OpenRaiser/Intern-Atlas huggingface dataset:
A general graph-based framework for top-N recommendation using content, temporal and trust information
Venue: arXiv cs.IR 2019
Year: 2019

Recommending appropriate items to users is crucial in many e-commerce platforms that contain implicit data as users' browsing, purchasing and streaming history. One common approach consists in selecting the N most relevant items to each user, for a given N, which is called top-N recommendation. To do so, recommender systems rely on various kinds of information, like item and user features, past interest of users for items, browsing history and trust between users. However, they often use only one or two such pieces of information, which limits their performance. In this paper, we design and implement GraFC2T2, a general graph-based framework to easily combine and compare various kinds of side information for top-N recommendation. It encodes content-based features, temporal and trust information into a complex graph, and uses personalized PageRank on this graph to perform recommendation. We conduct experiments on Epinions and Ciao datasets, and compare obtained performances using F1-score, Hit ratio and MAP evaluation metrics, to systems based on matrix factorization and deep learning. This shows that our framework is convenient for such explorations, and that combining different kinds of information indeed improves recommendation in general.
The Alt-Right and Global Information Warfare
Venue: arXiv cs.SI 2019
Year: 2019

The Alt-Right is a neo-fascist white supremacist movement that is involved in violent extremism and shows signs of engagement in extensive disinformation campaigns. Using social media data mining, this study develops a deeper understanding of such targeted disinformation campaigns and the ways they spread. It also adds to the available literature on the endogenous and exogenous influences within the US far right, as well as motivating factors that drive disinformation campaigns, such as geopolitical strategy. This study is to be taken as a preliminary analysis to indicate future methods and follow-on research that will help develop an integrated approach to understanding the strategies and associations of the modern fascist movement.
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image
Venue: arXiv cs.CV 2019
Year: 2019

We propose a deep inverse rendering framework for indoor scenes. From a single RGB image of an arbitrary indoor scene, we create a complete scene reconstruction, estimating shape, spatially-varying lighting, and spatially-varying, non-Lambertian surface reflectance. To train this network, we augment the SUNCG indoor scene dataset with real-world materials and render them with a fast, high-quality, physically-based GPU renderer to create a large-scale, photorealistic indoor dataset. Our inverse rendering network incorporates physical insights -- including a spatially-varying spherical Gaussian lighting representation, a differentiable rendering layer to model scene appearance, a cascade structure to iteratively refine the predictions and a bilateral solver for refinement -- allowing us to jointly reason about shape, lighting, and reflectance. Experiments show that our framework outperforms previous methods for estimating individual scene components, which also enables various novel applications for augmented reality, such as photorealistic object insertion and material editing. Code and data will be made publicly available.
Kendall Tau Sequence Distance: Extending Kendall Tau from Ranks to Sequences
Venue: arXiv cs.DM 2019
Year: 2019

An edit distance is a measure of the minimum cost sequence of edit operations to transform one structure into another. Edit distance is most commonly encountered within the context of strings, where Wagner and Fischer's string edit distance is perhaps the most well-known. However, edit distance is not limited to strings. For example, there are several edit distance measures for permutations, including Wagner and Fischer's string edit distance since a permutation is a special case of a string. However, another edit distance for permutations is Kendall tau distance, which is the number of pairwise element inversions. On permutations, Kendall tau distance is equivalent to an edit distance with adjacent swap as the edit operation. A permutation is often used to represent a total ranking over a set of elements. There exist multiple extensions of Kendall tau distance from total rankings (permutations) to partial rankings (i.e., where multiple elements may have the same rank), but none of these are suitable for computing distance between sequences. We set out to explore extending Kendall tau distance in a different direction, namely from the special case of permutations to the more general case of strings or sequences of elements from some finite alphabet. We name our distance metric Kendall tau sequence distance, and define it as the minimum number of adjacent swaps necessary to transform one sequence into the other. We provide two $O(n \lg n)$ algorithms for computing it, and experimentally compare their relative performance. We also provide reference implementations of both algorithms in an open source Java library.
Optimal Randomness in Swarm-Based Search
Venue: arXiv cs.NE 2019
Year: 2019

