Reviewerly/exHarmony huggingface dataset:
A Merging Strategy for Gaussian Process Extended Target Estimates in Multi-Sensor Applications

For the purpose of extended object tracking in multiple hypothesis tracking algorithms such as the Gaussian mixture probability hypothesis density filter (GMPHD), we develop an approach for the combination of different contour estimates. The developed approach works for tracking algorithms that represent target shapes using contour functions to describe the target shape as the distance of the contour to a reference point over the angle. In a heterogeneous multiple sensor setup, the individual sensors' measurements lead to different extent estimates due to their individual measurement principles. Thus a straight forward use of the extended object state in the traditional merging algorithm either results in unexpected shapes, or tracks cannot be merged due to the differing shape of the objects. Our merging procedure explicitly takes the extent estimates into account by using a merging function. The choice of the merging function provides the means to reach objectives such as a conservative or a generous extent estimate. We evaluate the approach using simulated multisensor data in a GMPHD filter. Compared to the traditional merging method, our approach results in better shape estimates.
Semantic Relatedness Based Re-ranker for Text Spotting

Applications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based neural approaches.We present a scenario where semantic similarity is not enough, and we devise a neural approach to learn semantic relatedness.The scenario is text spotting in the wild, where a text in an image (e.g.street sign, advertisement or bus destination) must be identified and recognized.Our goal is to improve the performance of vision systems by leveraging semantic information.Our rationale is that the text to be spotted is often related to the image context in which it appears (word pairs such as Delta-airplane, or quarters-parking are not similar, but are clearly related).We show how learning a word-to-word or word-to-sentence relatedness score can improve the performance of text spotting systems up to 2.9 points, outperforming other measures in a benchmark dataset.
Parallel Corpus Filtering Based on Fuzzy String Matching

In this paper, we describe the IIT Patna's submission to WMT 2019 shared task on parallel corpus filtering.This shared task asks the participants to develop methods for scoring each parallel sentence from a given noisy parallel corpus.Quality of the scoring method is judged based on the quality of SMT and NMT systems trained on smaller set of high-quality parallel sentences sub-sampled from the original noisy corpus.This task has two language pairs.We submit for both the Nepali-English and Sinhala-English language pairs.We define fuzzy string matching score between English and the translated (into English) source based on Levenshtein distance.Based on the scores, we sub-sample two sets (having 1 million and 5 millions English tokens) of parallel sentences from each parallel corpus, and train SMT systems for development purpose only.The organizers publish the official evaluation using both SMT and NMT on the final official test set.Total 10 teams participated in the shared task and according the official evaluation, our scoring method obtains 2nd position in the team ranking for 1-million Nepali-English NMT and 5-million Sinhala-English NMT categories.
Investigation of Item Selection Methods According to Test Termination Rules in CAT Applications

In this research, computerized adaptive testing item selection methods were investigated in regard to ability estimation methods and test termination rules. For this purpose, an item pool including 250 items and 2000 people were simulated (M = 0, SD = 1). A total of thirty computerized adaptive testing (CAT) conditions were created according to item selection methods (Maximum Fisher Information, a-stratification, Likelihood Weight Information Criterion, Gradual Information Ratio, and Kullback-Leibler), ability estimation methods (Maximum Likelihood Estimation, Expected a Posteriori Distribution), and test termination rules (40 items, SE &amp;lt; .20 and SE &amp;lt; .40). According to the fixed test-length stopping rule, the SE values that were obtained by using the Maximum Likelihood Estimation method were found to be higher than the SE values that were obtained by using the Expected a Posteriori Distribution ability estimation method. When ability estimation was Maximum Likelihood, the highest SE value was obtained from a-stratification item selection method when the test length is smaller then 30. Whereas, Kullback-Leibler item selection method yielded the highest SE value when the test length is larger then 30. According to Expected a Posteriori ability estimation method, the highest SE value was obtained from a-stratification item selection method in all test lengths. In the conditions where test termination rule was SE &amp;lt; .20, and Maximum Likelihood Ability Estimation method was used, the lowest and highest average number of items were obtained from the Gradual Information Ratio and Maximum Fisher Information item selection method, respectively. Furthermore, when the SE is lower than .20 and Expected a Posteriori ability estimation method was utilized, the lowest average number of items was obtained through Kullback-Leibler, and the highest was obtained through Likelihood Weight Information Criterion item selection method. In the conditions where the test termination rule was SE &amp;lt; .40, and ability estimation method was Maximum Likelihood Estimation, the maximum and minimum number of items were obtained by using Maximum Fisher Information and Kullback-Leibler item selection methods respectively. Additionally, when Expected a Posteriori ability estimation was used, the maximum and minimum number of items were obtained via Maximum Fisher Information and a-stratification item selection methods. For the cases where the stopping rule was SE &amp;lt; .20 and SE &amp;lt; .40 and Maximum Likelihood Estimation method was used, the average number of items were found to be highest in all item selection methods.
Quality and Coverage: The AFRL Submission to the WMT19 Parallel Corpus Filtering for Low-Resource Conditions Task

