Reviewerly/exHarmony huggingface dataset:
Stochastic first-order methods for convex and nonconvex functional constrained optimization

Functional constrained optimization is becoming more and more important in machine learning and operations research. Such problems have potential applications in risk-averse machine learning, semisupervised learning and robust optimization among others. In this paper, we first present a novel Constraint Extrapolation (ConEx) method for solving convex functional constrained problems, which utilizes linear approximations of the constraint functions to define the extrapolation (or acceleration) step. We show that this method is a unified algorithm that achieves the best-known rate of convergence for solving different functional constrained convex composite problems, including convex or strongly convex, and smooth or nonsmooth problems with stochastic objective and/or stochastic constraints. Many of these rates of convergence were in fact obtained for the first time in the literature. In addition, ConEx is a single-loop algorithm that does not involve any penalty subproblems. Contrary to existing primal-dual methods, it does not require the projection of Lagrangian multipliers into a (possibly unknown) bounded set. Second, for nonconvex functional constrained problems, we introduce a new proximal point method which transforms the initial nonconvex problem into a sequence of convex problems by adding quadratic terms to both the objective and constraints. Under certain MFCQ-type assumption, we establish the convergence and rate of convergence of this method to KKT points when the convex subproblems are solved exactly or inexactly. For large-scale and stochastic problems, we present a more practical proximal point method in which the approximate solutions of the subproblems are computed by the aforementioned ConEx method. Under a strong feasibility assumption, we establish the total iteration complexity of ConEx required by this inexact proximal point method for a variety of problem settings, including nonconvex smooth or nonsmooth problems with stochastic objective and/or stochastic constraints. To the best of our knowledge, most of these convergence and complexity results of the proximal point method for nonconvex problems also seem to be new in the literature.
A Fair Blockchain Based on Proof of Credit

Proof of work and proof of stake (PoS) are commonly used in the current permissionless blockchains. These consensus protocols can be abstracted into a random process of selecting a node for accounting in a blockchain ledger. However, they are generally faced with resource consumption and vulnerability issues. We present proof of credit (PoC), a fair blockchain protocol based on the PoC blockchain protocol. It is a special PoS protocol where the credit is a special kind of stake quantifying whether the node's activity is beneficial to the system. Any nodes cannot change their credits arbitrarily. We demonstrate that our PoC protocol satisfies the security properties, including common prefix, chain quality, and chain growth, under the assumption that the total credit the honest held is majority. In addition, we propose a self-audit mechanism and a hybrid incentive mechanism to enhance the security and stability. Finally, we explain the method by which the PoC protocol resists the double-spending attacks and the selfish mining attacks.
Gender Diversity and Community Smells: Insights From the Trenches

Given growing attention to gender diversity in software development teams, we asked practitioners if it was a useful tool to mitigate undesirable communication patterns. While many participants didn't consider gender diversity useful in this context, those who did were motivated by their own professional experience.
A Contrast-Guided Approach for the Enhancement of Low-Lighting Underwater Images

Underwater images are often acquired in sub-optimal lighting conditions, in particular at profound depths where the absence of natural light demands the use of artificial lighting. Low-lighting images impose a challenge for both manual and automated analysis, since regions of interest can have low visibility. A new framework capable of significantly enhancing these images is proposed in this article. The framework is based on a novel dehazing mechanism that considers local contrast information in the input images, and offers a solution to three common disadvantages of current single image dehazing methods: oversaturation of radiance, lack of scale-invariance and creation of halos. A novel low-lighting underwater image dataset, OceanDark, is introduced to assist in the development and evaluation of the proposed framework. Experimental results and a comparison with other underwater-specific image enhancement methods show that the proposed framework can be used for significantly improving the visibility in low-lighting underwater images of different scales, without creating undesired dehazing artifacts.
Performance Prediction for the Apache Kafka Messaging System

Apache Kafka is a highly scalable distributed messaging system that provides high throughput with low latency. Various kinds of cloud vendors provide Kafka as a service for users who need a messaging system. Given a certain hardware environment, how to set the configurations of Kafka properly will be the first concern of users. In this paper, we analyze the structure and workflow of Kafka and propose a queueing based packet flow model to predict performance metrics of Kafka cloud services. The input configuration parameters of this model contain the number of brokers in Kafka cluster, the number of partitions in a topic and the batch size of messages. Through this model users can obtain the impact of certain configuration parameters on the performance metrics including the producer throughput, the relative payload and overhead and the change of disk storage usage over time. We use queueing theory to evaluate the end-to-end latency of packets. In the experimental validation we see a strong correlation between packet sizes and packet send interval, and the service time of packets fits a phase-type distribution. The correlation and fitting results are substituted to the essential constants in the model. Experiments are performed with various configurations for observing their effects on performance metrics. The results show that our model achieves high accuracy in predicting throughput and latency.
Investigating the impacts of using a mobile interactive English learning system on the learning achievements and learning perceptions of student with different backgrounds

