Reviewerly/exHarmony huggingface dataset:
On the impact of Continuous Integration on refactoring practice: An exploratory study on TravisTorrent

The ultimate goal of Continuous Integration (CI) is to support developers in integrating changes into production constantly and quickly through automated build process. While CI provides developers with prompt feedback on several quality dimensions after each change, such frequent and quick changes may in turn compromise software quality without Refactoring. Indeed, recent work emphasized the potential of CI in changing the way developers perceive and apply refactoring. However, we still lack empirical evidence to confirm or refute this assumption. We aim to explore and understand the evolution of refactoring practices, in terms of frequency, size and involved developers, after the switch to CI in order to emphasize the role of this process in changing the way Refactoring is applied. We collect a corpus of 99,545 commits and 89,926 refactoring operations extracted from 39 open-source GitHub projects that adopt Travis CI and analyze the changes using Multiple Regression Analysis (MRA). Our study delivers several important findings. We found that the adoption of CI is associated with a drop in the refactoring size as recommended, while refactoring frequency as well as the number (and its related rate) of developers that perform refactoring are estimated to decrease after the shift to CI, indicating that refactoring is less likely to be applied in CI context. Our study uncovers insights about CI theory and practice and adds evidence to existing knowledge about CI practices related especially to quality assurance. Software developers need more customized refactoring tool support in the context of CI to better maintain and evolve their software systems.
Disentangled Speaker and Language Representations Using Mutual Information Minimization and Domain Adaptation for Cross-Lingual TTS

We propose a method for obtaining disentangled speaker and language representations via mutual information minimization and domain adaptation for cross-lingual text-to-speech (TTS) synthesis. The proposed method extracts speaker and language embeddings from acoustic features by a speaker encoder and a language encoder. Then the proposed method applies domain adaptation on the two embeddings to obtain language-invariant speaker embedding and speaker-invariant language embedding. To get more disentangled representations, the proposed method further uses mutual information minimization between the two embeddings to remove entangled information within each embedding. Disentangled representations of speaker and language are critical for cross-lingual TTS synthesis since entangled representations make it difficult to maintain speaker identity information when changing the language representation and consequently causes performance degradation. We evaluate the proposed method using English and Japanese multi-speaker datasets with a total of 207 speakers. Experimental result demonstrates that the proposed method significantly improves the naturalness and speaker similarity of both intra-lingual and cross-lingual TTS synthesis. Furthermore, we show that the proposed method has a good capability of maintaining the speaker identity between languages.
Using Machine Intelligence to Prioritise Code Review Requests

Modern Code Review (MCR) is the process of reviewing new code changes that need to be merged with an existing codebase. As a developer, one may receive many code review requests every day, i.e., the review requests need to be prioritised. Manually prioritising review requests is a challenging and time-consuming process. To address the above problem, we conducted an industrial case study at Ericsson aiming at developing a tool called Pineapple, which uses a Bayesian Network to prioritise code review requests. To validate our approach/tool, we deployed it in a live software development project at Ericsson, wherein more than 150 developers develop a telecommunication product. We focused on evaluating the predictive performance, feasibility, and usefulness of our approach. The results indicate that Pineapple has competent predictive performance (RMSE = 0.21 and MAE = 0.15). Furthermore, around 82.6% of Pineapple's users believe the tool can support code review request prioritisation by providing reliable results, and around 56.5% of the users believeit helps reducing code review lead time. As future work, we planto evaluate Pineapple's predictive performance, usefulness, and feasibility through a longitudinal investigation.
MulCode: A Multi-task Learning Approach for Source Code Understanding

Recent years have witnessed the significant rise of Deep Learning (DL) techniques applied to source code. Researchers exploit DL for a multitude of tasks and achieve impressive results. However, most tasks are explored separately, resulting in a lack of generalization of the solutions. In this work, we propose MulCode, a multi-task learning approach for source code understanding that learns unified representation space for tasks, with the pre-trained BERT model for the token sequence and the Tree-LSTM model for abstract syntax trees. Furthermore, we integrate two source code views into a hybrid representation via the attention mechanism and set learnable uncertainty parameters to adjust the tasks' relationship.We train and evaluate MulCode in three downstream tasks: comment classification, author attribution, and duplicate function detection. In all tasks, MulCode outperforms the state-of-the-art techniques. Moreover, experiments on three unseen tasks demonstrate the generalization ability of MulCode compared with state-of-the-art embedding methods.
Scheming an Proficient Auto Scaling Technique for Minimizing Response Time in Load Balancing on Amazon AWS Cloud

