Reviewerly/exHarmony huggingface dataset:
Learning Implicit Generative Models by Matching Perceptual Features

Perceptual features (PFs) have been used with great success in tasks such as transfer learning, style transfer, and super-resolution. However, the efficacy of PFs as key source of information for learning generative models is not well studied. We investigate here the use of PFs in the context of learning implicit generative models through moment matching (MM). More specifically, we propose a new effective MM approach that learns implicit generative models by performing mean and covariance matching of features extracted from pretrained ConvNets. Our proposed approach improves upon existing MM methods by: (1) breaking away from the problematic min/max game of adversarial learning; (2) avoiding online learning of kernel functions; and (3) being efficient with respect to both number of used moments and required minibatch size. Our experimental results demonstrate that, due to the expressiveness of PFs from pretrained deep ConvNets, our method achieves state-of-the-art results for challenging benchmarks.
Stochastic optimization on complex variables and pure-state quantum tomography

Abstract Real-valued functions of complex arguments violate the Cauchy-Riemann conditions and, consequently, do not have Taylor series expansion. Therefore, optimization methods based on derivatives cannot be directly applied to this class of functions. This is circumvented by mapping the problem to the field of the real numbers by considering real and imaginary parts of the complex arguments as the new independent variables. We introduce a stochastic optimization method that works within the field of the complex numbers. This has two advantages: Equations on complex arguments are simpler and easy to analyze and the use of the complex structure leads to performance improvements. The method produces a sequence of estimates that converges asymptotically in mean to the optimizer. Each estimate is generated by evaluating the target function at two different randomly chosen points. Thereby, the method allows the optimization of functions with unknown parameters. Furthermore, the method exhibits a large performance enhancement. This is demonstrated by comparing its performance with other algorithms in the case of quantum tomography of pure states. The method provides solutions which can be two orders of magnitude closer to the true minima or achieve similar results as other methods but with three orders of magnitude less resources.
Perceptual Compression for Video Storage and Processing Systems

Compressed videos constitute 70% of Internet traffic, and video upload growth rates far outpace compute and storage improvement trends. Past work in leveraging perceptual cues like saliency, i.e., regions where viewers focus their perceptual attention, reduces compressed video size while maintaining perceptual quality, but requires significant changes to video codecs and ignores the data management of this perceptual information.
Conjure

Refraction Networking (formerly known as "Decoy Routing") has emerged as a promising next-generation approach for circumventing Internet censorship. Rather than trying to hide individual circumvention proxy servers from censors, proxy functionality is implemented in the core of the network, at cooperating ISPs in friendly countries. Any connection that traverses these ISPs could be a conduit for the free flow of information, so censors cannot easily block access without also blocking many legitimate sites. While one Refraction scheme, TapDance, has recently been deployed at ISP-scale, it suffers from several problems: a limited number of "decoy" sites in realistic deployments, high technical complexity, and undesirable tradeoffs between performance and observability by the censor. These challenges may impede broader deployment and ultimately allow censors to block such techniques. We present Conjure, an improved Refraction Networking approach that overcomes these limitations by leveraging unused address space at deploying ISPs. Instead of using real websites as the decoy destinations for proxy connections, our scheme connects to IP addresses where no web server exists leveraging proxy functionality from the core of the network. These phantom hosts are difficult for a censor to distinguish from real ones, but can be used by clients as proxies. We define the Conjure protocol, analyze its security, and evaluate a prototype using an ISP testbed. Our results suggest that Conjure can be harder to block than TapDance, is simpler to maintain and deploy, and offers substantially better network performance.
Creating Content That Influences People: Considering User Experience and Behavioral Design in Technical Communication

As people today use information products in contexts with distractions, we need to design for people’s attention. User experience design routinely relies on behavioral design to engage distracted users and nudge them toward specific behavior. Although practiced in user experience design, behavioral design is less known in technical communication. In this article, we use the CHOICES (Context, Habits, Other people, Incentives, Congruence, Emotions, and Salience) framework developed by McKinsey’s Behavioral Lab to introduce students to learn about behavioral design principles that make use of cognitive biases to influence people. We maintain that behavioral design is useful for technical communicators because they create digital assets that are part of the user experience.
Computationally efficient method of tracking fibres in composite materials using digital image correlation