The WMT19 Parallel Corpus Filtering For Low-Resource Conditions Task aims to test various methods of filtering noisy parallel corpora, to make them useful for training machine translation systems.This year the noisy corpora are from the relatively low-resource language pairs of English-Nepali and English-Sinhala.This papers describes the Air Force Research Laboratory (AFRL) submissions, including preprocessing methods and scoring metrics.Numerical results indicate a benefit over baseline and the relative effects of different options.
Head-Controlled Menu in Mixed Reality with a HMD

We present a design-space and three new techniques for head-based interaction with menus in Mixed Reality (MR) with a Head-Mounted Display (HMD). Usual input modalities such as hand gestures and voice commands are not suitable in noisy MR contexts where the users have both hands occupied as in augmented surgery and machine maintenance. To address the two issues of noisy MR contexts and hand-free interaction, we systematically explore the design space of head-controlled menu interaction by considering two design factors: (1) head-controlled menu versus head-controlled cursor (2) virtual targets versus mixed targets anchored on physical objects. Based on the design space, we present three novel menu techniques that we compared with a baseline head-controlled cursor technique. Experimental results suggest that head-controlled menu and head-controlled cursor techniques offer similar performance. In addition, the study found that mixed targets do not impact ultimate user performance when users are trained enough, but improve the learning phase. When using virtual targets, users still progressed after the training phase by reducing their mean selection time by 0.84 s. When using mixed targets, the improvement was limited to 0.3 s.
Exploring the Effects of Replicating Shape, Weight and Recoil Effects on VR Shooting Controllers

Commercial Virtual Reality (VR) controllers with realistic force feedback are becoming available, to increase the realism and immersion of first-person shooting (FPS) games in VR. These controllers attempt to mimic not only the shape and weight of real guns but also their recoil effects (linear force feedback parallel to the barrel, when the gun is shot). As these controllers become more popular and affordable, this paper investigates the actual effects that these properties (shape, weight, and especially directional force feedback) have on performance for general VR users (e.g. users with no marksmanship experience), drawing conclusions for both consumers and device manufacturers. We created a prototype replicating the properties exploited by commercial VR controllers (i.e. shape, weight and adjustable force feedback) and used it to assess the effect of these parameters in user performance, across a series of user studies. We first analysed the benefits on user performance of adding weight and shape vs a conventional controller (e.g. Vive controller). We then explore the implications of adding linear force feedback (LFF), as well as replicating the shape and weight. Our studies show negligible effects on the immediate shooting performance with some improvements in subjective appreciation, which are already present with low levels of LFF. While higher levels of LFF do not increase subjective appreciations any further, they lead users to reach their maximum distance skillset more quickly. This indicates that while adding low levels of LFF can be enough to influence user’s immersion/engagement for gaming contexts, controllers with higher levels of LFF might be better suited for training environments and/or when dealing with particularly demanding aiming tasks.
Learning Object Scale With Click Supervision for Object Detection

Weakly-supervised object detection has recently attracted increasing attention since it only requires image-level annotations. However, the performance obtained by existing methods is still far from being satisfactory compared with fully-supervised object detection methods. To achieve a good trade-off between annotation cost and object detection performance, we propose a simple yet effective method which incorporates CNN visualization with click supervision to generate the pseudo ground-truths (i.e., bounding boxes). These pseudo ground-truths can be used to train a fully-supervised detector. To estimate the object scale, we firstly adopt a proposal selection algorithm to preserve high-quality proposals, and then generate Class Activation Maps (CAMs) for these preserved proposals by the proposed CNN visualization algorithm called Spatial Attention CAM. Finally, we fuse these CAMs together to generate pseudo ground-truths and train a fully-supervised object detector with these ground-truths. Experimental results on the PASCAL VOC 2007 and VOC 2012 datasets show that the proposed method can obtain much higher accuracy for estimating the object scale, compared with the state-of-the-art image-level based methods and the center-click based method.
Overview of Control and Game Theory in Adaptive Cyber Defenses

