Reviewerly/exHarmony huggingface dataset:
The "Shut the f**k up" Phenomenon: Characterizing Incivility in Open Source Code Review Discussions

Code review is an important quality assurance activity for software development. Code review discussions among developers and maintainers can be heated and sometimes involve personal attacks and unnecessary disrespectful comments, demonstrating, therefore, incivility. Although incivility in public discussions has received increasing attention from researchers in different domains, the knowledge about the characteristics, causes, and consequences of uncivil communication is still very limited in the context of software development, and more specifically, code review. To address this gap in the literature, we leverage the mature social construct of incivility as a lens to understand confrontational conflicts in open source code review discussions. For that, we conducted a qualitative analysis on 1,545 emails from the Linux Kernel Mailing List (LKML) that were associated with rejected changes. We found that more than half 66.66% of the non-technical emails included uncivil features. Particularly, frustration, name calling, and impatience are the most frequent features in uncivil emails. We also found that there are civil alternatives to address arguments, while uncivil comments can potentially be made by any people when discussing any topic. Finally, we identified various causes and consequences of such uncivil communication. Our work serves as the first study about the phenomenon of in(civility) in open source software development, paving the road for a new field of research about collaboration and communication in the context of software engineering activities.
Towards Transferable Adversarial Attacks on Vision Transformers

Vision transformers (ViTs) have demonstrated impressive performance on a series of computer vision tasks, yet they still suffer from adversarial examples. In this paper, we posit that adversarial attacks on transformers should be specially tailored for their architecture, jointly considering both patches and self-attention, in order to achieve high transferability. More specifically, we introduce a dual attack framework, which contains a Pay No Attention (PNA) attack and a PatchOut attack, to improve the transferability of adversarial samples across different ViTs. We show that skipping the gradients of attention during backpropagation can generate adversarial examples with high transferability. In addition, adversarial perturbations generated by optimizing randomly sampled subsets of patches at each iteration achieve higher attack success rates than attacks using all patches. We evaluate the transferability of attacks on state-of-the-art ViTs, CNNs and robustly trained CNNs. The results of these experiments demonstrate that the proposed dual attack can greatly boost transferability between ViTs and from ViTs to CNNs. In addition, the proposed method can easily be combined with existing transfer methods to boost performance.
ViViT: A Video Vision Transformer

We present pure-transformer based models for video classification, drawing upon the recent success of such models in image classification. Our model extracts spatio-temporal tokens from the input video, which are then encoded by a series of transformer layers. In order to handle the long sequences of tokens encountered in video, we propose several, efficient variants of our model which factorise the spatial- and temporal-dimensions of the input. Although transformer-based models are known to only be effective when large training datasets are available, we show how we can effectively regularise the model during training and leverage pretrained image models to be able to train on comparatively small datasets. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple video classification benchmarks including Kinetics 400 and 600, Epic Kitchens, Something-Something v2 and Moments in Time, outperforming prior methods based on deep 3D convolutional networks. To facilitate further research, we will release code and models.
Joint Semi-supervised and Active Learning for Segmentation of Gigapixel Pathology Images with Cost-Effective Labeling

The need for manual and detailed annotations limits the applicability of supervised deep learning algorithms in medical image analyses, specifically in the field of pathology. Semi-supervised learning (SSL) provides an effective way for leveraging unlabeled data to relieve the heavy reliance on the amount of labeled samples when training a model. Although SSL has shown good performance, the performance of recent state-of-the-art SSL methods on pathology images is still under study. The problem for selecting the most optimal data to label for SSL is not fully explored. To tackle this challenge, we propose a semi-supervised active learning framework with a region-based selection criterion. This framework iteratively selects regions for an-notation query to quickly expand the diversity and volume of the labeled set. We evaluate our framework on a grey-matter/white-matter segmentation problem using gigapixel pathology images from autopsied human brain tissues. With only 0.1% regions labeled, our proposed algorithm can reach a competitive IoU score compared to fully-supervised learning and outperform the current state-of-the-art SSL by more than 10% of IoU score and DICE coefficient.
Early Convolutions Help Transformers See Better

