Reviewerly/exHarmony huggingface dataset:
SAGA with Arbitrary Sampling

We study the problem of minimizing the average of a very large number of smooth functions, which is of key importance in training supervised learning models. One of the most celebrated methods in this context is the SAGA algorithm. Despite years of research on the topic, a general-purpose version of SAGA---one that would include arbitrary importance sampling and minibatching schemes---does not exist. We remedy this situation and propose a general and flexible variant of SAGA following the {\em arbitrary sampling} paradigm. We perform an iteration complexity analysis of the method, largely possible due to the construction of new stochastic Lyapunov functions. We establish linear convergence rates in the smooth and strongly convex regime, and under a quadratic functional growth condition (i.e., in a regime not assuming strong convexity). Our rates match those of the primal-dual method Quartz for which an arbitrary sampling analysis is available, which makes a significant step towards closing the gap in our understanding of complexity of primal and dual methods for finite sum problems.
ASSESSING THE RELATIVE IMPORTANCE OF AN E-LEARNING SYSTEM’S USABILITY DESIGN CHARACTERISTICS BASED ON STUDENTS' PREFERENCES

ASSESSING THE RELATIVE IMPORTANCE OF AN E-LEARNING SYSTEM’S USABILITY DESIGN CHARACTERISTICS BASED ON STUDENTS' PREFERENCES
QADiver: Interactive Framework for Diagnosing QA Models

Question answering (QA) extracting answers from text to the given question in natural language, has been actively studied and existing models have shown a promise of outperforming human performance when trained and evaluated with SQuAD dataset. However, such performance may not be replicated in the actual setting, for which we need to diagnose the cause, which is non-trivial due to the complexity of model. We thus propose a web-based UI that provides how each model contributes to QA performances, by integrating visualization and analysis tools for model explanation. We expect this framework can help QA model researchers to refine and improve their models.
VIVO: a system for research discovery

vee-voh] is member-supported, enterprise open source software and an ontology for representing scholarship.VIVO supports recording, editing, searching, browsing and visualizing scholarly activity.VIVO encourages research discovery, expert finding, network analysis and assessment of research impact.VIVO is easily extended to support additional domains of scholarly activity (Börner, Conlon, Corson-Rikert, & Ying Ding, 2012).VIVO uses an ontology to represent people, papers, grants, projects, datasets, resources, and other elements of research and scholarship as linked open data.The ontology can be used to create RDF that can be loaded into VIVO.VIVO RDF data is easily exported for use in other applications.VIVO includes Vitro (Project, 2019), a domain-free engine for managing linked open data, the JFact reasoner ("JFact DL Reasoner," 2018), SolR ("Apache Solr -," 2019) for search, SPARQL query ("SPARQL Query Language for RDF," 2008), Jena as a triple store ("Apache Jena -," 2011), supporting both TDB ("Apache Jena -Apache Jena -TDB," 2019) and SDB ("Apache Jena -SDB -persistent triple stores using relational databases," 2019) on MySQL ("MySQL," 2019), uses D3 (Bostock, 2015) for visualizations, and provides multiple APIs, including Triple Pattern Fragments (Verborgh et al., 2016) for rapid remote access to specified data.Using VIVO, organizations can represent the activities and accomplishments of their scholars as linked open data, and share that data with others.
Generic resource allocation metrics and methods for heterogeneous cloud infrastructures

With the advent of cloud computing, computation has become a commodity used by customers to access computing resources with no up-front investment, but as an on-demand and pay-as-you-go basis. Cloud providers make their infrastructure available to public so that anyone can obtain a virtual machine (VM) instance that can be remotely configured and managed. The cloud infrastructure is a large resource pool, allocated to VM instances on demand. In a multi-resource heterogeneous cloud, allocation state of the data center needs to be captured in metrics that can be used by allocation algorithms to make proper assignments of virtual machines to servers. In this paper, we propose two novel metrics reflecting the current state of VM allocation. These metrics can be used by online and offline VM placement algorithms in judging which placement would be better. We also propose multi-dimensional resource allocation heuristic algorithms showing how metrics can be used. We studied the performance of proposed methods and compared them with the methods from the literature. Results show that our metrics perform significantly better than the others and can be used to efficiently place virtual machines with high success rate.
New Fitness Functions in Genetic Programming for Classification with High-dimensional Unbalanced Data