A computationally efficient method based on digital image correlation (DIC) has been introduced in this paper for fast and accurate characterisation of fibre orientation fields from a series of optical mosaics of a continuous fibre-reinforced composite (CFRC) specimen, which were obtained using a well-established automated serial sectioning technique. The newly developed method has the capability of determining fibre paths from the sequence of mosaics in scenarios where the serial section spacing between the adjacent mosaics is atypically large, resulting in shifts in the fibre locations on the order of 90 pixels or twelve fibre diameters. The performance of the proposed method was quantitatively compared with a state-of-the-art fibre-tracking algorithm based on a Kalman filter, by applying them to the mosaic sequences from two CFRC material specimens. It was demonstrated from the fibre-tracking results that the proposed DIC-based method outperformed the Kalman filter algorithm in terms of both reliability and speed.
Revisiting the Hybrid Attack on Sparse Secret LWE and Application to HE Parameters

In the practical use of the Learning With Error (LWE) based cryptosystems, it is quite common to choose the secret to be extremely small: one popular choice is ternary ((±1,0),coefficient vector, and some further use ternary vector having only small numbers of nonzero coefficient, what is called \em sparse and ternary vector. This use of small secret also benefits to attack algorithms against LWE, and currently LWE-based cryptosystems including homomorphic encryptions (HE) set parameters based on the attack complexity of those improved attacks.
Multi-Modal fusion with multi-level attention for Visual Dialog

Given an input image, Visual Dialog is introduced to answer a sequence of questions in the form of a dialog. To generate accurate answers for questions in the dialog, we need to consider all information of the dialog history, the question, and the image. However, existing methods usually directly utilized the high-level semantic information of the whole sentence for the dialog history and the question, while ignoring the low-level detailed information of words in the sentence. Similarly, the detailed region information of the image in low level is also required to be considered for question answering. Therefore, we propose a novel visual dialog method, which focuses on both high-level and low-level information of the dialog history, the question, and the image. In our approach, we introduce three low-level attention modules, the goal of which is to enhance the representation of words in the sentence of the dialog history and the question based on the word-to-word connection and enrich the region information of the image based on the region-to-region relation. Besides, we design three high-level attention modules to select important words in the sentence of the dialog history and the question as the supplement of the detailed information for semantic understanding, as well as to select relevant regions in the image to provide the targeted visual information for question answering. We evaluate the proposed approach on two datasets: VisDial v0.9 and VisDial v1.0. The experimental results demonstrate that utilizing both low-level and high-level information really enhances the representation of inputs.
Seems Legit

The standard definition of security for digital signatures - existential unforgeability - does not ensure certain properties that protocol designers might expect. For example, in many modern signature schemes, one signature may verify against multiple distinct public keys. It is left to protocol designers to ensure that the absence of these properties does not lead to attacks.
From keywords to relational database content: A semantic mapping method

Keyword-based query specification to extract data from structured databases has attracted considerable attention from various researchers, and many interesting proposals may be found in the scientific literature. However, many of these studies focus on finding a set of interconnected tuples containing all or some of the query keywords. The architecture introduced by this paper covers from the selection of databases on the Web to ranked relevant results. The approach also includes important aspects such as the proximity between keywords, query segmentation, and the use of aggregate functions, among others. The empirical evaluation analyzes the relevance of results and proves competitive as regards related studies.
Developing a model for predicting optimum daily tilt angle of a PV solar system at different geometric, physical and dynamic parameters

Capturing the solar radiation that passed through the Earth’s atmosphere and received by solar panels depends on several parameters. In this paper, all governing parameters of the total daily solar radiation are provided in mathematical relations. In the first stage, the mathematical model is converted to a computer-based model by using the MathCAD program. In the second stage, however, the control variables, tilt angles, surface azimuth angles, day of year, and ground reflectance are identified. Based on the total daily solar radiation objective function, three scenarios are proposed in this study for different situations of variation of control variables. In the final stage the optimization flowchart is designed for the optimum daily tilt angle. The model’s innovation to simultaneously analyze the mean effects of control variables on the dynamic and optimum tilt angles simulation was designed based on 3 scenarios for a PV solar system of the building in Montreal in different seasons. After analyzing the correlation among the scenarios, values of the average optimum tilt angles for each scenario are simulated in angle ranges of 60° to 65° for the winter, 20° to 22.5° for the spring, 27.5° to 35° for the summer, and 68° to 75° for fall.
Application of Spherical-Radial Cubature Bayesian Filtering and Smoothing in Bearings Only Passive Target Tracking

