Reviewerly/exHarmony huggingface dataset:
Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff

Is chatbot able to completely replace the human agent? The short answer could be – ``it depends...''. For some challenging cases, e.g., dialogue's topical spectrum spreads beyond the training corpus coverage, the chatbot may malfunction and return unsatisfied utterances. This problem can be addressed by introducing the Machine-Human Chatting Handoff (MHCH) which enables human-algorithm collaboration. To detect the normal/transferable utterances, we propose a Difficulty-Assisted Matching Inference (DAMI) network, utilizing difficulty-assisted encoding to enhance the representations of utterances. Moreover, a matching inference mechanism is introduced to capture the contextual matching features. A new evaluation metric, Golden Transfer within Tolerance (GT-T), is proposed to assess the performance by considering the tolerance property of the MHCH. To provide insights into the task and validate the proposed model, we collect two new datasets. Extensive experimental results are presented and contrasted against a series of baseline models to demonstrate the efficacy of our model on MHCH.
A Clustering based Selection Framework for Cost Aware and Test-time Feature Elicitation

Most learning algorithms are optimized with generalization and predictive performance as the goal. However, in most real-world machine learning applications, obtaining features at test time can incur a cost. For example, in clinical tasks, acquiring certain features such as FMRI or certain lab tests for patients can be expensive, while other features like patient demography or history are easily obtained and do not have a cost involved. Motivated by this, we address the problem of test-time elicitation of features. We formulate the problem of cost-aware feature elicitation as an optimization problem with trade-off between performance and feature acquisition cost. We assume that the cost of the features has already been paid in obtaining the training data. We propose a Clustering based Cost Aware Test-time Feature Elicitation (CATE) algorithm, which can select the relevant feature set given the observed attributes of the test instance. Our experiments on four real-world tasks demonstrate the efficacy and effectiveness of our proposed approach in both cost and performance.
Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue

In many domains, dialogue systems need to work collaboratively with users to successfully reconstruct the meaning the user had in mind.In this paper, we show how cognitive models of users' communicative strategies can be leveraged in a reinforcement learning approach to dialogue planning to enable interactive systems to give targeted, effective feedback about the system's understanding.We describe a prototype system that collaborates on reference tasks that distinguish arbitrarily varying color patches from similar distractors, and use experiments with crowd workers and analyses of our learned policies to document that our approach leads to context-sensitive clarification strategies that focus on key missing information, elicit correct answers that the system understands, and contribute to increasing dialogue success.
Retos educativos y sociales en tiempos de confinamiento

espanolEl virus Covid-19 se ha extendido de manera vertiginosa por todos los paises. Ha afectado a todos los grupos sociales, pero de manera muy desigual. Una pandemia que ha traspasado las fronteras nacionales y que el primer mundo se ha mostrado incapacitado para frenar sus efectos devastadores. De una primera fase de shock, hemos pasado a otra de regulacion de un nuevo contexto que afecta a todos los ambitos humanos. Este articulo, realizado durante los dos primeros meses de incidencia de la pandemia en Espana, tiene por objeto desvelar e interpretar la incidencia y la gestion del Covid-19 en el ambito sociocultural y en el educativo a partir del analisis informaciones publicadas en la prensa diaria. EnglishThe Covid-19 virus has spread like wildfire through all the countries of the world. It has affected all social groups but in very different ways. It is a pandemic that has gone beyond national borders and that the first world has shown itself incapable of curbing its devastating effects. From the first phase of shock we have passed to one of regulating a new context that affects all spheres of human life. The aim of this article, written during the first two months of the pandemic in Spain, is to reveal and interpret the effect and the management of Covid-19 in the socio-cultural field and in that of education, starting from an analysis of the articles published in the daily press
Aspectuality Across Genre: A Distributional Semantics Approach

The interpretation of the lexical aspect of verbs in English plays a crucial role for recognizing textual entailment and learning discourse-level inferences.We show that two elementary dimensions of aspectual class, states vs. events, and telic vs. atelic events, can be modelled effectively with distributional semantics.We find that a verb's local context is most indicative of its aspectual class, and demonstrate that closed class words tend to be stronger discriminating contexts than content words.Our approach outperforms previous work on three datasets.Lastly, we contribute a dataset of human-human conversations annotated with lexical aspect and present experiments that show the correlation of telicity with genre and discourse goals.
Parameter optimization of support vector machine based on improved grid algorithm

