Reviewerly/exHarmony huggingface dataset:
A Nano-Pattern Language for Java

Patterns, micro-patterns, and nano-patterns have many applications: program comprehension, code transformations, documentation aids, improving code robustness, etc. This work revisits the notion of nano-patterns—originally an obiter dictum of the work on micro-patterns. Nano-patterns here are taken as more general than their previous definition in the literature: predicates on short code snippets that represent some common and elementary programming missions such as "for each m ∈ M do...", or, "use x (but if x is null, y is a substitute)", which represent small and recurring programming idioms. With this generalization, we offer a taxonomized languageof nanos nano-patterns for Java. We also describe the process of pattern harvesting we used and the underlying rationale, including our proposed prevalence threshold criterion, which, by capitalizing on Hirsch's famous h-index, makes a robust yard-stick of the pattern's significance. An empirical survey of 78 Open Source Java projects indicates that the nano-patterns of our proposed language described here have a substantial prevalence in the code. About a third of the commands (executable statements) and half of the methods are instances of nano-patterns in the proposed language. Also, the language's prevalence is typically higher than that of languages harvested in a project specific, automated machine learning process. Nano-patterns are implementation/language level details for most high level software engineering purposes. One contribution made by the present paper is in identifying the clutter made by the snippets, appreciating its presence, and imposing order on it. The language, the nano-patterns in it, and the contributed automatic tool for tracing nano-patterns in code may help to deal systematically with this low level, yet significant, portion of code.
Learning from Omission

Pragmatic reasoning allows humans to go beyond the literal meaning when interpreting language in context.Previous work has shown that such reasoning can improve the performance of already-trained language understanding systems.Here, we explore whether pragmatic reasoning during training can improve the quality of learned meanings.Our experiments on reference game data show that end-to-end pragmatic training produces more accurate utterance interpretation models, especially when data is sparse and language is complex.
ML defense

Machine learning (ML) has shown its impressive performance in the modern world, and many corporations leverage the technique of machine learning to improve their service quality, e.g., Facebook's DeepFace. Machine learning models with a collection of private data being processed by a training algorithm are deemed to be increasingly confidential. Confidential models are typically trained in a centralized cloud server but publicly accessible. ML-as-a-service (MLaaS) system is one of running examples, where users are allowed to access trained models and are charged on a pay-per-query basis.
Better Exploiting Latent Variables in Text Modeling

We show that sampling latent variables multiple times at a gradient step helps in improving a variational autoencoder and propose a simple and effective method to better exploit these latent variables through hidden state averaging.Consistent gains in performance on two different datasets, Penn Treebank and Yahoo, indicate the generalizability of our method. 1
The Effectiveness of Simple Hybrid Systems for Hypernym Discovery

Hypernymy modeling has largely been separated according to two paradigms, patternbased methods and distributional methods.However, recent works utilizing a mix of these strategies have yielded state-of-the-art results.This paper evaluates the contribution of both paradigms to hybrid success by evaluating the benefits of hybrid treatment of baseline models from each paradigm.Even with a simple methodology for each individual system, utilizing a hybrid approach establishes new stateof-the-art results on two domain-specific English hypernym discovery tasks and outperforms all non-hybrid approaches in a general English hypernym discovery task.
Co-Training Based Sequential Three-Way Decisions for Cost-Sensitive Classification

As a popular research topic in classification, face recognition has drawn great attention in recent years, which is researched mostly in verification and identification modes. In this paper, we focus on the identification problem. Traditionally, a low misclassification error rate remains a fundamental target of various face recognition systems. However, in some real applications, this target is far from reasonable on account of different misclassification costs and large numbers of unlabeled facial images. To solve this problem, we introduce a sequential three-way decisions model for cost-sensitive face recognition. Instead of achieving a low recognition error rate, we are concerned with seeking a minimum misclassification cost in each decision step. When labeled samples are insufficient, delayed decisions can be made, which make up the boundary region. To mitigate the problem of insufficient labeled samples, we use a co-training mechanism. With more labeled samples obtained, the delayed decisions will be converted to positive or negative decisions definitely. This sequential three-way decisions model is in accordance with the human decision-making process. Several experiments are performed to demonstrate the effectiveness of this research.
An Empirical Study on the Impact of Inconsistency Feedback during Model and Code Co-changing.