In this paper, an application of spherical radial cubature Bayesian filtering and smoothing algorithms is presented to solve a typical underwater bearings only passive target tracking problem effectively. Generally, passive target tracking problems in the ocean environment are represented with the state-space model having linear system dynamics merged with nonlinear passive measurements, and the system is analyzed with nonlinear filtering algorithms. In the present scheme, an application of spherical radial cubature Bayesian filtering and smoothing is efficiently investigated for accurate state estimation of a far-field moving target in complex ocean environments. The nonlinear model of a Kalman filter based on a Spherical Radial Cubature Kalman Filter (SRCKF) and discrete-time Kalman smoother known as a Spherical Radial Cubature Rauch–Tung–Striebel (SRCRTS) smoother are applied for tracking the semi-curved and curved trajectory of a moving object. The worth of spherical radial cubature Bayesian filtering and smoothing algorithms is validated by comparing with a conventional Unscented Kalman Filter (UKF) and an Unscented Rauch–Tung–Striebel (URTS) smoother. Performance analysis of these techniques is performed for white Gaussian measured noise variations, which is a significant factor in passive target tracking, while the Bearings Only Tracking (BOT) technology is used for modeling of a passive target tracking framework. Simulations based experiments are executed for obtaining least Root Mean Square Error (RMSE) among a true and estimated position of a moving target at every time instant in Cartesian coordinates. Numerical results endorsed the validation of SRCKF and SRCRTS smoothers with better convergence and accuracy rates than that of UKF and URTS for each scenario of passive target tracking problem.
What Are Links in Linked Open Data? A Characterization and Evaluation of Links between Knowledge Graphs on the Web

Linked Open Data promises to provide guiding principles to publish interlinked knowledge graphs on the Web in the form of findable, accessible, interoperable, and reusable datasets. We argue that while as such, Linked Data may be viewed as a basis for instantiating the FAIR principles, there are still a number of open issues that cause significant data quality issues even when knowledge graphs are published as Linked Data. First, to define boundaries of single coherent knowledge graphs within Linked Data, a principled notion of what a dataset is, or, respectively, what links within and between datasets are, has been missing. Second, we argue that to enable FAIR knowledge graphs, Linked Data misses standardised findability and accessability mechanism via a single entry link. To address the first issue, we (i) propose a rigorous definition of a naming authority for a Linked Data dataset, (ii) define different link types for data in Linked datasets, (iii) provide an empirical analysis of linkage among the datasets of the Linked Open Data cloud, and (iv) analyse the dereferenceability of those links. We base our analyses and link computations on a scalable mechanism implemented on top of the HDT format, which allows us to analyse quantity and quality of different link types at scale.
Anonymization and De-Anonymization of Mobility Trajectories: Dissecting the Gaps Between Theory and Practice

Human mobility trajectories are increasingly collected by ISPs to assist academic research and commercial applications. Meanwhile, there is a growing concern that individual trajectories can be de-anonymized when the data is shared, using information from external sources (e.g., online social networks). To understand this risk, prior works either estimate the theoretical privacy bound or simulate de-anonymization attacks on synthetically created datasets. However, it is not clear how well the theoretical estimations are preserved in practice. In this article, we collected a large-scale ground-truth trajectory dataset from 2,161,500 users of a cellular network, and two matched external trajectory datasets from a large social network (56,683 users) and a check-in/review service (45,790 users) on the same user population. The two sets of large ground-truth data provide a rare opportunity to extensively evaluate a variety of de-anonymization algorithms (nine in total). We find that their performance in the real-world dataset is far from the theoretical bound. Further analysis shows that most algorithms have under-estimated the impact of spatio-temporal mismatches between the data from different sources, and the high sparsity of user generated data also contributes to the under-performance. Based on these insights, we propose four new algorithms that are specially designed to tolerate spatial or temporal mismatches (or both) and model location contexts and time contexts. Extensive evaluations show that our algorithms achieve more than 17 percent performance gain over the best existing algorithms, confirming our insights. Further, we propose two new location-privacy preserving mechanisms utilizing the spatio-temporal mismatches to better protect users' privacy against the de-anonymization attack. Evaluation results show that our proposed mechanisms can reduce the performance of de-anonymization attacks by over 8.0 percent, demonstrating the effectiveness of our insights.
Sentiment Lexicon Enhanced Neural Sentiment Classification