Lévy flights is a random walk where the step-lengths have a probability distribution that is heavy-tailed. It has been shown that Lévy flights can maximize the efficiency of resource searching in uncertain environments, and also movements of many foragers and wandering animals have been shown to follow a Lévy distribution. The reason mainly comes from that the Lévy distribution, has an infinite second moment, and hence is more likely to generate an offspring that is farther away from its parent. However, the investigation into the efficiency of other different heavy-tailed probability distributions in swarm-based searches is still insufficient up to now. For swarm-based search algorithms, randomness plays a significant role in both exploration and exploitation, or diversification and intensification. Therefore, it's necessary to discuss the optimal randomness in swarm-based search algorithms. In this study, CS is taken as a representative method of swarm-based optimization algorithms, and the results can be generalized to other swarm-based search algorithms. In this paper, four different types of commonly used heavy-tailed distributions, including Mittag-Leffler distribution, Pareto distribution, Cauchy distribution, and Weibull distribution, are considered to enhance the searching ability of CS. Then four novel CS algorithms are proposed and experiments are carried out on 20 benchmark functions to compare their searching performances. Finally, the proposed methods are used to system identification to demonstrate the effectiveness.
Robust Dense Mapping for Large-Scale Dynamic Environments
Venue: arXiv cs.CV 2019
Year: 2019

We present a stereo-based dense mapping algorithm for large-scale dynamic urban environments. In contrast to other existing methods, we simultaneously reconstruct the static background, the moving objects, and the potentially moving but currently stationary objects separately, which is desirable for high-level mobile robotic tasks such as path planning in crowded environments. We use both instance-aware semantic segmentation and sparse scene flow to classify objects as either background, moving, or potentially moving, thereby ensuring that the system is able to model objects with the potential to transition from static to dynamic, such as parked cars. Given camera poses estimated from visual odometry, both the background and the (potentially) moving objects are reconstructed separately by fusing the depth maps computed from the stereo input. In addition to visual odometry, sparse scene flow is also used to estimate the 3D motions of the detected moving objects, in order to reconstruct them accurately. A map pruning technique is further developed to improve reconstruction accuracy and reduce memory consumption, leading to increased scalability. We evaluate our system thoroughly on the well-known KITTI dataset. Our system is capable of running on a PC at approximately 2.5Hz, with the primary bottleneck being the instance-aware semantic segmentation, which is a limitation we hope to address in future work. The source code is available from the project website (http://andreibarsan.github.io/dynslam).
Fixing Inclusivity Bugs for Information Processing Styles and Learning Styles
Venue: arXiv cs.HC 2019
Year: 2019

Most software systems today do not support cognitive diversity. Further, because of differences in problem-solving styles that cluster by gender, software that poorly supports cognitive diversity can also embed gender biases. To help software professionals fix gender bias "bugs" related to people's problem-solving styles for information processing and learning of new software we collected inclusivity fixes from three sources. The first two are empirical studies we conducted: a heuristics-driven user study and a field research industry study. The third is data that we obtained about a before/after user study of inclusivity bugs. The resulting seven potential inclusivity fixes show how to debug software to be more inclusive for diverse problem-solving styles.
Learning Cascaded Siamese Networks for High Performance Visual Tracking
Venue: arXiv cs.CV 2019
Year: 2019

Visual tracking is one of the most challenging computer vision problems. In order to achieve high performance visual tracking in various negative scenarios, a novel cascaded Siamese network is proposed and developed based on two different deep learning networks: a matching subnetwork and a classification subnetwork. The matching subnetwork is a fully convolutional Siamese network. According to the similarity score between the exemplar image and the candidate image, it aims to search possible object positions and crop scaled candidate patches. The classification subnetwork is designed to further evaluate the cropped candidate patches and determine the optimal tracking results based on the classification score. The matching subnetwork is trained offline and fixed online, while the classification subnetwork performs stochastic gradient descent online to learn more target-specific information. To improve the tracking performance further, an effective classification subnetwork update method based on both similarity and classification scores is utilized for updating the classification subnetwork. Extensive experimental results demonstrate that our proposed approach achieves state-of-the-art performance in recent benchmarks.
On the semi-proper orientations of graphs
Venue: arXiv cs.DM 2019
Year: 2019

A {\it semi-proper orientation} of a given graph $G$ is a function $(D,w)$ that assigns an orientation $D(e)$ and a positive integer weight $ w(e)$ to each edge $e$ such that for every two adjacent vertices $v$ and $u$, $S_{(D,w)}(v) \neq S_{(D,w)}(u) $, where $S_{(D,w)}(v) $ is the sum of the weights of edges with head $v$ in $D$. The {\it semi-proper orientation number} of a graph $G$, denoted by $\overrightarrowχ_s (G)$, is $ \min_{(D,w)\in Γ} \max_{v\in V(G)} S_{(D,w)}(v) $, where $Γ$ is the set of all semi-proper orientations of $G$. The {\it optimal semi-proper orientation} is a semi-proper orientation $(D,w)$ such that $ \max_{v\in V(G)} S_{(D,w)}(v)= \overrightarrowχ_s (G) $. In this work, we show that every graph $G$ has an optimal semi-proper orientation $(D,w)$ such that the weight of each edge is one or two. Next, we show that determining whether a given planar graph $G$ with $\overrightarrowχ_s (G)=2 $ has an optimal semi-proper orientation $(D,w)$ such that the weight of each edge is one is NP-complete. Finally, we prove that the problem of determining the semi-proper orientation number of planar bipartite graphs is NP-hard.
Cyber-All-Intel: An AI for Security related Threat Intelligence
Venue: arXiv cs.AI 2019
Year: 2019