Vision transformer (ViT) models exhibit substandard optimizability. In particular, they are sensitive to the choice of optimizer (AdamW vs. SGD), optimizer hyperparameters, and training schedule length. In comparison, modern convolutional neural networks are far easier to optimize. Why is this the case? In this work, we conjecture that the issue lies with the patchify stem of ViT models, which is implemented by a stride-p pxp convolution (p=16 by default) applied to the input image. This large-kernel plus large-stride convolution runs counter to typical design choices of convolutional layers in neural networks. To test whether this atypical design choice causes an issue, we analyze the optimization behavior of ViT models with their original patchify stem versus a simple counterpart where we replace the ViT stem by a small number of stacked stride-two 3x3 convolutions. While the vast majority of computation in the two ViT designs is identical, we find that this small change in early visual processing results in markedly different training behavior in terms of the sensitivity to optimization settings as well as the final model accuracy. Using a convolutional stem in ViT dramatically increases optimization stability and also improves peak performance (by ~1-2% top-1 accuracy on ImageNet-1k), while maintaining flops and runtime. The improvement can be observed across the wide spectrum of model complexities (from 1G to 36G flops) and dataset scales (from ImageNet-1k to ImageNet-21k). These findings lead us to recommend using a standard, lightweight convolutional stem for ViT models as a more robust architectural choice compared to the original ViT model design.
Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi

The widespread presence of offensive language on social media motivated the development of systems capable of recognizing such content automatically.Apart from a few notable exceptions, most research on automatic offensive language identification has dealt with English.To address this shortcoming, we introduce MOLD 1 , the Marathi Offensive Language Dataset.MOLD is the first dataset of its kind compiled for Marathi, thus opening a new domain for research in lowresource Indo-Aryan languages.We present results from several machine learning experiments on this dataset, including zero-short and other transfer learning experiments on state-ofthe-art cross-lingual transformers from existing data in Bengali, English, and Hindi.
Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing

The early and robust detection of anomalies occurring in discrete manufacturing processes allows operators to prevent harm, e.g. defects in production machinery or products. While current approaches for data-driven anomaly detection provide good results on the exact processes they were trained on, they often lack the ability to flexibly adapt to changes, e.g. in products. Continual learning promises such flexibility, allowing for an automatic adaption of previously learnt knowledge to new tasks. Therefore, this article discusses different continual learning approaches from the group of regularization strategies, which are implemented, evaluated, and compared based on a real industrial metal forming dataset.
Left behind? The effects of digital gaps on e-learning in rural secondary schools and remote communities across Nigeria during the COVID19 pandemic

The COVID19 pandemic has contributed to a digital economy by emphasising the importance of digital infrastructure while exposing the digital gaps between countries and communities. For example, during the period of COVID19, schools were closed with no option of online learning due to a deficit of infrastructure around educational technologies. Inequalities in digital access have impeded educational gains made towards achieving the Sustainable Development Goals (SDGs). Though significant studies have been carried out on digital learning, there is currently a dearth of knowledge on students' digital gaps and needs in remote communities in Nigeria. To this end, this research study explored the digital gaps and needs of rural secondary schools in remote communities and its implications on e-learning across 6 Nigerian states, namely Kwara, Ekiti, Ebonyi, Bayelsa, Adamawa, and Kano during the COVID19 era. The study adopted a concurrent embedded mixed method design approach to collect data from rural secondary schools from 6 Nigerian states. Findings from the study identified a lack of ICT strategies and policies in Nigeria, socioeconomic status, poor internet connectivity, electricity, and a high poverty level as the primary drivers of digital gaps in remote communities. Therefore, addressing the digital gaps among students in remote parts of Nigeria will be crucial to achieving the targets of SDGs, particularly SDG 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all by 2030.
SEMRES - A Triple Security Protected Blockchain Based Medical Record Exchange Structure

