Reviewerly/exHarmony huggingface dataset:
Primers or reminders?

In contemporary code review, the comments put by reviewers on a specific code change are immediately visible to the other reviewers involved. Could this visibility prime new reviewers' attention (due to the human's proneness to availability bias), thus biasing the code review outcome? In this study, we investigate this topic by conducting a controlled experiment with 85 developers who perform a code review and a psychological experiment. With the psychological experiment, we find that ≈70% of participants are prone to availability bias. However, when it comes to the code review, our experiment results show that participants are primed only when the existing code review comment is about a type of bug that is not normally considered; when this comment is visible, participants are more likely to find another occurrence of this type of bug. Moreover, this priming effect does not influence reviewers' likelihood of detecting other types of bugs. Our findings suggest that the current code review practice is effective because existing review comments about bugs in code changes are not negative primers, rather positive reminders for bugs that would otherwise be overlooked during code review. Data and materials: https://doi.org/10.5281/zenodo.3653856
ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection

In recent years, Convolutional Neural Networks (CNNs) have shown superior capability in visual learning tasks. While accuracy-wise CNNs provide unprecedented performance, they are also known to be computationally intensive and energy demanding for modern computer systems. In this paper, we propose Virtual Pooling (ViP), a model-level approach to improve speed and energy consumption of CNN-based image classification and object detection tasks, with a provable error bound. We show the efficacy of ViP through experiments on four CNN models, three representative datasets, both desktop and mobile platforms, and two visual learning tasks, i.e., image classification and object detection. For example, ViP delivers 2.1x speedup with less than 1.5% accuracy degradation in ImageNet classification on VGG16, and 1.8x speedup with 0.025 mAP degradation in PASCAL VOC object detection with Faster-RCNN. ViP also reduces mobile GPU and CPU energy consumption by up to 55% and 70%, respectively. As a complementary method to existing acceleration approaches, ViP achieves 1.9x speedup on ThiNet leading to a combined speedup of 5.23x on VGG16. Furthermore, ViP provides a knob for machine learning practitioners to generate a set of CNN models with varying trade-offs between system speed/energy consumption and accuracy to better accommodate the requirements of their tasks. Code is available at https://github.com/cmu-enyac/VirtualPooling.
FINITE FIELD EXTENSIONS WITH THE LINE OR TRANSLATE PROPERTY FOR -PRIMITIVE ELEMENTS

Abstract Let $r,n&gt;1$ be integers and $q$ be any prime power $q$ such that $r\mid q^{n}-1$ . We say that the extension $\mathbb{F}_{q^{n}}/\mathbb{F}_{q}$ possesses the line property for $r$ -primitive elements property if, for every $\unicode[STIX]{x1D6FC},\unicode[STIX]{x1D703}\in \mathbb{F}_{q^{n}}^{\ast }$ such that $\mathbb{F}_{q^{n}}=\mathbb{F}_{q}(\unicode[STIX]{x1D703})$ , there exists some $x\in \mathbb{F}_{q}$ such that $\unicode[STIX]{x1D6FC}(\unicode[STIX]{x1D703}+x)$ has multiplicative order $(q^{n}-1)/r$ . We prove that, for sufficiently large prime powers $q$ , $\mathbb{F}_{q^{n}}/\mathbb{F}_{q}$ possesses the line property for $r$ -primitive elements. We also discuss the (weaker) translate property for extensions.
Spam Email and Malware Elimination employing various Classification Techniques

Machine Learning (ML) is a subsection of Artificial Intelligence (AI) which is a scientific approach of using statistical models, that computer systems can apply and carry out specific task without human intervention. Since its inception email has been one of the most important and prominent means of communication, as with all other modes of communication email has also fallen victim to spam (Spiced-Ham). Studies show about 59.56% of all emails sent in 2018 where spam. Many of these spam emails also have attachments, which might have hidden malicious code which may execute on victims' computer when opened locally. The malicious code can help attackers gain sensitive information of the victim, leading to theft of money or worse identity theft. To solve the spam and malicious file problems we propose to use a subsection of ML known as supervised learning classification which performs binary signature analysis. In this paper we compare over ten different classification techniques like k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Naïve Bayes and Decision Tree. These algorithms are trained on previously labeled data and accuracy of the classifier is computed based on unseen data.
Lower bounds for testing graphical models: colorings and antiferromagnetic Ising models

