Reviewerly/exHarmony huggingface dataset:
Improving Students’ Communicative Competence through Inductive Method Using Authentic Materials

Students were often confused and forced into silence when they communicate in English. This condition became a problem during the teaching-learning process and may eventually lead to poor communicative competence. Inductive teaching method, which employs authentic materials can be a solution to address the aforementioned condition. The objective of this study is finding out the effects of implementing inductive teaching method. using authentic materials, to the students’ communicative competence. Participants were 61 fourth semester students (22 males and 39 females). enrolled in a three-year diploma program in Travel and Tourism Business Study Program, State Polytechnic of Bali. Data are collected through classroom action-based research procedures. By conducting a quasi-experimental design, the collected data were analyzed by paired t-tests and mixed design ANCOVA. The study reveals the English communicative competence of the students significantly increased, shown by the mean score on the post-test, namely 63.05 in the control group and 84.43 in the experimental group [t = -23.485, p=.000] at the level of p&lt;.05. In addition, their learning motivation and interest also increased as it is shown by the result of the questionnaires. Limitations and suggestions for successful applied action-integrated instruction and implications for future research are provided.
Watch out for this commit! A study of influential software changes

Abstract One single code change can significantly influence a wide range of software systems and their users. For example, (a) adding a new feature can spread defects in several modules, while (b) changing an API method can improve the performance of all client programs. Unfortunately, developers often may not clearly know whether code changes are influential at commit time. This paper investigates influential software changes and proposes an approach to identify them immediately when they are applied. Our goals are to (a) identify existing influential changes (ICs) in software projects, (b) understand their characteristics, and (c) build a classification model of ICs to help developers find and address them early. We first conduct a post‐mortem analysis to discover existing influential changes by using intuitions (eg, changes referred by other changes). Then, we re‐categorize all identified changes through an open‐card sorting process. Subsequently, we conduct a survey with about 100 developers to finalize a taxonomy. Finally, from our ground truth, we extract features, including metrics such as the complexity of changes and file centrality in co‐change graphs to build machine learning classifiers. The experiment results show that our classification model with random samples achieves 86.8% precision, 74% recall, and 80.4% F ‐measure, respectively.
Research on Facial Expression Recognition Based on LBP and DeepLearning

Traditional algorithm of LBP fails to take the role of the central pixel and relationship between it and the neighborhood pixel into consideration in comparing the gray value of the central pixel and that of the neighborhood pixel, which causes part facial features lost. This paper improves the algorithm of LBP by targeting at the problems above mentioned and puts forward a multidirectional feature of LBP. Through the redefinition of the calculation of the gray value of the central pixel and the neighborhood pixel, this paper obtains a new facial textural feature and designs a seven-layer convolutional neural networks (CNN) for the classification of the features so as to complete facial expression recognition. The experiment carries out verification with JAFFE and obtains the recognition rate of 86.7%, which proves the effectiveness and feasibility of this method.
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection

In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection. Theoretically, we prove that a differentially private learning algorithm used for training the GAN does not overfit to a certain degree, i.e., the generalization gap can be bounded. Moreover, some recent works, such as the Bayesian GAN, can be re-interpreted based on our theoretical insight from privacy protection. Quantitatively, to evaluate the information leakage of well-trained GAN models, we perform various membership attacks on these models. The results show that previous Lipschitz regularization techniques are effective in not only reducing the generalization gap but also alleviating the information leakage of the training dataset.
Ensemble of Random Binary Output Encoding for Adversarial Robustness

Despite the excellent classification performance, recent research has revealed that the Convolutional Neural Network (CNN) could be readily deceived by only the small adversarial perturbation. Its imperceptible to human eyes and transferability from one model to another actually threaten the security of a CNN-based system. In this paper, we propose to create multiple and independent random binary codes per input class and train ensemble of homogeneous CNN classifiers with these codes to improve the adversarial robustness of the networks. The proposed ensemble structure consists of replicas of the same learning architecture, but each network is trained with different random target outputs. The network model is simultaneously trained with their own unique binary codes, and optimized through a single and common objective function in an end-to-end manner. It is demonstrated with experimental results that assigning different encoded labels for each classifier in ensemble leverages the diversity and eventually improves the classification performance on adversarial attacks. We also conduct several performance analysis to understand how the different aspects can contribute to the robustness of the proposed algorithm. The proposed algorithm provides significantly improved classification accuracies as compared to the recent relevant studies, verified with various network architectures, datasets, and adversarial attacks.
Towards a distributed local-search approach for partitioning large-scale social networks

