OpenRaiser/Intern-Atlas huggingface dataset:
Exploratory Data Analysis of Urdu Poetry
Venue: arXiv cs.CL 2021
Year: 2021

The study presented here provides numerical insight into ghazal -- the most appreciated genre in Urdu poetry. Using 48,761 poetic works from 4,754 poets produced over a period of 800 years, this study explores the main features of Urdu ghazal that make it popular and admired more than other forms. A detailed explanation is provided as to the types of words used for expressing love, nature, birds, and flowers etc. Also considered is the way in which the poets addressed their loved ones in their poetry. The style of poetry is numerically analyzed using Multi Dimensional Scaling to reveal the lexical diversity and similarities/differences between the different poetic works that have drawn the attention of critics, such as Iqbal and Ghalib, Mir Taqi Mir and Mir Dard. The analysis produced here is particularly helpful for research in computational stylistics, neurocognitive poetics, and sentiment analysis.
On Submodular Contextual Bandits
Venue: arXiv cs.LG 2021
Year: 2021

We consider the problem of contextual bandits where actions are subsets of a ground set and mean rewards are modeled by an unknown monotone submodular function that belongs to a class $\mathcal{F}$. We allow time-varying matroid constraints to be placed on the feasible sets. Assuming access to an online regression oracle with regret $\mathsf{Reg}(\mathcal{F})$, our algorithm efficiently randomizes around local optima of estimated functions according to the Inverse Gap Weighting strategy. We show that cumulative regret of this procedure with time horizon $n$ scales as $O(\sqrt{n \mathsf{Reg}(\mathcal{F})})$ against a benchmark with a multiplicative factor $1/2$. On the other hand, using the techniques of (Filmus and Ward 2014), we show that an $ε$-Greedy procedure with local randomization attains regret of $O(n^{2/3} \mathsf{Reg}(\mathcal{F})^{1/3})$ against a stronger $(1-e^{-1})$ benchmark.
Counterfactual Fairness in Mortgage Lending via Matching and Randomization
Venue: arXiv cs.LG 2021
Year: 2021

Unfairness in mortgage lending has created generational inequality among racial and ethnic groups in the US. Many studies address this problem, but most existing work focuses on correlation-based techniques. In our work, we use the framework of counterfactual fairness to train fair machine learning models. We propose a new causal graph for the variables available in the Home Mortgage Disclosure Act (HMDA) data. We use a matching-based approach instead of the latent variable modeling approach, because the former approach does not rely on any modeling assumptions. Furthermore, matching provides us with counterfactual pairs in which the race variable is isolated. We first demonstrate the unfairness in mortgage approval and interest rates between African-American and non-Hispanic White sub-populations. Then, we show that having balanced data using matching does not guarantee perfect counterfactual fairness of the machine learning models.
A Game-Theoretic Approach for AI-based Botnet Attack Defence
Venue: arXiv cs.CR 2021
Year: 2021

The new generation of botnets leverages Artificial Intelligent (AI) techniques to conceal the identity of botmasters and the attack intention to avoid detection. Unfortunately, there has not been an existing assessment tool capable of evaluating the effectiveness of existing defense strategies against this kind of AI-based botnet attack. In this paper, we propose a sequential game theory model that is capable to analyse the details of the potential strategies botnet attackers and defenders could use to reach Nash Equilibrium (NE). The utility function is computed under the assumption when the attacker launches the maximum number of DDoS attacks with the minimum attack cost while the defender utilises the maximum number of defense strategies with the minimum defense cost. We conduct a numerical analysis based on a various number of defense strategies involved on different (simulated) cloud-band sizes in relation to different attack success rate values. Our experimental results confirm that the success of defense highly depends on the number of defense strategies used according to careful evaluation of attack rates.
PhishMatch: A Layered Approach for Effective Detection of Phishing URLs
Venue: arXiv cs.CR 2021
Year: 2021

