Reviewerly/exHarmony huggingface dataset:
Guidance Network with Staged Learning for Image enhancement

Many important yet not fully resolved problems in computational photography and image enhancement, e.g. generating well-lit images from their low-light counterparts or producing RGB images from their RAW camera inputs share a common nature: discovering a color mapping between input pixels to output pixels based on both global information and local details. We propose a novel deep neural network architecture to learn the RAW to RGB mapping based on this common nature. This architecture consists of both global and local sub-networks, where the first sub-network focuses on determining illumination and color mapping, the second sub-network deals with recovering image details. The result of the global network serves as a guidance to the local network to form the final RGB images. Our method outperforms state-of-the-art with a significantly smaller size of network features on various image enhancement tasks.
Hierarchical Video Prediction using Relational Layouts for Human-Object Interactions

Learning to model and predict how humans interact with objects while performing an action is challenging, and most of the existing video prediction models are ineffective in modeling complicated human-object interactions. Our work builds on hierarchical video prediction models, which disentangle the video generation process into two stages: predicting a high-level representation, such as pose sequence, and then learning a pose-to-pixels translation model for pixel generation. An action sequence for a human-object interaction task is typically very complicated, involving the evolution of pose, person’s appearance, object locations, and object appearances over time. To this end, we propose a Hierarchical Video Prediction model using Relational Layouts. In the first stage, we learn to predict a sequence of layouts. A layout is a high-level representation of the video containing both pose and objects’ information for every frame. The layout sequence is learned by modeling the relationships between the pose and objects using relational reasoning and recurrent neural networks. The layout sequence acts as a strong structure prior to the second stage that learns to map the layouts into pixel space. Experimental evaluation of our method on two datasets, UMD-HOI and Bimanual, shows significant improvements in standard video evaluation metrics such as LPIPS, PSNR, and SSIM. We also perform a detailed qualitative analysis of our model to demonstrate various generalizations.
Faulds: A Non-Parametric Iterative Classifier for Internet-Wide OS Fingerprinting

Recent work in OS fingerprinting has focused on overcoming random distortion in network and user features during Internet-scale SYN scans. These classification techniques work under an assumption that all parameters of the profiled network are known a-priori – the likelihood of packet loss, the popularity of each OS, the distribution of network delay, and the probability of user modification to each default TCP/IP header value. However, it is currently unclear how to obtain realistic versions of these parameters for the public Internet and/or customize them to a particular network being analyzed. To address this issue, we derive a non-parametric Expectation-Maximization (EM) estimator, which we call <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Faulds</i> , for the unknown distributions involved in single-probe OS fingerprinting and demonstrate its significantly higher robustness to noise compared to methods in prior work. We apply Faulds to a new scan of 67M webservers and discuss its findings.
Real-Time Adaptive Switching Mechanism Towards Viewport-Adaptive Omnidirectional Video Streaming

Viewport-adaptive streaming is now a widely used solution for the transmission of omnidirectional video. However, when the user’s viewport changes, there will be a delay in switching to high quality video. This delay will significantly affect the user’s experience. In this paper, a real-time adaptive switching mechanism was proposed to solve this problem, which can perform video quality switching at any instant. In the proposed real-time adaptive switching mechanism, a technique of Random Access P Frame (RAPF) is proposed, which can serve as the random access to help to achieve the real-time adaptive in practice. In the process of encoding RAPFs, inter prediction encoding method will be used to reduce the size of RAPFs, thereby reducing the bandwidth consumption. The experimental result shows that the proposed mechanism can reduce the delay to the quality switching, while just resulting in a quite small extra bandwidth consumption and negligible quality degradation.
Visual similarity-based fashion recommendation system

Fashion recommendation systems are designed to retrieve a ranked list of catalog images similar to the target catalog image. These systems are crucial in helping customers move toward potential buying decisions from recommended products. Catching true correlation between similar product recommendations and user satisfaction will exponentially improve sale experience of companies. One of the major tasks in fashion recommendation systems is accurately finding similar images for a given product image. In this chapter, we outline a visual similarity-based fashion recommendation system that can be used in e-commerce especially for shoe and handbag recommendations. The system consists of several modules including a generative adversarial network (GAN) module for creating image representations (feature vectors) and an effective vector search library that acts as a database for querying similar images. We provide comparisons for our GAN-based recommendation results with pretrained convolutional neural networks (CNNs) on new catalog shoe and handbag images. Major contributions of this chapter can be summarized as follows: by utilizing GANs we are able to develop a robust and reliable fashion recommendation system, visual similarity-based recommendations are employed to create similar product search for e-commerce applications, and we also provide detailed comparisons for extracting high-quality, accurate, and efficient vector feature representation of product images using deep neural networks for fashion domain.
Using multiple generative adversarial networks to build better-connected levels for mega man