Many scholars have highlighted the importance of motivation and anxiety in language learning. They have also indicated the advantages of integrating learning content into a mobile-assisted English learning system environment. Meanwhile, a few studies have explored the impacts of a mobile-assisted English learning system on the motivation and English anxiety of elementary school students. This study explores these factors in the English learning achievement of elementary school students by using a mobile-assisted English learning system that includes interesting and interactive games. This study also tries to improve students' learning motivations and decrease their English anxiety through the mobile-assisted English learning system. This quasi-experimental design required 6 weeks to collect the data from the participants. A total of 79 students participated in the experiment, and they were divided into an experimental group and a control group. The students in the experimental group used a mobile-assisted English learning system to learn. Then, they filled out a questionnaire and evaluated whether the system's operations and their motivation, English anxiety, and perceived usefulness affected their learning achievement. The experimental results indicate that the mobile-assisted English learning system benefits students' learning achievement. Moreover, students with lower levels of English anxiety have better learning achievement than those with higher levels of English anxiety, and higher levels of perceived usefulness result in better learning achievement. These findings could be used as a reference for relevant instructors to find better assisted learning tools and improve students' learning achievement.
Synthetic Augmentation and Feature-Based Filtering for Improved Cervical Histopathology Image Classification

Cervical intraepithelial neoplasia (CIN) grade of histopathology images is a crucial indicator in cervical biopsy results. Accurate CIN grading of epithelium regions helps pathologists with precancerous lesion diagnosis and treatment planning. Although an automated CIN grading system has been desired, supervised training of such a system would require a large amount of expert annotations, which are expensive and time-consuming to collect. In this paper, we investigate the CIN grade classification problem on segmented epithelium patches. We propose to use conditional Generative Adversarial Networks (cGANs) to expand the limited training dataset, by synthesizing realistic cervical histopathology images. While the synthetic images are visually appealing, they are not guaranteed to contain meaningful features for data augmentation. To tackle this issue, we propose a synthetic-image filtering mechanism based on the divergence in feature space between generated images and class centroids in order to control the feature quality of selected synthetic images for data augmentation. Our models are evaluated on a cervical histopathology image dataset with limited number of patch-level CIN grade annotations. Extensive experimental results show a significant improvement of classification accuracy from 66.3% to 71.7% using the same ResNet18 baseline classifier after leveraging our cGAN generated images with feature based filtering, which demonstrates the effectiveness of our models.
An assessment of digital literacy skills and knowledge-based competencies among librarians working in university libraries in Africa

Purpose The purpose of this paper is to assess digital literacy skills possessed by library and information professionals working in university libraries in Africa. Design/methodology/approach Online questionnaire was developed to collect data from 214 librarians working in various university libraries in English-speaking countries in Africa. Findings The study found that librarians working in university libraries in Africa rated their database search skills, uploading documents to online platforms, skills in using different social media, sending and receiving e-mails skill, digital library development skills, skills in applying new technologies into library services, ability to create different file formats and ability to use open source software as very high. While, metadata development skills, and library website development skills were rated to be moderate and low. Overall, the librarians rated their level of digital literacy skills possessed to be moderate, and differences emerged between librarians in Nigeria and South Africa with regard to digital literacy skills possessed. Practical implications This study attempts to identify skills that are central to librarians working in university libraries. The study will be useful for trainers who want to arrange training for academic librarians in Africa and other developing countries. For some library schools, it may help them to review their curriculum in accordance with the required skills and competencies for academic librarians in the market. Originality/value Findings will be helpful to explore the skills and competencies needed by information professionals and to act as a guideline for competency development and curriculum update in library schools in developing countries.
On the Robustness of Redundant Teacher-Student Frameworks for Semantic Segmentation

The trend towards autonomous systems in today's technology comes with the need for environment perception. Deep neural networks (DNNs) constantly showed state-of-the-art performance over the last few years in visual machine perception, e.g., semantic segmentation. While DNNs work fine on uncorrupted data, recently introduced adversarial examples (AEs) led to misclassification with high confidence. This lack of robustness against such adversarial attacks questions the use of DNNs in safety-critical autonomous systems, e.g., autonomous driving vehicles. In this work, we address the mentioned problem with the use of a redundant teacher-student framework, consisting of a static teacher network (T), a static student network (S), and a constantly adapting student network (A). By using this triplet in combination with a novel inverse feature matching (IFM) loss, we show that a significant robustness increase of student DNNs against adversarial attacks is achieveable, while maintaining semantic segmentation quality at a reasonably high level. With our approach, we manage to increase the mean intersection over union (mean IoU) ratio between static student adversarial examples and clean images from about 35 % to about 80 % on the Cityscapes dataset. Moreover, our proposed method can be integrated into any DNN-based perception mechanism to increase the (online) robustness in an adversarial environment, created from static model knowledge.
Speculative Taint Tracking (STT): A Comprehensive Protection for Speculatively Accessed Data