Phishing attacks continue to be a significant threat on the Internet. Prior studies show that it is possible to determine whether a website is phishing or not just by analyzing its URL more carefully. A major advantage of the URL based approach is that it can identify a phishing website even before the web page is rendered in the browser, thus avoiding other potential problems such as cryptojacking and drive-by downloads. However, traditional URL based approaches have their limitations. Blacklist based approaches are prone to zero-hour phishing attacks, advanced machine learning based approaches consume high resources, and other approaches send the URL to a remote server which compromises user's privacy. In this paper, we present a layered anti-phishing defense, PhishMatch, which is robust, accurate, inexpensive, and client-side. We design a space-time efficient Aho-Corasick algorithm for exact string matching and n-gram based indexing technique for approximate string matching to detect various cybersquatting techniques in the phishing URL. To reduce false positives, we use a global whitelist and personalized user whitelists. We also determine the context in which the URL is visited and use that information to classify the input URL more accurately. The last component of PhishMatch involves a machine learning model and controlled search engine queries to classify the URL. A prototype plugin of PhishMatch, developed for the Chrome browser, was found to be fast and lightweight. Our evaluation shows that PhishMatch is both efficient and effective.
Construct Informative Triplet with Two-stage Hard-sample Generation
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a robust sample generation scheme to construct informative triplets. The proposed hard sample generation is a two-stage synthesis framework that produces hard samples through effective positive and negative sample generators in two stages, respectively. The first stage stretches the anchor-positive pairs with piecewise linear manipulation and enhances the quality of generated samples by skillfully designing a conditional generative adversarial network to lower the risk of mode collapse. The second stage utilizes an adaptive reverse metric constraint to generate the final hard samples. Extensive experiments on several benchmark datasets verify that our method achieves superior performance than the existing hard-sample generation algorithms. Besides, we also find that our proposed hard sample generation method combining the existing triplet mining strategies can further boost the deep metric learning performance.
Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitation
Venue: arXiv cs.RO 2021
Year: 2021

In few-shot imitation learning (FSIL), using behavioral cloning (BC) to solve unseen tasks with few expert demonstrations becomes a popular research direction. The following capabilities are essential in robotics applications: (1) Behaving in compound tasks that contain multiple stages. (2) Retrieving knowledge from few length-variant and misalignment demonstrations. (3) Learning from a different expert. No previous work can achieve these abilities at the same time. In this work, we conduct FSIL problem under the union of above settings and introduce a novel stage conscious attention network (SCAN) to retrieve knowledge from few demonstrations simultaneously. SCAN uses an attention module to identify each stage in length-variant demonstrations. Moreover, it is designed under demonstration-conditioned policy that learns the relationship between experts and agents. Experiment results show that SCAN can learn from different experts without fine-tuning and outperform baselines in complicated compound tasks with explainable visualization.
An Annotated Video Dataset for Computing Video Memorability
Venue: arXiv cs.CV 2021
Year: 2021

Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1,275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant's ability to recall having seen the video previously when shown a collection of videos. The recognition tasks were performed on videos seen within the previous few minutes for short-term memorability and within the previous 24 to 72 hours for long-term memorability. Data includes the reaction times for each recognition of each video. Associated with each video are text descriptions (captions) as well as a collection of image-level features applied to 3 frames extracted from each video (start, middle and end). Video-level features are also provided. The dataset was used in the Video Memorability task as part of the MediaEval benchmark in 2020.
Ergodic Capacity of IRS-Assisted MIMO Systems with Correlation and Practical Phase-Shift Modeling
Venue: arXiv cs.IT 2021
Year: 2021

We focus on the maximization of the exact ergodic capacity (EC) of a point-to-point multiple-input multiple-output (MIMO) system assisted by an intelligent reflecting surface (IRS). In addition, we account for the effects of correlated Rayleigh fading and the intertwinement between the amplitude and the phase shift of the reflecting coefficient of each IRS element, which are usually both neglected despite their presence in practice. Random matrix theory tools allow to derive the probability density function (PDF) of the cascaded channel in closed form, and subsequently, the EC, which depend only on the large-scale statistics and the phase shifts. Notably, we optimize the EC with respect to the phase shifts with low overhead, i.e., once per several coherence intervals instead of the burden of frequent necessary optimization required by expressions being dependent on instantaneous channel information. Monte-Carlo (MC) simulations verify the analytical results and demonstrate the insightful interplay among the key parameters and their impact on the EC.
Generalized Binary Search Network for Highly-Efficient Multi-View Stereo
Venue: arXiv cs.CV 2021
Year: 2021