Model and code co-changing is about the coordinated modification of models and code during evolution.Intermittent inconsistencies are a common occurrence during co-changing.A partial co-change is the period in which the developer changed, say, the model but has not yet propagated the change to the code.Inconsistency feedback can be provided to developers for helping them to complete partial co-changes.However, there is no evidence whether such inconsistency feedback is useful to developers.To investigate this problem, we conducted a controlled experiment with 36 subjects who were required to complete ten partially completed change tasks between models and code of two non-trivial systems.The tasks were of different levels of complexity depending on how many model diagrams they affected.All subjects had to work on all change tasks but sometimes with and sometimes without inconsistency feedback.We then measured differences between task effort and correctness.We found that when subjects were given inconsistency feedback during tasks, they were 268% more likely to complete the co-change correctly compared to when they were not given inconsistency feedback.We also found that when subjects were not given inconsistency feedback, they nearly always failed in completing co-change tasks with high complexity where the partially completed changes were spread across different diagrams in the model.These findings suggest that inconsistency feedback (i.e.detection and repair) should form an integral part of co-changing, regardless of whether the code or the model changes first.Furthermore, these findings suggest that merely having access to changes (as with the given partially completed changes) is insufficient for effective co-changing.
Vision-Based Novelty Detection Using Deep Features and Evolved Novelty Filters for Specific Robotic Exploration and Inspection Tasks

One of the essential abilities in animals is to detect novelties within their environment. From the computational point of view, novelty detection consists of finding data that are different in some aspect to the known data. In robotics, researchers have incorporated novelty modules in robots to develop automatic exploration and inspection tasks. The visual sensor is one of the preferred sensors to perform this task. However, there exist problems as illumination changes, occlusion, and scale, among others. Besides, novelty detectors vary their performance depending on the specific application scenario. In this work, we propose a visual novelty detection framework for specific exploration and inspection tasks based on evolved novelty detectors. The system uses deep features to represent the visual information captured by the robots and applies a global optimization technique to design novelty detectors for specific robotics applications. We verified the performance of the proposed system against well-established state-of-the-art methods in a challenging scenario. This scenario was an outdoor environment covering typical problems in computer vision such as illumination changes, occlusion, and geometric transformations. The proposed framework presented high-novelty detection accuracy with competitive or even better results than the baseline methods.
Robust PCA for Face Recognition with Occlusion Using Symmetry Information

Recently, the accuracy and the speed of face recognition have been improved greatly. However, the recognition of faces with occlusions still remains an open problem. A fundamental important problem is the existing methods could not handle large scale occlusion situations well. To address this problem, we propose a novel face recognition methods based on RPCA and facial symmetry, called Symmetry-RPCA. First, we use RPCA to find the sparse errors areas of objective faces; Then, we use the threshold method and k NN method to find the occlusion areas from the sparse errors areas, and we use the mirror parts of face to fix those areas. To compensate for the unnatural characteristic, we do RPCA again with the mirror fixed faces. After the process, the result faces could both keep the traits from the samples and make full use of the information of the original occluded face image. After those, we use CRC for the classification. Experimental results on several popular data sets validate that the proposed methods are of high efficiency and robustness to the recognition of blocked faces, which exceeds existing methods greatly.
Tintin at SemEval-2019 Task 4: Detecting Hyperpartisan News Article with only Simple Tokens

Tintin, the system proposed by the CECL for the Hyperpartisan News Detection task of Se-mEval 2019, is exclusively based on the tokens that make up the documents and a standard supervised learning procedure.It obtained very contrasting results: poor on the main task, but much more effective at distinguishing documents published by hyperpartisan media outlets from unbiased ones, as it ranked first.An analysis of the most important features highlighted the positive aspects, but also some potential limitations of the approach.
Solving Fredholm integral equations of the first kind by using wavelet bases

In this paper, we used a project technique for solving integral equation of the first kind by wavelet families via regularization approach and we proved the convergence for the numerical method and error consideration. Semi-orthogonal B-spline scaling functions and wavelets of degree 4 and their dual functions are presented to approximate the solutions to integral equations. Sparse matrix will product of semi-orthoganality and vanishing moment properties of B-spline wavelets.
The Determinant of Economic Growth Evidence from Benin: Time Series Analysis from 1970 to 2017

