Reviewerly/exHarmony huggingface dataset:
Image completion using structure and texture GAN network

Recently, image completion using deep neural networks has become a hot research area. As structure (the underlying sketch) and texture (the pixel pattern mapped onto semantic structure) are two indispensable parts of images, completion algorithms must deal with them appropriately to produce realistic results. However, many current methods use the end-to-end (such as encode–decode) framework to repair an image, which do not pay special attention to texture and structure. Therefore, they often generate distorted structures and inconsistent textures. Moreover, they also do not allow a user to specify structure and texture information arbitrarily in the missing region. To this end, we propose a novel image completion method comprising a sketch completion network (sGAN) followed by a texture completion network (tGAN). sGAN focuses on repairing the sketch structures in the missing region of an image, and tGAN generates consistent texture information in the missing region based on the sketch output by sGAN and the surrounding incomplete image. By modeling the two parts separately in a deep neural network, not only the proposed method can successfully synthesize semantically valid and visually plausible contents in the missing region, but also it allows a user to manipulate the structure attributes freely in that region. Experimental results on the CelebA, Places2 and CelebA-HQ datasets demonstrate our method owns superior performance on challenging image completion examples and it provides far more user control flexibility than nearly all existing methods.
Branched convolutional neural networks incorporated with Jacobian deep regression for facial landmark detection

Facial landmark detection is to localize multiple facial key-points for a given facial image. While many methods have achieved remarkable performance in recent years, the accuracy remains unsatisfactory due to some uncontrolled conditions such as occlusion, head pose variations and illumination, under which, the L2 loss function is conventionally dominated by errors from those facial components on which the landmarks are hard predicted. In this paper, a novel branched convolutional neural network incorporated with Jacobian deep regression framework, hereafter referred to as BCNN-JDR, is proposed to solve the facial landmark detection problem. Our proposed framework consists of two parts: initialization stage and cascaded refinement stages. We firstly exploit branched convolutional neural networks as the robust initializer to estimate initial shape, which is incorporated with the knowledge of component-aware branches. By virtue of the component-aware branches mechanism, BCNN can effectively alleviate this issue of the imbalance errors among facial components and provide the robust initial face shape. Following the BCNN, a sequence of refinement stages are cascaded to fine-tune the initial shape within a narrow range. In each refinement stage, the local texture information is adopted to fit the facial local nonlinear variation. Moreover, our entire framework is jointly optimized via the Jacobian deep regression optimization strategy in an end-to-end manner. Jacobian deep regression optimization strategy has an ability to backward propagate the training error of the last stage to all previous stages, which implements a global optimization approach to our proposed framework. Experimental results on benchmark datasets demonstrate that the proposed BCNN-JDR is robust against uncontrolled conditions and outperforms the state-of-the-art approaches.
Detection of frame deletion in HEVC-Coded video in the compressed domain

In this paper, we propose an algorithm for detecting frame deletion in HEVC-coded video in the compressed domain. Specifically, we focus on the frame type changes occurring upon frame deletion, which cause slight differences between the coding patterns in original and forged video. Then, we identify discriminating coding patterns for use as features, which are classified by machine learning classifiers. Furthermore, we propose video sequence integrity detection on a group-of-picture basis, which is computationally efficient and robust even when applied to static videos and videos with genuine scene changes. The experimental results show that the proposed technique can classify HEVC-coded videos more accurately than previous methods. In addition, the results demonstrate that the selected features work harmoniously in discrimination and that the learning-based classifiers are more robust and reliable than model-based classifiers.
Aboriginal and Torres Strait Islander people and digital inclusion: what is the evidence and where is it?

Quantitative research on digital inclusion has grappled with significant gaps in data with respect to Aboriginal and Torres Strait Islander people. We compare the survey methods and questions of the Australian Digital Inclusion Index (ADII) data, the National Aboriginal Torres Strait Islander Social Survey (NATSISS) and the Census of Population and Housing to identify these gaps. While the ADII is the most comprehensive examination of digital inclusion, encompassing access, affordability, and digital ability measures, the general ADII dataset excludes Aboriginal and Torres Strait Islander people living in remote areas. In addition, we draw together findings from qualitative research on digital inclusion within particular Aboriginal and Torres Strait Islander communities, to demonstrate why digital choices—including the preference for mobile devices—are important for understanding statistical trends.
Neural fashion experts: I know how to make the complementary clothing matching

