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Graph query autocompletion (gQAC) generates a small list of ranked query suggestions during the graph query formulation process in a visual environment. The current state-of-the-art of gQAC provides suggestions that are formed by adding subgraph increments to arbitrary places of an existing (partial) user query. However, according to the research results on human-computer interaction (HCI), humans can only interact with a small number of recent software artifacts in hand. Hence, many of such suggestions could be irrelevant. In this paper, we present the GFocus framework that exploits a novel notion of user focus of graph query formulation (or simply focus). Intuitively, the focus is the subgraph that a user is working on. We formulate locality principles inspired by the HCI research to automatically identify and maintain the focus. We propose novel monotone submodular ranking functions for generating popular and comprehensive query suggestions only at the focus. In particular, the query suggestions of GFocus have high result counts (when they are used as queries) and maximally cover the possible suggestions at the focus. We propose efficient algorithms and an index for ranking the suggestions. Our results show that GFocus saves 12-32 percent more mouse clicks and is 35× more efficient than the state-of-the-art competitor. [ABSTRACT FROM AUTHOR]

Numerous single image super-resolution (SISR) algorithms have been proposed during the past years to reconstruct a high-resolution (HR) image from its low-resolution (LR) observation. However, how to fairly compare the performance of different SISR algorithms/results remains a challenging problem. So far, the lack of comprehensive human subjective study on large-scale real-world SISR datasets and accurate objective SISR quality assessment metrics makes it unreliable to truly understand the performance of different SISR algorithms. We in this paper make efforts to tackle these two issues. Firstly, we construct a real-world SISR quality dataset (i.e., RealSRQ) and conduct human subjective studies to compare the performance of the representative SISR algorithms. Secondly, we propose a new objective metric, i.e., KLTSRQA, based on the Karhunen-Loéve Transform (KLT) to evaluate the quality of SISR images in a no-reference (NR) manner. Experiments on our constructed RealSRQ and the latest synthetic SISR quality dataset (i.e., QADS) have demonstrated the superiority of our proposed KLTSRQA metric, achieving higher consistency with human subjective scores than relevant existing NR image quality assessment (NR-IQA) metrics. The dataset and the code will be made available at https://github.com/Zhentao-Liu/RealSRQ-KLTSRQA. [ABSTRACT FROM AUTHOR]

Recent advances in noiseless non-adaptive group testing have led to a precise asymptotic characterization of the number of tests required for high-probability recovery in the sublinear regime $k = n^{\theta }$ (with $\theta \in (0,1)$), with $n$ individuals among which $k$ are infected. However, the required number of tests may increase substantially under real-world practical constraints, notably including bounds on the maximum number $\Delta $ of tests an individual can be placed in, or the maximum number $\Gamma $ of individuals in a given test. While previous works have given recovery guarantees for these settings, significant gaps remain between the achievability and converse bounds. In this paper, we substantially or completely close several of the most prominent gaps. In the case of $\Delta $ -divisible items, we show that the definite defectives (DD) algorithm coupled with a random regular design is asymptotically optimal in dense scaling regimes, and optimal to within a factor of e more generally; we establish this by strengthening both the best known achievability and converse bounds. In the case of $\Gamma $ -sized tests, we provide a comprehensive analysis of the regime $\Gamma = \Theta (1)$ , and again establish a precise threshold proving the asymptotic optimality of SCOMP (a slight refinement of DD) equipped with a tailored pooling scheme. Finally, for each of these two settings, we provide near-optimal adaptive algorithms based on sequential splitting, and provably demonstrate gaps between the performance of optimal adaptive and non-adaptive algorithms. [ABSTRACT FROM AUTHOR]

Density Peak Clustering (DPC), a popular density-based clustering approach, has received considerable attention from the research community primarily due to its simplicity and fewer-parameter requirement. However, the resultant clusters obtained using DPC are influenced by the sensitive parameter $d_c$ d c , which depends on data distribution and requirements of different users. Besides, the original DPC algorithm requires visiting a large number of objects, making it slow. To this end, this paper investigates index-based solutions for DPC. Specifically, we propose two list-based index methods viz. (i) a simple List Index, and (ii) an advanced Cumulative Histogram Index. Efficient query algorithms are proposed for these indices which significantly avoids irrelevant comparisons at the cost of space. For memory-constrained systems, we further introduce an approximate solution to the above indices which allows substantial reduction in the space cost, provided that slight inaccuracies are admissible. Furthermore, owing to considerably lower memory requirements of existing tree-based index structures, we also present effective pruning techniques and efficient query algorithms to support DPC using the popular Quadtree Index and R-tree Index. Finally, we practically evaluate all the above indices and present the findings and results, obtained from a set of extensive experiments on six synthetic and real datasets. The experimental insights obtained can help to guide in selecting a befitting index. [ABSTRACT FROM AUTHOR]