Financial development is part of the private sector development strategy to stimulate economic growth and reduce poverty.So, the paper attempts to find out the determinant of economy growth and the explanatory variables, such as, the foreign direct investment, government expenditure, consumer price index and trade openness in Benin.Using time series as the econometric model and the data during 1970 and 2017 for the study.In addition, the technique that implemented to estimate the model was Ordinary Least Square.The result showed that the foreign direct investment and consumer index price have significantly and positively impact the Benin's economy growth but the trade openness and the government expenditure have significantly and negatively impacted the Benin's economy growth.I suggest that the government must invest more in infrastructure to attract more foreign direct investment.In term of trade Benin's policymaker should promote the local products and export to the world to adjust the balance between the import and export.
USING COLOR CODING TO IMPROVE STUDENTS' ENGLISH VOCABULARY ABILITY

English is a subject in Indonesian school that learned from junior high school until the universities. Therefore one of importance basic aspects and skills in learning English for students is to understand avariety of English vocabulary. In learning English, vocabulary plays an important role, it means vocabulary is the basic one to mastering all English Skill. This research intended to students at seventh grade of Madrasah Tsanawiyah (MTs) in Cimahi. And this study aims to determine the benefits of Color Coding technique in improving the ability of English vocabulary. Color Coding is a method that can be used in teaching vocabulary, one of the use of color is to identifying something, it can make the student easy in their learning vocabulary process. The design of this research is Classroom Action Research (CAR). The researcher employed qualitative research design and pre experimental pretest-posttest. Thedata were collected from the result of pretest and posttets. The result of the data showed: Mean of pretest was 63.00, mean of posttest was 82.00, The result also show the score of t-observed was higher than ttable (2.08 &gt; 1.72). It means color coding is effective to improve students’ vocabulary ability.Keywords: Vocabulary, Color Coding Technique, CAR
Subjective Assessment of Adaptive Media Playout for Video Streaming

Adaptive Media Playout (AMP), the adaptive modification of media playback speed, has been previously proposed as a technique to modify the playout delay of a user with respect to other users or with real time, thus having a crucial role in social TV and live video streaming. In this paper, we study the subjective impact of the playout rate change, which had received very limited attention in the past. In particular, we analyze the effect of the subject and the source content in the subjective opinion.We have found that most of the previous works underestimated the impact of AMP in perceived quality, and that the speed change rate should not be modified beyond 10% from the original, as a general rule. We have also developed a user scoring model which takes into account the variability between users and sources. Our results can help developing cost models for playout control systems based on AMP, as well as provide some insights to the analysis of other types of subjective assessment tests.
Vetting API Usages in C Programs with IMChecker

Libraries offer reusable functionality through application programming interfaces (APIs) with usage constraints such as call conditions and orders. Constraint violations, i.e., API misuses, commonly lead to bugs and even security issues. In this paper, we introduce IMChecker, a constraint-directed static analysis toolkit to vet API usages in C programs powered by a domain-specific language (DSL) to specify the API usages. First, we propose a DSL, which covers most API usage constraint types and enables straightforward but precise specification by studying real-world API-misuse bug patches. Then, we design and implement a static analysis engine to automatically parse specifications into checking targets, identify potential API misuses and prune the false positives with rich semantics. We have instantiated IMChecker for C programs with user-friendly graphic interfaces and evaluated the widely used benchmarks and real-world projects. The results show that IMChecker outperforms 4.78-36.25% in precision and 40.25-55.21% w.r.t. state-of-the-arts toolkits. We also found 75 previously unknown bugs in Linux kernel, OpenSSL and applications of Ubuntu, 61 of which have been confirmed by the corresponding development communities. Video: https://youtu.be/YGDxeyOEVIM.
Hadoop MapReduce Job Scheduling Algorithms Survey and Use Cases

Data is the fastest growing asset in the 21st century, extracting insights is becoming of the essence as the traditional ecosystems are incapable to process the resulting amounts, complying with different structural levels, and is rapidly produced. Along this paradigm, the need for processing mostly real time data among other factors highlights the need for optimized Job Scheduling Algorithms, which is the interest of this paper. It is one of the most important aspects to guarantee an efficient processing ecosystem with minimal execution time, while exploiting the available resources taking into consideration granting all the users a fair share of the dedicated resources. Through this work, we lay some needed background on the Hadoop MapReduce framework. We run a comparative analysis on different algorithms that are classified on different criteria. The light is shed on different classifications: Cluster Environment, Job Allocation Strategy, Optimization Strategy, and Metrics of Quality. We, also, construct use cases to showcase the characteristics of selected Job Scheduling Algorithms, then we present a comparative display featuring the details for the use cases.
Are Fictional Voices Distinguishable? Classifying Character Voices in Modern Drama

