OpenRaiser/Intern-Atlas huggingface dataset:
Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information
Venue: arXiv cs.CV 2020
Year: 2020

Recently, with the availability of cost-effective depth cameras coupled with real-time skeleton estimation, the interest in skeleton-based human action recognition is renewed. Most of the existing skeletal representation approaches use either the joint location or the dynamics model. Differing from the previous studies, we propose a new method called Denoising Autoencoder with Temporal and Categorical Constraints (DAE_CTC)} to study the skeletal representation in a view of skeleton reconstruction. Based on the concept of learning under privileged information, we integrate action categories and temporal coordinates into a stacked denoising autoencoder in the training phase, to preserve category and temporal feature, while learning the hidden representation from a skeleton. Thus, we are able to improve the discriminative validity of the hidden representation. In order to mitigate the variation resulting from temporary misalignment, a new method of temporal registration, called Locally-Warped Sequence Registration (LWSR), is proposed for registering the sequences of inter- and intra-class actions. We finally represent the sequences using a Fourier Temporal Pyramid (FTP) representation and perform classification using a combination of LWSR registration, FTP representation, and a linear Support Vector Machine (SVM). The experimental results on three action data sets, namely MSR-Action3D, UTKinect-Action, and Florence3D-Action, show that our proposal performs better than many existing methods and comparably to the state of the art.
SynCGAN: Using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images
Venue: arXiv cs.CV 2020
Year: 2020

One of the most challenging aspects of medical image analysis is the lack of a high quantity of annotated data. This makes it difficult for deep learning algorithms to perform well due to a lack of variations in the input space. While generative adversarial networks have shown promise in the field of synthetic data generation, but without a carefully designed prior the generation procedure can not be performed well. In the proposed approach we have demonstrated the use of automatically generated segmentation masks as learnable class-specific priors to guide a conditional GAN for the generation of patho-realistic samples for cytology image. We have observed that augmentation of data using the proposed pipeline called "SynCGAN" improves the performance of state of the art classifiers such as ResNet-152, DenseNet-161, Inception-V3 significantly.
Explaining Away Attacks Against Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

We investigate the problem of identifying adversarial attacks on image-based neural networks. We present intriguing experimental results showing significant discrepancies between the explanations generated for the predictions of a model on clean and adversarial data. Utilizing this intuition, we propose a framework which can identify whether a given input is adversarial based on the explanations given by the model. Code for our experiments can be found here: https://github.com/seansaito/Explaining-Away-Attacks-Against-Neural-Networks.
Jiskefet, a bookkeeping application for ALICE
Venue: arXiv cs.HC 2020
Year: 2020

A new bookkeeping system called Jiskefet is being developed for A Large Ion Collider Experiment (ALICE) during Long Shutdown 2, to be in production until the end of LHC Run 4 (2029). Jiskefet unifies two functionalities: a) gathering, storing and presenting metadata associated with the operations of the ALICE experiment and b) tracking the asynchronous processing of the physics data. It will replace the existing ALICE Electronic Logbook and AliMonitor, allowing for a technology refresh and the inclusion of new features based on the experience collected during Run 1 and Run 2. The front end leverages web technologies much in use nowadays such as TypeScript and NodeJS and is adaptive to various clients such as tablets, mobile devices and other screens. The back end includes an OpenAPI specification based REST API and a relational database. This paper will describe the organization of the work done by various student teams who work on Jiskefet in sequential and parallel semesters and how continuity is guaranteed by using guidelines on coding, documentation and development. It will also describe the current status of the development, the initial experience in detector stand-alone commissioning setups and the future plans.
Dependency-Aware Software Requirements Selection using Fuzzy Graphs and Integer Programming
Venue: arXiv cs.SE 2020
Year: 2020

Software requirements selection aims to find an optimal subset of the requirements with the highest value while respecting the project constraints. But the value of a requirement may depend on the presence or absence of other requirements in the optimal subset. Such Value Dependencies, however, are imprecise and hard to capture. In this paper, we propose a method based on integer programming and fuzzy graphs to account for value dependencies and their imprecision in software requirements selection. The proposed method, referred to as Dependency-Aware Software Requirements Selection (DARS), is comprised of three components: (i) an automated technique for the identification of value dependencies from user preferences, (ii) a modeling technique based on fuzzy graphs that allows for capturing the imprecision of value dependencies, and (iii) an Integer Linear Programming (ILP) model that takes into account user preferences and value dependencies identified from those preferences to reduce the risk of value loss in software projects. Our work is verified by studying a real-world software project. The results show that our proposed method reduces the value loss in software projects and is scalable to large requirement sets.
Cross-modal Multi-task Learning for Graphic Recognition of Caricature Face
Venue: arXiv cs.CV 2020
Year: 2020