We study the identity testing problem in the context of spin systems or undirected graphical models, where it takes the following form: given the parameter specification of the model $M$ and a sampling oracle for the distribution $\mu_{\hat{M}}$ of an unknown model $\hat{M}$, can we efficiently determine if the two models $M$ and $\hat{M}$ are the same? We consider identity testing for both soft-constraint and hard-constraint systems. In particular, we prove hardness results in two prototypical cases, the Ising model and proper colorings, and explore whether identity testing is any easier than structure learning. For the ferromagnetic (attractive) Ising model, Daskalakis et al. (2018) presented a polynomial time algorithm for identity testing. We prove hardness results in the antiferromagnetic (repulsive) setting in the same regime of parameters where structure learning is known to require a super-polynomial number of samples. In particular, for $n$-vertex graphs of maximum degree $d$, we prove that if $|\beta| d = \omega(\log{n})$ (where $\beta$ is the inverse temperature parameter), then there is no polynomial running time identity testing algorithm unless $RP=NP$. We also establish computational lower bounds for a broader set of parameters under the (randomized) exponential time hypothesis. Our proofs utilize insights into the design of gadgets using random graphs in recent works concerning the hardness of approximate counting by Sly (2010). In the hard-constraint setting, we present hardness results for identity testing for proper colorings. Our results are based on the presumed hardness of #BIS, the problem of (approximately) counting independent sets in bipartite graphs. In particular, we prove that identity testing is hard in the same range of parameters where structure learning is known to be hard.
Physical Features and Deep Learning-based Appearance Features for Vehicle Classification from Rear View Videos

Currently, there are many approaches for vehicle classification, but there is no specific study on automated, rear view, and video-based robust vehicle classification. The rear view is important for intelligent transportation systems since not all states in the United States require a frontal license plate on a vehicle. The classification of vehicles, from their rear views, is challenging since vehicles have only subtle appearance differences and there are changing illumination conditions and the presence of moving shadows. In this paper, we present a novel multi-class vehicle classification system that classifies a vehicle into one of four possible classes (sedan, minivan, SUV, and a pickup truck) from its rear view video, using physical and visual features. For a given geometric setup of the camera on highways, we make physical measurements on a vehicle. These measurements include visual rear ground clearance, the height of the vehicle, and the distance between the license plate and the rear bumper. We call these distances as the physical features. The visual features, also called appearance-based features, are extracted using convolutional neural networks from the input images. We achieve a classification accuracy of 93.22% and 91.52% using physical and visual features, respectively. Furthermore, we achieve a higher classification accuracy of 94.81% by fusing both the features together. The results are shown on a dataset consisting of 1831 rear view videos of vehicles and they are compared with various approaches, including deep learning techniques.
Accurate and Consistent Image-to-Image Conditional Adversarial Network

Image-to-image translation based on deep learning has attracted interest in the robotics and vision community because of its potential impact on terrain analysis and image representation, interpretation, modification, and enhancement. Currently, the most successful approach for generating a translated image is a conditional generative adversarial network (cGAN) for training an autoencoder with skip connections. Despite its impressive performance, it has low accuracy and a lack of consistency; further, its training is imbalanced. This paper proposes a balanced training strategy for image-to-image translation, resulting in an accurate and consistent network. The proposed approach uses two generators and a single discriminator. The generators translate images from one domain to another. The discriminator takes the input of three different configurations and guides both the generators to generate realistic images in their corresponding domains while ensuring high accuracy and consistency. Experiments are conducted on different datasets. In particular, the proposed approach outperforms the cGAN in realistic image translation in terms of accuracy and consistency in training.
Multi-class Novelty Detection Using Mix-up Technique

Multi-class novelty detection is increasingly becoming an important area of research due to the continuous increase in the number of object categories. It tries to answer the pertinent question: given a test sample, should we even try to classify it? We propose a novel solution using the concept of mix-up technique for novelty detection, termed as Segregation Network. During training, a pair of examples are selected from the training data and an interpolated data point using their convex combination is constructed. We develop a suitable loss function to train our model to predict its constituent classes. During testing, each input query is combined with the known class prototypes to generate mixed samples which are then passed through the trained network. Our model which is trained to reveal the constituent classes can then be used to determine whether the sample is novel or not. The intuition is that if a query comes from a known class and is mixed with the set of known class prototypes, then the prediction of the trained model for the correct class should be high. In contrast, for a query from a novel class, the predictions for all the known classes should be low. The proposed model is trained using only the available known class data and does not need access to any auxiliary dataset or attributes. Extensive experiments on two benchmark datasets, namely Caltech 256 and Stanford Dogs and comparisons with the state-of-the-art algorithms justifies the usefulness of our approach.
A Bottom-Up and Top-Down Integration Framework for Online Object Tracking

