the next best offer, insurance products have specific properties which imply that we must adopt a different approach. Our recommendation system combines the XGBoost algorithm and the Apriori algorithm to choose which customer should be recommended and which cover to recommend, respectively. It has been tested in a pilot phase of around 150 recommendations, which shows that the approach outperforms standard results for similar up-selling campaigns.
Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data

This paper presents the system used in our submission to the IWPT 2020 Shared Task.Our system is a graph-based parser with secondorder inference.For the low-resource Tamil corpus, we specially mixed the training data of Tamil with other languages and significantly improved the performance of Tamil.Due to our misunderstanding of the submission requirements, we submitted graphs that are not connected, which makes our system only rank 6th over 10 teams.However, after we fixed this problem, our system is 0.6 ELAS higher than the team that ranked 1st in the official results.
Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding

We present Køpsala, the Copenhagen-Uppsala system for the Enhanced Universal Dependencies Shared Task at IWPT 2020.Our system is a pipeline consisting of off-the-shelf models for everything but enhanced graph parsing, and for the latter, a transition-based graph parser adapted from Che et al. ( 2019).We train a single enhanced parser model per language, using gold sentence splitting and tokenization for training, and rely only on tokenized surface forms and multilingual BERT for encoding.While a bug introduced just before submission resulted in a severe drop in precision, its post-submission fix would bring us to 4th place in the official ranking, according to average ELAS.Our parser demonstrates that a unified pipeline is effective for both Meaning Representation Parsing and Enhanced Universal Dependencies.
Exploring Context-Free Languages via Planning: The Case for Automating Machine Learning

While Machine Learning has achieved considerable success in recent years, this success crucially relies on human experts to select appropriate features, workflows, algorithms with their hyper-parameters, etc. Automating the role of the human expert has seen some attention from the Machine Learning community, with a dedicated workshop running since 2014 at one of the top Machine Learning conferences. % In this work, we propose to exploit multiple AI Planning tools for automating the human expert, generating Machine Learning pipelines automatically. We start from a knowledge about possible valid pipelines encoded as Context-Free Grammar, translate the problem of generating the corresponding language into Hierarchical Task Network (HTN) Planning model, further translate the HTN Planning model into a classical planning model. We use existing planners to produce multiple plans for the classical planning task, translate these plans into Machine Learning pipelines, train and evaluate these pipelines. Based on pipelines' accuracy feedback we update the classical planning model to improve the quality of pipelines obtained in next iterations. Using planning tools allows us to exploit the flexibility of model update instead of solution modification. We present an application that helps users to focus pipelines' exploration process by allowing to encode additional constraints on desired pipelines. Our experimental evaluation shows the feasibility of using planning techniques in this context.