According to the literary theory of Mikhail Bakhtin, a dialogic novel is one in which characters speak in their own distinct voices, rather than serving as mouthpieces for their authors.We use text classification to determine which authors best achieve dialogism, looking at a corpus of plays from the late nineteenth and early twentieth centuries.We find that the SAGE model of text generation, which highlights deviations from a background lexical distribution, is an effective method of weighting the words of characters' utterances.Our results show that it is indeed possible to distinguish characters by their speech in the plays of canonical writers such as George Bernard Shaw, whereas characters are clustered more closely in the works of lesser-known playwrights.
HAD-Tübingen at SemEval-2019 Task 6: Deep Learning Analysis of Offensive Language on Twitter: Identification and Categorization

This paper describes the submissions of our team, HAD-Tübingen, for the SemEval 2019 -Task 6: "OffensEval: Identifying and Categorizing Offensive Language in Social Media".We participated in all the three sub-tasks: Sub-task A -"Offensive language identification", sub-task B -"Automatic categorization of offense types" and sub-task C -"Offense target identification".As a baseline model we used a Long short-term memory recurrent neural network (LSTM) to identify and categorize offensive tweets.For all the tasks we experimented with external databases in a postprocessing step to enhance the results made by our model.The best macro-average F 1 scores obtained for the sub-tasks A, B and C are 0.73, 0.52, and 0.37, respectively.
Cross-lingual Subjectivity Detection for Resource Lean Languages

Wide and universal changes in the web content due to the growth of web 2 applications increase the importance of user-generated content on the web. Therefore, the related research areas such as sentiment analysis, opinion mining and subjectivity detection receives much attention from the research community. Due to the diverse languages that web-users use to express their opinions and sentiments, research areas like subjectivity detection should present methods which are practicable on all languages. An important prerequisite to effectively achieve this aim is considering the limitations in resource-lean languages. In this paper, cross-lingual subjectivity detection on resource lean languages is investigated using two different approaches: a language-model based and a learning-to-rank approach. Experimental results show the impact of different factors on the performance of subjectivity detection methods using English resources to detect the subjectivity score of Persian documents. The experiments demonstrate that the proposed learning-to-rank method outperforms the baseline method in ranking documents based on their subjectivity degree.
Penerapan Media Pembelajaran Flash Card untuk Meningkatkan Perkembangan Bahasa Anak

&#x0D; This study aims to apply flash card learning media to improve children's language development. The method that I use to determine the research area is a purposive sampling. The function of the respondents is through the observation method, supplemented by interview methods and documentation. The results of the study, flash card learning media including attractive card images, easy to play so that children can respond and learn more actively. Flash card media is a teacher's tool for children to play and learn, can also be used at home with their playmates. The results of the school taught by the teacher are very good so that the results of children's language development are good, children can carry out 2-3 simple commands, can imitate sentences that are conveyed and can respond to questions well. Child language development can clearly state words, vocabulary is good and can speak well after the child enters school. Conclusion, the application of flash card learning media can improve children's language development.&#x0D; Keywords: Flash Card Media, Development, Children's Language&#x0D;
ParallelDots at SemEval-2019 Task 3: Domain Adaptation with feature embeddings for Contextual Emotion Analysis

This paper describes our proposed system & experiments performed to detect contextual emotion in texts for SemEval 2019 Task 3. We exploit sentiment information, syntactic patterns & semantic relatedness to capture diverse aspects of the text.Word level embeddings such as Glove, FastText, Emoji along with sentence level embeddings like Skip-Thought, DeepMoji & Unsupervised Sentiment Neuron were used as input features to our architecture.We democratize the learning using ensembling of models with different parameters to produce the final output.This paper discusses comparative analysis of the significance of these embeddings and our approach for the task.
A New Image Inpainting Approach based on Criminisi Algorithm

In patch-based inpainting methods, the order of filling the areas to be restored is very important.This filling order is defined by a priority function that integrates two parameters: confidence term and data term.The priority, as initially defined, is negatively affected by the mutual influence of confidence and data terms.In addition, the rapid decrease to zero of confidence term leads the numerical instability of algorithms.Finally, the data term depends only on the central pixel of the patch, without taking into account the influence of neighboring pixels.Our aim in this paper is to propose an algorithm to solve the problems mentioned above.This algorithm is based on a new definition of the priority function, a calculation of the average data term obtained from the elementary data terms in a patch and an update of the confidence term slowing its decrease and avoiding convergence to zero.We evaluated our method by comparing it with algorithms in the literature.The results show that our method provides better results both visually and in terms of the Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity index (SSIM).