Robust online object tracking entails integrating short-term memory based trackers and long-term memory based trackers in an elegant framework to handle structural and appearance variations of unknown objects in an online manner. The integration and synergy between short-term and long-term memory based trackers have yet studied well in the literature, especially in pre-training free settings. To address this issue, this paper presents a bottom-up and top-down integration framework. The bottom-up component realizes a data-driven approach for particle generation. It exploits a short-term memory based tracker to generate bounding box proposals in a new frame. In the top-down component, this paper presents a graph regularized sparse coding scheme as the long-term memory based tracker. The over-complete bases for sparse coding are composed of part-based representations learned from earlier tracking results and new observations to form a space with rich temporal context information. A particle graph is computed whose nodes are the bottom-up discriminative particles and edges are formed on-the-fly in terms of appearance and spatial-temporal similarities between particles. The particle graph induces a regularization term in optimizing the sparse coding coefficients for bottom-up particles. In experiments, the proposed method is tested on the widely used OTB-100 benchmark and the VOT2016 benchmark with better performance obtained than baselines including deep learning based trackers. In addition, the outputs from the top-down sparse coding are potentially useful for downstream tasks such as action recognition, multiple-object tracking, and object re-identification.
Quality Estimation Framework for Encrypted Traffic (Q2ET)

In the coming years, the development of the Internet of Things (IoT) will have relevance for transport, environment, health care, smart cities and also multimedia services (Multimedia Internet of Things (MIoT)). Nowadays, many ISP (Internet Service Provider) encrypt the data to make it secure during the transmission. However, it imposes some obstacles for the NSP (Network Service Provider) because of the lack of visibility for operators into network traffic. To resolve these issues, we proposed the Quality Estimation Framework for Encrypted Traffic (Q2ET) containing a classification module and a QoE assessment module. The first module inherited from our previous research works to classify the encrypted network traffic using CNN (Convolutional Neural Network). The second one applies the objective and subjective methods based on the statistical analysis and machine learning methods that combine application and network parameters to calculate user's QoE (Quality of Experience) in terms of MOS (Mean Opinion Score). The Q2ET allows the NSP to monitor the user's QoE to take the appropriate decisions when the QoE degradation happens in the network systems.
From Image to Video Face Inpainting: Spatial-Temporal Nested GAN (STN-GAN) for Usability Recovery

In this paper, we propose to use constrained inpainting methods to recover usability of corrupted images. Here we focus on the example of face images that are masked for privacy protection but complete images are required for further algorithm development. The task is tackled in a progressive manner: 1) the generated images should look realistic; 2) the generated images must satisfy spatial constraints, if available; 3) when applied to video data, temporal consistency should be retained. We first present a spatial inpainting framework to synthesize face images which can incorporate spatial constraints, provided as positions offa- cial markers and show that it outperforms state-of-the-art methods. Next, we propose Spatial-Temporal Nested GAN (STN-GAN) to adapt image inpainting framework, trained on ~200k images, to video data by incorporating temporal information using residual blocks. Experiments on multiple public datasets show STN-GAN attains spatio-temporal consistency effectively and efficiently. Furthermore, we show that the spatial constraints can be perturbed to obtain different inpainted results from a single source. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Automatic Essay Scoring Model Based on Two-Layer Bi-directional Long-Short Term Memory Network

Automatic essay scoring provides a cost-effective and consistent alternative to human correction. However, in order to obtain good performance, human experts are needed to extract features of text manually in traditional ways. We propose a two-layer bi-directional long-short term memory model that a fully automatic essay scoring model. The agreement of essay scores which marked by our model and human raters respectively has achieved to 0.870 based on metrics of Cohen's κ. This model can achieve excellent results like human beings' professional raters.
Understanding Security Vulnerability Awareness, Firm Incentives, and ICT Development in Pan-Asia

This paper investigates how the awareness of a security vulnerability index affects firms' security protection strategy and how the information awareness effect interacts with firm incentives and country-wide information technology (IT) development level. The security index is constructed based on outgoing spams and phishing website hosting, which may serve as an indicator of a firm's security controls. To study whether security vulnerability awareness causes firms to improve their security, we conducted a randomized field experiment on 1,262 firms in six Pan-Asian countries and regions. Among 631 randomly selected treated firms, we alerted them of their security vulnerability index and their relative rankings compared to their peers via advisory emails and websites. Difference-in-differences analyses show that compared with the controls, the treated firms improve their security over time, with a statistically significant reduction of outgoing spam volume according to one of the data sources but not phishing website hosting. However, a statistically significant reduction in phishing website hosting was observed among non-web hosting firms, suggesting that firms' underlying incentives play an important role in the treatment effect. Lastly, exploiting the multi-country nature of the data, we found that firms in countries with high information and communications technology (ICT) development are more responsive to our intervention because they have higher IT capabilities and more resources to resolve security issues. Our study provides cybersecurity policymakers with useful insights on how firm incentives and ICT environments play roles in firms' security measure adoption.
Sim-to-Real in Reinforcement Learning for Everyone