Clothing has gradually become the beauty enhancing product, while the harmonious clothing matching is critical for a suitable outfit. The existing clothing matching techniques mainly rely on the visual features but overlook the textual metadata, which may be insufficient to comprehensively encode the fashion items. Nowadays, fashion experts are enabled to share their fashion tips by demonstrating their outfit compositions on the fashion-oriented online communities. Each outfit usually consists of several complementary fashion items (e.g., a top, a bottom and a pair of shoes), which involves an image along with the textual metadata (e.g., the categories and titles). The rich fashion data provide us an opportunity for the clothing matching, especially the complementary fashion item matching. In this work, we propose a multiple autoencoder neural network based on the Bayesian Personalized Ranking, dubbed BPR-MAE. Seamlessly exploring the multi-modalities (i.e., the visual and textual modalities) of fashion items, this framework is able to not only comprehensively model the compatibility between fashion items (e.g., tops and bottoms, bottoms and shoes) but also fulfill the complementary fashion item matching among multiple fashion items. Experimental results on the real-world dataset FashionVC+ demonstrate the effectiveness of BPR-MAE, based on which we provide certain deep insights that can benefit the future research.
Statistical Performances Evaluation of APSO and Improved APSO for Short Term Hydrothermal Scheduling Problem

The Accelerated Particle Swarm Optimization (APSO) algorithm is an efficient and the easiest to implement variant of the famous Particle Swarm Optimization (PSO) algorithm. PSO and its variant APSO have been implemented on the famous Short-Term Hydrothermal Scheduling (STHTS) problem in recent research, and they have shown promising results. The APSO algorithm can be further modified to enhance its optimizing capability by deploying dynamic search space squeezing. This paper presents the implementation of the improved APSO algorithm that is based on dynamic search space squeezing, on the short-term hydrothermal scheduling problem. To give a quantitative comparison, a true statistical comparison based on comparing means is also presented to draw conclusions.
What is Happening to Our Norms Against Racist Speech?

Journal Article What is Happening to Our Norms Against Racist Speech? Get access Jennifer Saul Jennifer Saul Department of Philosophy, University of Sheffield, 45 Victoria Street, Sheffield s3 7qb, uk Email: j.saul@sheffield.ac.uk Search for other works by this author on: Oxford Academic Google Scholar Aristotelian Society Supplementary Volume, Volume 93, Issue 1, 2019, Pages 1–23, https://doi.org/10.1093/arisup/akz001 Published: 20 June 2019
New Performance Modeling Methods for Parallel Data Processing Applications

Predicting the performance of an application running on parallel computing platforms is increasingly becoming important because of its influence on development time and resource management. However, predicting the performance with respect to parallel processes is complex for iterative and multi-stage applications. This research proposes a performance approximation approach FiM to predict the calculation time with FiM-Cal and communication time with FiM-Com of an application running on a distributed framework. FiM-Cal consists of two key components that are coupled with each other: (1) a Stochastic Markov Model to capture non-deterministic runtime that often depends on parallel resources, e.g., number of processes, and (2) a machine-learning model that extrapolates the parameters for calibrating our Markov model when we have changes in application parameters such as dataset. Along with the parallel calculation time, parallel computing platforms consume some data transfer time to communicate among different nodes. FiM-Com consists of a simulation queuing model to quickly estimate communication time. Our new modeling approach considers different design choices along multiple dimensions, namely (i) process-level parallelism, (ii) distribution of cores on multi-processor platform, (iii) application related parameters, and (iv) characteristics of datasets. The major contribution of our prediction approach is that FiM can provide an accurate prediction of parallel processing time for the datasets that have a much larger size than that of the training datasets. We evaluate our approach with NAS Parallel Benchmarks and real iterative data processing applications. We compare the predicted results (e.g., end-to-end execution time) with actual experimental measurements on a real distributed platform. We also compare our work with an existing prediction technique based on machine learning. We rank the number of processes according to the actual and predicted results from FiM and calculate the correlation between the actual and predicted rankings. Our results show that FiM obtains a high correlation in the range of 0.80 to 0.99, which indicates considerable accuracy of our technique. Such prediction provides data analysts a useful insight of optimal configuration of parallel resources (e.g., number of processes and number of cores) and also helps system designers to investigate the impact of changes in application parameters on system performance.
An Empirical Method to Predict Sediment Grain Size From Inorganic Geochemical Measurements

