Reviewerly/exHarmony huggingface dataset:
Neural Duplicate Question Detection without Labeled Training Data

Supervised training of neural models to duplicate question detection in community Question Answering (cQA) requires large amounts of labeled question pairs, which are costly to obtain.To minimize this cost, recent works thus often used alternative methods, e.g., adversarial domain adaptation.In this work, we propose two novel methods: (1) the automatic generation of duplicate questions, and (2) weak supervision using the title and body of a question.We show that both can achieve improved performances even though they do not require any labeled data.We provide comprehensive comparisons of popular training strategies, which provides important insights on how to 'best' train models in different scenarios.We show that our proposed approaches are more effective in many cases because they can utilize larger amounts of unlabeled data from cQA forums.Finally, we also show that our proposed approach for weak supervision with question title and body information is also an effective method to train cQA answer selection models without direct answer supervision.
Balinese Historian Chatbot using Full-Text Search and Artificial Intelligence Markup Language Method

In the era of technology, various information could be obtained quickly and easily.The history of Bali is one of the information that could be obtained.Balinese have known their history through Babad and stories which are told through generations.Babad is traditionalhistorical writing which tells important event that has happened.As technology evolves, Balinese's interest in studying their own history has been decreased.It is caused by people interest in studying history books and chronicles tend to decrease over time.Therefore, an innovation of technology, which able to convert historical data from printed media to digital media, is needed.The technology that could be used is Chatbot technology; a computer program that could carry out conversations.Chatbot technology is used to make people learning history easily by using Instant Messenger LINE as a platform to communicate.This Chatbot uses two methods, namely the Artificial Intelligence Markup Language method and the Full-Text Search method.The Artificial Intelligence Markup Language method is used as the process of making characteristic of questions and answers.The Full-Text Search method is the process of matching answers based on user input.This chatbot only uses Indonesian to communicate.The results of this study are a Chatbot that could be accessed by using Instant Messenger LINE and could communicate like historian expert.
Demonstration of Krypton

In this demonstration, we present Krypton, a system for accelerating occlusion-based deep convolution neural network (CNN) explanation workloads. Driven by the success of CNNs in image understanding tasks, there is growing adoption of CNNs in various domains, including high stakes applications such as radiology. However, users of such applications often seek an "explanation" for why a CNN predicted a certain label. One of the most widely used approaches for explaining CNN predictions is the occlusion-based explanation (OBE) method. This approach is computationally expensive due to the large number of re-inference requests produced. Krypton reduces the runtime of OBE by up to 35x by enabling incremental and approximate inference optimizations that are inspired by classical database query optimization techniques. We allow the audience to interactively diagnose CNN predictions from several use cases, including radiology and natural images. A short video of our demonstration can be found here: https://youtu.be/1OWddbd4n6Y
Ranking Passages for Argument Convincingness

In data ranking applications, pairwise annotation is often more consistent than cardinal annotation for learning ranking models.We examine this in a case study on ranking text passages for argument convincingness.Our task is to choose text passages that provide the highest-quality, most-convincing arguments for opposing sides of a topic.Using data from a deployed system within the Bing search engine, we construct a pairwiselabeled dataset for argument convincingness that is substantially more comprehensive in topical coverage compared to existing public resources.We detail the process of extracting topical passages for queries submitted to a search engine, creating annotated sets of passages aligned to different stances on a topic, and assessing argument convincingness of passages using pairwise annotation.Using a state-of-the-art convincingness model, we evaluate several methods for using pairwiseannotated data examples to train models for ranking passages.Our results show pairwise training outperforms training that regresses to a target score for each passage.Our results also show a simple 'win-rate' score is a better regression target than the previously proposed page-rank target.Lastly, addressing the need to filter noisy crowd-sourced annotations when constructing a dataset, we show that filtering for transitivity within pairwise annotations is more effective than filtering based on annotation confidence measures for individual examples.
Fine-Grained Evaluation for Entity Linking

The Entity Linking (EL) task identifies entity mentions in a text corpus and associates them with an unambiguous identifier in a Knowledge Base.While much work has been done on the topic, we first present the results of a survey that reveal a lack of consensus in the community regarding what forms of mentions in a text and what forms of links the EL task should consider.We argue that no one definition of the Entity Linking task fits all, and rather propose a fine-grained categorization of different types of entity mentions and links.We then re-annotate three EL benchmark datasets -ACE2004, KORE50, and VoxEL -with respect to these categories.We propose a fuzzy recall metric to address the lack of consensus and conclude with finegrained evaluation results comparing a selection of online EL systems.
Modeling language learning using specialized Elo rating