Large-scale social graph data poses significant challenges for social analytic tools to monitor and analyze social networks. A feasible solution is to parallelize the computation and leverage distributed graph computing frameworks to process such big data. However, it is nontrivial to partition social graphs into multiple parts so that they can be computed on distributed platforms. In this paper, we propose a distributed local search algorithm, named dLS, which enables quality and efficient partition of large-scale social graphs. With the vertex-centric computing model, dLS can achieve massive parallelism. We employ a distributed graph coloring strategy to differentiate neighbor nodes and avoid interference during the parallel execution of each vertex. We convert the original graph into a small graph, Quotient Network, and obtain local search solution from processing the Quotient Network, thus further improving the partition quality and efficiency of dLS. We have evaluated the performance of dLS experimentally using real-life and synthetic social graphs, and the results show that dLS outperforms two state-of-the-art algorithms in terms of partition quality and efficiency.
Computationally Modeling the Impact of Task-Appropriate Language Complexity and Accuracy on Human Grading of German Essays

Computational linguistic research on the language complexity of student writing typically involves human ratings as a gold standard. However, educational science shows that teachers find it difficult to identify and cleanly separate accuracy, different aspects of complexity, contents, and structure. In this paper, we therefore explore the use of computational linguistic methods to investigate how task-appropriate complexity and accuracy relate to the grading of overall performance, content performance, and language performance as assigned by teachers. Based on texts written by students for the official school-leaving state examination (Abitur), we show that teachers successfully assign higher language performance grades to essays with higher task-appropriate language complexity and properly separate this from content scores. Yet, accuracy impacts teacher assessment for all grading rubrics, also the content score, overemphasizing the role of accuracy. Our analysis is based on broad computational linguistic modeling of German language complexity and an innovative theory- and data-driven feature aggregation method inferring task-appropriate language complexity.
Image Detector Based Automatic 3D Data Labeling and Training for Vehicle Detection on Point Cloud

Nowadays, a large amount of labeled data is crucial for deep neural network training. However, data labeling is still a time- and labor-consuming task, especially when labeling 3D point clouds. Meanwhile, object recognition has achieved great success on 2D images, even beyond the ability of humans. In this paper, we propose an effective framework to produce labeled data by using an image detector as a supervisor, and we train the network with a simple trick to eliminate noisy labels. For object-sparse scenes, this method is able to obtain good label data, while for object-dense scenes, we can use our training method to detect some of the corrupted labels. This is realized by building a cohesive camera and LiDAR system (named “Licam”) and performing target frustum region proposal on point clouds using the camera detection result. Efficient and effective vehicle detection is achieved based on this learning and training framework. We examine this method on the KITTI dataset [7] and our own road running data collected from a micro electro mechanical system (MEMS) LiDAR, demonstrating fast and accurate detection results. The results show that our automatic data labeling and training framework is effective and efficient. It provides the ability to obtain large-scale labeled data, and is easy to use for online learning.
Comparing “Challenge-Based” and “Code-Based” Internet Voting Verification Implementations

Internet-enabled voting introduces an element of invisibility and unfamiliarity into the voting process, which makes it very different from traditional voting. Voters might be concerned about their vote being recorded correctly and included in the final tally. To mitigate mistrust, many Internet-enabled voting systems build verifiability into their systems. This allows voters to verify that their votes have been cast as intended, stored as cast and tallied as stored at the conclusion of the voting period. Verification implementations have not been universally successful, mostly due to voter difficulties using them. Here, we evaluate two cast as intended verification approaches in a lab study: (1) "Challenge-Based" and (2) "Code-Based". We assessed cast-as-intended vote verification efficacy, and identified usability issues related to verifying and/or vote casting. We also explored acceptance issues post-verification, to see whether our participants were willing to engage with Internet voting in a real election. Our study revealed the superiority of the code-based approach, in terms of ability to verify effectively. In terms of real-life Internet voting acceptance, convenience encourages acceptance, while security concerns and complexity might lead to rejection.
DIGITAL LITERACY IN THE ENGLISH CURRICULUM: MODELS OF LEARNING ACTIVITIES