Dr. Wenjing Lou is currently the W. C. English Endowed Professor of Computer Science at Virginia Tech and a Fellow of the IEEE. Her research interests span many topics in the fields of wireless networks and cybersecurity. On wireless networks, her interests lie in distributed algorithms and lightweight protocols for wireless networks. Her previous works include algorithm and protocol design for ad hoc networks, multihop cooperative communications, opportunistic routing, and network coding. She has recently collaborated with other colleagues on interference cancellation, MIMO networks, and dynamic spectrum sharing, and 5G wireless communications. On cybersecurity, she has worked on many security and privacy topics in networked information systems, including anonymous authentication, encryption-based information access control and sharing, search over encrypted data, and confidential data computation. More recently, she has been working on block-chain and decentralized consensus protocols, securing 5G/nextG mobile networks, and adversarial machine learning and trustworthy AI systems. Her research goal is to develop innovative solutions to address fundamental research challenges in emerging technologies and critical infrastructures. [ABSTRACT FROM AUTHOR]

Algorithms determine which calculations computers use to solve problems and are one of the central pillars of computer science. As algorithms improve, they enable scientists to tackle larger problems and explore new domains and new scientific techniques ,. Bold claims have been made about the pace of algorithmic progress. For example, the President’s Council of Advisors on Science and Technology (PCAST), a body of senior scientists that advise the U.S. President, wrote in 2010 that “in many areas, performance gains due to improvements in algorithms have vastly exceeded even the dramatic performance gains due to increased processor speed”. However, this conclusion was supported based on data from progress in linear solvers , which is just a single example. With no guarantee that linear solvers are representative of algorithms in general, it is unclear how broadly conclusions, such as PCAST’s, should be interpreted. Is progress faster in most algorithms? Just some? How much on average? [ABSTRACT FROM AUTHOR]

One-class collaborative filtering (OCCF) problems are ubiquitous in real-world recommendation systems, such as news recommendation, but suffer from data sparsity and lack of negative items. To address the challenge, the state-of-the-art algorithm assigns uninteracted items with smaller weights of being negative and performs low-rank approximation over the user-item interaction matrix. However, the prior ratings are usually suggested to be zero but may not be well-defined. To avert the direct utilization of prior ratings for uninteracted items, we propose a novel ranking-based implicit regularizer by hypothesizing that users’ preference scores for uninteracted items should not deviate a lot from each other. The regularizer is then used in a ranking-based OCCF framework to penalize large differences of preference scores between uninteracted items. To efficiently optimize model parameters in this framework, we develop the scalable alternating least square algorithm and coordinate descent algorithm, whose time complexity is linearly proportional to the data size. Finally, we extensively evaluate the proposed algorithms on six public real-world datasets. The results show that the proposed regularizer significantly improves the recommendation quality of ranking-based OCCF algorithms, such as BPRMF and RankALS. Moreover, the ranking-based framework with the proposed regularizer outperforms the state-of-the-art recommendation algorithms for implicit feedback. [ABSTRACT FROM AUTHOR]

We present an efficient list decoding algorithm in the style of Guruswami-Sudan for algebraic geometry codes. Our decoder can decode any such code using $\tilde{\mathcal {O}} (s\ell ^{\omega }\mu ^{\omega -1}(n+g))$ operations in the underlying finite field, where $n$ is the code length, $g$ is the genus of the function field used to construct the code, $s$ is the multiplicity parameter, $\ell $ is the designed list size and $\mu $ is the smallest positive element in the Weierstrass semigroup at some chosen place; the “soft-O” notation $\tilde{\mathcal {O}} (\cdot)$ is similar to the “big-O” notation $\mathcal {O}(\cdot)$ , but ignores logarithmic factors. For the interpolation step, which constitutes the computational bottleneck of our approach, we use known algorithms for univariate polynomial matrices, while the root-finding step is solved using existing algorithms for root-finding over univariate power series. [ABSTRACT FROM AUTHOR]