In reinforcement learning (RL), it remains a challenge to have a robotic agent perform a task in the real world for which it was trained in simulation. In this paper, we present our work training a low-cost robotic arm in simulation to move towards a predefined target in space, represented by a red ball in an RGB image, and transferring the capability to the real arm. We exercised the entire end-to-end flow including the 3D modeling of the arm, training of a state-of-the-art RL policy in simulation with multiple actors in a distributed fashion, domain randomization in order to close the sim-to-real gap, and finally the execution of the trained model in the real robot. We also implemented a mechanism to edit the image captured from the camera before sending it to the model for inference, which allowed us to automate reward computation in the physical world. Our work highlights important challenges of training RL agents and moving them to the real world, validating important aspects shown by other works as well as detailing steps not explained by some of them (e.g. how to compute the reward in the real world). The conducted experiments show the improvements observed as the techniques were added to the final solution.
Pedestrian Tracking Algorithm for Dense Crowd based on Deep Learning

The development of Dense Crowd Visual Tracking algorithm based on Deep Learning (DTDL) is introduced. The main research contents of this paper are as follows: (l) Dense crowd Based on Single Shot Multi-Box Detector (D-SSD). The D-SSD detection algorithm was proposed to reconstruct the pedestrian data set, mark the head and shoulder of pedestrians in the crowd, and modify the size of the candidate box close to the proportion of head and shoulder of pedestrians, so as to obtain a new data set for training. (2) visual tracking framework (Kalman Based on Kernel Correlation Filters, K-KCF): KCF tracking framework is more popular in recent years, KCF used in visual tracking under the common scene both in tracking performance and tracking speed performance is good, but there are severe target block KCF will tracking failure, in the dense population under the background of such inevitable condition. The addition of Kalman filter can largely solve the tracking failure caused by the occlusion of pedestrians. In this paper, Kalman filter is proposed as an auxiliary K-KCF visual tracking framework.
Unsupervised Cross-Dataset Adaptation via Probabilistic Amodal 3D Human Pose Completion

Despite remarkable success of supervised deep learning models for 3D human pose estimation, performance of such models is mostly limited to constrained laboratory settings. Such models not only exhibit an alarming level of dataset bias, but also fail to operate on unconstrained videos in the presence of external variations such as camera motion, partial body visibility, occlusion, etc. Acknowledging these shortcomings, firstly, we aim to formalize a motion representation learning framework by effectively utilizing both constrained and artificially generated unconstrained video samples for datasets with 3D pose annotation. Without ignoring the inherent uncertainty in pose estimation for the truncated video frames, we devise a novel probabilistic amodal pose completion framework to enable generation of multiple plausible pose-filling outcomes. Secondly, to address dataset bias, the probabilistic amodal framework is reutilized to design novel self-supervised objectives. This not only enables adaptation of the model to target unannotated datasets (wild YouTube videos) but also encourages learning of generic motion representations beyond the available supervised data even in unconstrained scenarios. Such a training regime helps us achieve state-of-the art performance on unsupervised cross-dataset pose estimation, with a significant improvement in partially-visible unconstrained scenarios.
Fast parallel vessel segmentation

Background and Objective: Accurate and fast vessel segmentation from liver slices remain challenging and important tasks for clinicians. The algorithms from the literature are slow and less accurate. We propose fast parallel gradient based seeded region growing for vessel segmentation. Seeded region growing is tedious when the inter connectivity between the elements is unavoidable. Parallelizing region growing algorithms are essential towards achieving real time performance for the overall process of accurate vessel segmentation. Methods: The parallel implementation of seeded region growing for vessel segmentation is iterative and hence time consuming process. Seeded region growing is implemented as kernel termination and relaunch on GPU due to its iterative mechanism. The iterative or recursive process in region growing is time consuming due to intermediate memory transfers between CPU and GPU. We propose persistent and grid-stride loop based parallel approach for region growing on GPU. We analyze static region of interest of tiles on GPU for the acceleration of seeded region growing. Results: We aim fast parallel gradient based seeded region growing for vessel segmentation from CT liver slices. The proposed parallel approach is 1.9x faster compared to the state-of-the-art. Conclusion: We discuss gradient based seeded region growing and its parallel implementation on GPU. The proposed parallel seeded region growing is fast compared to kernel termination and relaunch and accurate in comparison to Chan-Vese and Snake model for vessel segmentation.