Multi-view Stereo (MVS) with known camera parameters is essentially a 1D search problem within a valid depth range. Recent deep learning-based MVS methods typically densely sample depth hypotheses in the depth range, and then construct prohibitively memory-consuming 3D cost volumes for depth prediction. Although coarse-to-fine sampling strategies alleviate this overhead issue to a certain extent, the efficiency of MVS is still an open challenge. In this work, we propose a novel method for highly efficient MVS that remarkably decreases the memory footprint, meanwhile clearly advancing state-of-the-art depth prediction performance. We investigate what a search strategy can be reasonably optimal for MVS taking into account of both efficiency and effectiveness. We first formulate MVS as a binary search problem, and accordingly propose a generalized binary search network for MVS. Specifically, in each step, the depth range is split into 2 bins with extra 1 error tolerance bin on both sides. A classification is performed to identify which bin contains the true depth. We also design three mechanisms to respectively handle classification errors, deal with out-of-range samples and decrease the training memory. The new formulation makes our method only sample a very small number of depth hypotheses in each step, which is highly memory efficient, and also greatly facilitates quick training convergence. Experiments on competitive benchmarks show that our method achieves state-of-the-art accuracy with much less memory. Particularly, our method obtains an overall score of 0.289 on DTU dataset and tops the first place on challenging Tanks and Temples advanced dataset among all the learning-based methods. The trained models and code will be released at https://github.com/MiZhenxing/GBi-Net.
Considering user dynamic preferences for mitigating negative effects of long tail in recommender systems
Venue: arXiv cs.IR 2021
Year: 2021

Nowadays, with the increase in the amount of information generated in the webspace, many web service providers try to use recommender systems to personalize their services and make accessing the content convenient. Recommender systems that only try to increase the accuracy (i.e., the similarity of items to users' interest) will face the long tail problem. It means that popular items called short heads appear in the recommendation lists more than others since they have many ratings. However, unpopular items called long-tail items are used less than popular ones as they reduce accuracy. Other studies that solve the long-tail problem consider users' interests constant while their preferences change over time. We suggest that users' dynamic preferences should be taken into account to prevent the loss of accuracy when we use long-tail items in recommendation lists. This study shows that the two reasons lie in the following: 1) Users rate for different proportions of popular and unpopular items over time. 2) Users of all ages have various interests in popular and unpopular items. As a result, recommendation lists can be created over time with a different portion of long-tail and short-head items. Besides, we predict the age of users based on item ratings to use more long-tail items. The results show that by considering these two reasons, the accuracy of recommendation lists reaches 91%. At the same time, the long tail problem is better improved than other related research and provides better diversity in recommendation lists in the long run.
Gated2Gated: Self-Supervised Depth Estimation from Gated Images
Venue: arXiv cs.CV 2021
Year: 2021

Gated cameras hold promise as an alternative to scanning LiDAR sensors with high-resolution 3D depth that is robust to back-scatter in fog, snow, and rain. Instead of sequentially scanning a scene and directly recording depth via the photon time-of-flight, as in pulsed LiDAR sensors, gated imagers encode depth in the relative intensity of a handful of gated slices, captured at megapixel resolution. Although existing methods have shown that it is possible to decode high-resolution depth from such measurements, these methods require synchronized and calibrated LiDAR to supervise the gated depth decoder -- prohibiting fast adoption across geographies, training on large unpaired datasets, and exploring alternative applications outside of automotive use cases. In this work, we fill this gap and propose an entirely self-supervised depth estimation method that uses gated intensity profiles and temporal consistency as a training signal. The proposed model is trained end-to-end from gated video sequences, does not require LiDAR or RGB data, and learns to estimate absolute depth values. We take gated slices as input and disentangle the estimation of the scene albedo, depth, and ambient light, which are then used to learn to reconstruct the input slices through a cyclic loss. We rely on temporal consistency between a given frame and neighboring gated slices to estimate depth in regions with shadows and reflections. We experimentally validate that the proposed approach outperforms existing supervised and self-supervised depth estimation methods based on monocular RGB and stereo images, as well as supervised methods based on gated images.
Deep Learning on Mobile Devices Through Neural Processing Units and Edge Computing
Venue: arXiv cs.NI 2021
Year: 2021

Deep Neural Network (DNN) is becoming adopted for video analytics on mobile devices. To reduce the delay of running DNNs, many mobile devices are equipped with Neural Processing Units (NPU). However, due to the resource limitations of NPU, these DNNs have to be compressed to increase the processing speed at the cost of accuracy. To address the low accuracy problem, we propose a Confidence Based Offloading (CBO) framework for deep learning video analytics. The major challenge is to determine when to return the NPU classification result based on the confidence level of running the DNN, and when to offload the video frames to the server for further processing to increase the accuracy. We first identify the problem of using existing confidence scores to make offloading decisions, and propose confidence score calibration techniques to improve the performance. Then, we formulate the CBO problem where the goal is to maximize accuracy under some time constraint, and propose an adaptive solution that determines which frames to offload at what resolution based on the confidence score and the network condition. Through real implementations and extensive evaluations, we demonstrate that the proposed solution can significantly outperform other approaches.
Augmentation-Free Self-Supervised Learning on Graphs
Venue: arXiv cs.LG 2021
Year: 2021