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Memory-Efficient Dataflow Inference for Deep CNNs on FPGA

Custom dataflow Convolutional Neural Network (CNN) inference accelerators on FPGA are tailored to a specific CNN topology and store parameters in On-Chip Memory (OCM), creating the potential for high energy efficiency and low inference latency. However, in these accelerators the shapes of parameter memories are dictated by throughput constraints and do not map well to the underlying OCM, which becomes an implementation bottleneck. In this work, we propose an accelerator design methodology - Frequency Compensated Memory Packing (FCMP) - which improves the OCM utilization efficiency of dataflow accelerators with minimal reduction in throughput and no modifications to the physical structure of FPGA OCM. To validate our methodology, we apply it to several realizations of medium-sized CIFAR-10 inference accelerators and demonstrate up to 30% reduction in OCM utilization without loss of inference throughput, allowing us to port the accelerators from Xilinx Zynq 7020 to 7012S, reducing application cost. We also implement a custom dataflow FPGA inference accelerator for a quantized ResNet-50 CNN, utilizing on-chip weights, the largest topology ever implemented with this accelerator architecture. We demonstrate that by applying FCMP to the ResNet accelerator, the OCM bottleneck is alleviated which enables the accelerator to be ported from Alveo U250 to the smaller Alveo U280 board with less throughput loss compared to alternative techniques. By providing a finer-grained trade off between throughput and OCM requirements, FCMP increases the flexibility of custom dataflow CNN inference designs on FPGA.
Good-Eye: A Device for Automatic Prediction and Detection of Elderly Falls in Smart Homes

This is an extended abstract for a Research Demo Session based on our published work [1]. It is important to be able to reduce the frequency of elderly falls and their disastrous effects. Good-Eye proposes an edge device which predicts or detects falls based on image orientation and physiological sensors using the Internet-of-Medical-Things (IoMT). Using LED lights, the user is notified with the decision of fall prediction and detection if the observed change is greater than a set threshold. Along with a camera attached to the wearable, the Good-Eye system proposes a remote off-line on-wall camera to make more accurate prediction and detection of falls.
Toxic Speech Detection using Traditional Machine Learning Models and BERT and fastText Embedding with Deep Neural Networks

The introduction of social media brought about a revolution in the world of digitalization and communication. These platforms were initially developed with a purpose of connecting people across the global boundaries while allowing them to express their views and opinions and learn from others' ideas. With the incoming of the pandemic, the usage of these sites has risen significantly be it by the businesses, educational institutions, students or general public. The increasing ubiquity of social media platforms like Twitter and Facebook has been an issue of major concern since a long time. Along with providing a way for enhanced communication, these platforms also allow internet users to voice their opinions which get circulated among the masses within seconds. Moreover, given the different backgrounds, believes, ethnicity and cultures that the users on these platforms come from, many of them tend to use mean, aggressive and hateful content during their discussions with people not hailing from a background similar to theirs. The amount of hate speech and offensive content has been increasing exponentially. Terms like "profane", "hate", and "offensive" are used interchangeably, and hence these have been classified under a broader category of "Toxic" content. A major part of our dataset focuses on conversations prevailing among the youth. After the preprocessing of this dataset using NLP and embeddings (Bert and fastText), a bunch of Machine Learning (LR, SVM, DT, RF, XGBoost) and Deep Learning algorithms (CNN, MLP, LSTM) have been performed, with CNN giving the best results.
Breakdowns and Breakthroughs: Observing Musicians’ Responses to the COVID-19 Pandemic

This paper describes results from a study examining how musicians have been affected by the restrictive environments imposed by the COVID-19 pandemic, and the corresponding technological implications. Through a survey of 29 musicians and additional interviews with 7 professional improvisation musicians, we observe the challenges musicians face in 1) finding the technological infrastructure and shared spaces necessary for dynamic, remote creative collaborations, 2) producing the mindset, free time, and sources of inspiration necessary for initiating creativity during times of stress, and 3) maintaining (and potentially expanding) the social connections and creative community vital to creative practice. We also report on how some musicians have creatively leveraged the radical changes in their lives to drive new artistic practices and styles of music. Collectively, these data illustrate the resilience of certain existing remote collaboration and creative tools, while suggesting ways other digital tools could more flexibly accommodate new use cases. The data also suggest the value of tools and services that help people creatively cope with radical changes to their lives and livelihoods.
Exploring User-Centered Security Design for Usable Authentication Ceremonies