Face recognition of realistic visual images has been well studied and made a significant progress in the recent decade. Unlike the realistic visual images, the face recognition of the caricatures is far from the performance of the visual images. This is largely due to the extreme non-rigid distortions of the caricatures introduced by exaggerating the facial features to strengthen the characters. The heterogeneous modalities of the caricatures and the visual images result the caricature-visual face recognition is a cross-modal problem. In this paper, we propose a method to conduct caricature-visual face recognition via multi-task learning. Rather than the conventional multi-task learning with fixed weights of tasks, this work proposes an approach to learn the weights of tasks according to the importance of tasks. The proposed multi-task learning with dynamic tasks weights enables to appropriately train the hard task and easy task instead of being stuck in the over-training easy task as conventional methods. The experimental results demonstrate the effectiveness of the proposed dynamic multi-task learning for cross-modal caricature-visual face recognition. The performances on the datasets CaVI and WebCaricature show the superiority over the state-of-art methods.
Topological Effects on Attacks Against Vertex Classification
Venue: arXiv cs.LG 2020
Year: 2020

Vertex classification is vulnerable to perturbations of both graph topology and vertex attributes, as shown in recent research. As in other machine learning domains, concerns about robustness to adversarial manipulation can prevent potential users from adopting proposed methods when the consequence of action is very high. This paper considers two topological characteristics of graphs and explores the way these features affect the amount the adversary must perturb the graph in order to be successful. We show that, if certain vertices are included in the training set, it is possible to substantially an adversary's required perturbation budget. On four citation datasets, we demonstrate that if the training set includes high degree vertices or vertices that ensure all unlabeled nodes have neighbors in the training set, we show that the adversary's budget often increases by a substantial factor---often a factor of 2 or more---over random training for the Nettack poisoning attack. Even for especially easy targets (those that are misclassified after just one or two perturbations), the degradation of performance is much slower, assigning much lower probabilities to the incorrect classes. In addition, we demonstrate that this robustness either persists when recently proposed defenses are applied, or is competitive with the resulting performance improvement for the defender.
SAHRTA: A Supervisory-Based Adaptive Human-Robot Teaming Architecture
Venue: arXiv cs.RO 2020
Year: 2020

Supervisory-based human-robot teams are deployed in various dynamic and extreme environments (e.g., space exploration). Achieving high task performance in such environments is critical, as a mistake may lead to significant monetary loss or human injury. Task performance may be augmented by adapting the supervisory interface's interactions or autonomy levels based on the human supervisor's workload level, as workload is related to task performance. Typical adaptive systems rely solely on the human's overall or cognitive workload state to select what adaptation strategy to implement; however, overall workload encompasses many dimensions (i.e., cognitive, physical, visual, auditory, and speech) called workload components. Selecting an appropriate adaptation strategy based on a complete human workload state (rather than a single workload dimension) may allow for more impactful adaptations that ensure high task performance. A Supervisory-Based Adaptive Human-Robot Teaming Architecture (SAHRTA) that selects an appropriate level of autonomy or system interaction based on a complete real-time multi-dimensional workload estimate and predicted future task performance is introduced. SAHRTA was shown to improve overall task performance in a physically expanded version of the NASA Multi-Attribute Task Battery.
Control-flow Flattening Preserves the Constant-Time Policy (Extended Version)
Venue: arXiv cs.PL 2020
Year: 2020

Obfuscating compilers protect a software by obscuring its meaning and impeding the reconstruction of its original source code. The typical concern when defining such compilers is their robustness against reverse engineering and the performance of the produced code. Little work has been done in studying whether the security properties of a program are preserved under obfuscation. In this paper we start addressing this problem: we consider control-flow flattening, a popular obfuscation technique used in industrial compilers, and a specific security policy, namely constant-time. We prove that this obfuscation preserves the policy, i.e., that every program satisfying the policy still does after the transformation.
Top-1 Solution of Multi-Moments in Time Challenge 2019
Venue: arXiv cs.CV 2020
Year: 2020

In this technical report, we briefly introduce the solutions of our team 'Efficient' for the Multi-Moments in Time challenge in ICCV 2019. We first conduct several experiments with popular Image-Based action recognition methods TRN, TSN, and TSM. Then a novel temporal interlacing network is proposed towards fast and accurate recognition. Besides, the SlowFast network and its variants are explored. Finally, we ensemble all the above models and achieve 67.22\% on the validation set and 60.77\% on the test set, which ranks 1st on the final leaderboard. In addition, we release a new code repository for video understanding which unifies state-of-the-art 2D and 3D methods based on PyTorch. The solution of the challenge is also included in the repository, which is available at https://github.com/Sense-X/X-Temporal.
A Deductive Verification Framework for Circuit-building Quantum Programs
Venue: arXiv cs.PL 2020
Year: 2020