Inspired by the recent success of self-supervised methods applied on images, self-supervised learning on graph structured data has seen rapid growth especially centered on augmentation-based contrastive methods. However, we argue that without carefully designed augmentation techniques, augmentations on graphs may behave arbitrarily in that the underlying semantics of graphs can drastically change. As a consequence, the performance of existing augmentation-based methods is highly dependent on the choice of augmentation scheme, i.e., hyperparameters associated with augmentations. In this paper, we propose a novel augmentation-free self-supervised learning framework for graphs, named AFGRL. Specifically, we generate an alternative view of a graph by discovering nodes that share the local structural information and the global semantics with the graph. Extensive experiments towards various node-level tasks, i.e., node classification, clustering, and similarity search on various real-world datasets demonstrate the superiority of AFGRL. The source code for AFGRL is available at https://github.com/Namkyeong/AFGRL.
Provisioning Fog Services to 3GPP Subscribers: Authentication and Application Mobility
Venue: arXiv cs.CR 2021
Year: 2021

Multi-Access Edge computing (MEC) and Fog computing provide services to subscribers at low latency. There is a need to form a federation among 3GPP MEC and fog to provide better coverage to 3GPP subscribers. This federation gives rise to two issues - third-party authentication and application mobility - for continuous service during handover from 3GPP MEC to fog without re-authentication. In this paper, we propose: 1) a proxy-based state transfer and third-party authentication (PS3A) that uses a transparent proxy to transfer the authentication and application state information, and 2) a token-based state transfer and proxy-based third-party authentication (TSP3A) that uses the proxy to transfer the authentication information and tokens to transfer the application state from 3GPP MEC to the fog. The proxy is kept transparent with virtual counterparts, to avoid any changes to the existing 3GPP MEC and fog architectures. We implemented these solutions on a testbed and results show that PS3A and TSP3A provide authentication within 0.345-2.858s for a 0-100 Mbps proxy load. The results further show that TSP3A provides application mobility while taking 40-52% less time than PS3A using state tokens. TSP3A and PS3A also reduce the service interruption latency by 82.4% and 84.6%, compared to the cloud-based service via tokens and prefetching.
Design Trade-offs for a Robust Dynamic Hybrid Hash Join (Extended Version)
Venue: arXiv cs.DB 2021
Year: 2021

The Join operator, as one of the most expensive and commonly used operators in database systems, plays a substantial role in Database Management System (DBMS) performance. Among the many different Join algorithms studied over the last decades, Hybrid Hash Join (HHJ) has proven to be one of the most efficient and widely-used join algorithms. While the performance of HHJ depends largely on accurate statistics and information about the input relations, it may not always be practical or possible for a system to have such information available. The design of HHJ depends on many details to perform well. This paper is an experimental and analytical study of the trade-offs in designing a robust and dynamic HHJ operator. We revisit the design and optimization techniques suggested by previous studies through extensive experiments, comparing them with other algorithms designed by us or used in related studies. We explore the impact of the number of partitions on the performance of HHJ and propose a lower bound and a default value for the number of partitions. We continue by designing and evaluating different partition insertion techniques to maximize memory utilization with the least CPU cost. In addition, we consider a comprehensive set of algorithms for dynamically selecting a partition to spill and compare the results against previously published studies. We then present two alternative growth policies for spilled partitions and study their effectiveness using experimental and model-based analyses. These algorithms have been implemented in the context of Apache AsterixDB and evaluated under different scenarios such as variable record sizes, different distributions of join attributes, and different storage types, including HDD, SSD, and Amazon Elastic Block Store (Amazon EBS).
Exploring Complicated Search Spaces with Interleaving-Free Sampling
Venue: arXiv cs.LG 2021
Year: 2021

The existing neural architecture search algorithms are mostly working on search spaces with short-distance connections. We argue that such designs, though safe and stable, obstacles the search algorithms from exploring more complicated scenarios. In this paper, we build the search algorithm upon a complicated search space with long-distance connections, and show that existing weight-sharing search algorithms mostly fail due to the existence of \textbf{interleaved connections}. Based on the observation, we present a simple yet effective algorithm named \textbf{IF-NAS}, where we perform a periodic sampling strategy to construct different sub-networks during the search procedure, avoiding the interleaved connections to emerge in any of them. In the proposed search space, IF-NAS outperform both random sampling and previous weight-sharing search algorithms by a significant margin. IF-NAS also generalizes to the micro cell-based spaces which are much easier. Our research emphasizes the importance of macro structure and we look forward to further efforts along this direction.