Security technology often follows a systems design approach that focuses on components instead of users. As a result, the users' needs and values are not sufficiently addressed, which has implications on security usability. In this paper, we report our lessons learned from applying a user-centered security design process to a well-understood security usability challenge, namely key authentication in secure instant messaging. Users rarely perform these key authentication ceremonies, which makes their end-to-end encrypted communication vulnerable. Our approach includes collaborative design workshops, an expert evaluation, iterative storyboard prototyping, and an online evaluation.
Deep Tone-Mapping Operator Using Image Quality Assessment Inspired Semi-Supervised Learning

Tone-mapping operator (TMO) is intended to convert high dynamic range (HDR) content into a lower dynamic range so that it can be displayed on a standard dynamic range (SDR) device. The tone-mapped result of HDR content is usually stored as SDR image. For different HDR scenes, traditional TMOs are able to obtain a satisfying SDR image only under manually fine-tuned parameters. In this paper, we address this problem by proposing a learning-based TMO using deep convolutional neural network (CNN). We explore different CNN structure and adopt multi-scale and multi-branch fully convolutional design. When training deep CNN, we introduce image quality assessments (IQA), specifically, tone-mapped image quality assessment, and implement it as semi-supervised loss terms. We discuss and prove the effectiveness of semi-supervised loss terms, CNN structure, data pre-processing, etc. by several experiments. Finally, we demonstrate that our approach can produce appealing results under diversified HDR scenes.
Revisiting Distance Perception with Scaled Embodied Cues in Social Virtual Reality

Previous research on distance estimation in virtual reality (VR) has well established that even for geometrically accurate virtual objects and environments users tend to systematically mis-estimate distances. This has implications for Social VR, where it introduces variables in personal space and proxemics behavior that change social behaviors compared to the real world. One yet unexplored factor is related to the trend that avatars' embodied cues in Social VR are often scaled, e.g., by making one's head bigger or one's voice louder, to make social cues more pronounced over longer distances. In this paper we investigate how the perception of avatar distance is changed based on two means for scaling embodied social cues: visual head scale and verbal volume scale. We conducted a human-subject study employing a mixed factorial design with two Social VR avatar representations (full-body, head-only) as a between factor as well as three visual head scales and three verbal volume scales (up-scaled, accurate, down-scaled) as within factors. For three distances from social to far-public space, we found that visual head scale had a significant effect on distance judgments and should be tuned for Social VR, while conflicting verbal volume scales did not, indicating that voices can be scaled in Social VR without immediate repercussions on spatial estimates. We discuss the interactions between the factors and implications for Social VR.
Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network

Super resolution (SR) enables to generate a high-resolution (HR) image from one or more low-resolution (LR) images. Since a variety of CNN models have been recently studied in the areas of computer vision, these approaches have been combined with SR in order to provide higher image restoration. In this paper, we propose a lightweight CNN-based SR method, named multi-scale channel dense network (MCDN). In order to design the proposed network, we extracted the training images from the DIVerse 2K (DIV2K) dataset and investigated the trade-off between the SR accuracy and the network complexity. The experimental results show that the proposed method can significantly reduce the network complexity, such as the number of network parameters and total memory capacity, while maintaining slightly better or similar perceptual quality compared to the previous methods.
Co-attentive representation learning for web services classification

The rapid adoption of services-related technologies, such as cloud computing, has lead to the explosive growth of web services. Automated service classification that groups web services by similar functionality is a widely used technique to facilitate the management and discovery of web services within a large-scale repository. The existing service classification approaches primarily focus on learning the isolated representations of service features but ignored their internal semantic correlations. To address the aforementioned issue, we propose a novel deep neural network with the Co-Attentive Representation Learning (CARL-Net) mechanism for effectively classifying services by learning interdependent characteristics of service without feature engineering. Specifically, we propose a service data augmentation mechanism by extracting informative words from the service description using information gain theory. Such a mechanism can learn a correlation matrix among embedded augmented data and description, thereby obtaining their interdependent semantic correlation representations for service classification. We evaluate the effectiveness of CARL-Net by comprehensive experiments based on a real-world dataset collected from ProgrammableWeb, which includes 10,943 web services. Compared with seven web service classification baselines based on CNN, LSTM, Recurrent-CNN, C-LSTM, BLSTM, ServeNet and ServeNet-BERT, the CARL-Net can achieve an improvement of 5.66%–172.21% in the F-measure of web service classification.
Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN

Fecal samples can easily be collected and are representative of a person's current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits the general examination speed; therefore, automatic analysis is needed. Nevertheless, recognition exhaustion time and accuracy remain major challenges in automatic testing. Here, we introduce a fast and efficient cell-detection algorithm based on the Faster-R-CNN technique: the Resnet-152 convolutional neural network architecture. Additionally, a region proposal network and a network combined with principal component analysis are proposed for cell location and recognition in microscopic images. Our algorithm achieved a mean average precision of 84% and a 723 ms detection time per sample for 40,560 fecal images. Thus, this approach may provide a solid theoretical basis for real-time detection in routine clinical examinations while accelerating the process to satisfy increasing demand.
SOAR: A Synthesis Approach for Data Science API Refactoring

With the growth of the open-source data science community, both the number of data science libraries and the number of versions for the same library are increasing rapidly. To match the evolving APIs from those libraries, open-source organizations often have to exert manual effort to refactor the APIs used in the code base. Moreover, due to the abundance of similar open-source libraries, data scientists working on a certain application may have an abundance of libraries to choose, maintain and migrate between. The manual refactoring between APIs is a tedious and error-prone task. Although recent research efforts were made on performing automatic API refactoring between different languages, previous work relies on statistical learning with collected pairwise training data for the API matching and migration. Using large statistical data for refactoring is not ideal because such training data will not be available for a new library or a new version of the same library. We introduce Synthesis for Open-Source API Refactoring (SOAR), a novel technique that requires no training data to achieve API migration and refactoring. SOAR relies only on the documentation that is readily available at the release of the library to learn API representations and mapping between libraries. Using program synthesis, SOAR automatically computes the correct configuration of arguments to the APIs and any glue code required to invoke those APIs. SOAR also uses the interpreter's error messages when running refactored code to generate logical constraints that can be used to prune the search space. Our empirical evaluation shows that SOAR can successfully refactor 80% of our benchmarks corresponding to deep learning models with up to 44 layers with an average run time of 97.23 seconds, and 90% of the data wrangling benchmarks with an average run time of 17.31 seconds.
Digital Competence of a Teacher in a Pandemic

The article considers changes in the education system during the COVID-19 coronavirus infection pandemic. The term “digital competence of a teacher” is disclosed and a comparative analysis of the attitude to distance learning of teachers and students using a sociological survey is made. The teachers identified the lack of direct contact with the student as the main disadvantage of distance learning, and students feel discomfort due to the inability to receive knowledge directly from the teacher. A number of problems of distance learning have been identified and a course program has been created to improve the digital competence of a teacher. The course content covers the basics of pedagogical design, rubrics of educational results, platforms for collaboration, tools for creating a video lecture, platforms for monitoring and collecting information, digital footprint and analysis, online resources of virtual laboratory work. As a result of learning, 680 university teachers and teachers of secondary schools in Kazakhstan designed their own course. At the end of the first semester, the approbation of these courses was considered. The authors performed a two-step data analysis procedure using deductive and inductive analysis methods. According to the course indicators, many of the identified problems have significantly decreased.
Balancing Privacy and Accountability in Blockchain Identity Management

The lack of privacy in the first generation of cryptocurrencies such as Bitcoin, Ethereum, etc. is a well known problem in cryptocurrency research. To overcome this problem, several new cryptocurrencies were designed to guarantee transaction privacy and anonymity for their users (examples include ZCash, Monero, etc.).However, the anonymity provided by such systems appears to be fundamentally problematic in current business and legislation settings: banks and other financial institutions must follow rules such as “Know Your Customer” (KYC), “Anti Money Laundering” (AML), etc. It is also well known that the (alleged or real) anonymity guarantees provided by cryptocurrencies have attracted ill-intentioned individuals to this space, who look at cryptocurrencies as a way of facilitating illegal activities (tax-evasion, ransom-ware, trading of illegal substances, etc.).The fact that current cryptocurrencies do not comply with such regulations can in part explain why traditional financial institutions have so far been very sceptical of the ongoing cryptocurrency and Blockchain revolution.In this paper, we propose a novel design principle for identity management in Blockchains. The goal of our design is to maintain privacy, while still allowing compliance with current regulations and preventing exploitations of Blockchain technology for purposes which are incompatible with the social good.