Reviewerly/exHarmony huggingface dataset:
Multi-reviewing pull-requests: An exploratory study on GitHub OSS projects

Context:GitHub has enabled developers to easily contribute their review comments on multiple pull-requests and switch their review focus between different pull-requests, i.e., multi-reviewing. Reviewing multiple pull-requests simultaneously may enhance work efficiency. However, multi-reviewing also relies on developers’ rationally allocating their focus, which may bring a different influence to the resolution of pull-requests. Objective: In this paper, we present an ongoing study of the impact of multi-reviewing on pull-request resolution in GitHub open source projects. Method: We collected and analyzed 1,836,280 pull-requests from 760 GitHub projects to explore how multi-reviewing affects the resolution of a pull-request. Results: We find that multi-reviewing is a common behavior in GitHub. However, more multi-reviewing behaviors tend to bring longer pull-request resolution latency. Conclusion: Multi-reviewing is a complex behavior of developers, and has an important impact on the efficiency of pull-request resolution. Our study motivates the need for more research on multi-reviewing.
Secure Transfer of Color Images Using Horizontal and Vertical Scan

The aim of this contribution is to enhance the security of encrypted image and decrease the execution time.The proposed cryptosystem based on the keystream generator (KSG) inspired from achterbahn-128 characterized by some good properties used for generating the key.The scan pattern is utilized for the mapping of the image encryption.After the generation of the keystream, the image is encrypted vertically using the scan pattern partition, and then the encrypted image is permuted and rescanned horizontally.Several tests are done using MATLAB software in order to prove performances of the system, including the statistical tests, the sensitivity analysis, and the security analysis.The proposed technique is simple to implement and has high encryption rate.
Dyned Programme as Computer Assisted Language Learning (CALL) for University Students: A Perception and Its Impact

This paper explores the student’s perception on the application of Dyned Programme as Computer Assisted Language Learning (CALL) in English department of Universitas PGRI Semarang, Indonesia. This study is descriptive qualitative. Otherwise, it needs the statistical data in calculating the result percentage. It took 85 students of the first semester students who have already joined Integrated Course (IC) as the sample. IC is a subject which involves ICT-based learning. In this case, Dyned Programme is the ICT-based learning used as CALL. The result of this study shows that the students are satisfied with ICT facilities provided as the media in English teaching and learning. In addition, the use of Dyned Programme in English teaching and learning is well-accepted by the students. Moreover, it comes to the students’ perception that shows us how important the media of ICT in English teaching learning process in order to make the students interest in learning English.
Learning the Relation Between Code Features and Code Transforms With Structured Prediction

To effectively guide the exploration of the code transform space for automated code evolution techniques, we present in this article the first approach for structurally predicting code transforms at the level of AST nodes using conditional random fields (CRFs). Our approach first learns offline a probabilistic model that captures how certain code transforms are applied to certain AST nodes, and then uses the learned model to predict transforms for arbitrary new, unseen code snippets. Our approach involves a novel representation of both programs and code transforms. Specifically, we introduce the formal framework for defining the so-called AST-level code transforms and we demonstrate how the CRF model can be accordingly designed, learned, and used for prediction. We instantiate our approach in the context of repair transform prediction for Java programs. Our instantiation contains a set of carefully designed code features, deals with the training data imbalance issue, and comprises transform constraints that are specific to code. We conduct a large-scale experimental evaluation based on a dataset of bug fixing commits from real-world Java projects. The results show that when the popular evaluation metric <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">top-3</i> is used, our approach predicts the code transforms with an accuracy varying from 41% to 53% depending on the transforms. Our model outperforms two baselines based on history probability and neural machine translation (NMT), suggesting the importance of considering code structure in achieving good prediction accuracy. In addition, a proof-of-concept synthesizer is implemented to concretize some repair transforms to get the final patches. The evaluation of the synthesizer on the Defects4j benchmark confirms the usefulness of the predicted AST-level repair transforms in producing high-quality patches.
Learning Through Deterministic Assignment of Hidden Parameters

Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameters determine the nonlinear mechanism of an estimator, while the bright parameters characterize the linear mechanism. In a traditional learning paradigm, hidden and bright parameters are not distinguished and trained simultaneously in one learning process. Such a one-stage learning (OSL) brings a benefit of theoretical analysis but suffers from the high computational burden. In this paper, we propose a two-stage learning scheme, learning through deterministic assignment of hidden parameters (LtDaHPs), suggesting to deterministically generate the hidden parameters by using minimal Riesz energy points on a sphere and equally spaced points in an interval. We theoretically show that with such a deterministic assignment of hidden parameters, LtDaHP with a neural network realization almost shares the same generalization performance with that of OSL. Then, LtDaHP provides an effective way to overcome the high computational burden of OSL. We present a series of simulations and application examples to support the outperformance of LtDaHP.
Concentration Bounds for High Sensitivity Functions Through Differential Privacy