In this era of information, communication, and technology, digital literacy is a must for the internet users including students.Digital literacy means the abilities to both select information on the internet and is aware of online safety.Despite the concerns about digital literacy, there is a paucity in the curriculum which does not state it as a learning goal.Therefore, this paper attempts to propose two models of learning activities to promote teaching digital literacy in an English classroom.After referring to several theories related to digital literacy and text types stated in Indonesian recently used curriculum, two models for teaching English at a senior high school level that promote digital literacy teaching are offered.The models comprise approaches to teach digital literacy, grades, text types, objectives, steps of learning activities, and assessments to assess students' learning.
Occupancy-Map-Based Rate Distortion Optimization for Video-Based Point Cloud Compression

The state-of-the-art video-based point cloud compression scheme projects the 3D point cloud to 2D patch by patch and organizes the patches into frames to compress them using the efficient video compression scheme. Such a scheme shows a good trade-off between the number of points projected and the video continuity to utilize the video compression scheme. However, some unoccupied pixels between different patches are compressed using almost the same quality with the occupied pixels, which will lead to the waste of lots of bits since the unoccupied pixels are useless for the reconstructed point cloud. In this paper, we propose to consider only the rate instead of the rate distortion cost for the unoccupied pixels during the rate distortion optimization process. The proposed scheme can be applied to both the geometry and attribute frames. The experimental results show that the proposed algorithm can achieve an average of 11:9% and 15:4% bitrate savings for the geometry and attribute, respectively.
Combine Conflicting Evidence Based on the Belief Entropy and IOWA Operator

Evidence theory is widely used in information fusion. However, how to combine highly conflicting evidence is still an open issue. In this paper, a modified average method is proposed to address this issue based on the belief entropy and induced ordered weighted averaging operator. One of the advantages of the proposed method is that both the uncertainty and reliability of evidence are considered. In addition, it provides a right for the decision maker to combine the evidence based on the requirements for the precision of the results. A numerical example is shown to illustrate the use of the proposed method and an application based on sensor fusion in fault diagnosis is given to demonstrate the efficiency of our proposed method.
A Multi-Pairwise Extension of Procrustes Analysis for Multilingual Word Translation

In this paper we present a novel approach to simultaneously representing multiple languages in a common space.Procrustes Analysis (PA) is commonly used to find the optimal orthogonal word mapping in the bilingual case.The proposed Multi Pairwise Procrustes Analysis (MPPA) is a natural extension of the PA algorithm to multilingual word mapping.Unlike previous PA extensions that require a k-way dictionary, this approach requires only pairwise bilingual dictionaries that are much easier to construct in either a supervised or an unsupervised way.The improved performance of the MPPA algorithm is demonstrated on two standard multilingual tasks.
Segmentation of Argumentative Texts with Contextualised Word Representations

The segmentation of argumentative units is an important subtask of argument mining, which is frequently addressed at a coarse granularity, usually assuming argumentative units to be no smaller than sentences.Approaches focusing at the clause-level granularity, typically address the task as sequence labeling at the token level, aiming to classify whether a token begins, is inside, or is outside of an argumentative unit.Most approaches exploit highly engineered, manually constructed features, and algorithms typically used in sequential tagging -such as Conditional Random Fields, while more recent approaches try to exploit manually constructed features in the context of deep neural networks.In this context, we examined to what extend recent advances in sequential labelling allow to reduce the need for highly sophisticated, manually constructed features, and whether limiting features to embeddings, pre-trained on large corpora is a promising approach.Evaluation results suggest the examined models and approaches can exhibit comparable performance, minimising the need for feature engineering.1 Also known as "Argumentative Discourse Units -ADUs" (Peldszus and Stede, 2013).
The IIIT-H Gujarati-English Machine Translation System for WMT19

This paper describes the Neural Machine Translation system of IIIT-Hyderabad for the Gujarati→English news translation shared task of WMT19. Our system is basedon encoder-decoder framework with attention mechanism. We experimented with Multilingual Neural MT models. Our experiments show that Multilingual Neural Machine Translation leveraging parallel data from related language pairs helps in significant BLEU improvements upto 11.5, for low resource language pairs like Gujarati-English
What’s There in the Dark