Abstract Grain size is one of the most fundamental properties of sediments. It is frequently used in paleoclimate, paleoceanographic, and paleoenvironmental research as a proxy for river discharge, current and wind strength, and to identify mass flow deposits. Measuring grain size is, however, time‐consuming and destructive. Given the strong influence of grain size on sediment inorganic geochemistry, single‐element variations measured by, for example, X‐ray fluorescence core scanning are increasingly used to estimate grain‐size variations at high resolution in sediment cores. This approach is however limited to a narrow grain‐size range since individual elements only monotonically relate to grain size over a narrow size range. Here, we present a simple, code‐free, multielement method based on partial least squares regression to predict sediment mean grain size from inorganic geochemical data over the range of sizes commonly encountered in sedimentary basins (clay to sand). The method was first tested using river sediment samples separated in 11 grain‐size fractions, and it was later successfully applied to two sediment cores from the Chilean fjords. Our method only requires measuring grain size on a limited number (around 10) of selected training samples, and it allows to predict mean grain size at X‐ray fluorescence core scanner resolution. This method has the potential to be applied to any lake or marine sediment core, provided sediment provenance, weathering, and diagenesis remain relatively stable through time, and we anticipate that it will result in a significant increase in the resolution of sediment proxy records of climate and environmental change.
SMFrWF: Segmented Modified Fractional Wavelet Filter: Fast Low-Memory Discrete Wavelet Transform (DWT)

This paper proposes a novel algorithm to compute the 2-D discrete wavelet transform (DWT) of high-resolution (HR) images on low-cost visual sensor and Internet of Things (IoT) nodes. The main advantages of the proposed segmented modified fractional wavelet filter (SMFrWF) are reduced computation (time) complexity and energy consumption compared to the state-of-the-art low-memory 2-D DWT computation methods. In particular, the conventional convolution-based DWT is very fast but requires large random access memory (RAM), as the entire image needs to be in the system memory. The fractional wavelet filter (FrWF) requires only a small RAM but has high complexity due to multiple readings of image lines. The proposed SMFrWF avoids the multiple readings of image lines, thus reducing the memory read access time and, thereby, the complexity. We evaluated the proposed SMFrWF through MATLAB simulations with 70 popular gray-scale test images of dimensions ranging from 256×256 up to 8192×8192 pixels. The results show that for images of size 2048 × 2048 pixels, the proposed SMFrWF (with four segments per line) has 16.8% and 53.6% lower time complexities than the conventional DWT and FrWF, respectively. The proposed SMFrWF has also been modeled in a hardware description language (HDL) and implemented on an Artix-7 field-programmable gate array (FPGA) platform to evaluate the hardware performance. We observed that the proposed SMFrWF has 65% lower energy consumption than the FrWF (both implemented on the same board). Thus, the proposed SMFrWF appears suitable for computing the wavelet transform coefficients of HR images on low-cost visual sensors and IoT platforms.
Analysis and Estimation of Video QoE in Wireless Cellular Networks using Machine Learning

The use of video streaming services are increasing in the cellular networks, inferring a need to monitor video quality to meet users’ Quality of Experience (QoE). The so-called no-reference (NR) models for estimating video quality metrics mainly rely on packet-header and bitstream information. However, there are situations where the availability of such information is limited due to tighten security and encryption, which necessitates exploration of alternative parameters for conducting video QoE assessment. In this study we collect real-live in-smartphone measurements describing the radio link of the LTE connection while streaming reference videos in uplink. The radio measurements include metrics such as RSSI, RSRP, RSRQ, and CINR. We then use these radio metrics to train a Random Forrest machine learning model against calculated video quality metrics from the reference videos. The aim is to estimate the Mean Opinion Score (MOS), PSNR, Frame delay, Frame skips, and Blurriness. Our result show 94% classification accuracy, and 85% model accuracy (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value) when predicting the MOS using regression. Correspondingly, we achieve 89%, 84%, 85%, and 82% classification accuracy when predicting PSNR, Frame delay, Frame Skips, and Blurriness respectively. Further, we achieve 81%, 77%, 79%, and 75% model accuracy (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value) regarding the same parameters using regression.
Tight Verifiable Delay Functions

A Verifiable Delay Function (VDF) is a function that takes at least T sequential steps to evaluate and produces a unique output that can be verified efficiently, in time essentially independent of T. In this work we study tight VDFs, where the function can be evaluated in time not much more than the sequentiality bound T. On the negative side, we show the impossibility of a black-box construction from random oracles of a VDF that can be evaluated in time $$T + O(T^\delta )$$ for any constant $$\delta < 1$$ . On the positive side, we show that any VDF with an inefficient prover (running in time cT for some constant c) that has a natural self-composability property can be generically transformed into a VDF with a tight prover efficiency of $$T+O(1)$$ . Our compiler introduces only a logarithmic factor overhead in the proof size and in the number of parallel threads needed by the prover. As a corollary, we obtain a simple construction of a tight VDF from any succinct non-interactive argument combined with repeated hashing. This is in contrast with prior generic constructions (Boneh et al., CRYPTO 2018) that required the existence of incremental verifiable computation, which entails stronger assumptions and complex machinery.
A Generative Adversarial Density Estimator