Abstract Support vector machines SVM) is a very popular algorithm used widely in the classification problem. The basic idea of SVM is to construct two parallel hyperplanes to separate two classes of instances and maximize the distance between the hyperplanes. In this paper, we propose a new algorithm called SVM-ICC-DBTCPRP one to solve the parameter optimization problem by improving the grid algorithm used by Fayed and Atiya. In the new algorithm there are two main sub-algorithms that are used to preselect the support vectors for reducing the time of training and preselect the parameter range to reduce the number of training respectively. Six typical data sets are selected to verify the effectiveness of our algorithm. The computed results show that our algorithm has the obvious advantage on the aspect of elapsed time than the one of Fayed and Atiya.
Fixing Vulnerabilities Automatically with Linters

Static analysis is a tried-and-tested approach to eliminate vulnerabilities in software. However, despite decades of successful use by experts, mainstream programmers often deem static analysis too costly to use. Mainstream programmers do routinely use linters, which are static analysis tools geared towards identifying simple bugs and stylistic issues in software. Can linters serve as a medium for delivering vulnerability detection to mainstream programmers? We investigate the extent of which linters can be leveraged to help programmers write secure software. We present new rules for ESLint that detect—and automatically fix—certain classes of cross-site scripting, SQL injection, and misconfiguration vulnerabilities in JavaScript. Evaluating our experience, we find that there is enormous potential in using linters to eliminate vulnerabilities in software, due to the relative ease with which linter rules can be implemented and shared to the community. We identify several open challenges, including third-party library dependencies and linter configuration, and propose ways to address them.
Adaptive Image-based Visual Servoing of Mobile Manipulator with an Uncalibrated Fixed Camera

This paper studied the problem of image-based visual servoing of mobile manipulator. In our system, a camera without any prior knowledge is fixed on the ceiling board in an arbitrary pose. In addition, the relative position of manipulator and mobile platform is also unknown, neither is the feature point position which is marked before on the manipulator end-effector. To overcome the difficulties mentioned above, we proposed a vision-based controller to make the vision feature point move toward the desired site on the camera image plane. And an update law of unknown parameters is designed to estimate parameters online. Lyapunov theory is utilized to make the analysis of system convergence. And simulation is provided to show the validity of our approach.
ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text

Code mixing is a common phenomena in multilingual societies where people switch from one language to another for various reasons.Recent advances in public communication over different social media sites have led to an increase in the frequency of code-mixed usage in written language.In this paper, we present the Generative Morphemes with Attention (GenMA) Model sentiment analysis system contributed to SemEval 2020 Task 9 SentiMix.The system aims to predict the sentiments of the given English-Hindi code-mixed tweets without using word-level language tags instead inferring this automatically using a morphological model.The system is based on a novel deep neural network (DNN) architecture, which has outperformed the baseline F1-score on the test data-set as well as the validation data-set.Our results can be found under the user name "koustava" on the "Sentimix Hindi English" 1 page.
Nonclassical oscillations in pre- and post-selected quantum walks

Quantum walks are counterparts of classical random walks. They spread faster, which can be exploited in information processing tasks, and constitute a versatile simulation platform for many quantum systems. Yet, some of their properties can be emulated with classical light. This raises a question: which aspects of the model are truly nonclassical? We address it by carrying out a photonic experiment based on a pre- and post-selection paradox. The paradox implies that if somebody could choose to ask either if the particle is at position $x=0$ at even time steps or at position $x=d$ $(d&gt;1)$ at odd time steps, the answer would be positive, no matter the question asked. Therefore, the particle seems to undergo long distance oscillations despite the fact that the model allows it to jump one position at a time. We translate this paradox into a Bell-like inequality and then into a contextuality witness. Finally, we experimentally verify this witness up to eight standard deviations.
Inherent Importance of Early Visual Features in Attraction of Human Attention

Local contrasts attract human attention to different areas of an image. Studies have shown that orientation, color, and intensity are some basic visual features which their contrasts attract our attention. Since these features are in different modalities, their contribution in the attraction of human attention is not easily comparable. In this study, we investigated the importance of these three features in the attraction of human attention in synthetic and natural images. Choosing 100% percent detectable contrast in each modality, we studied the competition between different features. Psychophysics results showed that, although single features can be detected easily in all trials, when features were presented simultaneously in a stimulus, orientation always attracts subject’s attention. In addition, computational results showed that orientation feature map is more informative about the pattern of human saccades in natural images. Finally, using optimization algorithms we quantified the impact of each feature map in construction of the final saliency map.
Memory Attentive Fusion: External Language Model Integration for Transformer-based Sequence-to-Sequence Model