While recent progress in quantum hardware open the door for significant speedup in certain key areas, quantum algorithms are still hard to implement right, and the validation of such quantum programs is a challenge. Early attempts either suffer from the lack of automation or parametrized reasoning, or target high-level abstract algorithm description languages far from the current de facto consensus of circuit-building quantum programming languages. As a consequence, no significant quantum algorithm implementation has been currently verified in a scale-invariant manner. We propose Qbricks, the first formal verification environment for circuit-building quantum programs, featuring clear separation between code and proof, parametric specifications and proofs, high degree of proof automation and allowing to encode quantum programs in a natural way, i.e. close to textbook style. Qbricks builds on best practice of formal verification for the classical case and tailor them to the quantum case: we bring a new domain-specific circuit-building language for quantum programs, namely Qbricks-DSL, together with a new logical specification language Qbricks-Spec and a dedicated Hoare-style deductive verification rule named Hybrid Quantum Hoare Logic. Especially, we introduce and intensively build upon HOPS, a higher-order extension of the recent path-sum symbolic representation, used for both specification and automation. To illustrate the opportunity of Qbricks, we implement the first verified parametric implementations of several famous and non-trivial quantum algorithms, including the quantum part of Shor integer factoring (Order Finding - Shor-OF), quantum phase estimation (QPE) - a basic building block of many quantum algorithms, and Grover search. These breakthroughs were amply facilitated by the specification and automated deduction principles introduced within Qbricks.
Stackelberg Equilibria for Two-Player Network Routing Games on Parallel Networks
Venue: arXiv cs.GT 2020
Year: 2020

We consider a two-player zero-sum network routing game in which a router wants to maximize the amount of legitimate traffic that flows from a given source node to a destination node and an attacker wants to block as much legitimate traffic as possible by flooding the network with malicious traffic. We address scenarios with asymmetric information, in which the router must reveal its policy before the attacker decides how to distribute the malicious traffic among the network links, which is naturally modeled by the notion of Stackelberg equilibria. The paper focuses on parallel networks, and includes three main contributions: we show that computing the optimal attack policy against a given routing policy is an NP-hard problem; we establish conditions under which the Stackelberg equilibria lead to no regret; and we provide a metric that can be used to quantify how uncertainty about the attacker's capabilities limits the router's performance.
ÆGIS: Shielding Vulnerable Smart Contracts Against Attacks
Venue: arXiv cs.CR 2020
Year: 2020

In recent years, smart contracts have suffered major exploits, costing millions of dollars. Unlike traditional programs, smart contracts are deployed on a blockchain. As such, they cannot be modified once deployed. Though various tools have been proposed to detect vulnerable smart contracts, the majority fails to protect vulnerable contracts that have already been deployed on the blockchain. Only very few solutions have been proposed so far to tackle the issue of post-deployment. However, these solutions suffer from low precision and are not generic enough to prevent any type of attack. In this work, we introduce ÆGIS, a dynamic analysis tool that protects smart contracts from being exploited during runtime. Its capability of detecting new vulnerabilities can easily be extended through so-called attack patterns. These patterns are written in a domain-specific language that is tailored to the execution model of Ethereum smart contracts. The language enables the description of malicious control and data flows. In addition, we propose a novel mechanism to streamline and speed up the process of managing attack patterns. Patterns are voted upon and stored via a smart contract, thus leveraging the benefits of tamper-resistance and transparency provided by the blockchain. We compare ÆGIS to current state-of-the-art tools and demonstrate that our solution achieves higher precision in detecting attacks. Finally, we perform a large-scale analysis on the first 4.5 million blocks of the Ethereum blockchain, thereby confirming the occurrences of well reported and yet unreported attacks in the wild.
Calibration-Free Positioning Technique Using Wi-Fi Ranging and Built-in Sensors of Mobile Devices
Venue: arXiv cs.NI 2020
Year: 2020

As positioning solutions integrate multiple components to improve accuracy, the number of parameters that require calibration has increased. This paper studies a calibration-free positioning technique using Wi-Fi ranging and pedestrian dead reckoning (PDR), where every parameter in the system is optimized in real-time. This significantly decreases the time and effort required to perform manual calibration procedures and enables the positioning solution to achieve robust performance in various situations. Additionally, this paper studies an efficient way of performing irregular Wi-Fi ranging procedures to improve battery life and network performance of mobile devices. The positioning performance of the proposed method was verified using a real-time Android application on several mobile devices under a large indoor office environment. Without any calibration, the proposed method achieved up to 1.38 m average positioning accuracy for received signal strength (RSS)-based ranging scenarios, which differs only by 30 cm from the benchmark assuming perfect calibration. In addition, the proposed method achieved up to 1.04 m accuracy for round trip time (RTT)-based ranging scenarios with a 40 MHz bandwidth configuration, which differs only by 10 cm from the benchmark.
Comb Diagrams for Discrete-Time Feedback
Venue: arXiv cs.LO 2020
Year: 2020