Speculative execution attacks present an enormous security threat, capable of reading arbitrary program data under malicious speculation, and later exfiltrating that data over microarchitectural covert channels. This article proposes speculative taint tracking (STT), a high-security and high-performance hardware mechanism to block these attacks. The main idea is that it is safe to execute and selectively forward the results of speculative instructions that read secrets, as long as we can prove that the forwarded results do not reach potential covert channels. The technical core of the article is a new abstraction to help identify all covert channels, and an architecture to quickly identify when a covert channel is no longer a threat. We further conduct a detailed formal analysis on the scheme and prove security in a companion document. When evaluated on SPEC06 workloads, STT incurs 8.5% or 14.5% performance overhead relative to an insecure machine.
Asymmetric Joint GANs for Normalizing Face Illumination From a Single Image

Illumination normalization for face recognition is very important when a face is captured under harsh lighting conditions. Instead of designing hand-crafted features, in this paper we formulate face illumination normalization as an image-to-image translation task. A great challenge of face normalization is that human facial structures are particularly sensitive to image structure distortion, which frequently occurs in traditional image-to-image translation tasks. Unfortunately, sometimes even slight facial structure distortions may prohibit human eyes and machine face recognition methods from identifying face identities. To address this issue, a novel GAN- based network architecture called the asymmetric joint generative adversarial network (AJGAN) is developed to normalize face images under arbitrary illumination conditions, without known face geometry and albedo information. In addition, an illumination normalization GAN $G_1$ and an asymmetric relighting GAN $G_2$ that maps a frontal-illuminated image to images with various lighting conditions are incorporated in AJGAN to maintain personalized facial structures. To avoid image blurring caused by the under-constrained relighting mapping, we introduce a scheme of one-hot lighting labels into $G_2$ and enforce label classification loss. Furthermore, the number of training images starting from a very limited number of labels is dynamically extended by the combination of different lighting labels. Qualitative and quantitative experiments on three databases validate that AJGAN significantly outperforms the state-of-the-art methods.
IntelliMerge: a refactoring-aware software merging technique

In modern software development, developers rely on version control systems like Git to collaborate in the branch-based development workflow. One downside of this workflow is the conflicts occurred when merging contributions from different developers: these conflicts are tedious and error-prone to be correctly resolved, reducing the efficiency of collaboration and introducing potential bugs. The situation becomes even worse, with the popularity of refactorings in software development and evolution, because current merging tools (usually based on the text or tree structures of source code) are unaware of refactorings. In this paper, we present IntelliMerge, a graph-based refactoring-aware merging algorithm for Java programs. We explicitly enhance this algorithm's ability in detecting and resolving refactoring-related conflicts. Through the evaluation on 1,070 merge scenarios from 10 popular open-source Java projects, we show that IntelliMerge reduces the number of merge conflicts by 58.90% comparing with GitMerge (the prevalent unstructured merging tool) and 11.84% comparing with jFSTMerge (the state-of-the-art semi-structured merging tool) without sacrificing the auto-merging precision (88.48%) and recall (90.22%). Besides, the evaluation of performance shows that IntelliMerge takes 539 milliseconds to process one merge scenario on the median, which indicates its feasibility in real-world applications.
Protecting IoTs from Mirai Botnet Attacks Using Blockchains

The exponential growth of Internet of Things (IoT) devices with limited computing resources and poor security configurations make them vulnerable to different cyber-attacks. Mirai Botnet malware exploits vulnerabilities of IoT devices resulting in massive Distributed Denial of Service (DDoS) attacks. Various techniques have been proposed to mitigate Mirai botnet attacks, but most of them are centralized or just provide precautionary steps to secure the IoT devices. This paper addresses IoT security against Mirai Botnet attacks using a novel blockchain based architecture. In our proposed approach, the network is divided into different Autonomous Systems (AS), through which host connectivity is established. Blockchains are used to store and share a list of Internet Protocol (IP) addresses of different hosts connected to an AS, indicating which of these have been identified as malicious. Every AS monitors the communication activity inside the network using the proposed approach and determines whether a host is infected with malware or not by comparing the total number of packets sent by the host with a specified threshold value. The proposed approach is simulated on a custom developed simulator which is used to determine a suitable value for the malicious threshold. The results indicate that the proposed solution works effectively in blocking malicious packets from the infected host so that they do not affect the response time of the victim. Furthermore, a scalability analysis is conducted and the block propagation delay is evaluated for different AS sizes and consensus algorithms.
A Benchmark Study Of Backdoor Data Poisoning Defenses For Deep Neural Network Classifiers And A Novel Defense