A new line of work demonstrates how differential privacy can be used as a mathematical tool for guaranteeing generalization in adaptive data analysis. Specifically, if a differentially private analysis is applied on a sample S of i.i.d. examples to select a low-sensitivity function f, then w.h.p. f(S) is close to its expectation, even though f is being chosen adaptively, i.e., based on the data.&#x0D; Very recently, Steinke and Ullman observed that these generalization guarantees can be used for proving concentration bounds in the non-adaptive setting, where the low-sensitivity function is fixed beforehand. In particular, they obtain alternative proofs for classical concentration bounds for low-sensitivity functions, such as the Chernoff bound and McDiarmid's Inequality. In this work, we extend this connection between differential privacy and concentration bounds, and show that differential privacy can be used to prove concentration of high-sensitivity functions.
Multilingual segmentation based on neural networks and pre-trained word embeddings

The DISPRT 2019 workshop has organized a shared task aiming to identify cross-formalism and multilingual discourse segments. Elementary Discourse Units (EDUs) are quite similar across different theories. Segmentation is the very first stage on the way of rhetorical annotation. Still, each annotation project adopted several decisions with consequences not only on the annotation of the relational discourse structure but also at the segmentation stage. In this shared task, we have employed pre-trained word embeddings, neural networks (BiLSTM+CRF) to perform the segmentation. We report F1 results for 6 languages: Basque (0.853), English (0.919), French (0.907), German (0.913), Portuguese (0.926) and Spanish (0.868 and 0.769). Finally, we also pursued an error analysis based on clause typology for Basque and Spanish, in order to understand the performance of the segmenter.
$t$ -Exponential Memory Networks for Question-Answering Machines

Recent advances in deep learning have brought to the fore models that can make multiple computational steps in the service of completing a task; these are capable of describing long-term dependencies in sequential data. Novel recurrent attention models over possibly large external memory modules constitute the core mechanisms that enable these capabilities. Our work addresses learning subtler and more complex underlying temporal dynamics in language modeling tasks that deal with sparse sequential data. To this end, we improve upon these recent advances by adopting concepts from the field of Bayesian statistics, namely, variational inference. Our proposed approach consists in treating the network parameters as latent variables with a prior distribution imposed over them. Our statistical assumptions go beyond the standard practice of postulating Gaussian priors. Indeed, to allow for handling outliers, which are prevalent in long observed sequences of multivariate data, multivariate t-exponential distributions are imposed. On this basis, we proceed to infer corresponding posteriors; these can be used for inference and prediction at test time, in a way that accounts for the uncertainty in the available sparse training data. Specifically, to allow for our approach to best exploit the merits of the t-exponential family, our method considers a new t-divergence measure, which generalizes the concept of the Kullback-Leibler divergence. We perform an extensive experimental evaluation of our approach, using challenging language modeling benchmarks, and illustrate its superiority over existing state-of-the-art techniques.
Very Hard Electoral Control Problems

It is important to understand how the outcome of an election can be modified by an agent with control over the structure of the election. Electoral control has been studied for many election systems, but for all these systems the winner problem is in P, and so control is in NP. There are election systems, such as Kemeny, that have many desirable properties, but whose winner problems are not in NP. Thus for such systems control is not in NP, and in fact we show that it is typically complete for ∑p2 (i.e., NPNP, the second level of the polynomial hierarchy). This is a very high level of complexity. Approaches that perform quite well for solving NP problems do not necessarily work for ∑p2-complete problems. However, answer set programming is suited to express problems in ∑p2, and we present an encoding for Kemeny control.
Computational performance of a projection and rescaling algorithm

This paper documents a computational implementation of a projection and rescaling algorithm for finding most interior solutions to the pair of feasibility problems find x∈L∩R+nandfind xˆ∈L⊥∩R+n, where L denotes a linear subspace in Rn and L⊥ denotes its orthogonal complement. The projection and rescaling algorithm is a recently developed method that combines a basic procedure involving only low-cost operations with a periodic rescaling step. We give a full description of a MATLAB implementation of this algorithm and present multiple sets of numerical experiments on synthetic problem instances with varied levels of conditioning. Our computational experiments provide promising evidence of the effectiveness of the projection and rescaling algorithm. Our MATLAB code is publicly available. Furthermore, the simplicity of the algorithm makes a computational implementation in other environments completely straightforward.
An Energy-Efficient Programmable Mixed-Signal Accelerator for Machine Learning Algorithms