Density estimation is a challenging unsupervised learning problem. Current maximum likelihood approaches for density estimation are either restrictive or incapable of producing high-quality samples. On the other hand, likelihood-free models such as generative adversarial networks, produce sharp samples without a density model. The lack of a density estimate limits the applications to which the sampled data can be put, however. We propose a Generative Adversarial Density Estimator, a density estimation approach that bridges the gap between the two. Allowing for a prior on the parameters of the model, we extend our density estimator to a Bayesian model where we can leverage the predictive variance to measure our confidence in the likelihood. Our experiments on challenging applications such as visual dialog where the density and the confidence in predictions are crucial shows the effectiveness of our approach.
Scaling Intel® Software Guard Extensions Applications with Intel® SGX Card

Cloud computing revolutionized the way internet-scale services are deployed and scaled. However, general security concerns and protecting business critical data are still major factors holding companies back from moving their IT infrastructure to the cloud. Intel® Software Guard Extensions (Intel® SGX) technology provides a hardware enforced trusted execution environment specifically developed to compute on confidential data in untrusted public clouds. To date, Intel SGX is available only on single-socket platforms and its secure memory limited to 128 MB.
Latent Patterns in Activities: A Field Study of How Developers Manage Context

In order to build efficient tools that support complex programming tasks, it is imperative that we understand how developers program. We know that developers create a context around their programming task by gathering relevant information. We also know that developers decompose their tasks recursively into smaller units. However, important gaps exist in our knowledge about: (1) the role that context plays in supporting smaller units of tasks, (2) the relationship that exists among these smaller units, and (3) how context flows across them. The goal of this research is to gain a better understanding of how developers structure their tasks and manage context through a field study of ten professional developers in an industrial setting. Our analysis reveals that developers decompose their tasks into smaller units with distinct goals, that specific patterns exist in how they sequence these smaller units, and that developers may maintain context between those smaller units with related goals.
Multi-view Embedding-based Synonyms for Email Search

Synonym expansion is a technique that adds related words to search queries, which may lead to more relevant documents being retrieved, thus improving recall. There is extensive prior work on synonym expansion for web search, however very few studies have tackled its application for email search. Synonym expansion for private corpora like emails poses several unique research challenges. First, the emails are not shared across users, which precludes us from directly employing query-document bipartite graphs, which are standard in web search synonym expansion. Second, user search queries are of personal nature, and may not be generalizable across users. Third, the size of the underlying corpora from which the synonyms may be mined is relatively small (i.e., user's private email inbox) compared to the size of the web corpus. Therefore, in this paper, we propose a solution tailored to the challenges of synonym expansion for email search. We formulate it as a multi-view learning problem, and propose a novel embedding-based model that joins information from multiple sources to obtain the optimal synonym candidates. To demonstrate the effectiveness of the proposed technique, we evaluate our model using both explicit human ratings as well as a live experiment using the Gmail Search service, one of the world's largest email search engines.
A Mapping Study on Product Owners in Industry: Identifying Future Research Directions

Product Owners in the Scrum framework - respectively the On-site Customer when applying eXtreme Programming - have an important role in the development process. The Scrum Guide states that this role is responsible for the requirements and maximizing the value of the product. However, the implementation of this role depends on the individual, the organization and the team and is perceived as difficult in industry. Various research approaches provide insights in the applied PO role in industry - yet, a conclusive bigger picture of the studies and reports on this issue is missing. To fill this gap, we conducted a systematic mapping study. Our findings structure the research area of Product Owners in industry in terms of research topics and applied research methods. In total, we identified 30 contributions addressing seven research topics and generated consolidated answers for each of them. While some of those topics provide congruent results, others point to gaps in current research: So is the PO role in large-scale projects clearly defined as a group effort, but questions regarding the leadership and management responsibilities of POs remain unanswered. Also, the impact of external circumstances on the PO role is a question that is worth to answer in future work.
Granger-causality: An efficient single user movement recognition using a smartphone accelerometer sensor

In this research paper, a novel framework is proposed to classify and analyze human activities. Granger-causality is applied on a smartphone for the recognition of single user activity. It is done in two different ways. In the first one, human activity is recognized on the basis of casual relationships among X-Y-Z Cartesian axes while the second one is based on the casual relationships among the activities. The graphical representation allowed the understanding of mutual dependencies among activities. A tri-axial accelerometer sensor embedded in a smartphone is used to record the acceleration signal. Six human activities successfully classified are walking, walking-upstairs, walking-downstairs, sitting, standing and lying.