This paper presents a novel fusion method for integrating an external language model (LM) into the Transformer based sequence-to-sequence (seq2seq) model. While paired data are basically required to train the seq2seq model, the external LM can be trained with only unpaired data. Thus, it is important to leverage memorized knowledge in the external LM for building the seq2seq model, since it is hard to prepare a large amount of paired data. However, the existing fusion methods assume that the LM is integrated with recurrent neural network-based seq2seq models instead of the Transformer. Therefore, this paper proposes a fusion method that can explicitly utilize network structures in the Transformer. The proposed method, called memory attentive fusion, leverages the Transformer-style attention mechanism that repeats source-target attention in a multi-hop manner for reading the memorized knowledge in the LM. Our experiments on two text-style conversion tasks demonstrate that the proposed method performs better than conventional fusion methods.
Optimized Candidate Generation for Frequent Subgraph Mining in a Single Graph

Mining frequent subgraphs from graph databases is a basic task with broad applications. Frequent subgraph mining is defined as finding all subgraphs that appear more than specified threshold value. It consists of mainly two steps, candidate generation and frequency calculation. In candidate generation step, most of the existing work starts with a frequent edge or vertex to generate frequent candidate patterns. This process is not scalable due to exponential number of candidate patterns generation. In this paper, an optimized algorithm is presented to generate candidate patterns for mining frequent subgraphs from a large single graph. The proposed algorithm starts and extends candidates with frequent subgraphs. The proposed algorithm uses graph invariant properties and symmetries present in a graph to generate candidate subgraphs thus reducing generation of enormous amount of candidate subgraphs. Subgraphs are extended by adding another frequent subgraph determined by the symmetry mapping of subgraph there by reduces the complexities involved in candidate generation and frequency counting. An evaluation study on datasets explores the strengths and limitations of the proposed work. The results make sure that, this is an optimized approach to generate candidate subgraphs directly using invariant properties.
Reviewing the Effects of Leader-Member Exchange on Job Crafting via ModeratedMediation Analysis: A Study on Teachers

Purpose -Main purpose of this study is to define the antecedents of job crafting.Within this context, it is questioned whether self-efficacy has a moderating role on the indirect effect leader-member exchange on job crafting, when combined with perceived supervisor support.Design/methodology/approach -The data were collected via questionnaires and simple random sampling while including middle school teachers in a city of Turkey as participants.In addition, data were gathered in October and November 2019.The reason why teachers were selected for this study is the fact that there is limited amount of studies in the literature doing so.In order to test the hypotheses, hierarchical regression analysis, sobel test and moderated mediation analysis were used.Findings -The findings show that leader-member exchange positively and significantly affects job crafting and additionally perceived supervisor support has a mediating role in this relationship.Along with this, it is also defined that supervisor support affects job crafting positively and significantly while self-efficacy has a moderating role in this relationship.Finally, it is seen that selfefficacy has a moderating role in the indirect effect of leader-member exchange on job crafting, when combined with supervisor support.Discussion -The study tries to put forward the effects of leader-member exchange, perceived supervisor support and self-efficacy on job crafting in a single model with a multilevel perspective.Additionally, there was no studies found in the literature that takes all these variables into consideration together and that looks into the relationship between these variables for teachers.For this reason, it can be stated that the obtained findings are important both for the literature and for practitioners.
Drugs, brands and consumer culture: the sign-value of the products sold on the darknet marketplaces

Purpose The purpose of this study is to explore drug consumption from a cultural perspective, in the context of the consumer culture. It aims to identify if, through the branding process, cryptomarket vendors are attaching a sign-value to their products to facilitate the process by which consumers will recognize and appreciate it. Design/methodology/approach The study was done by performing a qualitative content analysis loosely inspired from semiotics on a sample of 40 seller pages from the Dream Market and samples of their listings, collected in 2018. The vendors who had over 1,000 successful transactions were selected, as they were considered to be the ones who have gained a certain level of trust on the cryptomarket and were considered to having to compete by differentiating their services through their brands of choice. Findings The results have shown that the sign-value attached to the drugs sold by the vendors from this sample can be divided in two different types of sign-systems: the popular culture and the drug cultures. The popular culture includes sign-value borrowed from established brands, popular media and media representations of crime worlds. The drug cultures include values from three types of subcultural systems: cannabis, party and psychonaut subculture. Originality/value The study is trying to stir the discussions around the regulation of the drug markets by looking at the market forces within them as rather a product of consumer capitalism and not as processes that happen outside the postmodern cultural and societal trends.
Constrained and composite optimization via adaptive sampling methods