High-dimensionality and class imbalance represent two main challenges in classification. Recently, there is a growing number of datasets exhibiting the characteristics of the combination of the class imbalance and high-dimensionality. Genetic programming (GP) has been successfully applied to solve high-dimensional classification tasks. However, most existing GP methods may also suffer from a performance bias if the class distribution is unbalanced. Using fitness functions for cost adjustment is one of the most important methods in GP to address the class imbalance issue. This paper develops new fitness functions in GP to address the class imbalance issue in classification with high-dimensional unbalanced data. Two fitness functions are proposed to increase the performance of the traditional accuracy measures, and one fitness function is proposed to approximate Area Under Curve (AUC) with the goal to save the training time. Experiments on six high-dimensional unbalanced datasets show the better performance of the proposed fitness functions, compared to existing fitness functions.
Enabling Identity-Aware Tracking via Fusion of Visual and Inertial Features

Person identification and tracking (PIT) is an essential issue in computer vision and robotic applications. It has long been studied and achieved by technologies such as RFID or face/fingerprint/iris recognition. These approaches, however, have their limitations due to environmental constraints (such as lighting and obstacles) or require close contact to specific devices. Therefore, their recognition accuracy highly depends on use scenarios. In this work, we propose RCU (Robot Catch yoU), an accompanyist robot system that provides follow-me or guide-me services. Such robots are capable of distinguishing users' profiles in front of them and keep tracking a specific target person. We study a more challenging scenario where the target person may be under occlusion from time to time. To enable robust PIT, we develop a data fusion technique that integrates two types of sensors, an RGB-D camera and wearable inertial sensors. Since the data generated by these sensors share common features, we are able to fuse them to achieve identity-aware tracking. Practical issues, such as time synchronization and coordinate calibration, are also addressed. We implement our design on a robotic platform and show that it can track a target person even when no biological feature is captured by the RGB-D camera. Our experimental evaluation shows a recognition rate of 95% and a following rate of 88%.
Changes of Photovoltaic Performance as a Function of Positioning Relative to the Focus Points of a Concentrator PV Module: Case Study

This article examines the positioning features of polycrystalline, monocrystalline, and amorphous silicon modules relative to the focus points of concentrator photovoltaic modules under real meteorological conditions using a dual tracking system. The performance of the photovoltaic modules mounted on a dual-axis tracking system was regarded as a function of module orientation where the modules were moved step by step up to a point where their inclination differed by 30° compared to the ideal focus point position of the reference concentrator photovoltaic module. The inclination difference relative to the ideal focus point position was determined by the perfect perpendicularity to the rays of the sun. Technology-specific results show the accuracy of a sun tracking photovoltaic system that is required to keep the loss in power yield below a defined level. The loss in power yield, determined as a function of the measurement results, also showed that the performance insensitivity thresholds of the monocrystalline, polycrystalline, and amorphous silicon modules depended on the direction of the alignment changes. The performance deviations showed clear azimuth dependence. Changing the tilt of the modules towards north and south showed little changes in results, but inclination changes towards northwest, southwest, southeast, and northeast produced results diverging more markedly from each other. These results may make the planning of solar tracking sensor investments easier and help with the estimate calculations of the total investment and operational costs and their return concerning monocrystalline, polycrystalline, and amorphous silicon photovoltaic systems. The results also provide guidance for the tracking error values of the solar tracking sensor.
Deep Model Transferability from Attribution Maps

Exploring the transferability between heterogeneous tasks sheds light on their intrinsic interconnections, and consequently enables knowledge transfer from one task to another so as to reduce the training effort of the latter. In this paper, we propose an embarrassingly simple yet very efficacious approach to estimating the transferability of deep networks, especially those handling vision tasks. Unlike the seminal work of \emph{taskonomy} that relies on a large number of annotations as supervision and is thus computationally cumbersome, the proposed approach requires no human annotations and imposes no constraints on the architectures of the networks. This is achieved, specifically, via projecting deep networks into a \emph{model space}, wherein each network is treated as a point and the distances between two points are measured by deviations of their produced attribution maps. The proposed approach is several-magnitude times faster than taskonomy, and meanwhile preserves a task-wise topological structure highly similar to the one obtained by taskonomy. Code is available at \url{https://github.com/zju-vipa/TransferbilityFromAttributionMaps}.
ShotgunWSD 2.0: An Improved Algorithm for Global Word Sense Disambiguation

