Reviewerly/exHarmony huggingface dataset:
How Domain-Specific Modeling Languages Address Variability in Product Line Development

Domain-Specific Modeling raises the level of abstraction beyond programming by specifying the solution directly with domain concepts. Within product lines domain-specific approaches are applied to specify variability and then generate final products together with commonality. Such automated product derivation is possible because both the modeling language and generator are made for a particular product line --- often inside a single company. In this paper we examine which kinds of reuse and product line approaches are applied in industry with domain-specific modeling. Our work is based on empirical analysis of 23 cases and the languages and models created there. The analysis reveals a wide variety and some commonalities in the size of languages and in the ways they apply reuse and product line approaches.
A Superpixel-Wise Approach for Face Sketch Synthesis

Face sketch synthesis from an input photo has drawn great attention in law enforcement and digital entertainment applications. Currently, the input photo is simply divided into overlapped rectangular patches and transformed to a sketch through weighted average of corresponding sketch patches. However, the regular patches lead to defects in the structure of synthesized sketch. In addition, existing methods need to cross the whole training dataset in order to collect the corresponding sketch patches, which limits their usability with the big training datasets. In this paper, a superpixel-wise approach based on the Superpixel technique incorporated into the Locality-constraint Linear Coding (LLC), termed as SuperLLC, is proposed to enhance the facial structure of synthesized sketch and simultaneously maintain fixed computational complexity regardless of the training dataset size. First, the input photo is segmented into overlapped superpixels to find their corresponding sketch superpixels from the training dataset. The LLC is then imposed to regularize proper weights to reconstruct the target sketch from these sketch superpixels, with averaging the overlapped areas between adjacent superpixels. But before all these, for each input photo, a sub-training set is selected based on facial landmarks distance between the input photo and the training photos set. This insures a steady synthesis process. Both subjective and objective experiments on public face sketch databases are finally carried out to reveal the superior performance of the proposed SuperLLC method compared to state-of-the-art methods.
P5: Portable Progressive Parallel Processing Pipelines for Interactive Data Analysis and Visualization

We present P5, a web-based visualization toolkit that combines declarative visualization grammar and GPU computing for progressive data analysis and visualization. To interactively analyze and explore big data, progressive analytics and visualization methods have recently emerged. Progressive visualizations of incrementally refining results have the advantages of allowing users to steer the analysis process and make early decisions. P5 leverages declarative grammar for specifying visualization designs and exploits GPU computing to accelerate progressive data processing and rendering. The declarative specifications can be modified during progressive processing to create different visualizations for analyzing the intermediate results. To enable user interactions for progressive data analysis, P5 utilizes the GPU to automatically aggregate and index data based on declarative interaction specifications to facilitate effective interactive visualization. We demonstrate the effectiveness and usefulness of P5 through a variety of example applications and several performance benchmark tests.
A Novel Plaintext-Related Image Encryption Algorithm Based on Stochastic Signal Insertion and Block Swapping

Image encryption is an important method for protecting private data during communication. This paper proposes a novel hyperchaotic image encryption algorithm based on stochastic signal insertion and block permutation. First, the 5D hyperchaotic system is applied to generate pseudorandom number sequences. The SHA-256 hash function and secret keys are used to produce the initial values of the cryptosystem. The hash values can effectively enhance the sensitivity to plain image. To enlarge the key space and change orbit of cryptosystem, some stochastic signals are inserted during iteration. The plain image is equally divided into two parts. An X-coordinate, a Y-coordinate and a control table are established with produced pseudonumber sequences. The pixel is swapped with another pixel in the current block or another block depending on the control table. Cyclic shift is performed during the diffusion process. Performance and security analyses are executed to verify the effect of the proposed scheme. It is clear that the proposed scheme has a large key space and is highly sensitive to plain image and secret keys. Moreover, the cryptosystem has low computation complexity and can resist correlation analysis, entropy analysis, statistical attack, differential attack, noise and data loss attacks.
IBWH: An Intermittent Block Withholding Attack with Optimal Mining Reward Rate

