Reviewerly/exHarmony huggingface dataset:
Type-Based Complexity Analysis of Probabilistic Functional Programs

We show that complexity analysis of probabilistic higher-order functional programs can be carried out compositionally by way of a type system. The introduced type system is a significant extension of refinement types. On the one hand, the presence of probabilistic effects requires adopting a form of dynamic distribution type, subject to a coupling-based subtyping discipline. On the other hand, recursive definitions are proved terminating by way of Lyapunov ranking functions. We prove not only that the obtained type system, called l\pmbRPCF, provides a sound methodology for average case complexity analysis, but also that it is extensionally complete, in the sense that any average case nolytime Turing machines can be encoded as a term typable in l\pmbRPCF.
Boundary-Aware CNN for Semantic Segmentation

Semantic segmentation has always been a fundamental and critical task to scene understanding. Current deep convolutional neural networks (DCNN) are able to successfully learn context from very large receptive fields due to convolutions with deep layers. However, current convolutions in DCNNs does not consider local object boundaries that are the borders among different semantic regions. Convolution with equal contribution on the pixels across the boundary may lead to inferior segmentation results. In this paper, a novel boundary-aware convolution is proposed. It is able to effectively fuse features by adaptively assigning contributions from pixels within receptive fields according to the boundary similarity map. A new semantic segmentation network based on classical FCN8S is then designed by employing multi-scale boundary-aware convolution. The whole network is implemented end-to-end and evaluated with heterogeneous RGB and depth input. Experiments conducted on multiple datasets show that our boundary-aware CNN can effectively improve the semantic segmentation performance.
Understanding source code comments at large-scale

Source code comments are important for any software, but the basic patterns of writing comments across domains and programming languages remain unclear. In this paper, we take a first step toward understanding differences in commenting practices by analyzing the comment density of 150 projects in 5 different programming languages. We have found that there are noticeable differences in comment density, which may be related to the programming language used in the project and the purpose of the project.
MementoMap Framework for Flexible and Adaptive Web Archive Profiling

In this work we proposed MementoMap, a flexible and adaptive framework to summarize holdings of a web archive efficiently. We described a simple, yet extensible, file format suitable for MementoMap. We used the complete index of the Arquivo.pt comprising 5B mementos (archived web pages/files) to understand the nature and shape of its holdings. We generated MementoMaps with varying amount of detail from its HTML pages that have an HTTP status code of 200 OK. Additionally, we designed a single-pass, memory-efficient, and parallelization-friendly algorithm to compact a large MementoMap into a small one and an in-file binary search method for efficient lookup. We analyzed more than three years of MemGator (a Memento aggregator) logs to understand the response behavior of 14 public web archives. We evaluated MementoMaps by measuring their Accuracy using 3.3M unique URIs from MemGator logs. We found that a MementoMap of less than 1.5% Relative Cost (as compared to the comprehensive listing of all the unique original URIs) can correctly identify the presence or absence of 60% of the lookup URIs in the corresponding archive while maintaining 100% Recall (i.e., zero false negatives).
Learning with Limited Data for Multilingual Reading Comprehension

This paper studies the problem of supporting question answering in a new language with limited training resources.As an extreme scenario, when no such resource exists, one can (1) transfer labels from another language, and (2) generate labels from unlabeled data, using translator and automatic labeling function respectively.However, these approaches inevitably introduce noises to the training data, due to translation or generation errors, which require a judicious use of data with varying confidence.To address this challenge, we propose a weakly-supervised framework that quantifies such noises from automatically generated labels, to deemphasize or fix noisy data in training.On reading comprehension task, we demonstrate the effectiveness of our model on low-resource languages with varying similarity to English, namely, Korean and French.
Measurement of media pedagogical competences of adult educators

Media pedagogical competence is critical for the modern-day adult educator. In the process of adult learning, both the use of digital media in the classroom and the transfer of knowledge in dealing with media are the basis for social participation and individual development that must be provided by teachers. However, at present little or no research has been conducted that assess media pedagogical competence of adult educators. Moreover, an instrument to measure media pedagogical competence was lacking. In order to redress these concerns, in the present paper an instrument for objectively measuring media pedagogical competence is designed and piloted with adult educators (n=622). The study provides the first results concerning objective measurement of adult educator media pedagogical competence.
Analyzing Linguistic Complexity and Accuracy in Academic Language Development of German across Elementary and Secondary School