ShotgunWSD is a recent unsupervised and knowledge-based algorithm for global word sense disambiguation (WSD). The algorithm is inspired by the Shotgun sequencing technique, which is a broadly-used whole genome sequencing approach. ShotgunWSD performs WSD at the document level based on three phases. The first phase consists of applying a brute-force WSD algorithm on short context windows selected from the document, in order to generate a short list of likely sense configurations for each window. The second phase consists of assembling the local sense configurations into longer composite configurations by prefix and suffix matching. In the third phase, the resulting configurations are ranked by their length, and the sense of each word is chosen based on a majority voting scheme that considers only the top configurations in which the respective word appears. In this paper, we present an improved version (2.0) of ShotgunWSD which is based on a different approach for computing the relatedness score between two word senses, a step that stays at the core of building better local sense configurations. For each sense, we collect all the words from the corresponding WordNet synset, gloss and related synsets, into a sense bag. We embed the collected words from all the sense bags in the entire document into a vector space using a common word embedding framework. The word vectors are then clustered using k-means to form clusters of semantically related words. At this stage, we consider that clusters with fewer samples (with respect to a given threshold) represent outliers and we eliminate these clusters altogether. Words from the eliminated clusters are also removed from each and every sense bag. Finally, we compute the median of all the remaining word embeddings in a given sense bag to obtain a sense embedding for the corresponding word sense. We compare the improved ShotgunWSD algorithm (version 2.0) with its previous version (1.0) as well as several state-of-the-art unsupervised WSD algorithms on six benchmarks: SemEval 2007, Senseval-2, Senseval-3, SemEval 2013, SemEval 2015, and overall (unified). We demonstrate that ShotgunWSD 2.0 yields better performance than ShotgunWSD 1.0 and some other recent unsupervised or knowledge-based approaches. We also performed paired McNemar's significance tests, showing that the improvements of ShotgunWSD 2.0 over ShotgunWSD 1.0 are in most cases statistically significant, with a confidence interval of 0.01.
Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space

A notable property of word embeddings is that word relationships can exist as linear substructures in the embedding space.For example, gender corresponds to womanman and queenking.This, in turn, allows word analogies to be solved arithmetically: kingman + woman ≈ queen.This property is notable because it suggests that models trained on word embeddings can easily learn such relationships as geometric translations.However, there is no evidence that models exclusively represent relationships in this manner.We document an alternative way in which downstream models might learn these relationships: orthogonal and linear transformations.For example, given a translation vector for gender, we can find an orthogonal matrix R, representing a rotation and reflection, such that R( king) ≈ queen and R( man) ≈ woman.Analogical reasoning using orthogonal transformations is almost as accurate as using vector arithmetic; using linear transformations is more accurate than both.Our findings suggest that these transformations can be as good a representation of word relationships as translation vectors.
Recurrent Space-time Graph Neural Networks

Learning in the space-time domain remains a very challenging problem in machine learning and computer vision. Current computational models for understanding spatio-temporal visual data are heavily rooted in the classical single-image based paradigm. It is not yet well understood how to integrate information in space and time into a single, general model. We propose a neural graph model, recurrent in space and time, suitable for capturing both the local appearance and the complex higher-level interactions of different entities and objects within the changing world scene. Nodes and edges in our graph have dedicated neural networks for processing information. Nodes operate over features extracted from local parts in space and time and over previous memory states. Edges process messages between connected nodes at different locations and spatial scales or between past and present time. Messages are passed iteratively in order to transmit information globally and establish long range interactions. Our model is general and could learn to recognize a variety of high level spatio-temporal concepts and be applied to different learning tasks. We demonstrate, through extensive experiments and ablation studies, that our model outperforms strong baselines and top published methods on recognizing complex activities in video. Moreover, we obtain state-of-the-art performance on the challenging Something-Something human-object interaction dataset.
Filtering Pseudo-References by Paraphrasing for Automatic Evaluation of Machine Translation

In this paper, we introduce our participation in the WMT 2019 Metric Shared Task.We propose a method to filter pseudo-references by paraphrasing for automatic evaluation of machine translation (MT).We use the outputs of off-the-shelf MT systems as pseudoreferences filtered by paraphrasing in addition to a single human reference (gold reference).We use BERT fine-tuned with paraphrase corpus to filter pseudo-references by checking the paraphrasability with the gold reference.Our experimental results of the WMT 2016 and 2017 datasets show that our method achieved higher correlation with human evaluation than the sentence BLEU (Sent-BLEU) baselines with a single reference and with unfiltered pseudo-references.
Joint Entity and Relation Extraction Based on Reinforcement Learning

Information extraction is a crucial technology to construct a knowledge base. In this paper, a novel model was proposed to extract entities and relations from plain text. This model consists of two components: a joint network and a reinforcement learning agent. The joint network is designed end-to-end, which can extract entities and relations simultaneously. In the joint model, a new tagging scheme was adopted, then the entity and relation extraction can be modeled as a joint sequence tagging problem. To enhance the robustness of the model, we also introduced a reinforcement learning (RL) agent to remove the noisy data from the training dataset. The RL agent aims at determining whether a candidate instance should be removed from the training dataset or not. When the agent completes a selection process, the training dataset will be divided into two parts: clean data and noisy data. Then the joint network can be trained again on the clean dataset to generate a better model. To assess the validity of the model we proposed, extensive experiments were conducted on the New York Times dataset (NYT10 and NYT 11). The experimental results showed that the model we proposed is superior compared with the baselines, achieving the F1 value on NYT10 and NYT11 with 0.612 and 0.549, respectively.
Evolutionary-Fuzzy-Integral-Based Convolutional Neural Networks for Facial Image Classification