The data for many useful bidirectional constructions in applied category theory (optics, learners, games, quantum combs) can be expressed in terms of diagrams containing "holes" or "incomplete parts", sometimes known as comb diagrams. We give a possible formalization of what these circuits with incomplete parts represent in terms of symmetric monoidal categories, using the dinaturality equivalence relations arising from a coend. Our main idea is to extend this formal description to allow for infinite circuits with holes indexed by the natural numbers. We show how infinite combs over an arbitrary symmetric monoidal category form again a symmetric monoidal category where notions of delay and feedback can be considered. The constructions presented here are still preliminary work.
An R Package for generating covariance matrices for maximum-entropy sampling from precipitation chemistry data
Venue: arXiv cs.MS 2020
Year: 2020

We present an open-source R package (MESgenCov v 0.1.0) for temporally fitting multivariate precipitation chemistry data and extracting a covariance matrix for use in the MESP (maximum-entropy sampling problem). We provide multiple functionalities for modeling and model assessment. The package is tightly coupled with NADP/NTN (National Atmospheric Deposition Program / National Trends Network) data from their set of 379 monitoring sites, 1978--present. The user specifies the sites, chemicals, and time period desired, fits an appropriate user-specified univariate model for each site and chemical selected, and the package produces a covariance matrix for use by MESP algorithms.
Investigating Error Injection to Enhance the Effectiveness of Mobile Text Entry Studies of Error Behaviour
Venue: arXiv cs.HC 2020
Year: 2020

During lab studies of text entry methods it is typical to observer very few errors in participants' typing - users tend to type very carefully in labs. This is a problem when investigating methods to support error awareness or correction as support mechanisms are not tested. We designed a novel evaluation method based around injection of errors into the users' typing stream and report two user studies on the effectiveness of this technique. Injection allowed us to observe a larger number of instances and more diverse types of error correction behaviour than would normally be possible in a single study, without having a significant impact on key input behaviour characteristics. Qualitative feedback from both studies suggests that our injection algorithm was successful in creating errors that appeared realistic to participants. The use of error injection shows promise for the investigation of error correction behaviour in text entry studies.
Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning
Venue: arXiv cs.AI 2020
Year: 2020

Smart home environments equipped with distributed sensor networks are capable of helping people by providing services related to health, emergency detection or daily routine management. A backbone to these systems relies often on the system's ability to track and detect activities performed by the users in their home. Despite the continuous progress in the area of activity recognition in smart homes, many systems make a strong underlying assumption that the number of occupants in the home at any given moment of time is always known. Estimating the number of persons in a Smart Home at each time step remains a challenge nowadays. Indeed, unlike most (crowd) counting solution which are based on computer vision techniques, the sensors considered in a Smart Home are often very simple and do not offer individually a good overview of the situation. The data gathered needs therefore to be fused in order to infer useful information. This paper aims at addressing this challenge and presents a probabilistic approach able to estimate the number of persons in the environment at each time step. This approach works in two steps: first, an estimate of the number of persons present in the environment is done using a Constraint Satisfaction Problem solver, based on the topology of the sensor network and the sensor activation pattern at this time point. Then, a Hidden Markov Model refines this estimate by considering the uncertainty related to the sensors. Using both simulated and real data, our method has been tested and validated on two smart homes of different sizes and configuration and demonstrates the ability to accurately estimate the number of inhabitants.
Sky High 5G: New Radio for Air-to-Ground Communications
Venue: arXiv cs.NI 2020
Year: 2020

Today, mobile operators are starting to deploy Fifth-Generation (5G) networks to expand the coverage ubiquity of broadband wireless service. In contrast, in-flight connectivity remains limited and its quality of service does not always meet the expectations. Embracing 5G New Radio (NR) in Air-to-Ground (A2G) communication systems can help narrow the gap between airborne and ground connectivity. In this article, we focus on 5G NR based direct A2G communications. We first provide an overview of the existing A2G systems which are based on earlier generations of mobile technologies. Then we confirm the feasibility of NR A2G systems with a performance study in a range of bands from below 7 GHz to millimeter wave frequencies. The results show that NR A2G systems can provide significantly improved data rates for in-flight connectivity. We also identify the major challenges associated with NR A2G communications, discuss enhancements to counteract the challenges, and point out fruitful avenues for future research.