OpenRaiser/Intern-Atlas huggingface dataset:
Rich-Item Recommendations for Rich-Users: Exploiting Dynamic and Static Side Information
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we study the problem of recommendation system where the users and items to be recommended are rich data structures with multiple entity types and with multiple sources of side-information in the form of graphs. We provide a general formulation for the problem that captures the complexities of modern real-world recommendations and generalizes many existing formulations. In our formulation, each user/document that requires a recommendation and each item or tag that is to be recommended, both are modeled by a set of static entities and a dynamic component. The relationships between entities are captured by several weighted bipartite graphs. To effectively exploit these complex interactions and learn the recommendation model, we propose MEDRES- a multiple graph-CNN based novel deep-learning architecture. MEDRES uses AL-GCN, a novel graph convolution network block, that harnesses strong representative features from the underlying graphs. Moreover, in order to capture highly heterogeneous engagement of different users with the system and constraints on the number of items to be recommended, we propose a novel ranking metric pAp@k along with a method to optimize the metric directly. We demonstrate effectiveness of our method on two benchmarks: a) citation data, b) Flickr data. In addition, we present two real-world case studies of our formulation and the MEDRES architecture. We show how our technique can be used to naturally model the message recommendation problem and the teams recommendation problem in the Microsoft Teams (MSTeams) product and demonstrate that it is 5-6% points more accurate than the production-grade models.
Automated Proof of Bell-LaPadula Security Properties
Venue: arXiv cs.SE 2020
Year: 2020

Almost fifty years ago, D.E. Bell and L. LaPadula published the first formal model of a secure system, known today as the Bell-LaPadula (BLP) model. BLP is described as a state machine by means of first-order logic and set theory. The authors also formalize two state invariants known as security condition and *-property. Bell and LaPadula prove that all the state transitions preserve these invariants. In this paper we present a fully automated proof of the security condition and the *-property for all the model operations. The model and the proofs are coded in the {log} tool. As far as we know this is the first time such proofs are automated. Besides, we show that the {log} model is also an executable prototype. Therefore we are providing an automatically verified executable prototype of BLP.
Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
Venue: arXiv cs.LG 2020
Year: 2020

The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and proteins, and solves it with Graph Convolutional Networks (GCNs). However, due to the complex relationships in POSE, this method has high computational cost and memory demand. This paper proposes a flexible Tri-graph Information Propagation (TIP) model that operates on three subgraphs to learn representations progressively by propagation from protein-protein graph to drug-drug graph via protein-drug graph. Experiments show that TIP improves accuracy by 7%+, time efficiency by 83$\times$, and space efficiency by 3$\times$.
Path Query Data Structures in Practice
Venue: arXiv cs.DS 2020
Year: 2020

We perform experimental studies on data structures that answer path median, path counting, and path reporting queries in weighted trees. These query problems generalize the well-known range median query problem in arrays, as well as the $2d$ orthogonal range counting and reporting problems in planar point sets, to tree structured data. We propose practical realizations of the latest theoretical results on path queries. Our data structures, which use tree extraction, heavy-path decomposition and wavelet trees, are implemented in both succinct and pointer-based form. Our succinct data structures are further specialized to be plain or entropy-compressed. Through experiments on large sets, we show that succinct data structures for path queries may present a viable alternative to standard pointer-based realizations, in practical scenarios. Compared to na{ï}ve approaches that compute the answer by explicit traversal of the query path, our succinct data structures are several times faster in path median queries and perform comparably in path counting and path reporting queries, while being several times more space-efficient. Plain pointer-based realizations of our data structures, requiring a few times more space than the na{ï}ve ones, yield up to $100$-times speed-up over them.
Systematic Review of Approaches to Improve Peer Assessment at Scale
Venue: arXiv cs.CY 2020
Year: 2020

Peer Assessment is a task of analysis and commenting on student's writing by peers, is core of all educational components both in campus and in MOOC's. However, with the sheer scale of MOOC's & its inherent personalised open ended learning, automatic grading and tools assisting grading at scale is highly important. Previously we presented survey on tasks of post classification, knowledge tracing and ended with brief review on Peer Assessment (PA), with some initial problems. In this review we shall continue review on PA from perspective of improving the review process itself. As such rest of this review focus on three facets of PA namely Auto grading and Peer Assessment Tools (we shall look only on how peer reviews/auto-grading is carried), strategies to handle Rogue Reviews, Peer Review Improvement using Natural Language Processing. The consolidated set of papers and resources so used are released in https://github.com/manikandan-ravikiran/cs6460-Survey-2.
Regularization Helps with Mitigating Poisoning Attacks: Distributionally-Robust Machine Learning Using the Wasserstein Distance
Venue: arXiv cs.LG 2020
Year: 2020