Keeping up with threat intelligence is a must for a security analyst today. There is a volume of information present in `the wild' that affects an organization. We need to develop an artificial intelligence system that scours the intelligence sources, to keep the analyst updated about various threats that pose a risk to her organization. A security analyst who is better `tapped in' can be more effective. In this paper we present, Cyber-All-Intel an artificial intelligence system to aid a security analyst. It is a system for knowledge extraction, representation and analytics in an end-to-end pipeline grounded in the cybersecurity informatics domain. It uses multiple knowledge representations like, vector spaces and knowledge graphs in a 'VKG structure' to store incoming intelligence. The system also uses neural network models to pro-actively improve its knowledge. We have also created a query engine and an alert system that can be used by an analyst to find actionable cybersecurity insights.
Multimodal Semantic Attention Network for Video Captioning
Venue: arXiv cs.CV 2019
Year: 2019

Inspired by the fact that different modalities in videos carry complementary information, we propose a Multimodal Semantic Attention Network(MSAN), which is a new encoder-decoder framework incorporating multimodal semantic attributes for video captioning. In the encoding phase, we detect and generate multimodal semantic attributes by formulating it as a multi-label classification problem. Moreover, we add auxiliary classification loss to our model that can obtain more effective visual features and high-level multimodal semantic attribute distributions for sufficient video encoding. In the decoding phase, we extend each weight matrix of the conventional LSTM to an ensemble of attribute-dependent weight matrices, and employ attention mechanism to pay attention to different attributes at each time of the captioning process. We evaluate algorithm on two popular public benchmarks: MSVD and MSR-VTT, achieving competitive results with current state-of-the-art across six evaluation metrics.
Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-assisted Networks
Venue: arXiv cs.IT 2019
Year: 2019

Unmanned aerial vehicles (UAVs) are expected to be a key component of the next-generation wireless systems. Due to their deployment flexibility, UAVs are being considered as an efficient solution for collecting information data from ground nodes and transmitting it wirelessly to the network. In this paper, a UAV-assisted wireless network is studied, in which energy-constrained ground nodes are deployed to observe different physical processes. In this network, a UAV that has a time constraint for its operation due to its limited battery, moves towards the ground nodes to receive status update packets about their observed processes. The flight trajectory of the UAV and scheduling of status update packets are jointly optimized with the objective of achieving the minimum weighted sum for the age-of-information (AoI) values of different processes at the UAV, referred to as weighted sum-AoI. The problem is modeled as a finite-horizon Markov decision process (MDP) with finite state and action spaces. Since the state space is extremely large, a deep reinforcement learning (RL) algorithm is proposed to obtain the optimal policy that minimizes the weighted sum-AoI, referred to as the age-optimal policy. Several simulation scenarios are considered to showcase the convergence of the proposed deep RL algorithm. Moreover, the results also demonstrate that the proposed deep RL approach can significantly improve the achievable sum-AoI per process compared to the baseline policies, such as the distance-based and random walk policies. The impact of various system design parameters on the optimal achievable sum-AoI per process is also shown through extensive simulations.
A Genetic Algorithm Enabled Similarity-Based Attack on Cancellable Biometrics
Venue: arXiv cs.CV 2019
Year: 2019

Cancellable biometrics (CB) as a means for biometric template protection approach refers to an irreversible yet similarity preserving transformation on the original template. With similarity preserving property, the matching between template and query instance can be performed in the transform domain without jeopardizing accuracy performance. Unfortunately, this trait invites a class of attack, namely similarity-based attack (SA). SA produces a preimage, an inverse of transformed template, which can be exploited for impersonation and cross-matching. In this paper, we propose a Genetic Algorithm enabled similarity-based attack framework (GASAF) to demonstrate that CB schemes whose possess similarity preserving property are highly vulnerable to similarity-based attack. Besides that, a set of new metrics is designed to measure the effectiveness of the similarity-based attack. We conduct the experiment on two representative CB schemes, i.e. BioHashing and Bloom-filter. The experimental results attest the vulnerability under this type of attack.
PiNet: A Permutation Invariant Graph Neural Network for Graph Classification
Venue: arXiv cs.LG 2019
Year: 2019