A trend in clustering mining power into mining pools in proof-of-work (PoW) blockchain systems raises severe concerns on the stability and security of these systems. Such pools can misbehave by strategically withhold found solutions (i.e., blocks). It has been shown that the reward of a large malicious mining pool can be significantly increased by deviating from the honest mining strategy. Moreover, a malicious pool can launch the block withholding (BWH) attack making the target’s pool reward system unfair by letting malicious participants receive unearned shares while pretending to contribute work. Although these attacks are well-known, they are usually investigated in a simplified static reward model. This paper gives a detailed analysis of the dynamic reward of the BWH attacker while considering a more realistic model with the computing power changing incessantly. We propose a novel attack called the intermittent block withholding (IBWH) attack and we prove that this attack is optimal in our model. IBWH is a strategy where an attacker influences the reward period time, consequently enlarging the reward rate. Furthermore, in our model, we include the dynamics of the Bitcoin network’s computing power, and even with the changing attacker’s reward rates, we show that the IBWH’s reward rate remains optimal. We consider both the selfish mining attack and the fork after withholding (FAW) attack, and we show that these attacks do not outperform IBWH.
Online news recommendations based on topic modeling and online interest adjustment

Purpose Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms. Design/methodology/approach A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing. Findings This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation. Originality/value The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.
Bayesian Network for Managing Runway Overruns in Aviation Safety

Runway excursions at landing constitute a major threat to aviation safety.Among them, runway overruns, defined as those occurrences when an aircraft departs the end of a runway, are the most frequent events.Although their occurrence rate is low, the entailed consequences may be very severe in terms of lives and aircraft damage.The main contributing factors to this event and their relationships are studied with the aid of a Bayesian network, while also modeling the nodes' conditional distributions.Then, inferences and predictions are made for the quantities of interest.The issues uncovered suggest several operational recommendations to reduce the probability of facing a runway overrun when landing.
Points2Pix: 3D Point-Cloud to Image Translation Using Conditional GANs

We present the first approach for 3D point-cloud to image translation based on conditional Generative Adversarial Networks (cGAN). The model handles multi-modal information sources from different domains, i.e. raw point-sets and images. The generator is capable of processing three conditions, whereas the point-cloud is encoded as raw point-set and camera projection. An image background patch is used as constraint to bias environmental texturing. A global approximation function within the generator is directly applied on the point-cloud (Point-Net). Hence, the representative learning model incorporates global 3D characteristics directly at the latent feature space. Conditions are used to bias the background and the viewpoint of the generated image. This opens up new ways in augmenting or texturing 3D data to aim the generation of fully individual images. We successfully evaluated our method on the KITTI and SunRGBD dataset with an outstanding object detection inception score.
Automating System Configuration of Distributed Machine Learning

The performance of distributed machine learning systems is dependent on their system configuration. However, configuring the system for optimal performance is challenging and time consuming even for experts due to the diverse runtime factors such as workloads or the system environment. We present cost-based optimization to automatically find a good system configuration for parameter server (PS) machine learning (ML) frameworks. We design and implement Cruise that applies the optimization technique to tune distributed PS ML execution automatically. Evaluation results on three ML applications verify that Cruise automates the system configuration of the applications to achieve good performance with minor reconfiguration costs.
A convolutional neural network based method for low-illumination image enhancement

Nowadays, images can be conveniently captured by various image acquisition devices. Weak lighting conditions and devices with poor filling flash will produce low-illumination images. These degraded images are difficult to identify, and must be processed by some methods through the computer. With the inspiring performance of convolutional neural network (CNN) in image classification, object detection and tracking, some studies have been made to enhance low-illumination images by using CNN in recent years. In this paper, based on the existing researches of CNN based low-illumination image enhancement, an improved Unet model is proposed to enhance low-illumination images. At the same time, this paper introduces two new loss functions: Peak signal-to-noise ratio (PSNR) loss and multi-scale Structural similarity (MS-SSIM) loss, and use a mixture of these two loss functions as loss function in our model. Our method can effectively balance the brightness of the processed image, accurately restore the color, so that the enhanced image have a better perception. Results demonstrate that the proposed method outperforms other enhancement methods.
Scalable and customizable arbitrary waveform generator for superconducting quantum computing

Superconducting quantum processors are manufactured based on a semiconductor process, which makes qubit integration possible. At the same time, this kind of qubit exhibits high-performance fidelity and decoherence time and requires a programmable arbitrary waveform generator (AWG). This paper presents the implementation of an AWG with a sampling rate of two-gigabit samples per second as well as 16-bit vertical resolution digital-to-analog converters. The AWGs are designed for a scaled-up usage scenario by integrating them with separate microwave devices onto a single backplane. A special waveform sequence output controller is designed to realize seamless waveform switching and arbitrary waveform generation. The jitter of multiple AWG channels is around 10 ps, and the integral nonlinearity and differential nonlinearity are both about 2 least significant bits. This customizable AWG has been used in several superconducting quantum processors, and the result of multiple qubits’ measurement verifies that the AWG is qualified for controlling tens of superconducting qubits.
A Quality Metric for Visualization of Clusters in Graphs