We use distributionally-robust optimization for machine learning to mitigate the effect of data poisoning attacks. We provide performance guarantees for the trained model on the original data (not including the poison records) by training the model for the worst-case distribution on a neighbourhood around the empirical distribution (extracted from the training dataset corrupted by a poisoning attack) defined using the Wasserstein distance. We relax the distributionally-robust machine learning problem by finding an upper bound for the worst-case fitness based on the empirical sampled-averaged fitness and the Lipschitz-constant of the fitness function (on the data for given model parameters) as regularizer. For regression models, we prove that this regularizer is equal to the dual norm of the model parameters. We use the Wine Quality dataset, the Boston Housing Market dataset, and the Adult dataset for demonstrating the results of this paper.
Shifted Pruning for List Decoding of Polar Codes
Venue: arXiv cs.IT 2020
Year: 2020

In successive cancellation list (SCL) decoding, the tree pruning operation retains the L best paths with respect to metric at every decoding step. However, the correct path might be among the L worst paths due to imposed penalties. In this case, the correct path is pruned and the decoding process fails. In this work, we propose a scheme for additional decoding attempts when decoding fails, in which the pruning window does not necessarily select the L best paths, but this window is shifted between positions 1 and 2L in the sorted list. In the simplest form, the L worst paths are selected at the decoding step where the probability of elimination of the correct paths is high. Additionally, we generalize the scheme and propose a number of variants such as constrained shifting, nested shifting and shifting under segmented decoding, aiming to reduce the computational complexity. The numerical results for polar codes of length 512 with code rates 0.5 and 0.8 and list sizes L=2, 8, 32, show that the shifted-pruning scheme can provide 0.25-0.5 dB gain in error correction performance, while the average computational complexity approaches the conventional list decoding complexity at practical FER ranges.
Virtual KITTI 2
Venue: arXiv cs.CV 2020
Year: 2020

This paper introduces an updated version of the well-known Virtual KITTI dataset which consists of 5 sequence clones from the KITTI tracking benchmark. In addition, the dataset provides different variants of these sequences such as modified weather conditions (e.g. fog, rain) or modified camera configurations (e.g. rotated by 15 degrees). For each sequence, we provide multiple sets of images containing RGB, depth, class segmentation, instance segmentation, flow, and scene flow data. Camera parameters and poses as well as vehicle locations are available as well. In order to showcase some of the dataset's capabilities, we ran multiple relevant experiments using state-of-the-art algorithms from the field of autonomous driving. The dataset is available for download at https://europe.naverlabs.com/Research/Computer-Vision/Proxy-Virtual-Worlds.
A New Arc-Routing Algorithm Applied to Winter Road Maintenance
Venue: arXiv cs.AI 2020
Year: 2020

This paper studies large scale instances of a fairly general arc-routing problem as well as incorporate practical constraints in particular coming from the scheduling problem of the winter road maintenance (e.g. different priorities for and methods of road maintenance). We develop a new algorithm based on a bin-packing heuristic which is well-scalable and able to solve road networks on thousands of crossroads and road segments in few minutes. Since it is impossible to find an optimal solution for such a large instances to compare it with a result of our algorithm, we also develop techniques to compute lower bounds which are based on Integer Linear Programming and Lazy Constraints.
H-OWAN: Multi-distorted Image Restoration with Tensor 1x1 Convolution
Venue: arXiv cs.CV 2020
Year: 2020

It is a challenging task to restore images from their variants with combined distortions. In the existing works, a promising strategy is to apply parallel "operations" to handle different types of distortion. However, in the feature fusion phase, a small number of operations would dominate the restoration result due to the features' heterogeneity by different operations. To this end, we introduce the tensor 1x1 convolutional layer by imposing high-order tensor (outer) product, by which we not only harmonize the heterogeneous features but also take additional non-linearity into account. To avoid the unacceptable kernel size resulted from the tensor product, we construct the kernels with tensor network decomposition, which is able to convert the exponential growth of the dimension to linear growth. Armed with the new layer, we propose High-order OWAN for multi-distorted image restoration. In the numerical experiments, the proposed net outperforms the previous state-of-the-art and shows promising performance even in more difficult tasks.
On Learning Vehicle Detection in Satellite Video
Venue: arXiv cs.CV 2020
Year: 2020