Generative Adversarial Networks (GANs) can generate levels for a variety of games. This paper focuses on combining GAN-generated segments in a snaking pattern to create levels for Mega Man. Adjacent segments in such levels can be orthogonally adjacent in any direction, meaning that an otherwise fine segment might impose a barrier between its neighbor depending on what sorts of segments in the training set are being most closely emulated: horizontal, vertical, or corner segments. To pick appropriate segments, multiple GANs were trained on different types of segments to ensure better flow between segments. Flow was further improved by evolving the latent vectors for the segments being joined in the level to maximize the length of the level's solution path. Using multiple GANs to represent different types of segments results in significantly longer solution paths than using one GAN for all segment types, and a human subject study verifies that these levels are more fun and have more human-like design than levels produced by one GAN.
Scalable and Usable Relational Learning With Automatic Language Bias

A large body of machine learning and AI is focused on learning models composed of (probabilistic) logical rules, i.e., relational models, over relational databases and knowledge bases. To learn effective relational models over the huge space of possible ones efficiently, users of the current learning systems must restrict the structure of the candidate models using language bias. ML experts have to spend a long time inspecting the data and performing many rounds of trial and error to develop an effective language bias. We propose AutoBias, a system that leverages information in the underlying data to generate the language bias. As its induced language bias may not restrict the set of candidate models as tightly as the manually-written ones, learning may not scale to large datasets. Thus, we design novel and efficient methods to sample and learn effective relational models over large data. Our extensive empirical study shows that AutoBias delivers the same accuracy as using manually-written language bias by imposing only a slight overhead on the learning time.
A collaborative approach for national cybersecurity incident management

Purpose Collaborative-based national cybersecurity incident management benefits from the huge size of incident information, large-scale information security devices and aggregation of security skills. However, no existing collaborative approach has been able to cater for multiple regulators, divergent incident views and incident reputation trust issues that national cybersecurity incident management presents. This paper aims to propose a collaborative approach to handle these issues cost-effectively. Design/methodology/approach A collaborative-based national cybersecurity incident management architecture based on ITU-T X.1056 security incident management framework is proposed. It is composed of the cooperative regulatory unit with cooperative and third-party management strategies and an execution unit, with incident handling and response strategies. Novel collaborative incident prioritization and mitigation planning models that are fit for incident handling in national cybersecurity incident management are proposed. Findings Use case depicting how the collaborative-based national cybersecurity incident management would function within a typical information and communication technology ecosystem is illustrated. The proposed collaborative approach is evaluated based on the performances of an experimental cyber-incident management system against two multistage attack scenarios. The results show that the proposed approach is more reliable compared to the existing ones based on descriptive statistics. Originality/value The approach produces better incident impact scores and rankings than standard tools. The approach reduces the total response costs by 8.33% and false positive rate by 97.20% for the first attack scenario, while it reduces the total response costs by 26.67% and false positive rate by 78.83% for the second attack scenario.
CondenseNet with exclusive lasso regularization

Group convolution has been widely used in deep learning community to achieve computation efficiency. In this paper, we develop CondenseNet-elasso to eliminate feature correlation among different convolution groups and alleviate neural network's overfitting problem. It applies exclusive lasso regularization on CondenseNet. The exclusive lasso regularizer encourages different convolution groups to use different subsets of input channels therefore learn more diversified features. Our experiment results on CIFAR10, CIFAR100 and Tiny ImageNet show that CondenseNets-elasso are more efficient than CondenseNets and other DenseNet' variants.
PostMan: Rapidly Mitigating Bursty Traffic via On-Demand Offloading of Packet Processing

