OpenRaiser/Intern-Atlas huggingface dataset:
Nondestructive Testing of Composite Fibre Materials with Hyperspectral Imaging : Evaluative Studies in the EU H2020 FibreEUse Project
Venue: arXiv cs.CV 2021
Year: 2021

Through capturing spectral data from a wide frequency range along with the spatial information, hyperspectral imaging (HSI) can detect minor differences in terms of temperature, moisture and chemical composition. Therefore, HSI has been successfully applied in various applications, including remote sensing for security and defense, precision agriculture for vegetation and crop monitoring, food/drink, and pharmaceuticals quality control. However, for condition monitoring and damage detection in carbon fibre reinforced polymer (CFRP), the use of HSI is a relatively untouched area, as existing non-destructive testing (NDT) techniques focus mainly on delivering information about physical integrity of structures but not on material composition. To this end, HSI can provide a unique way to tackle this challenge. In this paper, with the use of a near-infrared HSI camera, applications of HSI for the non-destructive inspection of CFRP products are introduced, taking the EU H2020 FibreEUse project as the background. Technical challenges and solutions on three case studies are presented in detail, including adhesive residues detection, surface damage detection and Cobot based automated inspection. Experimental results have fully demonstrated the great potential of HSI and related vision techniques for NDT of CFRP, especially the potential to satisfy the industrial manufacturing environment.
Control of a fly-mimicking flyer in complex flow using deep reinforcement learning
Venue: arXiv cs.LG 2021
Year: 2021

An integrated framework of computational fluid-structural dynamics (CFD-CSD) and deep reinforcement learning (deep-RL) is developed for control of a fly-scale flexible-winged flyer in complex flow. Dynamics of the flyer in complex flow is highly unsteady and nonlinear, which makes modeling the dynamics challenging. Thus, conventional control methodologies, where the dynamics is modeled, are insufficient for regulating such complicated dynamics. Therefore, in the present study, the integrated framework, in which the whole governing equations for fluid and structure are solved, is proposed to generate a control policy for the flyer. For the deep-RL to successfully learn the control policy, accurate and ample data of the dynamics are required. However, satisfying both the quality and quantity of the data on the intricate dynamics is extremely difficult since, in general, more accurate data are more costly. In the present study, two strategies are proposed to deal with the dilemma. To obtain accurate data, the CFD-CSD is adopted for precisely predicting the dynamics. To gain ample data, a novel data reproduction method is devised, where the obtained data are replicated for various situations while conserving the dynamics. With those data, the framework learns the control policy in various flow conditions and the learned policy is shown to have remarkable performance in controlling the flyer in complex flow fields.
Pirouette: Higher-Order Typed Functional Choreographies
Venue: arXiv cs.PL 2021
Year: 2021

We present Pirouette, a language for typed higher-order functional choreographic programming. Pirouette offers programmers the ability to write a centralized functional program and compile it via endpoint projection into programs for each node in a distributed system. Moreover, Pirouette is defined generically over a (local) language of messages, and lifts guarantees about the message type system to its own. Message type soundness also guarantees deadlock freedom. All of our results are verified in Coq.
ActFloor-GAN: Activity-Guided Adversarial Networks for Human-Centric Floorplan Design
Venue: arXiv cs.GR 2021
Year: 2021

We present a novel two-stage approach for automated floorplan design in residential buildings with a given exterior wall boundary. Our approach has the unique advantage of being human-centric, that is, the generated floorplans can be geometrically plausible, as well as topologically reasonable to enhance resident interaction with the environment. From the input boundary, we first synthesize a human-activity map that reflects both the spatial configuration and human-environment interaction in an architectural space. We propose to produce the human-activity map either automatically by a pre-trained generative adversarial network (GAN) model, or semi-automatically by synthesizing it with user manipulation of the furniture. Second, we feed the human-activity map into our deep framework ActFloor-GAN to guide a pixel-wise prediction of room types. We adopt a re-formulated cycle-consistency constraint in ActFloor-GAN to maximize the overall prediction performance, so that we can produce high-quality room layouts that are readily convertible to vectorized floorplans. Experimental results show several benefits of our approach. First, a quantitative comparison with prior methods shows superior performance of leveraging the human-activity map in predicting piecewise room types. Second, a subjective evaluation by architects shows that our results have compelling quality as professionally-designed floorplans and much better than those generated by existing methods in terms of the room layout topology. Last, our approach allows manipulating the furniture placement, considers the human activities in the environment, and enables the incorporation of user-design preferences.
AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family
Venue: arXiv cs.LG 2021
Year: 2021