Vehicle detection in aerial and satellite images is still challenging due to their tiny appearance in pixels compared to the overall size of remote sensing imagery. Classical methods of object detection very often fail in this scenario due to violation of implicit assumptions made such as rich texture, small to moderate ratios between image size and object size. Satellite video is a very new modality which introduces temporal consistency as inductive bias. Approaches for vehicle detection in satellite video use either background subtraction, frame differencing or subspace methods showing moderate performance (0.26 - 0.82 $F_1$ score). This work proposes to apply recent work on deep learning for wide-area motion imagery (WAMI) on satellite video. We show in a first approach comparable results (0.84 $F_1$) on Planet's SkySat-1 LasVegas video with room for further improvement.
Optimal Augmented-Channel Puncturing for Low-Complexity Soft-Output MIMO Detectors
Venue: arXiv cs.IT 2020
Year: 2020

We propose a computationally-efficient soft-output detector for multiple-input multiple-output channels based on augmented channel puncturing in order to reduce tree processing complexity. The proposed detector, dubbed augmented WL detector (AWLD), employs a punctured channel with a special structure derived by triangulizing the original channel in augmented form, followed by Gaussian elimination. We prove that these punctured channels are optimal in maximizing the lower-bound on the achievable information rate (AIR) based on a newly proposed mismatched detection model. We show that the AWLD decomposes into a minimum mean-square error (MMSE) prefilter and channel-gain compensation stages, followed by a regular unaugmented WL detector (WLD). It attains the same performance as the existing AIR partial marginalization (AIR-PM) detector, but with much simpler processing.
HoaxItaly: a collection of Italian disinformation and fact-checking stories shared on Twitter in 2019
Venue: arXiv cs.SI 2020
Year: 2020

We released over 1 million tweets shared during 2019 and containing links to thousands of news articles published on two classes of Italian outlets: (1) disinformation websites, i.e. outlets which have been repeatedly flagged by journalists and fact-checkers for producing low-credibility content such as false news, hoaxes, click-bait, misleading and hyper-partisan stories; (2) fact-checking websites which notably debunk and verify online news and claims. The dataset, which includes also title and body for approximately 37k news articles, is publicly available at https://doi.org/10.7910/DVN/ PGVDHX.
Exploitation and Exploration Analysis of Elitist Evolutionary Algorithms: A Case Study
Venue: arXiv cs.NE 2020
Year: 2020

Known as two cornerstones of problem solving by search, exploitation and exploration are extensively discussed for implementation and application of evolutionary algorithms (EAs). However, only a few researches focus on evaluation and theoretical estimation of exploitation and exploration. Considering that exploitation and exploration are two issues regarding global search and local search, this paper proposes to evaluate them via the success probability and the one-step improvement rate computed in different domains of integration. Then, case studies are performed by analyzing performances of (1+1) random univariate search and (1+1) evolutionary programming on the sphere function and the cheating problem. By rigorous theoretical analysis, we demonstrate that both exploitation and exploration of the investigated elitist EAs degenerate exponentially with the problem dimension $n$. Meanwhile, it is also shown that maximization of exploitation and exploration can be achieved by setting an appropriate value for the standard deviation $σ$ of Gaussian mutation, which is positively related to the distance from the present solution to the center of the promising region.
The benefits of synthetic data for action categorization
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we study the value of using synthetically produced videos as training data for neural networks used for action categorization. Motivated by the fact that texture and background of a video play little to no significant roles in optical flow, we generated simplified texture-less and background-less videos and utilized the synthetic data to train a Temporal Segment Network (TSN). The results demonstrated that augmenting TSN with simplified synthetic data improved the original network accuracy (68.5%), achieving 71.8% on HMDB-51 when adding 4,000 videos and 72.4% when adding 8,000 videos. Also, training using simplified synthetic videos alone on 25 classes of UCF-101 achieved 30.71% when trained on 2500 videos and 52.7% when trained on 5000 videos. Finally, results showed that when reducing the number of real videos of UCF-25 to 10% and combining them with synthetic videos, the accuracy drops to only 85.41%, compared to a drop to 77.4% when no synthetic data is added.
FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation
Venue: arXiv cs.CV 2020
Year: 2020