The motivation for this paper stems from the desire to develop an adaptive sampling method for solving constrained optimization problems in which the objective function is stochastic and the constraints are deterministic. The method proposed in this paper is a proximal gradient method that can also be applied to the composite optimization problem min f(x) + h(x), where f is stochastic and h is convex (but not necessarily differentiable). Adaptive sampling methods employ a mechanism for gradually improving the quality of the gradient approximation so as to keep computational cost to a minimum. The mechanism commonly employed in unconstrained optimization is no longer reliable in the constrained or composite optimization settings because it is based on pointwise decisions that cannot correctly predict the quality of the proximal gradient step. The method proposed in this paper measures the result of a complete step to determine if the gradient approximation is accurate enough; otherwise a more accurate gradient is generated and a new step is computed. Convergence results are established both for strongly convex and general convex f. Numerical experiments are presented to illustrate the practical behavior of the method.
Ensemble of LSTMs and Feature Selection for Human Action Prediction

As robots are becoming more and more ubiquitous in human environments, it will be necessary for robotic systems to better understand and predict human actions. However, this is not an easy task, at times not even for us humans, but based on a relatively structured set of possible actions, appropriate cues, and the right model, this problem can be computationally tackled. In this paper, we propose to use an ensemble of long-short term memory (LSTM) networks for human action prediction. To train and evaluate models, we used the MoGaze ( https://humans-to-robots-motion.github.io/mogaze/ ) dataset – currently the most comprehensive dataset capturing poses of human joints and the human gaze. We have thoroughly analyzed the MoGaze dataset and selected a reduced set of cues for this task. Our model can predict (i) which of the labeled objects the human is going to grasp, and (ii) which of the macro locations the human is going to visit (such as table or shelf). We have exhaustively evaluated the proposed method and compared it to individual cue baselines. The results suggest that our LSTM model slightly outperforms the gaze baseline in single object picking accuracy, but achieves better accuracy in macro object prediction. Furthermore, we have also analyzed the prediction accuracy when the gaze is not used, and in this case, the LSTM model considerably outperformed the best single cue baseline.
Memory Augmentation Through Lifelogging: Opportunities and Challenges

Perfect memory is a dream long sought and which has become especially prevalent in our knowledge society. While only a few are blessed with a photographic memory, most people compensate for the fallacies of their memory with the use of tools and tricks to organize information, schedule reminders and remember facts. Most aspects of our lives, however, leave behind some sort of data trail. We explicitly document our trips and experiences while our phones implicitly record our movements and messages. The sum of these recordings paints a holistic picture of our activities in a so-called lifelog. Lifelogging technologies have long been praised for extending our memory’s capacity by allowing us to recall and browse our recorded experiences. But despite positive, well-documented effects on memory and well-being, lifelogging largely remains a niche activity with few commercially successful applications. Over recent years, we built a series of prototypes to investigate the challenges and opportunities of lifelogging technologies. We investigated the positioning of on-body cameras, the collection of implicit and explicit lifelogging data, requirements for effective lifelog reviews, automated summaries and ways to navigate multimedia lifelogs. In this chapter, we discuss the potential of these technologies to augment memory and the obstacles of bringing memory prosthetics to a broad audience.
Comparison between Voting Classifier and Deep Learning methods for Arabic Dialect Identification

In this paper, we present three methods developed for the NADI shared task on Arabic Dialect Identification for tweets. The first and the second method use respectively a machine learning model based on a Voting Classifier with words and character level features and a deep learning model at word level. The third method uses only character-level features. We explored different text representation such as Tf-idf (first model) and word embeddings (second model). The Voting Classifier was the most powerful prediction model, achieving the best macro-average F1 score of 18.8% and an accuracy of 36.54% on the official test. Our model ranked 9 on the challenge and in conclusion we propose some ideas to improve its results.
Query Intent Classification with Short Sentences in Agglutinative Languages

In this work, a new approach is presented for the classification of query sentences under limited data conditions which often occurs in frequently asked questions or community question and answer platforms. Also, the accuracy of this classification system on a validation set is measured and summarized in an evolutionary way. The techniques are especially designed for the agglutinative structure of Turkish language. However, the base approach can easily be applied to other languages with similar agglutinative morphology. The method is comprised of three major steps: bag-of-terms extraction from query texts in the training set, generation of Term Frequency- Inverse Document Frequency of these filtered terms, and the matching score computation of query sentences in the validation and test set against unique intents existing in the training set. The training set contains 4500 query samples where each sample is matched with only one of 1500 different intents. With the best configuration of the proposed method, the intent classification accuracy measured on validation and test sets have been 75.7% and 68.0%, respectively.