Unexpected bursty traffic brought by certain sudden events, such as news in the spotlight on a social network or discounted items on sale, can cause severe load imbalance in backend services. Migrating hot data - the standard approach to achieve load balance - meets a challenge when handling such unexpected load imbalance, because migrating data will slow down the server that is already under heavy pressure. This article proposes PostMan, an alternative approach to rapidly mitigate load imbalance for services processing small requests. Motivated by the observation that processing large packets incurs far less CPU overhead than processing small ones, PostMan deploys a number of middleboxes called helpers to assemble small packets into large ones for the heavily-loaded server. This approach essentially offloads the overhead of packet processing from the heavily-loaded server to helpers. To minimize the overhead, PostMan activates helpers on demand, only when bursty traffic is detected. The heavily-loaded server determines when clients connect/disconnect to/from helpers based on the real-time load statistics. To tolerate helper failures, PostMan can migrate connections across helpers and can ensure packet ordering despite such migration. Driven by real-world workloads, our evaluation shows that, with the help of PostMan, a Memcached server can mitigate bursty traffic within hundreds of milliseconds, while migrating data takes tens of seconds and increases the latency during migration.
The Python/Jupyter Ecosystem: Today’s Problem-Solving Environment for Computational Science

The EiC looks back on a concept presented in CiSE’s inaugural issue: that of problem-solving environments, with a promise to be ‘all things to all people’ in computational science. Reviewing the progress over the past decades of the Python ecosystem for scientific computing, and the coverage in CiSE, she makes the case that Python and Jupyter today provide that rich environment that can make computational scientists most productive. This editorial anticipates the next issue, when CiSE will feature scientific applications that embody the powerful idea of combining high researcher productivity via Python and high performance through code generation, just-in-time compilation, or exploiting advanced Python libraries.
Turbo: Fraud Detection in Deposit-free Leasing Service via Real-Time Behavior Network Mining

Online deposit-free leasing service has witnessed rapid growth in China and shows a promising market in the future. While eliminating the requirement of a deposit does attract more users to the service, it also lowers the cost for fraudsters. Since the emergence of this service is relatively new, there are few works in literature focusing on detecting fraud transactions in it. Existing efforts mainly fall into hard-coded solutions such as block-listing or scorecard methods, which can be impotent in the face of the diverse fraud tactics, e.g., identity theft, or even suffering concept drift problem as the tactics evolve. In this paper, we contribute Turbo, an efficient graph-based anti-fraud system, to fully exploit the abundant user behavior logs in a real-time manner. Turbo is able to additionally make use of the implicit user relationships beyond the user features in the logs. To capture the user relationships, we first propose a novel algorithm to construct a time-evolving user behavior network called BN. Empirical analysis demonstrates that fraudsters in BN exhibit unique temporal aggregation and homophilic patterns, which inspires us to develop a novel heterogeneous adaptive graph neural network algorithm called HAG. Specifically, in HAG two graph operators are presented to mitigate the over-smoothing problem and make better use of the heterogeneous behavior relations in BN. Extensive experiments on a real-world dataset show that our method outperforms state-of-the-art methods significantly and can give a response in seconds for each detection request.
Work-in-Progress–—A Preliminary Eye Tracking and HMD Orientation Comparison to Determine Focus on a Cardiac Auscultation Training Environment

Cardiac auscultation is a routine medical examination whereby using a stethoscope placed over various regions of the patient's chest, a health care professional listens to the patient's hear murmurs to diagnose their heart condition. Cardiac auscultation training has evolved from relying on patient and audio recordings to employing simulation manikins, multimedia, mobile applications, and most recently digital simulations, including virtual reality (VR) that allows training without the limitations of physical laboratories. Typically, at a consumer-level VR headset orientation is used to identify areas of attention and trigger interactions, while high-end headsets make use of eye tracking. This paper presents a work in progress comparison between headset orientation and eye tracking employing two different VR controllers to understand focus on items on screen. Preliminary results indicate that participants spent a significant amount of time browsing the scene rather than relevant objects for the examination. With respect to the tracking methods, eye tracking was the most reliable, thus highlighting the need for thoughtful headset tracking. Future work will focus on a larger study to develop a tool to enhance VR headset interactions by understating how they compare to gaze captured data for more effective interactions and navigation in VR for successful training.
Total Eclipse of the Enclave: Detecting Eclipse Attacks From Inside TEEs

Enclave applications that rely on blockchains for integrity and availability are vulnerable to eclipse attacks. In this paper, we present an approach for reliably detecting extended eclipse attacks, even when the adversary controls all network connectivity. By monitoring changes to the difficulty parameter in Proof-of-Work (PoW) protocols, our algorithm detects suppression of new blocks, as well as difficulty-lowering attacks that attempt to force an enclave client onto a malicious fork mined solely by an attacker. We present analysis that attackers have negligible probability of evading our block monitoring algorithm, and demonstrate its robustness to most historical fluctuations in difficulty on the Ethereum blockchain, resulting in a very low false-positive rate.
Examining the Characteristics That Differentiate Jihadi-Associated Cyberattacks Using Routine Activities Theory