We track the development of writing complexity and accuracy in German students' early academic language development from first to eighth grade.Combining an empirically broad approach to linguistic complexity with the high-quality error annotation included in the Karlsruhe Children's Text corpus (Lavalley et al., 2015) used, we construct models of German academic language development that successfully identify the student's grade level.We show that classifiers for the early years rely more on accuracy development, whereas development in secondary school is better characterized by increasingly complex language in all domains: linguistic system, language use, and human sentence processing characteristics.We demonstrate the generalizability and robustness of models using such a broad complexity feature set across writing topics.
On the rate of convergence of fully connected very deep neural network regression estimates

Recent results in nonparametric regression show that deep learning, i.e., neural network estimates with many hidden layers, are able to circumvent the so-called curse of dimensionality in case that suitable restrictions on the structure of the regression function hold. One key feature of the neural networks used in these results is that their network architecture has a further constraint, namely the network sparsity. In this paper we show that we can get similar results also for least squares estimates based on simple fully connected neural networks with ReLU activation functions. Here either the number of neurons per hidden layer is fixed and the number of hidden layers tends to infinity suitably fast for sample size tending to infinity, or the number of hidden layers is bounded by some logarithmic factor in the sample size and the number of neurons per hidden layer tends to infinity suitably fast for sample size tending to infinity. The proof is based on new approximation results concerning deep neural networks.
SoftRegex: Generating Regex from Natural Language Descriptions using Softened Regex Equivalence

Jun-U Park, Sang-Ki Ko, Marco Cognetta, Yo-Sub Han. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Sequential Learning of Convolutional Features for Effective Text Classification

Text classification has been one of the major problems in natural language processing.With the advent of deep learning, convolutional neural network (CNN) has been a popular solution to this task.However, CNNs which were first proposed for images, face many crucial challenges in the context of text processing, namely in their elementary blocks: convolution filters and max pooling.These challenges have largely been overlooked by the most existing CNN models proposed for text classification.In this paper, we present an experimental study on the fundamental blocks of CNNs in text categorization.Based on this critique, we propose Sequential Convolutional Attentive Recurrent Network (SCARN).The proposed SCARN model utilizes both the advantages of recurrent and convolutional structures efficiently in comparison to previously proposed recurrent convolutional models.We test our model on different text classification datasets across tasks like sentiment analysis and question classification.Extensive experiments establish that SCARN outperforms other recurrent convolutional architectures with significantly less parameters.Furthermore, SCARN achieves better performance compared to equally large various deep CNN and LSTM architectures.
Redcoat: A Collaborative Annotation Tool for Hierarchical Entity Typing

We introduce Redcoat, a web-based annotation tool that supports collaborative hierarchical entity typing.As an annotation tool, Redcoat also facilitates knowledge elicitation by allowing the creation and continuous refinement of concept hierarchies during annotation.It aims to minimise not only annotation time but the time it takes for project creators to set up and distribute projects to annotators.Projects created using the web-based interface can be rapidly distributed to a list of email addresses.Redcoat handles the propagation of documents amongst annotators and automatically scales the annotation workload depending on the number of active annotators.In this paper we discuss these key features and outline Redcoat's system architecture.We also highlight Redcoat's unique benefits over existing annotation tools via a qualitative comparison.
Neighborhood-aware web service quality prediction using deep learning

With the rapid growth of web services on the Internet, it becomes more difficult for users who want to choose the high-quality web services from a large number of functionally equivalent candidate services. Therefore, the prediction of quality of service (QoS) values according to the history of web services has received extensive attention. In recent years, deep learning has achieved great success in speech recognition, image processing, and natural language understanding. However, it is rarely applied to the service recommendation field. Therefore, a novel approach for QoS prediction named NDL (neighborhood-aware deep learning) is proposed. NDL first gets the Top-k neighbors of the user and the service through the Pearson correlation coefficient according to the service QoS information. Then, it extracts the potential features of the user neighbor and the service neighbor; after that, it inputs the QoS values of the user and the user neighbor as well as the QoS values of the service and service neighbors as a convolutional neural network. The results of experiments conducted on a real-world dataset demonstrate that the NDL significantly outperforms the current QoS prediction method in prediction accuracy.
A Novel Anomaly Score for Isolation Forests