Automatic assessment of the proficiency levels of the learner is a critical part of Intelligent Tutoring Systems.We present methods for assessment in the context of language learning.We use a specialized Elo formula used in conjunction with educational data mining.We simultaneously obtain ratings for the proficiency of the learners and for the difficulty of the linguistic concepts that the learners are trying to master.From the same data we also learn a graph structure representing a domain model capturing the relations among the concepts.This application of Elo provides ratings for learners and concepts which correlate well with subjective proficiency levels of the learners and difficulty levels of the concepts.
Entity resolution for noisy ASR transcripts

Arushi Raghuvanshi, Vijay Ramakrishnan, Varsha Embar, Lucien Carroll, Karthik Raghunathan. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations. 2019.
Steadiface: Real-Time Face-Centric Stabilization On Mobile Phones

We present Steadiface, a new real-time face-centric video stabilization method that simultaneously removes hand shake and keeps subject's head stable. We use a CNN to estimate the face landmarks and use them to optimize a stabilized head center. We then formulate an optimization problem to find a virtual camera pose that locates the face to the stabilized head center while retains smooth rotation and translation transitions across frames. We test the proposed method on fieldtest videos and show it stabilizes both the head motion and background. It is robust to large head pose, occlusion, facial appearance variations, and different kinds of camera motions. We show our method advances the state of art in selfie video stabilization by comparing against alternative methods. The whole process runs very efficiently on a modern mobile phone (8.1 ms/frame).
SAO WMT19 Test Suite: Machine Translation of Audit Reports

This paper describes a machine translation test set of documents from the auditing domain and its use as one of the "test suites" in the WMT19 News Translation Task for translation directions involving Czech, English and German.Our evaluation suggests that current MT systems optimized for the general news domain can perform quite well even in the particular domain of audit reports.The detailed manual evaluation however indicates that deep factual knowledge of the domain is necessary.For the naked eye of a non-expert, translations by many systems seem almost perfect and automatic MT evaluation with one reference is practically useless for considering these details.Furthermore, we show on a sample document from the domain of agreements that even the best systems completely fail in preserving the semantics of the agreement, namely the identity of the parties.
Modeling Multi-Action Policy for Task-Oriented Dialogues

Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system.In most existing approaches, the agent predicts only one DM policy action per turn.This significantly limits the expressive power of the conversational agent and introduces unwanted turns of interactions that may challenge users' patience.Longer conversations also lead to more errors and the system needs to be more robust to handle them.In this paper, we compare the performance of several models on the task of predicting multiple acts for each turn.A novel policy model is proposed based on a recurrent cell called gated Continue-Act-Slots (gCAS) that overcomes the limitations of the existing models.Experimental results show that gCAS 1 outperforms other approaches.
Pareto Self-Paced Learning Based on Differential Evolution

Inspired by the learning rules of humans/animals, self-paced learning (SPL) ranks the samples from easy to complex and assigns real-valued weights to the samples. The current SPL regimes adopt an increasing pace parameter to select the training samples. Obviously, it is difficult to tune the pace parameter during iterations. Furthermore, the current SPL regimes cannot go back to the previous stage even when the current model is worse than the previous one. In this article, a novel Pareto SPL (PSPL) approach is proposed to address the above issues. In PSPL, the SPL problem is considered as a multiobjective optimization problem. Then, a Pareto-based differential evolution algorithm is utilized to optimize the self-paced function and the weighted loss function simultaneously. In PSPL, a new representation method is designed to assign nonpositive weights to the unselected samples. Then, a modified mutation operator inspired by the SPL idea is proposed to generate new population based on ranking the individuals and assigning weights. No pace parameters are introduced in the proposed technique and PSPL can obtain entire solution spectrum to automatically adjust the solution path. The effectiveness of the PSPL has been demonstrated through rigorous studies with matrix factorization and multiclass classification problems.
Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings

Distributed representations of words which map each word to a continuous vector have proven useful in capturing important linguistic information not only in a single language but also across different languages.Current unsupervised adversarial approaches show that it is possible to build a mapping matrix that aligns two sets of monolingual word embeddings without high quality parallel data, such as a dictionary or a sentence-aligned corpus.However, without an additional step of refinement, the preliminary mapping learnt by these methods is unsatisfactory, leading to poor performance for typologically distant languages.In this paper, we propose a weakly-supervised adversarial training method to overcome this limitation, based on the intuition that mapping across languages is better done at the concept level than at the word level.We propose a concept-based adversarial training method which improves the performance of previous unsupervised adversarial methods for most languages, and especially for typologically distant language pairs.
A Scenario-Based Requirement Model for Crossover Healthcare Service

As the population ages, eldercare and healthcare have become major issues in recent years. Crossover healthcare services, instead of individual ones, have become the main form of service provision. In this work, a scenario-based requirement model (SBRM) is proposed for crossover healthcare service. A DSL and a prototype system are designed based on the model as well. Our model defines the requirements as: WHO, in what SCENARIOs, what PROCESSes need to be performed, and what RULEs need to be satisfied. We verify our model in the real case of the MEH (medical, eldercare, healthcare) crossover service. SBRM supports the service better in our cases and shows satisfactory efficiency, effectiveness, and reusability.
The Modulation Technology of Chaotic Multi-Tone and its Application in Covert Communication System

With the development of communication countermeasure technology, the existing communication mode is exposed to intercept or decipher in the covert transmission of information.Therefore, the development of a new concept and principle covert communication technology with low interception probability and anti-non-cooperative demodulation has drawn increasing attention.In this treatise, we propose a chaotic multi-tone modulation and demodulation technology thus present a method to construct the covert communication system.The technology constructs chaotic multi-tone sets to express the baseband information with the multi-value mapping based on the dynamics principle, which can express the same baseband information with different multi-tone groups.Signal modulation is accomplished by combining frequency hopping and baseband information scrambling technology.Multi-value mapping is adopted to complete chaotic multi-tone modulation.Specifically it has strong information concealment and anti-noncooperative demodulation characteristics.In order to solve the demodulation problem of the cooperative party, we present a demodulation scheme based on chaotic multi-tone and provide a complete chaotic multitone communication system construction scheme.Simulation results verify the performance of the proposed method.
A word embedding topic model for topic detection and summary in social networks

The aim of topic detection is to automatically identify the events and hot topics in social networks and continuously track known topics. Applying the traditional methods such as Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis is difficult given the high dimensionality of massive event texts and the short-text sparsity problems of social networks. The problem also exists of unclear topics caused by the sparse distribution of topics. To solve the above challenge, we propose a novel word embedding topic model by combining the topic model and the continuous bag-of-words mode (Cbow) method in word embedding method, named Cbow Topic Model (CTM), for topic detection and summary in social networks. We conduct similar word clustering of the target social network text dataset by introducing the classic Cbow word vectorization method, which can effectively learn the internal relationship between words and reduce the dimensionality of the input texts. We employ the topic model-to-model short text for effectively weakening the sparsity problem of social network texts. To detect and summarize the topic, we propose a topic detection method by leveraging similarity computing for social networks. We collected a Sina microblog dataset to conduct various experiments. The experimental results demonstrate that the CTM method is superior to the existing topic model method.
Learning and Recovery in the ReLU Model

Rectified linear units, or ReLUs, have become a preferred activation function for artificial neural networks. In this paper we consider two basic learning problems assuming that the underlying data follow a generative model based on a simple network with ReLU activations. The first problem we study corresponds to learning a generative model in the presence of nonlinearity (modeled by the ReLU functions). Given a set of signal vectors y <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> ∈ R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> , i = 1, 2, . . . , n, we aim to learn the network parameters, i.e., the d × k matrix A, under the model y <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> = ReLU (Ac <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> + b), where b ∈ R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> is a random bias vector. We show that it is possible to recover the column space of A within an error of O(d) (in Frobenius norm) under certain conditions on the distribution of b. The second problem we consider is that of robust recovery of the signal in the presence of outliers. In this setting, we are interested in recovering the latent vector c from its noisy nonlinear images of the form v = ReLU(Ac) + e + w, where e E Rd denotes the outliers with sparsity s and w E Rd denote the dense but small noise. We show that the LASSO algorithm recovers c E Rk within an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -error of O(√((k + s) log d)/d) when A is a random Gaussian matrix.
Conceptual Change and Distributional Semantic Models: an Exploratory Study on Pitfalls and Possibilities