This study applies routine activities theory to determine whether the characteristics of jihadi-inspired web defacements in the United States vary from all other defacements performed against IP addresses hosted within the United States from 2012 to 2016. We focus on target suitability variables and use a sample of over 2.2 million defacements reported by the independent website Zone-H. We estimated a binary logistic regression model and found that jihadi cyberattacks were rare among all the defacements performed in this 5-year period. Additionally, these findings demonstrated jihadists were more likely to target organizational websites and utilized specific attack methods compared to all other defacers. We contextualize our findings and outline a number of avenues for future research.
Evolutionary meta reinforcement learning for portfolio optimization

Portfolio optimization is a control problem whose objective is to find the optimal strategy for the process of selecting the proportions of assets that can provide the maximum return. Conventional approaches formulate the problem as a single Markov decision process and apply reinforcement learning methods to provide solutions. However, it is well known that financial markets involve non-stationary processes, leading to violations of this assumption in these methods. In this work, we reformulate the portfolio optimization problem to deal with the non-stationary nature of financial markets. In our approach, we divide a long-term process into multiple short-term processes to adapt to context changes and consider the portfolio optimization problem as a multitask control problem. Thereafter, we propose an evolutionary meta reinforcement learning approach to search for an initial policy that can quickly adapt to the upcoming target tasks. We model the policies as convolutional networks that can score the match of the patterns in market data charts. Finally, we test our approach using real-world cryptographic currency data and show that it adapts well to the changes in the market and leads to better profitability.
Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

The software inevitably encounters the crash, which will take developers a large amount of effort to find the fault causing the crash (short for crashing fault). Developing automatic methods to identify the residence of the crashing fault is a crucial activity for software quality assurance. Researchers have proposed methods to predict whether the crashing fault resides in the stack trace based on the features collected from the stack trace and faulty code, aiming at saving the debugging effort for developers. However, previous work usually neglected the feature preprocessing operation towards the crash data and only used traditional classification models. In this paper, we propose a novel crashing fault residence prediction framework, called ConDF, which consists of a consistency based feature subset selection method and a state-of-the-art deep forest model. More specifically, first, the feature selection method is used to obtain an optimal feature subset and reduce the feature dimension by reserving the representative features. Then, a simplified deep forest model is employed to build the classification model on the reduced feature set. The experiments on seven open source software projects show that our ConDF method performs significantly better than 17 baseline methods on three performance indicators.
Automated Media Segmentation in Intravascular Ultrasound Images using Geometric Principal Component Analysis

Cardiovascular diseases are some of the most common diseases with a high mortality rate. The correct diagnosis of aortic stenosis can help patients get appropriate treatment, and can decrease the hazard of death. Intravascular ultrasound (IVUS) images can be used to evaluate plaque which is intermediate between the lumen and media adventitia (MA). This paper applies morphology processing to extract areas expected to be MA regions, and represents them with an ellipse by applying the Principal Component Analysis method to locate the MA regions. We evaluated our method on the standard publicly available IVUS image dataset (more than 100 images) using three common evaluation metrics JM, HD, and PAD. The automatic segmentation results are compared with manual segmentation delineated by experts. The proposed method also was compared with other methods including Watershed, Distance Regularized Level Set Evolution, and Region Growing. The results showed that the proposed method yielded satisfactory results with JM = 0.79, HD = 12.72, and PAD = 1.51. Most of the results were better than the other methods and effective in identifying MA.
Chosen Ciphertext Secure Keyed Two-Level Homomorphic Encryption

Homomorphic encryption (HE) is a useful variant of public key encryption (PKE), but it has a drawback that HE cannot fully achieve IND-CCA2 security, a standard security notion for PKE. Emura et al. (PKC 2013) proposed a “keyed" version of HE, called KH-PKE, which introduces a separate key for homomorphic evaluation and then achieves security close to IND-CCA2. Current KH-PKE schemes are classified into ones supporting only a single kind of homomorphic operation (addition or multiplication) and others that are fully homomorphic but are consequently not very efficient; no intermediate schemes with both efficiency and richer functionality are known so far. In this paper, we propose a “two-level" KH-PKE scheme for evaluating degree-two polynomials, by cleverly combining Emura et al.’s generic framework with a recent efficient two-level HE by Attrapadung et al. (ASIACCS 2018).