The purpose of this chapter is to introduce cyber security researchers to key concepts in modern control and game theory that are relevant to Moving Target Defenses and Adaptive Cyber Defense. We begin by observing that there are fundamental differences between control models and game models that are important for security practitioners to understand. Those differences will be illustrated through simple but realistic cyber operations scenarios, especially with respect to the types and amounts of data require for modeling. In addition to modeling differences, there are a variety of ways to think about what constitutes a “solution.” Moreover, there are significant differences in the computational and information requirements to compute solutions for various types of Adaptive Cyber Defense problems. This material is presented in the context of the advances documented in this book, the various chapters of which describe advances made in the 2012 ARO ACD MURI.
Robust Mutual Learning Hashing

With the advances in deep learning, deep hashing methods have achieved promising results in recent years. However, tackling the distribution gap between train data and test data still remains unsolved. In this paper, motivated by Spatial Transformer Networks (STN) and mutual learning, we propose a novel robust hashing method (RMLH) for effective image retrieval. Specifically, the network learns more flexible transformation and makes itself generalize better to test data by plugging STN module. Then the mutual learning strategy is introduced to stabilize the training process. Furthermore, we relax the binary variables into continuous variables to avoid introducing any auxiliary variable. Finally, the experimental results show that our method has achieved the state-of-the-art performance on benchmark datasets.
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis

In transductive learning, an unlabeled test set is used for model training.While this setting deviates from the common assumption of a completely unseen test set, it is applicable in many real-world scenarios, where the texts to be processed are known in advance.However, despite its practical advantages, transductive learning is underexplored in natural language processing.Here, we conduct an empirical study of transductive learning for neural models and demonstrate its utility in syntactic and semantic tasks.Specifically, we fine-tune language models (LMs) on an unlabeled test set to obtain test-set-specific word representations.Through extensive experiments, we demonstrate that despite its simplicity, transductive LM fine-tuning consistently improves state-ofthe-art neural models in both in-domain and out-of-domain settings.
Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers

This paper explores the task of leveraging typology in the context of cross-lingual dependency parsing.While this linguistic information has shown great promise in preneural parsing, results for neural architectures have been mixed.The aim of our investigation is to better understand this stateof-the-art.Our main findings are as follows: 1) The benefit of typological information is derived from coarsely grouping languages into syntactically-homogeneous clusters rather than from learning to leverage variations along individual typological dimensions in a compositional manner; 2) Typology consistent with the actual corpus statistics yields better transfer performance; 3) Typological similarity is only a rough proxy of crosslingual transferability with respect to parsing.
NJU Submissions for the WMT19 Quality Estimation Shared Task

In this paper, we describe the submissions of the team from Nanjing University for the WMT19 sentence-level Quality Estimation (QE) shared task on English-German language pair.We develop two approaches based on a two-stage neural QE model consisting of a feature extractor and a quality estimator.More specifically, one of the proposed approaches employs the translation knowledge between the two languages from two different translation directions; while the other one employs extra monolingual knowledge from both source and target sides, obtained by pretraining deep self-attention networks.To efficiently train these two-stage models, a joint learning training method is applied.Experiments show that the ensemble model of the above two models achieves the best results on the benchmark dataset of the WMT17 sentence-level QE shared task and obtains competitive results in WMT19, ranking 3rd out of 10 submissions.
TellMeWhy: Learning to Explain Corrective Feedback for Second Language Learners

We present a writing prototype feedback system, TellMeWhy, to provide explanations of errors in submitted essays.In our approach, the sentence with corrections is analyzed to identify error types and problem words, aimed at customizing explanations based on the context of the error.The method involves learning the relation of errors and problem words, generating common feedback patterns, and extracting grammar patterns, collocations and example sentences.At run-time, a sentence with corrections is classified, and the problem word and template are identified to provide detailed explanations.Preliminary evaluation shows that the method has potential to improve existing commercial writing services.
Bayesian wavelet de-noising with the caravan prior