Isolation Forests represent a recent variant of Random Forests, specifically designed for one-class classification problems. In the original version, this method builds a set of extremely randomized trees to describe the set of points, subsequently measuring the “anomaly” of a testing point by looking at how much deep it arrives in each tree. Even if few extensions have been recently proposed – mainly aimed at improving the training stage – in most cases the anomaly score is still kept in its original formulation, which does not completely exploit all the information contained in the trained forest. This paper is focused on improving this aspect, and proposes a new approach for the computation of the anomaly score, which exploits the different information linked to the different nodes of the trees of the forest. We investigate three different variants of the novel anomaly score, evaluating them with twelve UCI benchmark datasets, with encouraging results.
A Reaction Attack Against Cryptosystems Based on LRPC Codes

Rank metric is a very promising research direction for code-based cryptography. In fact, thanks to the high complexity of generic decoding attacks against codes in this metric, it is possible to easily select parameters that yield very small data sizes. In this paper we analyze cryptosystems based on Low-Rank Parity-Check (LRPC) codes, one of the classes of codes that are efficiently decodable in the rank metric. We show how to exploit the decoding failure rate, which is an inherent feature of these codes, to devise a reaction attack aimed at recovering the private key. As a case study, we cryptanalyze the recent McNie submission to NIST's Post-Quantum Standardization process. Additionally, we provide details of a simple implementation to validate our approach.
Differentially Private Meta-Learning

Parameter-transfer is a well-known and versatile approach for meta-learning, with applications including few-shot learning, federated learning, and reinforcement learning. However, parameter-transfer algorithms often require sharing models that have been trained on the samples from specific tasks, thus leaving the task-owners susceptible to breaches of privacy. We conduct the first formal study of privacy in this setting and formalize the notion of task-global differential privacy as a practical relaxation of more commonly studied threat models. We then propose a new differentially private algorithm for gradient-based parameter transfer that not only satisfies this privacy requirement but also retains provable transfer learning guarantees in convex settings. Empirically, we apply our analysis to the problems of federated learning with personalization and few-shot classification, showing that allowing the relaxation to task-global privacy from the more commonly studied notion of local privacy leads to dramatically increased performance in recurrent neural language modeling and image classification.
A tactile interaction concept for in-car passenger infotainment systems

Many modern cars offer in-vehicle infotainment systems to enable information and entertainment features. Often, these systems use touchscreen-based interaction concepts, which can be tedious (holding the arm) and imprecise due to the mobile context. In addition, most systems are driver-targeted and neglect the interaction by passengers. In this paper, we therefore investigate the use of an absolute indirect touch interaction concept with tactile feedback to enable passenger interaction with an infotainment system with the goal to ease screen navigation and improve user experience. Results from an experiment (N=18) reveal that this approach performs well regarding usability and user experience for both entertainment and infotainment functions.
Development of Android-Based Interactive Physics Mobile Learning Media (IPMLM) with Scaffolding Learning Approach to Improve HOTS of high school students in Indonesia

The objectives of this study are to: a) Produce an Android-based Interactive Physics Mobile Learning Media (IPMLM) that is eligible for improving Higher-order Thinking Skills (HOTS) of high school students; b) Determine the effectiveness of the use of Android-based interactive physics mobile learning media with a scaffolding learning approach in improving Higher-order Thinking Skills (HOTS) for high school students. The development procedure in this study was modified from the 4D development model (defining, designing, developing, disseminating). The research subjects were 1070 high school students in five regencies/cities, they are Sleman Regency, Bengkulu City, Pontianak City, Kupang City, and Bima City. In each city was chosen four schools for extensive trial and limited trial. The products developed were Interactive Physics Mobile Learning Media (IPMLM) applications, lesson plan, student worksheets, higher-order thinking skills test instruments in the form of reasonable multiple-choice questions, student questionnaire responses towards media and learning activities as well as validation sheets and product eligibility. The technique of analyzing the data of empirical test questions used item response theory analysis by looking at the compatibility of items with the model based on the INFIT MNSQ (infit mean square) value. Extensive test data were analyzed with descriptive statistics and inferential statistics. Inferential statistics were performed using the ANAVA mixed design test with a significance level of α = 0.05. The results show that a) Android-based interactive physics mobile learning media applications and learning devices are appropriate to be used to improve higher-order thinking skills; b) the use of android-based interactive physics mobile learning media (IPMLM) with an effective scaffolding learning approach in improving higher-order thinking skills. The effective contribution of the experimental group in increasing the ability of HOTS is 84.80%. The effective contribution of the control group that uses learning tools with the direct learning assisted by Physics textbooks in increasing HOTS ability is 55.50%.
DATA AUGMENTATION APPROACHES FOR SATELLITE IMAGE SUPER-RESOLUTION