This paper addresses the importance of full-image supervision for monocular depth estimation. We propose a semi-supervised architecture, which combines both unsupervised framework of using image consistency and supervised framework of dense depth completion. The latter provides full-image depth as supervision for the former. Ego-motion from navigation system is also embedded into the unsupervised framework as output supervision of an inner temporal transform network, making monocular depth estimation better. In the evaluation, we show that our proposed model outperforms other approaches on depth estimation.
SLO-ML: A Language for Service Level Objective Modelling in Multi-cloud Applications
Venue: arXiv cs.DC 2020
Year: 2020

Cloud modelling languages (CMLs) are designed to assist customers in tackling the diversity of services in the cloud market. While many CMLs have been proposed in the literature, they lack practical support for automating the selection of services based on the specific service level objectives of a customer's application. We put forward SLO-ML, a novel and generative CML to capture service level requirements and, subsequently, to select the services to honour customer requirements and generate the deployment code appropriate to these services. We present the architectural design of SLO-ML and the associated broker that realises the deployment operations. We rigorously evaluate SLO-ML using a mixed methods approach. First, we exploit an experimental case study with a group of researchers and developers using a real-world cloud application. We also assess overheads through an exhaustive set of empirical scalability tests. Through expressing the levels of gained productivity and experienced usability, we highlight SLO-ML's profound potential in enabling user-centric cloud brokers. We also discuss limitations as application requirements grow.
Developing an Augmented Reality Tourism App through User-Centred Design (Extended Version)
Venue: arXiv cs.HC 2020
Year: 2020

Augmented Reality (AR) bridges the gap between the physical and virtual world. Through overlaying graphics on natural environments, users can immerse themselves in a tailored environment. This offers great benefits to mobile tourism, where points of interest (POIs) can be annotated on a smartphone screen. While a variety of apps currently exist, usability issues can discourage users from embracing AR. Interfaces can become cluttered with icons, with POI occlusion posing further challenges. In this paper, we use user-centred design (UCD) to develop an AR tourism app. We solicit requirements through a synthesis of domain analysis, tourist observation and semi-structured interviews. Whereas previous user-centred work has designed mock-ups, we iteratively develop a full Android app. This includes overhead maps and route navigation, in addition to a detailed AR browser. The final product is evaluated by 20 users, who participate in a tourism task in a UK city. Users regard the system as usable and intuitive, and suggest the addition of further customisation. We finish by critically analysing the challenges of a user-centred methodology.
Multi-Participant Multi-Class Vertical Federated Learning
Venue: arXiv cs.LG 2020
Year: 2020

Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants. Vertical federated learning (VFL) deals with the case where participants sharing the same sample ID space but having different feature spaces, while label information is owned by one participant. Current studies of VFL only support two participants, and mostly focus on binaryclass logistic regression problems. In this paper, we propose the Multi-participant Multi-class Vertical Federated Learning (MMVFL) framework for multi-class VFL problems involving multiple parties. Extending the idea of multi-view learning (MVL), MMVFL enables label sharing from its owner to other VFL participants in a privacypreserving manner. To demonstrate the effectiveness of MMVFL, a feature selection scheme is incorporated into MMVFL to compare its performance against supervised feature selection and MVL-based approaches. Experiment results on real-world datasets show that MMVFL can effectively share label information among multiple VFL participants and match multi-class classification performance of existing approaches.
AVATAR -- Machine Learning Pipeline Evaluation Using Surrogate Model
Venue: arXiv cs.LG 2020
Year: 2020

The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution.
On the Importance of Word Order Information in Cross-lingual Sequence Labeling
Venue: arXiv cs.CL 2020
Year: 2020

Word order variances generally exist in different languages. In this paper, we hypothesize that cross-lingual models that fit into the word order of the source language might fail to handle target languages. To verify this hypothesis, we investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages. To do so, we reduce the source language word order information fitted to sequence encoders and observe the performance changes. In addition, based on this hypothesis, we propose a new method for fine-tuning multilingual BERT in downstream cross-lingual sequence labeling tasks. Experimental results on dialogue natural language understanding, part-of-speech tagging, and named entity recognition tasks show that reducing word order information fitted to the model can achieve better zero-shot cross-lingual performance. Furthermore, our proposed methods can also be applied to strong cross-lingual baselines, and improve their performances.