Various optimization methods and network architectures are used by convolutional neural networks (CNNs). Each optimization method and network architecture style have their own advantages and representation abilities. To make the most of these advantages, evolutionary-fuzzy-integral-based convolutional neural networks (EFI-CNNs) are proposed in this paper. The proposed EFI-CNNs were verified by way of face classification of age and gender. The trained CNNs’ outputs were set as inputs of a fuzzy integral. The classification results were operated using either Sugeno or Choquet output rules. The conventional fuzzy density values of the fuzzy integral were decided by heuristic experiments. In this paper, particle swarm optimization (PSO) was used to adaptively find optimal fuzzy density values. To combine the advantages of each CNN type, the evaluation of each CNN type in EFI-CNNs is necessary. Three CNN structures, AlexNet, very deep convolutional neural network (VGG16), and GoogLeNet, and three databases, computational intelligence application laboratory (CIA), Morph, and cross-age celebrity dataset (CACD2000), were used in experiments to classify age and gender. The experimental results show that the proposed method achieved 5.95% and 3.1% higher accuracy, respectively, in classifying age and gender.
Location-Based Parallel Sequential Pattern Mining Algorithm

Given a data sequence, sequential pattern mining, which finds frequent sequence patterns among them, is an important data mining problem. However, in the existing sequential pattern mining, only the purchase order of the items is considered, and the position where the item is purchased is not considered. In this paper, we developed a sequential pattern mining algorithm using Apache spark. The proposed algorithm finds frequent sequential patterns in parallel by distributing data to several machines. Experimentally, we performed a comprehensive performance study on the proposed algorithm by varying various parameter values using various synthetic data. Experimental results show that the proposed algorithm shows a linear speed improvement over the number of machines.
An Incremental Turn-Taking Model for Task-Oriented Dialog Systems

In a human-machine dialog scenario, deciding the appropriate time for the machine to take the turn is an open research problem.In contrast, humans engaged in conversations are able to timely decide when to interrupt the speaker for competitive or non-competitive reasons.In state-of-the-art turn-by-turn dialog systems the decision on the next dialog action is taken at the end of the utterance.In this paper, we propose a token-by-token prediction of the dialog state from incremental transcriptions of the user utterance.To identify the point of maximal understanding in an ongoing utterance, we a) implement an incremental Dialog State Tracker which is updated on a token basis (iDST) b) re-label the Dialog State Tracking Challenge 2 (DSTC2) dataset and c) adapt it to the incremental turn-taking experimental scenario.The re-labeling consists of assigning a binary value to each token in the user utterance that allows to identify the appropriate point for taking the turn.Finally, we implement an incremental Turn Taking Decider (iTTD) that is trained on these new labels for the turn-taking decision.We show that the proposed model can achieve a better performance compared to a deterministic handcrafted turn-taking algorithm.
Detecting harassment in real-time as conversations develop

We developed a machine-learning-based method to detect video game players that harass teammates or opponents in chat earlier in the conversation.This real-time technology would allow gaming companies to intervene during games, such as issue warnings or muting or banning a player.In a proof-of-concept experiment on League of Legends data we compute and visualize evaluation metrics for a machine learning classifier as conversations unfold, and observe that the optimal precision and recall of detecting toxic players at each moment in the conversation depends on the confidence threshold of the classifier: the threshold should start low, and increase as the conversation unfolds.How fast this sliding threshold should increase depends on the training set size.
Mitigating Noisy Inputs for Question Answering

Natural language processing systems are often downstream of unreliable inputs: machine translation, optical character recognition, or speech recognition. For instance, virtual assistants can only answer your questions after understanding your speech. We investigate and mitigate the effects of noise from Automatic Speech Recognition systems on two factoid Question Answering (QA) tasks. Integrating confidences into the model and forced decoding of unknown words are empirically shown to improve the accuracy of downstream neural QA systems. We create and train models on a synthetic corpus of over 500,000 noisy sentences and evaluate on two human corpora from Quizbowl and Jeopardy! competitions.
Narrative Generation in the Wild: Methods from NaNoGenMo

In text generation, generating long stories is still a challenge.Coherence tends to decrease rapidly as the output length increases.Especially for generated stories, coherence of the narrative is an important quality aspect of the output text.In this paper we examine how narrative coherence is attained in the submissions of NaNoGenMo 2018, an online text generation event where participants are challenged to generate a 50,000 word novel.We list the main approaches that were used to generate coherent narratives and link them to scientific literature.Finally, we give recommendations on when to use which approach.