While data poisoning attacks on classifiers were originally proposed to degrade a classifier's usability, there has been strong recent interest in backdoor data poisoning attacks, where the classifier learns to classify to a target class whenever a backdoor pattern (e.g., a watermark or innocuous pattern) is added to an example from some class other than the target class. In this paper, we conduct a benchmark experimental study to assess the effectiveness of backdoor attacks against deep neural network (DNN) classifiers for images (CIFAR-10 domain), as well as of anomaly detection defenses against these attacks, assuming the defender has access to the (poisoned) training set. We also propose a novel defense scheme (cluster impurity (CI)) based on two ideas: i) backdoor patterns may cluster in a DNN's (e.g. penultimate) deep layer latent space; ii) image filtering (or additive noise) may remove the backdoor patterns, and thus alter the class decision produced by the DNN. We demonstrate that largely imperceptible single-pixel backdoor attacks are highly successful, with no effect on classifier usability. However, the CI approach is highly effective at detecting these attacks, and more successful than previous backdoor detection methods.
DeblurGAN+: Revisiting blind motion deblurring using conditional adversarial networks

This work studies dynamic scene deblurring (DSD) of a single photograph, mainly motivated by the very recent DeblurGAN method. It is discovered that training the generator alone of DeblurGAN will result in both regular checkerboard effects and irregular block color excursions unexpectedly. In this paper, two aspects of endeavors are made for a more effective and robust adversarial learning approach to DSD. On the one hand, a kind of opposite-channel-based discriminative priors is developed, improving the deblurring performance of DeblurGAN without additional computational burden in the testing phase. On the other hand, a computationally more efficient while architecturally more robust auto-encoder is developed as a substitute of the original generator in DeblurGAN, promoting DeblurGAN to a new state-of-the-art method for DSD. For brevity, the proposed approach is dubbed as DeblurGAN+. Experimental results on the benchmark GoPro dataset validate that DeblurGAN+ achieves more than 1.5 dB improvement than DeblurGAN in terms of PSNR as trained utilizing the same amount of data. More importantly, the results on realistic non-uniform blurred images demonstrate that DeblurGAN+ is really more effective than DeblurGAN as well as most of variational model-based methods in terms of both blur removal and detail recovery.
Local and Global Consistency Regularized Mean Teacher for Semi-supervised Nuclei Classification

Nucleus classification is a fundamental task in pathology diagnosis for cancers, e.g., Ki-67 index estimation. Supervised deep learning methods have achieved promising classification accuracy. However, the success of these methods heavily relies on massive manually annotated data. Manual annotation for nucleus classification are usually time consuming and laborious. In this paper, we propose a novel semi-supervised deep learning method that can learn from small portion of labeled data and large-scale unlabeled data for nucleus classification. Our method is inspired by the recent state-of-the-art self-ensembling (SE) methods. These methods learn from unlabeled data by enforcing consistency of predictions under different perturbations while ignoring local and global consistency hidden in data structure. In our work, a label propagation (LP) step is integrated into the SE method, and a graph is constructed using the LP predictions that encode the local and global data structure. Finally, a Siamese loss is used to learn the local and global consistency from the graph. Our implementation is based on the state-of-the-art SE method Mean Teacher. Extensive experiments on two nucleus datasets demonstrate that our method outperforms the state-of-the-art SE methods, and achieves $$F_1$$ scores close to the supervised methods using only 5%–25% labeled data.
A Simple Baseline for Audio-Visual Scene-Aware Dialog

The recently proposed audio-visual scene-aware dialog task paves the way to a more data-driven way of learning virtual assistants, smart speakers and car navigation systems. However, very little is known to date about how to effectively extract meaningful information from a plethora of sensors that pound the computational engine of those devices. Therefore, in this paper, we provide and carefully analyze a simple baseline for audio-visual scene-aware dialog which is trained end-to-end. Our method differentiates in a data-driven manner useful signals from distracting ones using an attention mechanism. We evaluate the proposed approach on the recently introduced and challenging audio-visual scene-aware dataset, and demonstrate the key features that permit to outperform the current state-of-the-art by more than 20% on CIDEr.