Traditionally, graph quality metrics focus on readability, but recent studies show the need for metrics which are more specific to the discovery of patterns in graphs. Cluster analysis is a popular task within graph analysis, yet there is no metric yet explicitly quantifying how well a drawing of a graph represents its cluster structure. We define a clustering quality metric measuring how well a node-link drawing of a graph represents the clusters contained in the graph. Experiments with deforming graph drawings verify that our metric effectively captures variations in the visual cluster quality of graph drawings. We then use our metric to examine how well different graph drawing algorithms visualize cluster structures in various graphs; the results confirm that some algorithms which have been specifically designed to show cluster structures perform better than other algorithms.
MRC4: A Modified RC4 Algorithm Using Symmetric Random Function Generator for Improved Cryptographic Features

The Rivest Cipher 4 (RC4) has been one of the most popular stream ciphers for providing symmetric key encryption, and is now proposed as an efficient cipher within light-weight cryptography.As an algorithm it has been considered to be one of the fastest stream ciphers and one of the easiest to implement.Unfortunately, despite its simplicity of usage, a number of attacks on it have been found.Therefore, various improvements of this algorithm exist in cryptography, but none of them use proper randomness.This paper outlines modified version of RC4 and which has the desirable features of an efficient stream cipher algorithm, and which integrates the Symmetric Random Function Generator (SRFG) method.Though RC4 uses pseudorandom features with an initialisation vector and a seed value, the use of true randomness in RC4 is novel in this domain.Therefore, this paper proposes a modified RC4 as MRC4, and which then evaluates the statistical features of MRC4 based upon parameters such as non-linearity, resiliency, balancedness, propagation and immunity.Further, we have compared the security features and confusiondiffusion attributes with some recent variants of RC4 and have found that MRC4 is efficient in withstanding against attacks.The experimental results show that MRC4 supports a 60% better confusion property and 50% better diffusion as compared to the original RC4 method.
Classifying Inconsistency Measures Using Graphs

The aim of measuring inconsistency is to obtain an evaluation of the imperfections in a set of formulas, and this evaluation may then be used to help decide on some course of action (such as rejecting some of the formulas, resolving the inconsistency, seeking better sources of information, etc). A number of proposals have been made to define measures of inconsistency. Each has its rationale. But to date, it is not clear how to delineate the space of options for measures, nor is it clear how we can classify measures systematically. To address these problems, we introduce a general framework for comparing syntactic measures of inconsistency. It is based on the notion of an inconsistency graph for each knowledgebase (a bipartite graph with a set of vertices representing formulas in the knowledgebase, a set of vertices representing minimal inconsistent subsets of the knowledgebase, and edges representing that a formula belongs to a minimal inconsistent subset). We then show that various measures can be computed using the inconsistency graph. Then we introduce abstractions of the inconsistency graph and use them to construct a hierarchy of syntactic inconsistency measures. Furthermore, we extend the inconsistency graph concept with a labeling that extends the hierarchy to include some other types of inconsistency measures.
Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance

The writing process consists of several stages such as drafting, revising, editing, and proofreading. Studies on writing assistance, such as grammatical error correction (GEC), have mainly focused on sentence editing and proofreading, where surface-level issues such as typographical errors, spelling errors, or grammatical errors should be corrected. We broaden this focus to include the earlier revising stage, where sentences require adjustment to the information included or major rewriting and propose Sentence-level Revision (SentRev) as a new writing assistance task. Well-performing systems in this task can help inexperienced authors by producing fluent, complete sentences given their rough, incomplete drafts. We build a new freely available crowdsourced evaluation dataset consisting of incomplete sentences authored by non-native writers paired with their final versions extracted from published academic papers for developing and evaluating SentRev models. We also establish baseline performance on SentRev using our newly built evaluation dataset.
A Method for Blockchain Transactions Analysis

A ransomware is a kind of malware used for digital extortion purposes, where victims must make a payment with acryptocurrency in exchange of information or compromised systems. This article presents a method to analyze thedynamic of Bitcoin transactions related to ransomware, through the representation of the blockchain as a directednetwork, and the measurement of the corresponding parameters, as well as statistical analysis and a transaction tracking.
Synthesizing replacement classes