Abstract. Data augmentation is a well known technique that is frequently used in machine learning tasks to increase the number of training instances and hence decrease model over-fitting. In this paper we propose a data augmentation technique that can further boost the performance of satellite image super resolution tasks. A super-resolution convolutional neural network (SRCNN) was adopted as a state-of-the-art deep learning model to test the proposed data augmentation technique. Different augmentation techniques were studied to investigate their relative importance and accuracy gains. We categorized the augmentation methods into instance based and channel based augmentation methods. The former refers to the standard approach of creating new data instances through applying image transformations to the original images such as adding artificial noise, rotations and translations to training samples, while in the latter we fuse auxiliary channels (or custom bands) with each training instance, which helps the model learn useful representations. Fusing auxiliary derived channels to a satellite image RGB combination can be seen as a spectral-spatial fusion process as we explain later. Several experiments were carried out to evaluate the efficacy of the proposed fusion-based augmentation method compared with traditional data augmentation techniques such as rotation, flip and noisy training inputs. The reconstruction quality of the high resolution output was quantitatively evaluated using Peak-Signal-To-Noise-Ratio (PSNR) and qualitatively through visualisation of test samples before and after super-resolving.
Nonlocal Coherent Perfect Absorption

Coherent absorption that occurs at two spatially separated, macroscopic lossy beam splitters is described. A superposition mode of any phase can be chosen as the absorbed or transparent mode. For a nonclassical two-photon NOON-state input, a superposition of two photons entering via one of two separate input modes, a single photon can survive the pair of beam splitters with certainty, implying nonlocal absorption of a single photon, which can be detected via the interference in the two-photon survival probability.
An Analysis of the Most Common Essay Writing Errors among EFL Saudi Female Learners (Majmaah University)

This study was conducted to explore and analyze the most common essay writing errors among Saudi female learners at the departments of English, Majmaa'h University, Kingdom of Saudi Arabia.Therefore, the aim has been to identify those difficulties from an error analysis standpoint and identify the sources underlying them.An analysis of a written corpus of forty students' written essays was thoroughly conducted.Types of errors were categorized, and the factors that contributed to them were analyzed.The participants were English majors in their third year of study enrolled in an advanced writing course during the first term pertinent to the academic year 2018-2019.Three essays were given to each participant to write about two to four pages using the narrative, descriptive, and compare/contrast organization.A writing difficulties questionnaire was further employed.The findings showed that the most frequent types of errors made by the participants were: punctuation errors forming the most troublesome area, followed by spelling errors, preposition errors, article errors, wrong verb tense, wrong word form respectively.The findings suggested that writing in English as a foreign language is quite challenging for students.Interlingual and intralingual transfer was found to be the source underlying the most common errors.
Tackling In-Camera Downsizing for Reliable Camera ID Verification

The photo-response non-uniformity (PRNU) of an imaging sensor can be regarded as a biometric identifier unique to each camera. This modality is referred to as camera ID. The underlying process for estimating and matching camera IDs is now well established, and its robustness has been studied under a variety of processing. However, the effect of in-camera downsizing on camera ID verification has not yet been methodologically addressed. In this work, we investigate limitations imposed by built-in camera downsizing methods and tackle the question of how to obtain a camera ID so that attribution is possible with lower resolution media. For this purpose, we developed an application that gathers photos and videos at all supported resolutions by controlling camera settings. Analysis of media obtained from 21 smartphone and tablet cameras shows that downsizing of photos by a factor of 4 or higher suppresses PRNU pattern significantly. On the contrary, it is observed that source of unstabilized videos can be verified quite reliably at almost all resolutions. We combined our observations in a camera ID verification procedure considering downsized media.