Scene Parsing is an important cog for modern autonomous driving systems. Most of the works in semantic segmentation pertains to day-time scenes with favourable weather and illumination conditions. In this paper, we propose a novel deep architecture, NiSeNet, that performs semantic segmentation of night scenes using a domain mapping approach of synthetic to real data. It is a dual-channel network, where we designed a Real channel using DeepLabV3+ coupled with an MSE loss to preserve the spatial information. In addition, we used an Adaptive channel reducing the domain gap between synthetic and real night images, which also complements the failures of Real channel output. Apart from the dual channel, we introduced a novel fusion scheme to fuse the outputs of two channels. In addition to that, we compiled a new dataset Urban Night Driving Dataset (UNDD); it consists of 7125 unlabelled day and night images; additionally, it has 75 night images with pixel-level annotations having classes equivalent to Cityscapes dataset. We evaluated our approach on the Berkley Deep Drive dataset, the challenging Mapillary dataset and UNDD dataset to exhibit that the proposed method outperforms the state-of-the-art techniques in terms of accuracy and visual quality.
Multilingual word translation using auxiliary languages

Current multilingual word translation methods are focused on jointly learning mappings from each language to a shared space.The actual translation, however, is still performed as an isolated bilingual task.In this study we propose a multilingual translation procedure that uses all the learned mappings to translate a word from one language to another.For each source word, we first search for the most relevant languages.We then use the auxiliary translations to these languages to form an improved representation of the source word.Finally, this representation is used for the actual translation to the target language.Experiments on a standard multilingual word translation benchmark demonstrate that our model outperforms state of the art results.
Random Projections with Asymmetric Quantization

The method of random projection has been a popular tool for data compression, similarity search, and machine learning. In many practical scenarios, applying quantization on randomly projected data could be very helpful to further reduce storage cost and facilitate more efficient retrievals, while only suffering from little loss in accuracy. In real-world applications, however, data collected from different sources may be quantized under different schemes, which calls for a need to study the asymmetric quantization problem. In this paper, we investigate the cosine similarity estimators derived in such setting under the Lloyd-Max (LM) quantization scheme. We thoroughly analyze the biases and variances of a series of estimators including the basic simple estimators, their normalized versions, and their debiased versions. Furthermore, by studying the monotonicity, we show that the expectation of proposed estimators increases with the true cosine similarity, on a broader family of stair-shaped quantizers. Experiments on nearest neighbor search justify the theory and illustrate the effectiveness of our proposed estimators.
MMFO: modified moth flame optimization algorithm for region based RGB color image segmentation

&lt;span lang="EN-US"&gt;Region-based color image segmentation is elementary steps in image processing and computer vision. Color image segmentation is a region growing approach in which RGB color image is divided into the different cluster based on their pixel properties. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced an improved algorithm MMFO (Modified Moth Flame Optimization) Algorithm for RGB color image Segmentation which is based on bio-inspired techniques for color image segmentation. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different no. of clusters is used during the segmentation process.&lt;/span&gt;
Single Training Dimension Selection for Word Embedding with PCA

In this paper, we present a fast and reliable method based on PCA to select the number of dimensions for word embeddings. First, we train one embedding with a generous upper bound (e.g. 1,000) of dimensions. Then we transform the embeddings using PCA and incrementally remove the lesser dimensions one at a time while recording the embeddings’ performance on language tasks. Lastly, we select the number of dimensions, balancing model size and accuracy. Experiments using various datasets and language tasks demonstrate that we are able to train about 10 times fewer sets of embeddings while retaining optimal performance. Researchers interested in training the best-performing embeddings for downstream tasks, such as sentiment analysis, question answering and hypernym extraction, as well as those interested in embedding compression should find the method helpful.
Team JUST at the MADAR Shared Task on Arabic Fine-Grained Dialect Identification

In this paper, we describe our team's effort on the MADAR Shared Task on Arabic Fine-Grained Dialect Identification.The task requires building a system capable of differentiating between 25 different Arabic dialects in addition to MSA.Our approach is simple.After preprocessing the data, we use Data Augmentation (DA) to enlarge the training data six times.We then build a language model and extract n-gram word-level and character-level TF-IDF features and feed them into an MNB classifier.Despite its simplicity, the resulting model performs really well producing the 4th highest F-measure and region-level accuracy and the 5th highest precision, recall, city-level accuracy and country-level accuracy among the participating teams.