Reviewerly/exHarmony huggingface dataset:
Interactive Question Clarification in Dialogue via Reinforcement Learning

Coping with ambiguous questions has been a perennial problem in real-world dialogue systems.Although clarification by asking questions is a common form of human interaction, it is hard to define appropriate questions to elicit more specific intents from a user.In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query.We first formulate a collection partitioning problem to select a set of labels enabling us to distinguish potential unambiguous intents.We list the chosen labels as intent phrases to the user for further confirmation.The selected label along with the original user query then serves as a refined query, for which a suitable response can more easily be identified.The model is trained using reinforcement learning with a deep policy network.We evaluate our model based on real-world user clicks and demonstrate significant improvements across several different experiments.
The Bayesian Inference of Pareto Models Based on Information Geometry

Bayesian methods have been rapidly developed due to the important role of explicable causality in practical problems. We develope geometric approaches to Bayesian inference of Pareto models, and give an application to the analysis of sea clutter. For Pareto two-parameter model, we show the non-existence of α-parallel prior in general, hence we adopt Jeffreys prior to deal with the Bayesian inference. Considering geodesic distance as the loss function, an estimation in the sense of minimal mean geodesic distance is obtained. Meanwhile, by involving Al-Bayyati’s loss function we gain a new class of Bayesian estimations. In the simulation, for sea clutter, we adopt Pareto model to acquire various types of parameter estimations and the posterior prediction results. Simulation results show the advantages of the Bayesian estimations proposed and the posterior prediction.
Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing

This paper presents AutoGOAL, a system for automatic machine learning (AutoML) that uses heterogeneous techniques.In contrast with existing AutoML approaches, our contribution can automatically build machine learning pipelines that combine techniques and algorithms from different frameworks, including shallow classifiers, natural language processing tools, and neural networks.We define the heterogeneous AutoML optimization problem as the search for the best sequence of algorithms that transforms specific input data into the desired output.This provides a novel theoretical and practical approach to AutoML.Our proposal is experimentally evaluated in diverse machine learning problems and compared with alternative approaches, showing that it is competitive with other AutoML alternatives in standard benchmarks.Furthermore, it can be applied to novel scenarios, such as several NLP tasks, where existing alternatives cannot be directly deployed.The system is freely available and includes in-built compatibility with a large number of popular machine learning frameworks, which makes our approach useful for solving practical problems with relative ease and effort.
Unsupervised Fine-tuning for Text Clustering

Fine-tuning with pre-trained language models (e.g.BERT) has achieved great success in many language understanding tasks in supervised settings (e.g.text classification).However, relatively little work has been focused on applying pre-trained models in unsupervised settings, such as text clustering.In this paper, we propose a novel method to fine-tune pre-trained models unsupervisedly for text clustering, which simultaneously learns text representations and cluster assignments using a clustering oriented loss.Experiments on three text clustering datasets (namely TREC-6, Yelp, and DBpedia) show that our model outperforms the baseline methods and achieves stateof-the-art results.
Empowering Teachers to Augment Students’ Reading Experience

This chapter discusses an attempt to empower teachers to ‘augment’ students’ reading experiences as part of the project The Living Book – Augmenting Reading for Life (Erasmus+). The project’s overall aim is to address the under-achievement of European students in reading by developing an innovative approach that combines offline activities promoting reading literacy with online experiences of books’ ‘virtual augmentation.’ More specifically, recognizing the important role of teachers in any educational reform effort, the project aims at strengthening teachers’ profile and competences in adopting the Living Book approach and in dealing with diversified groups of learners, and particularly with pupils from disadvantaged backgrounds, through a series of training sessions. The chapter outlines the theoretical premises of the Living Book and provides an overview of the theoretical framework underlying the design of the ‘Augmented Teacher’ professional development course. It also describes the content and structure of the course, and the process of evaluation currently taking place in the project partner countries.
Amobee at SemEval-2020 Task 7: Regularization of Language Model Based Classifiers

This paper describes Amobee's participation in SemEval-2020 task 7: "Assessing Humor in Edited News Headlines", sub-tasks 1 and 2. The goal of this task was to estimate the funniness of human modified news headlines.In this paper we present methods to fine-tune and ensemble various language models (LM) based classifiers for this task.This technique used for both sub-tasks and reached the second place (out of 49) in sub-tasks 1 with RMSE score of 0.5, and the second (out of 32) place in sub-task 2 with accuracy of 66% without using any additional data except the official training set.
The fractal geometry of fitness landscapes at the local optima level