According to both domain expert knowledge and empirical evidence, wavelet coefficients of real signals tend to exhibit clustering patterns, in that they contain connected regions of coefficients of similar magnitude (large or small). A wavelet de-noising approach that takes into account such a feature of the signal may in practice outperform other, more vanilla methods, both in terms of the estimation error and visual appearance of the estimates. Motivated by this observation, we present a Bayesian approach to wavelet de-noising, where dependencies between neighbouring wavelet coefficients are a priori modelled via a Markov chain-based prior, that we term the caravan prior. Posterior computations in our method are performed via the Gibbs sampler. Using representative synthetic and real data examples, we conduct a detailed comparison of our approach with a benchmark empirical Bayes de-noising method (due to Johnstone and Silverman). We show that the caravan prior fares well and is therefore a useful addition to the wavelet de-noising toolbox.
Special Issue on Big Data Applications in Cyber Security and Threat Intelligence – Part 1

The papers in this special section examine Big Data applications in cyber security and threat intelligence. This last decade has witnessed a tremendous rapid increase in volume, veracity, velocity and variety of data generated by different cyber security solutions and as part of cyber investigation cases. When a significant amount of data is collected from or generated by different devices and sources, intelligent big-data analytical techniques are necessary to mine, interpret and visualize such data. To mitigate existing cyber security threats, it is important for big-data analytical techniques to keep pace. Therefore, in special issue we focus on cutting-edge from both academia and industry, with a particular emphasis on novel techniques to mine, interpret and visualize big-data from a wide range of sources and can be applied in cyber security, cyber forensics and threat intelligence context.
Video Dialog via Progressive Inference and Cross-Transformer

Video dialog is a new and challenging task, which requires the agent to answer questions combining video information with dialog history.And different from single-turn video question answering, the additional dialog history is important for video dialog, which often includes contextual information for the question.Existing visual dialog methods mainly use RNN to encode the dialog history as a single vector representation, which might be rough and straightforward.Some more advanced methods utilize hierarchical structure, attention and memory mechanisms, which still lack an explicit reasoning process.In this paper, we introduce a novel progressive inference mechanism for video dialog, which progressively updates query information based on dialog history and video content until the agent think the information is sufficient and unambiguous.In order to tackle the multimodal fusion problem, we propose a crosstransformer module, which could learn more fine-grained and comprehensive interactions both inside and between the modalities.And besides answer generation, we also consider question generation, which is more challenging but significant for a complete video dialog system.We evaluate our method on two largescale datasets, and the extensive experiments show the effectiveness of our method.
Constraint-based Learning of Phonological Processes

Phonological processes are context-dependent sound changes in natural languages.We present an unsupervised approach to learning human-readable descriptions of phonological processes from collections of related utterances.Our approach builds upon a technique from the programming languages community called constraint-based program synthesis.We contribute a novel encoding of the learning problem into Satisfiablity Modulo Theory constraints, which enables both data efficiency and fast inference.We evaluate our system on textbook phonology problems and lexical databases, and show that it achieves high accuracy at speeds two orders of magnitude faster than state-of-the-art approaches.
QR Code Based Teaching Materials for Organizational Classes and Game Systems

To support the learning process of a course must be supported by teaching materials that are cheap, simple, easy to learn, easily accessible and quickly accessed by students. Along with the rapid advances in technology, many media have been developed both in print and online to support the learning process. One technology that is now widely known is the QR Code. This study aims to produce QR Code-based teaching materials that are easy to learn, easy and quickly accessible to improve learning motivation in organizational subjects and match systems. The research conducted is development research. The development model used is the 4D model developed by Thiagarajan, et al. 4D stages consisting of define, design, develop, and disseminate. In summary the data obtained from the evaluation of material experts showed 83%, from the evaluation of linguists 83%, evaluation of media experts 79% so it can be concluded that the product is feasible and good.
Patch graph-based wavelet inpainting for color images

We propose a novel inpainting process for color images. Our algorithm is based on the graph-based wavelet regularization and the non-local mean approach. At each step damaged structures are estimated by computing a graph of patches and applying a regularization model using a wavelet transform on graphs. Our approach uses color information of the image to reconstruct missing data according to local geometry. We show that the graph can be used to model geometry information in the frame of inpainting and to merge candidate pixels from a graph-based wavelet regularization. We provide details on numerical approaches and the results highlight an improvement of the geometrical information reconstruction of color images.
DEVELOPING STUDENTS SPEAKING SKILL TROUGH GUESSING GAMES

This study aims at describing how the students can improve their speaking skill and that also part of the teachers’ job. A teacher is expected to have right teaching techniques to provide students with appropriate teaching materials and to create a positive classroom environment. The data of this study were collected through observations, planning, action, and reflection, some of the researchers were conducted in two cycles. The data were obtained by interviewing the English teachers and students. The result of this study showed that the speaking skill can improved through guessing games. These were confirmed by the data on the finding and discussion in the researchers’ article