Suppose that $\mathcal {P}$ is a property that may be satisfied by a random code $C \subset \Sigma ^{n}$. For example, for some $p \in (0,1)$ , $\mathcal {P}$ might be the property that there exist three elements of $C$ that lie in some Hamming ball of radius $pn$. We say that $R^{\ast}$ is the threshold rate for $\mathcal {P}$ if a random code of rate $R^{\ast} + \varepsilon $ is very likely to satisfy $\mathcal {P}$ , while a random code of rate $R^{\ast} - \varepsilon $ is very unlikely to satisfy $\mathcal {P}$. While random codes are well-studied in coding theory, even the threshold rates for relatively simple properties like the one above are not well understood. We characterize threshold rates for a rich class of properties. These properties, like the example above, are defined by the inclusion of specific sets of codewords which are also suitably “symmetric.” For properties in this class, we show that the threshold rate is in fact equal to the lower bound that a simple first-moment calculation obtains. Our techniques not only pin down the threshold rate for the property $\mathcal {P}$ above, they give sharp bounds on the threshold rate for list-recovery in several parameter regimes, as well as an efficient algorithm for estimating the threshold rates for list-recovery in general. [ABSTRACT FROM AUTHOR]

Security is a key problem for the transmission, interchange, and storage process of multimedia systems and applications. In 2018, Li et al. proposed in-depth security analysis on an image encryption algorithm based on a first-order time-delay system (IEATD) and gave a specific chosen-plaintext attack on it. Moreover, an enhanced version called as IEACD was designed to fix the reported security defects. This article analyzes the essential structures of the two algorithms and evaluates their real security performances: 1) no efficient nonlinear operations are adopted to assure the sensibility of keystream; 2) the equivalent secret key of IEATD can be efficiently recovered from one known plain-image and the corresponding cipher-image; and 3) IEACD can still be efficiently cracked with a chosen-plaintext attack. Both rigorous theoretical analyses and detailed experimental results are provided to demonstrate effectiveness of the advanced cryptanalytic methods. [ABSTRACT FROM AUTHOR]

Variational autoencoders (VAEs) have demonstrated their superiority in unsupervised learning for image processing in recent years. The performance of the VAEs highly depends on their architectures, which are often handcrafted by the human expertise in deep neural networks (DNNs). However, such expertise is not necessarily available to each of the end users interested. In this article, we propose a novel method to automatically design optimal architectures of VAEs for image classification, called evolving deep convolutional VAE (EvoVAE), based on a genetic algorithm (GA). In the proposed EvoVAE algorithm, the traditional VAEs are first generalized to a more generic and asymmetrical one with four different blocks, and then a variable-length gene encoding mechanism of the GA is presented to search for the optimal network depth. Furthermore, an effective genetic operator is designed to adapt to the proposed variable-length gene encoding strategy. To verify the performance of the proposed algorithm, nine variants of AEs and VAEs are chosen as the peer competitors to perform the comparisons on MNIST, street view house numbers, and CIFAR-10 benchmark datasets. The experiments reveal the superiority of the proposed EvoVAE algorithm, which wins 21 times out of the 24 comparisons and outperforms the best competitors by 1.39%, 14.21%, and 13.03% on the three benchmark datasets, respectively. [ABSTRACT FROM AUTHOR]

We give a linear-time erasure list-decoding algorithm for expander codes. More precisely, let r > 0 be any integer. Given an inner code C0 of length d, and a d-regular bipartite expander graph G with n vertices on each side, we give an algorithm to list-decode the code C = C(G, C0) of length nd from approximately δ δr nd erasures in time n ⋅ poly (d2r/δ), where δ and δr are the relative distance and the r’th generalized relative distance of C0, respectively. To the best of our knowledge, this is the first linear-time algorithm that can list-decode expander codes from erasures beyond their (designed) distance of approximately δ2nd. To obtain our results, we show that an approach similar to that of (Hemenway and Wootters, Information and Computation, 2018) can be used to obtain such an erasure-list-decoding algorithm with an exponentially worse dependence of the running time on r and δ; then we show how to improve the dependence of the running time on these parameters. [ABSTRACT FROM AUTHOR]