Abstract A local optima network (LON) encodes local optima connectivity in the fitness landscape of a combinatorial optimisation problem. Recently, LONs have been studied for their fractal dimension . Fractal dimension is a complexity index where a non-integer dimension can be assigned to a pattern. This paper investigates the fractal nature of LONs and how that nature relates to metaheuristic performance on the underlying problem. We use visual analysis, correlation analysis, and machine learning techniques to demonstrate that relationships exist and that fractal features of LONs can contribute to explaining and predicting algorithm performance. The results show that the extent of multifractality and high fractal dimensions in the LON can contribute in this way when placed in regression models with other predictors. Features are also individually correlated with search performance, and visual analysis of LONs shows insight into this relationship.
Flexible parametric implantation of voicing in whispered speech under scarce training data

Whispered-voice to normal-voice conversion is typically achieved using codec-based analysis and re-synthesis, using statistical conversion of important spectral and prosodic features, or using data-driven end-to-end signal conversion. These approaches are however highly constrained by the architecture of the codec, the statistical projection, or the size and quality of the training data. In this paper, we presume direct implantation of voiced phonemes in whispered speech and we focus on fully flexible parametric models that i) can be independently controlled, ii) synthesize natural and linguistically correct voiced phonemes, iii) preserve idiosyncratic characteristics of a given speaker, and iv) are amenable to co-articulation effects through simple model interpolation. We use natural spoken and sung vowels to illustrate these capabilities in a signal modeling and re-synthesis process where spectral magnitude, phase structure, F0 contour and sound morphing can be independently controlled in arbitrary ways.
Visual Object Tracking with Colored Measurement Noise using Kalman and UFIR Filters

Visual object tracking is commonly accompanied with large variations in the image frame size and position, as caused by the object dynamics and low frame rate. In this paper, we treat such variations as a Gauss-Markov colored measurement noise (CMN), modify the Kalman filter and unbiased finite impulse response filter using the backward Euler method and employing measurement differencing, and apply to some simulated and benchmark data. It is shown that the modified filters can suppress efficiently both the slow frame variations associated with CMN and fast variations associated with white noise. Extensive experimental investigations conducted for the "Car4" benchmark database has demonstrated a high efficiency of the modified algorithms.
Space-time Map based Path Planning Scheme in Large-scale Intelligent Warehouse System

As an important part of large-scale intelligent warehouse system, path planning by considering the cooperation among automated guided vehicles (AGVs) becomes an important factor to enhance the efficiency of the system. To this end, we propose a novel path planning scheme based on space-time map with the target of improving the path planning efficiency. Specifically, we first model the time dimension and construct a space-time map to obtain the planned path information of the intelligent warehouse system. Then, by taking the size of AGV and turning cost into consideration, we design a node extension algorithm to limit the search direction of AGVs. To decrease the complexity of the proposed algorithm and improve the efficiency of head-on conflict avoidance, a time window based piecewise path planning method and a mechanism of protected zone are developed, respectively. Simulation results show that the proposed space-time map based path planning scheme has a better performance than the conventional method in terms of the number of turns, the system running time and the moving distance of AGVs.
Natural Language Generation in Dialogue Systems for Customer Care

In this paper we discuss the role of natural language generation (NLG) in modern dialogue systems (DSs). In particular, we will study the role that a linguistically sound NLG architecture can have in a DS. Using real examples from a new corpus of dialogue in customer-care domain, we will study how the non-linguistic contextual data can be exploited by using NLG.
An Adaptive Fuzzy for PMSM to Overcome the Changing Load

This paper proposes a robust Fuzzy PI to detect the changing of load and provide an appropriate control signal to stabilize the motor speed. First, the mathematical model of Permanent magnet synchronous motor (PMSM) and the structure of the Mamdani fuzzy controller were proved. Secondly, this controller will be combined with PI controller to adjust K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> , K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> when the load changing. Finally, the speed response and overshoot will be discussed and compared with the PI method.
SOIL GEOCHEMISTRY ADJACENT TO CARBONATITES FROM CAÇAPAVA DO SUL, RS, BY FLUORESCENCE OF X-RAYS BY DISPERSIVE ENERGY

Currently, carbonatites are being severally studied in a prospective matter due to their high chance of containing calcium carbonate, magnesium and iron. They are also considered extremely rich in phosphate and minerals rich in Rare Earth Elements (ETR). In this context, the municipality of Caçapava do Sul has two occurrences of carbonatites: Picada de Tocos and Passo Feio. In order to enrich knowledge and assist in carbonate prospecting, this work aimed to characterize the soil geochemistry at the occurrences of the carbonatites in Caçapava do Sul. The data obtained revealed that there is an anomalous presence of Ca, Mg, Fe, Ti, P, Mn, Co, Th and Ce elements. This anomaly is directly linked to the presence of carbonatites in the region and their chemical composition.
IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection

This paper describes the system proposed for addressing the research problem posed in Task 10 of SemEval-2020 1 : Emphasis Selection For Written Text in Visual Media.We propose an end-to-end model that takes as input the text and corresponding to each word gives the probability of the word to be emphasized.Our results show that transformer-based models are particularly effective in this task.We achieved the best Match m score (described in section 2.2) of 0.810 and were ranked third on the leader-board.
Attacking Machine Learning Models for Social Good

As machine learning (ML) techniques are becoming widely used, awareness of the harmful effect of automation is growing. Especially, in problem domains where critical decisions are made, machine learning-based applications may raise ethical issues with respect to fairness and privacy. Existing research on fairness and privacy in the ML community mainly focuses on providing remedies during the ML model training phase. Unfortunately, such remedies may not be voluntarily adopted by the industry that is concerned about the profits. In this paper, we propose to apply, from the user’s end, a fair and legitimate technique to “game” the ML system to ameliorate its social accountability issues. We show that although adversarial attacks can be exploited to tamper with ML systems, they can also be used for social good. We demonstrate the effectiveness of our proposed technique on real world image and credit data.
A Comprehensive Analysis of Crowdsourcing for Subjective Evaluation of Tone Mapping Operators

Tone mapping operators (TMO) are pivotal in rendering High Dynamic Range (HDR) content on limited dynamic range media. Analysing the quality of tone mapped images depends on several objective factors and a combination of several subjective factors like aesthetics, fidelity etc. Objective Image quality assessment (IQA) metrics are often used to evaluate TMO quality but they do not always reflect the ground truth. A robust alternative to objective IQA metrics is subjective quality assessment. Although, subjective experiments provide accurate results, they can be time-consuming and expensive to conduct. Over the last decade, crowdsourcing experiments have become more popular for collecting large amount of data within a shorter period of time for a lesser cost. Although they provide more data requiring less resources, lack of controlled environment for the experiment results in noisy data. In this work 1 , we propose a comprehensive analysis of crowdsourcing experiments with two different groups of participants. Our contributions include a comparative study and a collection of methods to detect unreliable participants in crowdsourcing experiments in a TMO quality evaluation scenario. These methods can be utilized by the scientific community to increase the reliability of the gathered data.
Development of training content for master students in course "mechatronics and robotics" at the University

In the conditions of active development of Informatization and technologization of society there is urgent need for specialists carrying out pedagogical activity with the help of mechanical systems and The purpose of the article is to identify the features of content development for training master students in course Mechatronics and Robotics at pedagogical University. On the basis of Federal state educational standard, relevant documentation and necessary methodological support were prepared in higher educational institution. The article presents the process of developing the content of training masters in course Mechanics and robotics. The development of a new profile of training is based on competence, activity and system approaches taking into account aspects of personal-oriented training and practice focus. On the basis of Federal state educational standard basic educational program, modules of educational programs, test content and methodical support are developed. The article reveals requirements and procedure for the development of theoretical content of training, as well as educational and industrial practices.
KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension and Generation

This paper presents our strategies in SemEval 2020 Task 4: Commonsense Validation and Explanation.We propose a novel way to search for evidence and choose the different large-scale pre-trained models as the backbone for three subtasks.The results show that our evidencesearching approach improves model performance on commonsense explanation task.Our team ranks 2nd in subtask C according to human evaluation score. 1
The Two Shades of Dubbing in Neural Machine Translation

Dubbing has two shades; synchronisation constraints are applied only when the actor's mouth is visible on screen, while the translation is unconstrained for off-screen dubbing.Consequently, different synchronisation requirements, and therefore translation strategies, are applied depending on the type of dubbing.In this work, we manually annotate an existing dubbing corpus (Heroes) for this dichotomy.We show that, even though we did not observe distinctive features between on-and off-screen dubbing at the textual level, on-screen dubbing is more difficult for MT (-4 BLEU points).Moreover, synchronisation constraints dramatically decrease translation quality for off-screen dubbing.We conclude that, distinguishing between on-screen and off-screen dubbing is necessary for determining successful strategies for dubbing-customised Machine Translation.
Bilingual Subword Segmentation for Neural Machine Translation