We propose PROMISE, the first end-to-end design of a PROgrammable MIxed-Signal accElerator from Instruction Set Architecture to high-level language compiler for acceleration of diverse machine learning algorithms by exploiting the advantage of the superior energy efficiency from analog/mixed-signal processing.
HAMBATAN DAN TANTANGAN PEMBELAJARAN BERBASIS TEKNOLOGI INFORMASI DAN KOMUNIKASI

Along with the development of Information and Communication Technology (ICT) that is increasingly rapid, the need for a concept and mechanism for ICT-based teaching and learning (educators) becomes inevitable. The concept which was later known as e-learning brought the influence of the process of converting conventional education into digital form, both in terms of contents and systems. E-learning as a new learning model in education provides a great role and function for the world of education which has been charged with the many shortcomings and weaknesses of education, including limited space and time in the education process. E-learning is a term used for learning that utilizes communication and information technology in various ways to support and improve the learning process. There are various kinds of technologies that can be used, ranging from online and offline computer applications, presentation technologies such as Microsoft PowerPoint applications or projectors, World Wide Web, web conferences, multimedia materials such as photos or animations, tools to assess student work, games, and still much more. In implementing ICT-based learning, there are many challenges and obstacles. One of the challenges and obstacles in the application of ICT-based learning is that the inequality of infrastructure that supports the application of ICT in the education sector is an initial problem that must be resolved by the authorities, because without the infrastructure that supports the implementation of ICT in education mere dreams.
Protection of entanglement for a two-qutrit V-type open system on the basis of system-reservoir bound states

In this work, a mechanism for long time protection of entanglement of a two-qutrit V-type atomic system each of which interacts independently with a dissipative reservoir is investigated. It is shown that the protection process is related exclusively on the formation of bound states in system-reservoir energy spectrum. It is demonstrated that the degree of boundedness for the system-reservoir bound states is well-controlled by entering other V-type qutrits into the reservoirs, and also through the manipulation of the spontaneously generated interference related to decay channels of the V-type atoms.
NL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion Mining

In this paper, we present neural model architecture submitted to the SemEval-2019 Task 9 competition: "Suggestion Mining from Online Reviews and Forums".We participated in both subtasks for domain specific and also crossdomain suggestion mining.We proposed a recurrent neural network architecture that employs Bi-LSTM layers and also self-attention mechanism.Our architecture tries to encode words via word representations using ELMo and ensembles multiple models to achieve better results.We performed experiments with different setups of our proposed model involving weighting of prediction classes for loss function.Our best model achieved in official test evaluation score of 0.6816 for subtask A and 0.6850 for subtask B. In official results, we achieved 12th and 10th place in subtasks A and B, respectively.
Online Algorithm for Unsupervised Sensor Selection

In many security and healthcare systems, the detection and diagnosis systems use a sequence of sensors/tests. Each test outputs a prediction of the latent state and carries an inherent cost. However, the correctness of the predictions cannot be evaluated due to unavailability of the ground-truth annotations. Our objective is to learn strategies for selecting a test that gives the best trade-off between accuracy and costs in such unsupervised sensor selection (USS) problems. Clearly, learning is feasible only if ground truth can be inferred (explicitly or implicitly) from the problem structure. It is observed that this happens if the problem satisfies the ‘Weak Dominance’ (WD) property. We set up the USS problem as a stochastic partial monitoring problem and develop an algorithm with sub-linear regret under the WD property. We argue that our algorithm is optimal and evaluate its performance on problem instances generated from synthetic and real-world datasets.
GIRNet: Interleaved Multi-Task Recurrent State Sequence Models

In several natural language tasks, labeled sequences are available in separate domains (say, languages), but the goal is to label sequences with mixed domain (such as code-switched text). Or, we may have available models for labeling whole passages (say, with sentiments), which we would like to exploit toward better position-specific label inference (say, target-dependent sentiment annotation). A key characteristic shared across such tasks is that different positions in a primary instance can benefit from different ‘experts’ trained from auxiliary data, but labeled primary instances are scarce, and labeling the best expert for each position entails unacceptable cognitive burden. We propose GIRNet, a unified position-sensitive multi-task recurrent neural network (RNN) architecture for such applications. Auxiliary and primary tasks need not share training instances. Auxiliary RNNs are trained over auxiliary instances. A primary instance is also submitted to each auxiliary RNN, but their state sequences are gated and merged into a novel composite state sequence tailored to the primary inference task. Our approach is in sharp contrast to recent multi-task networks like the crossstitch and sluice networks, which do not control state transfer at such fine granularity. We demonstrate the superiority of GIRNet using three applications: sentiment classification of code-switched passages, part-of-speech tagging of codeswitched text, and target position-sensitive annotation of sentiment in monolingual passages. In all cases, we establish new state-of-the-art performance beyond recent competitive baselines.
A Self-Controllable and Balanced Data Sharing Model