Sentiment classification is an important task in the sentiment analysis field. Many deep learning based sentiment classification methods have been proposed in recent years. However, these methods usually rely on massive labeled texts to train sentiment classifiers, which are expensive and time-consuming to annotate. Luckily, many high-quality sentiment lexicons have been constructed and can cover a large number of sentiment words. Since sentiment words are the basic units to convey sentiments in texts, these sentiment lexicons have the potential to improve the performance of neural sentiment classification. In this paper, we propose two approaches to exploit sentiment lexicons to enhance neural sentiment classification. In our first approach we use sentiment lexicons to learn sentiment-aware attentions. We propose a word sentiment classification task to classify the sentiments of words in a sentence based on their hidden representations in the attention network of neural sentiment classification models. We jointly train this task with neural sentiment classifier to facilitate the attention network to recognize and highlight sentiment-bearing words. In our second approach we use sentiment lexicons to learn sentiment-aware word embeddings. We design an auxiliary task to classify the sentiments of words in sentiment lexicons based on their word embeddings, and jointly train this task with neural sentiment classifier to encode sentiment information in sentiment lexicons to word embeddings. Extensive experiments on three benchmark datasets validate the effectiveness of our approach.
The Work of Bilingual Parent-Education Liaisons

In this paper, we examine the information work that bilingual parent-education liaisons perform to create connections towards assisting immigrant parents in the United States. As part of formal and informal educational institutions, liaisons operate between different social worlds---within and beyond the domain of education---to maximize immigrant parents' engagement in their children's academic lives. Drawing upon ethnographic fieldwork with low-income Latino immigrants and their liaisons, we explore how liaisons leverage technology, content, and people to align these worlds; bringing them closer to mutual understanding. We borrow inspiration from the analytical lens of seams suggested by Vertesi to explore how liaisons manage the "seams'' between worlds. This analysis provides a rich and unique perspective of the work that successfully engages immigrant parents with their children's education, as well as the tensions that challenge that engagement. This understanding of the work that liaisons do with and across seams provides insights to inform design directions for technology that could facilitate and amplify their efforts. Finally, we highlight how exploring the seams that liaisons work with can inform the field of Computer-Supported Collaborative Work (CSCW) on the role of technology in mediation.
DCUDA

In clouds and data centers, GPU servers which consist of multiple GPUs are widely deployed. Current state-of-the-art GPU scheduling algorithm are "static" in assigning applications to different GPUs. These algorithms usually ignore the dynamics of the GPU utilization and are often inaccurate in estimating resource demand before assigning/running applications, so there is a large opportunity to further load balance and to improve GPU utilization. Based on CUDA (Compute Unified Device Architecture), we develop a runtime system called DCUDA which supports "dynamic" scheduling of running applications between multiple GPUs. In particular, DCUDA provides a realtime and lightweight method to accurately monitor the resource demand of applications and GPU utilization. Furthermore, it provides a universal migration facility to migrate "running applications" between GPUs with negligible overhead. More importantly, DCUDA transparently supports all CUDA applications without changing their source codes. Experiments with our prototype system show that DCUDA can reduce 78.3% of overloaded time of GPUs on average. As a result, for different workloads consisting of a wide range applications we studied, DCUDA can reduce the average execution time of applications by up to 42.1%. Furthermore, DCUDA also reduces 13.3% energy in the light load scenario.
Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU

Recently, interactive recommender systems are becoming increasingly popular. The insight is that, with the interaction between users and the system, (1) users can actively intervene the recommendation results rather than passively receive them, and (2) the system learns more about users so as to provide better recommendation.
The effect of server energy proportionality on data center power oversubscription

Modern data centers improve resource utilization with power oversubscription. The power hierarchy in a data center is oversubscribed by installing more servers than allowed by the power budget based on server peak power consumption. Power oversubscription is possible due to the statistically low likelihood of simultaneous peak power operation of multiple servers. As future servers become more energy proportional, the opportunity for greater power oversubscription increases. The challenge is to quantify the level of oversubscription that can be attained. In this paper, we quantify the level of oversubscription possible for a given acceptable probability of power overload for servers characterized by energy proportionality metric and workload distribution. We develop a theoretical framework to characterize and predict the relationship between server energy proportionality and power oversubscription. We verify our framework through an extensive empirical study using publicly available SPECpower benchmark data for over 500 server models and publicly available Google cluster utilization data. Using our framework, a data center operator can predict the additional power oversubscription possible when replacing existing servers with a newer model of more energy proportional servers.