Studying conceptual change using embedding models has become increasingly popular in the Digital Humanities community, while critical observations about them have received less attention.This paper investigates what the impact of known pitfalls can be on the conclusions drawn in a digital humanities study through the use case of "Racism" in the 20th century.In addition, we suggest an approach for modeling a complex concept in terms of words and relations representative of the conceptual system.Our results show that different models created from the same data yield different results, but also indicate that (i) using different model architectures, (ii) comparing different corpora and (iii) comparing to control words and relations can help to identify which results are solid and which may be due to artefacts.We propose guidelines to conduct similar studies, but also note that more work is needed to fully understand how we can distinguish artefacts from actual conceptual changes.'naive' data models control nn of racialdifferent no no no racialdifferent ←→ values yes no yes
Particle swarm optimization and spiral dynamic algorithm-based interval type-2 fuzzy logic control of triple-link inverted pendulum system: A comparative assessment

This paper presents investigations into the development of an interval type-2 fuzzy logic control (IT2FLC) mechanism integrated with particle swarm optimization and spiral dynamic algorithm. The particle swarm optimization and spiral dynamic algorithm are used for enhanced performance of the IT2FLC by finding optimised values for input and output controller gains and parameter values of IT2FLC membership function as comparison purpose in order to identify better solution for the system. A new model of triple-link inverted pendulum on two-wheels system, developed within SimWise 4D software environment and integrated with Matlab/Simulink for control purpose. Several tests comprising system stabilization, disturbance rejection and convergence accuracy of the algorithms are carried out to demonstrate the robustness of the control approach. It is shown that the particle swarm optimization-based control mechanism performs better than the spiral dynamic algorithm-based control in terms of system stability, disturbance rejection and reduce noise. Moreover, the particle swarm optimization-based IT2FLC shows better performance in comparison to previous research. It is envisaged that this system and control algorithm can be very useful for the development of a mobile robot with extended functionality.
At the Lower End of Language—Exploring the Vulgar and Obscene Side of German

In this paper, we describe a workflow for the data-driven acquisition and semantic scaling of a lexicon that covers lexical items from the lower end of the German language registerterms typically considered as rough, vulgar or obscene.Since the fine semantic representation of grades of obscenity can only inadequately be captured at the categorical level (e.g., obscene vs. non-obscene, or rough vs. vulgar), our main contribution lies in applying best-worst scaling, a rating methodology that has already been shown to be useful for emotional language, to capture the relative strength of obscenity of lexical items.We describe the empirical foundations for bootstrapping such a low-end lexicon for German by starting from manually supplied lexicographic categorizations of a small seed set of rough and vulgar lexical items and automatically enlarging this set by means of distributional semantics.We then determine the degrees of obscenity for the full set of all acquired lexical items by letting crowdworkers comparatively assess their pejorative grade using best-worst scaling.This semi-automatically enriched lexicon already comprises 3,300 lexical items and incorporates 33,000 vulgarity ratings.Using it as a seed lexicon for fully automatic lexical acquisition, we were able to raise its coverage up to slightly more than 11,000 entries.
Pervasive augmented reality for indoor uninterrupted experiences

Augmented Reality (AR) adds additional layers of information on top of real environments. Recently, Pervasive AR extends this concept through an AR experience that is continuous in space, being aware of and responsive to the user's context and pose (position and orientation). This paper focus on an exploratory user study with 27 participants meant to better understand some aspects of Pervasive AR, such as how users explore, select, recognize and manipulate virtual content in uninterrupted AR experiences, as well as their preferences. The approach used to provide this sort of engaging experiences allows the creation of indoor persistent location-based experiences, with a high level of accuracy and resilience to changes in dynamic environments. Results concerning user acceptance of uninterrupted AR experiences were encouraging. In particular, users were positively impressed by the continuous display of virtual content and were willing to use this technology more often and in different contexts.
A Platform Agnostic Dual-Strand Hate Speech Detector

Hate speech detectors must be applicable across a multitude of services and platforms, and there is hence a need for detection approaches that do not depend on any information specific to a given platform.For instance, the information stored about the text's author may differ between services, and so using such data would reduce a system's general applicability.The paper thus focuses on using exclusively text-based input in the detection, in an optimised architecture combining Convolutional Neural Networks and Long Short-Term Memory-networks.The hate speech detector merges two strands with character ngrams and word embeddings to produce the final classification, and is shown to outperform comparable previous approaches.