We present a new technique for automatically synthesizing replacement classes. The technique starts with an original class O and a potential replacement class R, then uses R to synthesize a new class that implements the same interface and provides the same functionality as O. Critically, our technique works with a synthe- sized inter-class equivalence predicate between the states of O and R. It uses this predicate to ensure that original and synthesized methods leave corresponding O and R objects in equivalent states. The predicate therefore enables the technique to synthesize individual replacement methods in isolation while still obtain- ing a replacement class that leaves the original and replacement objects in equivalent states after arbitrarily long method invocation sequences. We have implemented the technique as part of a tool, named Mask, and evaluated it using open-source Java classes. The results highlight the effectiveness of Mask in synthesizing replacement classes.
Editable Neural Networks

These days deep neural networks are ubiquitously used in a wide range of tasks, from image classification and machine translation to face identification and self-driving cars. In many applications, a single model error can lead to devastating financial, reputational and even life-threatening consequences. Therefore, it is crucially important to correct model mistakes quickly as they appear. In this work, we investigate the problem of neural network editing - how one can efficiently patch a mistake of the model on a particular sample, without influencing the model behavior on other samples. Namely, we propose Editable Training, a model-agnostic training technique that encourages fast editing of the trained model. We empirically demonstrate the effectiveness of this method on large-scale image classification and machine translation tasks.
Helmet Detection Using ML &amp; IoT

This paper is about detecting two-wheeler riders without helmet with the help of machine learning and provide them with a user interface to pay challans. The proposed approach first captures the real time image of road traffic and then differentiates the two wheelers from other vehicles in the road. It then processes to check whether the rider and pillion rider are wearing helmet or not using OpenCV. If any one of the riders and pillion rider found not wearing the helmet, their vehicle number plate is processed using optical character recognition (OCR). After extracting the vehicle registration number, a challan will be generated against respective vehicle and all the details of the challan will be sent via E-mail and SMS to the concerned person. An user interface (an app and a website) will also be provided to pay their challans.
Expression Robust 3D Facial Landmarking via Progressive Coarse-to-Fine Tuning

Facial landmarking is a fundamental task in automatic machine-based face analysis. The majority of existing techniques for such a problem are based on 2D images; however, they suffer from illumination and pose variations that may largely degrade landmarking performance. The emergence of 3D data theoretically provides an alternative to overcome these weaknesses in the 2D domain. This article proposes a novel approach to 3D facial landmarking, which combines both the advantages of feature-based methods as well as model-based ones in a progressive three-stage coarse-to-fine manner (initial, intermediate, and fine stages). For the initial stage, a few fiducial landmarks (i.e., the nose tip and two inner eye corners) are robustly detected through curvature analysis, and these points are further exploited to initialize the subsequent stage. For the intermediate stage, a statistical model is learned in the feature space of three normal components of the facial point-cloud rather than the smooth original coordinates, namely Active Normal Model (ANM). For the fine stage, cascaded regression is employed to locally refine the landmarks according to their geometry attributes. The proposed approach can accurately localize dozens of fiducial points on each 3D face scan, greatly surpassing the feature-based ones, and it also improves the state of the art of the model-based ones in two aspects: sensitivity to initialization and deficiency in discrimination. The proposed method is evaluated on the BU-3DFE, Bosphorus, and BU-4DFE databases, and competitive results are achieved in comparison with counterparts in the literature, clearly demonstrating its effectiveness.
Keypoint Detection Using Higher Order Laplacian of Gaussian

This paper presents a keypoint detection method based on the Laplacian of Gaussian (LoG).In contrast to the Difference of Gaussian (DoG)-based keypoint detection method used in Scale Invariant Feature Transform (SIFT), we focus on the LoG operator and its higher order derivatives.We provide mathematical analogies between higher order DoG (HDoG) and higher order LoG (HLoG) and experimental results to show the effectiveness of the proposed HLoG-based keypoint detection method.The performance of the HLoG is evaluated with four different tests: i) a repeatability test of the keypoints detected across images under various transformations, ii) image retrieval, iii) panorama stitching and iv) 3D reconstruction.The proposed HLoG method provides comparable performance to HDoG and the combination of HLoG and HDoG provides significant improvements in various keypoint-related computer vision problems.