COVID-19, a serious infectious disease outbreak started in the end of 2019, has caused a strong impact on the overall medical system, which reflects the gap in the volume and capacity of medical services and highlights the importance of clinical data ex-change and application. The most important concerns of medical records in the medical field include data privacy, data correctness, and data security. By realizing these three goals, medical records can be made available to different hospital information systems to achieve the most complete medical care services. The privacy and protection of health data require detailed specification and usage requirements, which is particularly important for cross-agency data exchange.This research is composed of three main modules. "Combined Encryption and Decryption Architecture", which includes the hybrid double encryption mechanism of AES and RSA, and encrypts medical records to produce "Secured Encrypted Medical Record". "Decentralize EMR Repository", which includes data decryption and an exchange mechanism. After a data transmission is completed, the content verification and data decryption process will be launched to confirm the correctness of the data and obtain the data. A blockchain architecture is used to store the hash value of the encrypted EMR, and completes the correctness verification of the EMR after transmission through the hash value.The results of this study provide an efficient triple encryption mechanism for electronic medical records. SEMRES ensures the correctness of data through the non-repudiation feature of a blockchain open ledger, and complete integrated information security protection and data verification architecture, in order that medical data can be exchanged, verified, and applied in different locations. After the patient receives medical services, the medical record is re-encrypted and verified and stored in the patient's medical record. The blockchain architecture is used to ensure the verification of non-repudiation of medical service, and finally to complete the payment for medical services.The main aim of this study was to complete a security architecture for medical data, and develop a triple encryption authentication architecture to help data owners easily and securely share personal medical records with medical service personnel.
Guessing Words

Multiple accounts have been proposed to model the mental lexicon, and these models have mainly been validated by comparing how well they can describe human data derived in an experimental setting. Alternatively, we present two experiments where word stimuli were generated using different models and compare their quality in a familiar word guessing game. A text-corpus-based model and a word association model were used to generate lists of closely related words to a target word. Consecutively, these words were used as hints in a word guessing game where the aim was to find the target word. Both models succeeded in generating good quality hints, but those generated from the word association model led to more accurate responses and fewer hints needed. Potential explanations for why the word association model provided a better approximation of the mental lexicon are discussed.
Explanation-Driven HCI Model to Examine the Mini-Mental State for Alzheimer’s Disease

Directing research on Alzheimer’s disease toward only early prediction and accuracy cannot be considered a feasible approach toward tackling a ubiquitous degenerative disease today. Applying deep learning (DL), Explainable artificial intelligence, and advancing toward the human-computer interface (HCI) model can be a leap forward in medical research. This research aims to propose a robust explainable HCI model using SHAPley additive explanation, local interpretable model-agnostic explanations, and DL algorithms. The use of DL algorithms—logistic regression (80.87%), support vector machine (85.8%), k -nearest neighbor (87.24%), multilayer perceptron (91.94%), and decision tree (100%)—and explainability can help in exploring untapped avenues for research in medical sciences that can mold the future of HCI models. The presented model’s results show improved prediction accuracy by incorporating a user-friendly computer interface into decision-making, implying a high significance level in the context of biomedical and clinical research.
Optimized cancer detection on various magnified histopathological colon images based on DWT features and FCM clustering

Due to the morphological characteristics and other biological aspects in histopathological images, the computerized diagnosis of colon cancer in histopathology images has gained popularity. The images acquired using the histopathology microscope may differ for greater visibility by magnifications. This causes a change in morphological traits leading to intra and inter-observer variability. An automatic colon cancer diagnosis system for various magnification is therefore crucial. This work proposes a magnification independent segmentation approach based on the connected component area and double density dual tree DWT (discrete wavelet transform) coefficients are derived from the segmented region. The derived features are reduced further shortened with fuzzy c-means. Further, with the aid of artificial neural network (ANN) optimized with salp swarm optimization (SSO), images are classified into normal and abnormal ones. This magnification independent proposed framework is evaluated across four different datasets (two realtime datasets and two public datasets) with different magnifications and the outcomes of all datasets were substantial when compared with the existing techniques. The proposed framework has shown strong concordance for cancer detection and can assist pathologists with a second opinion.
Investigating Students’ Motivation toward the Use of Zoom Meeting Application as English Learning Media During Covid-19 Pandemic

This research was a the students’ motivation in learning English by using zoom platform during covid-19 pandemic. During covid-19, all teaching and learning process have been conducted from offline to online methods. The increasing of using the media in learning process such as android become a new phenomena, but the motivation of students in learning become less since the teachers did not meet and face-to-face with the students in the learning process. Because this study investigates students' motivation in English online learning via the Zoom app based on their experiences, a qualitative technique was chosen as an appropriate approach and descriptive research was conducted. In this research, the source of data was taken from primary source data. The primary source data was the data which the researchers took data of this research directly in the field of the problem’s concern, namely in grade X MIA 2 of SMA Negeri 5 Pematangsiantar. The findings of this study are provided descriptively in order to provide a thorough picture of students' motivation. According to the data analysis offered, kids had two motivations: extrinsic motivation, which consists of a person's point of view and attitude, and extrinsic motivation, which consists of parents, school facilities, and teachers.
Key Factors in the Success of Virtualization of Teaching in Spanish Universities During the COVID-19 Pandemic