The widespread acceptance of data sharing has promoted the relevant research in both academia and industry. “Data sharing” is the process of interchanging data among multiple data sources in a controllable access manner. Any such system provides common functionality of storage and access; however, there are prominent differences in non-functionalities, such as self-control, transparency, cost-effectiveness, incentive, and auditing, which make data sharing in a dilemma. In this paper, we propose a distributed data sharing model based on blockchain technology: data owner publishes data with an additional control policy; data consumer inquires data with an access request; and consensus nodes evaluate the request and make decisions. After formalizing the system model, we discuss the procedure of data sharing, reputation management, and proposed solution. With the access control policy, price compensation, and reputation management mechanism, we achieve self-control and price balance in sharing data. Our model is equally a closed-loop control system that acts as a regulator of supervising its participants. The analyses and experimental results show, comparing with the existing sharing systems, that our model is practical and can spur agents to participate actively in sharing data.
A Combination Model Based on EEMD-PE and Echo State Network for Chaotic Time Series Prediction

Prediction of chaotic time series has broad application prospects and becomes a research hotspot. Since chaotic time series is strongly non-stationary and nonlinear, it's difficult to predict based on any single model. Therefore, the empirical mode decomposition (EMD)-based combination model becomes an important means of prediction. To reduce the scale of prediction models of conventional combination method, this paper proposes a high-efficiency combination model using ensemble EMD (EEMD), permutation entropy (PE) and echo state network(ESN). EEMD decomposes the original time series into a group of intrinsic mode functions (IMFs), and the number of IMFs is consistent with the number of predictors. On account of the complexity of the chaotic time series, there is a large demand of predictors. Through complexity analysis by PE, we combine some IMFs whose complexities are similar, and predict the combined signals instead of the initial ones based on ESNs. Finally, these obtained estimates are assembled as the ultimate prediction results. In the experiment, we use real-world dataset to examine the proposed model. The experimental results confirm that our combination approach outperforms existing single models, and efficiently reduces the scale of prediction models comparing to the EEMD-ESN.
Machine-learning denoising in feature film production

We present our experience deploying and using machine learning denoising of Monte Carlo renders in the production of animated feature films such as Pixar's Toy Story 4, Disney Animation's Ralph Breaks the Internet and Industrial Light & Magic's visual effects work on photo-realistic films such as Aladdin (2019). We show what it took to move from an R&D implementation of "Denoising with Kernel Prediction and Asymmetric Loss Functions" [Vogels et al. 2018] to a practical tool in a production pipeline.
When the Signal is in the Noise: Exploiting Diffix's Sticky Noise

Anonymized data is highly valuable to both businesses and researchers. A large body of research has however shown the strong limits of the de-identification release-and-forget model, where data is anonymized and shared. This has led to the development of privacy-preserving query-based systems. Based on the idea of sticky Diffix has been recently proposed as a novel query-based mechanism satisfying alone the EU Article~29 Working Party's definition of anonymization. According to its authors, Diffix adds less noise to answers than solutions based on differential privacy while allowing for an unlimited number of queries. This paper presents a new class of noise-exploitation attacks, exploiting the noise added by the system to infer private information about individuals in the dataset. Our first differential attack uses samples extracted from Diffix in a likelihood ratio test to discriminate between two probability distributions. We show that using this attack against a synthetic best-case dataset allows us to infer private information with 89.4% accuracy using only 5 attributes. Our second cloning attack uses dummy conditions that conditionally strongly affect the output of the query depending on the value of the private attribute. Using this attack on four real-world datasets, we show that we can infer private attributes of at least 93% of the users in the dataset with accuracy between 93.3% and 97.1%, issuing a median of 304 queries per user. We show how to optimize this attack, targeting 55.4% of the users and achieving 91.7% accuracy, using a maximum of only 32 queries per user. Our attacks demonstrate that adding data-dependent noise, as done by Diffix, is not sufficient to prevent inference of private attributes. We furthermore argue that Diffix alone fails to satisfy Art. 29 WP's definition of anonymization. [...]