We propose an end-to-end deep learning learning model for graph classification and representation learning that is invariant to permutation of the nodes of the input graphs. We address the challenge of learning a fixed size graph representation for graphs of varying dimensions through a differentiable node attention pooling mechanism. In addition to a theoretical proof of its invariance to permutation, we provide empirical evidence demonstrating the statistically significant gain in accuracy when faced with an isomorphic graph classification task given only a small number of training examples. We analyse the effect of four different matrices to facilitate the local message passing mechanism by which graph convolutions are performed vs. a matrix parametrised by a learned parameter pair able to transition smoothly between the former. Finally, we show that our model achieves competitive classification performance with existing techniques on a set of molecule datasets.
Bayesian Optimization for Polynomial Time Probabilistically Complete STL Trajectory Synthesis
Venue: arXiv cs.RO 2019
Year: 2019

In recent years, Signal Temporal Logic (STL) has gained traction as a practical and expressive means of encoding control objectives for robotic and cyber-physical systems. The state-of-the-art in STL trajectory synthesis is to formulate the problem as a Mixed Integer Linear Program (MILP). The MILP approach is sound and complete for bounded specifications, but such strong correctness guarantees come at the price of exponential complexity in the number of predicates and the time bound of the specification. In this work, we propose an alternative synthesis paradigm that relies on Bayesian optimization rather than mixed integer programming. This relaxes the completeness guarantee to probabilistic completeness, but is significantly more efficient: our approach scales polynomially in the STL time-bound and linearly in the number of predicates. We prove that our approach is sound and probabilistically complete, and demonstrate its scalability with a nontrivial example.
Generalized formal model of big data
Venue: arXiv cs.DB 2019
Year: 2019

This article dwells on the basic characteristic features of the Big Data technologies. It is analyzed the existing definition of the "big data" term. The article proposes and describes the elements of the generalized formal model of big data. It is analyzed the peculiarities of the application of the proposed model components. It described the fundamental differences between Big Data technology and business analytics. Big Data is supported by the distributed file system Google File System technology, Cassandra, HBase, Lustre and ZFS, by the MapReduce and Hadoop programming constructs and many other solutions. According to the experts, such as McKinsey Institute, the manufacturing, healthcare, trade, administration and control of individual movements undergo the transformations under the influence of the Big Data.
Cloud Service ranking using Checkpoint based Load balancing in real time scheduling of Cloud Computing
Venue: arXiv cs.NI 2019
Year: 2019

Cloud computing has been gaining popularity in the recent years. Several studies are being proceeded to build cloud applications with exquisite quality based on users demands. In achieving the same, one of the applied criteria is checkpoint based load balancing in real time scheduling through which suitable cloud service is chosen from a group of cloud services candidates. Valuable information can be collected to rank the services within this checkpoint based load balancing. In order to attain ranking, different services are needed to be invoked in the cloud, which is time consuming and wastage of services invocation. To avoid the same, this chapter proposes an algorithm for predicting the ranks of different cloud services by using the values from previously offered services.
Key-agreement based on automaton groups
Venue: arXiv cs.CR 2019
Year: 2019

We suggest several automaton groups as key-agreement platforms for Anshl-Anshel-Goldfeld metascheme, they include Grigorchuk and universal Grigorchuk groups, Hanoi 3-Towers group, Basilica group and a subgroup of the affine group with the unsolvable conjugacy problem
Vision-based Unscented FastSLAM for Mobile Robot
Venue: arXiv cs.RO 2019
Year: 2019

This paper presents a vision-based Unscented FastSLAM (UFastSLAM) algorithm combing the Rao-Blackwellized particle filter and Unscented Kalman filte(UKF). The landmarks are detected by a binocular vision to integrate localization and mapping. Since such binocular vision system generally inherits larger measurement errors, it is suitable to adopt Unscented FastSLAM to improve the performance of localization and mapping. Unscented FastSLAM takes advantage of UKF instead of the linear approximations of the nonlinear function where the effective number of particles is used as the criteria to reduce the particle degeneration. Simulations and experiments are carried out to demonstrate that the Unscented FastSLAM algorithm can achieve much better performance in the vision-based system than FastSLAM2.0 algorithm on the accuracy and robustness.
Endoscopy artifact detection (EAD 2019) challenge dataset
Venue: arXiv cs.CV 2019
Year: 2019

Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs. Precise detection of specific artifacts like pixel saturations, motion blur, specular reflections, bubbles and debris is essential for high-quality frame restoration and is crucial for realizing reliable computer-assisted tools for improved patient care. At present most videos in endoscopy are currently not analyzed due to the abundant presence of multi-class artifacts in video frames. Through the endoscopic artifact detection (EAD 2019) challenge, we address this key bottleneck problem by solving the accurate identification and localization of endoscopic frame artifacts to enable further key quantitative analysis of unusable video frames such as mosaicking and 3D reconstruction which is crucial for delivering improved patient care. This paper summarizes the challenge tasks and describes the dataset and evaluation criteria established in the EAD 2019 challenge.