The virtualization of university teaching in Spain caused by the pandemic was a success, but the urgent nature of the transition from face-to-face teaching to online teaching meant that some peculiarities that should have been taken into account were omitted. Thus, in order to find out and analyze the factors causing the success of this virtualization and take measures to address the weak points of this process as teachers may want to maintain the use of online teaching as support for their face-to-face teaching, these factors were located through a literature review and were classified according to their level of importance in this success through a causal study carried out with the methodology of fuzzy cognitive maps. The results achieved indicated the determining factors in students, involving their attitude, aptitude and predisposition, which are factors where the work of the teacher is of main relevance (as it exerts the greatest influence on these factors). For this reason, technological attitude and aptitude, teaching style, and the creation and structuring of content should be reinforced in teachers.
Scalable Network for Simultaneous Pairwise Quantum Key Distribution via Entanglement-Based Time-Bin Coding

We present a scalable star-shaped quantum key distribution (QKD) optical fiber network. We use wavelength-division demultiplexing (WDM) of broadband photon pairs to establish key exchange between multiple pairs of participants simultaneously. Our QKD system is the first entanglement-based network of four participants using BBM92 time-bin coding and the first network achieving timing synchronization solely by clock recovery based on the photon arrival times. We demonstrate simultaneous bipartite key exchange between any possible combination of participants and show that the quantum bit error rate (QBER) itself can be used to stabilize the phase in the interferometers by small temperature adjustments. The key distribution is insensitive to polarization fluctuations in the network, enabling key distribution using deployed fibers even under challenging environmental conditions. We show that our network can be readily extended to 34 participants by using a standard arrayed-waveguide grating for WDM with 100 GHz channel spacing and that reconfigurable network connections are possible with a wavelength-selective switch. In a field test we demonstrate secure key rates of 6.3 bit/s with a QBER of 4.5% over a total fiber length of 108 km with 26.8 km of deployed fiber between two participants with high stability. Our system features a relatively simple design of the receiver modules and enables scaling QKD networks without a trusted nodes to distances up to more than 100 km and to more than 100 users. With such a network, a secure communication infrastructure on a metropolitan scale can be established.
MegaPortraits: One-shot Megapixel Neural Head Avatars

In this work, we advance the neural head avatar technology to the megapixel resolution while focusing on the particularly challenging task of cross-driving synthesis, i.e., when the appearance of the driving image is substantially different from the animated source image. We propose a set of new neural architectures and training methods that can leverage both medium-resolution video data and high-resolution image data to achieve the desired levels of rendered image quality and generalization to novel views and motion. We demonstrate that suggested architectures and methods produce convincing high-resolution neural avatars, outperforming the competitors in the cross-driving scenario. Lastly, we show how a trained high-resolution neural avatar model can be distilled into a lightweight student model which runs in real-time and locks the identities of neural avatars to several dozens of pre-defined source images. Real-time operation and identity lock are essential for many practical applications head avatar systems.
CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer

In this paper, we aim to devise a universally versatile style transfer method capable of performing artistic, photo-realistic, and video style transfer jointly, without seeing videos during training. Previous single-frame methods assume a strong constraint on the whole image to maintain temporal consistency, which could be violated in many cases. Instead, we make a mild and reasonable assumption that global inconsistency is dominated by local inconsistencies and devise a generic Contrastive Coherence Preserving Loss (CCPL) applied to local patches. CCPL can preserve the coherence of the content source during style transfer without degrading stylization. Moreover, it owns a neighbor-regulating mechanism, resulting in a vast reduction of local distortions and considerable visual quality improvement. Aside from its superior performance on versatile style transfer, it can be easily extended to other tasks, such as image-to-image translation. Besides, to better fuse content and style features, we propose Simple Covariance Transformation (SCT) to effectively align second-order statistics of the content feature with the style feature. Experiments demonstrate the effectiveness of the resulting model for versatile style transfer, when armed with CCPL.