This paper proposed a new subword segmentation method for neural machine translation, "Bilingual Subword Segmentation," which tokenizes sentences to minimize the difference between the number of subword units in a sentence and that of its translation.While existing subword segmentation methods tokenize a sentence without considering its translation, the proposed method tokenizes a sentence by using subword units induced from bilingual sentences; this method could be more favorable to machine translation.Evaluations on WAT Asian Scientific Paper Excerpt Corpus (ASPEC) English-to-Japanese and Japanese-to-English translation tasks and WMT14 English-to-German and German-to-English translation tasks show that our bilingual subword segmentation improves the performance of Transformer neural machine translation (up to +0.81 BLEU).
System simulation and synchronization for optimal evolutionary design of nonlinear controlled systems

Due to the influence of nonlinearity and time-variation, it is difficult to establish an accurate model of concrete frame structures that adopt active controllers. Fuzzy theory is a relatively appropriate method but susceptible to human subjective experience to decrease the performance. This paper proposes a novel artificial intelligence based EBA (Evolved Bat Algorithm) controller with machine learning matched membership functions in the complex nonlinear system. The proposed affine transformed membership functions are adopted and stabilization and performance criterion of the closed-loop fuzzy systems are obtained through a new parametrized linear matrix inequality which is rearranged by machine learning affine matched membership functions. The trajectory of the closed-loop dithered system and that of the closed-loop fuzzy relaxed system can be made as close as desired. This enables us to get a rigorous prediction of stability of the closed-loop dithered system by establishing that of the closed-loop fuzzy relaxed system.
A Novel Collaborative Filtering Algorithm and Its Application for Recommendations in E-Commerce

With the rapid development of the Internet, the amount of data recorded on the Internet has increased dramatically.It is becoming more and more urgent to effectively obtain the specific information we need from the vast ocean of data.In this study, we propose a novel collaborative filtering algorithm for generating recommendations in e-commerce.This study has two main innovations.First, we propose a mechanism that embeds temporal behavior information to find a neighbor set in which each neighbor has a very significant impact on the current user or item.Second, we propose a novel collaborative filtering algorithm by injecting the neighbor set into probability matrix factorization.We compared the proposed method with several state-of-the-art alternatives on real datasets.The experimental results show that our proposed method outperforms the prevailing approaches.
Approximate Nearest Neighbors Beyond Space Partitions

We show improved data structures for the high-dimensional approximate nearest neighbor search problem (ANN) for ℓp distances for “large” values of p and for generalized Hamming distances. The previous best data structures proceeded by embedding a metric of interest into the ℓ∞ space or an ℓ∞-direct sum with simple summands, and then using data structures of Indyk (FOCS 1998, SoCG 2002) for ℓ∞-ANN. In contrast to this, we bypass the embedding step and proceed by extending the technique underlying the ℓ∞ data structures to handle ℓp and generalized Hamming distances directly. The resulting data structures are randomized, in contrast to Indyk's result for ℓ∞-ANN, and replicate input points, in contrast with Locality Sensitive Hashing. This leads to ANN data structures with significantly improved approximations over those implied by embeddings, as well as those obtained using all known approaches based on random space partitions.
Calibration of Probabilistic Predictive Models

Predicting unknown and unobserved events is a common task in many domains. Mathematically, the uncertainties arising in such prediction tasks can be described by probabilistic predictive models. Id ...
Predicting the System Failures using Machine Learning Algorithms

Quick recuperation stays one of the key difficulties to architects and administrators of vast organized framework. Before recuperation can happen, notwithstanding, one should  first identify and analyze the disappointment, so without arrange disappointment expectation it is  hard to recoup the failure. Failure in any piece of the system result in slow down the system. Failure Prediction helps in early detection of failure in the system, so that we can avoid the damage caused by the failure in that particular system. By predicting the failure we can avoid loss of data, application services. By prediction we can also decide if the framework will breakdown as indicated by the examination on the assessment of authentic investigation and current conduct of the framework.
Incorporating societal topics in software engineering education: A case study of a customer-driven course

Context: This full research paper presents a review of the project descriptions from a projectbased course designed around close collaboration with students and external customers. Our master course is based on four decades of software engineering experience and teaching at Norwegian University of Science and Technology. In the scope of this work, we analyzed 45 customers’ project proposals over the past three years.Objectives: More precisely, we looked into 1) how many societal topics are present in project customers’ descriptions? 2) Which sustainable development goals if any, are addressed from the customers? 3) How do the trends of societal topics addressing SDGs in customer projects change over time? 4) Which categories of individuals do project descriptions target?Methods: We conducted a deductive thematic analysis utilizing open coding of the customers’ project descriptions. Results: We found that most project descriptions provided by the customers had a technical focus with a moderate portion of projects addressing societal topics for specific target groups.Contribution: The study’s overall outcomes contribute to the course’s future improvement and informs customers about the prospective socially relevant project proposals.