State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models. However, widespread use is constrained by device hardware limitations, resulting in a substantial performance gap between state-of-the-art models and those that can be effectively deployed on small devices. While Knowledge Distillation (KD) theoretically enables small student models to emulate larger teacher models, in practice selecting a good student architecture requires considerable human expertise. Neural Architecture Search (NAS) appears as a natural solution to this problem but most approaches can be inefficient, as most of the computation is spent comparing architectures sampled from the same distribution, with negligible differences in performance. In this paper, we propose to instead search for a family of student architectures sharing the property of being good at learning from a given teacher. Our approach AutoKD, powered by Bayesian Optimization, explores a flexible graph-based search space, enabling us to automatically learn the optimal student architecture distribution and KD parameters, while being 20x more sample efficient compared to existing state-of-the-art. We evaluate our method on 3 datasets; on large images specifically, we reach the teacher performance while using 3x less memory and 10x less parameters. Finally, while AutoKD uses the traditional KD loss, it outperforms more advanced KD variants using hand-designed students.
Optimal Approximate Distance Oracle for Planar Graphs
Venue: arXiv cs.DS 2021
Year: 2021

A $(1+ε)$-approximate distance oracle of an edge-weighted graph is a data structure that returns an approximate shortest path distance between any two query vertices up to a $(1+ε)$ factor. Thorup (FOCS 2001, JACM 2004) and Klein (SODA 2002) independently constructed a $(1+ε)$-approximate distance oracle with $O(n\log n)$ space, measured in number of words, and $O(1)$ query time when $G$ is an undirected planar graph with $n$ vertices and $ε$ is a fixed constant. Many follow-up works gave $(1+ε)$-approximate distance oracles with various trade-offs between space and query time. However, improving $O(n\log n)$ space bound without sacrificing query time remains an open problem for almost two decades. In this work, we resolve this problem affirmatively by constructing a $(1+ε)$-approximate distance oracle with optimal $O(n)$ space and $O(1)$ query time for undirected planar graphs and fixed $ε$. We also make substantial progress for planar digraphs with non-negative edge weights. For fixed $ε> 0$, we give a $(1+ε)$-approximate distance oracle with space $o(n\log(Nn))$ and $O(\log\log(Nn)$ query time; here $N$ is the ratio between the largest and smallest positive edge weight. This improves Thorup's (FOCS 2001, JACM 2004) $O(n\log(Nn)\log n)$ space bound by more than a logarithmic factor while matching the query time of his structure. This is the first improvement for planar digraphs in two decades, both in the weighted and unweighted setting.
AGPCNet: Attention-Guided Pyramid Context Networks for Infrared Small Target Detection
Venue: arXiv cs.CV 2021
Year: 2021

Infrared small target detection is an important problem in many fields such as earth observation, military reconnaissance, disaster relief, and has received widespread attention recently. This paper presents the Attention-Guided Pyramid Context Network (AGPCNet) algorithm. Its main components are an Attention-Guided Context Block (AGCB), a Context Pyramid Module (CPM), and an Asymmetric Fusion Module (AFM). AGCB divides the feature map into patches to compute local associations and uses Global Context Attention (GCA) to compute global associations between semantics, CPM integrates features from multi-scale AGCBs, and AFM integrates low-level and deep-level semantics from a feature-fusion perspective to enhance the utilization of features. The experimental results illustrate that AGPCNet has achieved new state-of-the-art performance on two available infrared small target datasets. The source codes are available at https://github.com/Tianfang-Zhang/AGPCNet.
Nonnegative Matrix Factorization to understand Spatio-Temporal Traffic Pattern Variations during COVID-19: A Case Study
Venue: arXiv cs.LG 2021
Year: 2021

Due to the rapid developments in Intelligent Transportation System (ITS) and increasing trend in the number of vehicles on road, abundant of road traffic data is generated and available. Understanding spatio-temporal traffic patterns from this data is crucial and has been effectively helping in traffic plannings, road constructions, etc. However, understanding traffic patterns during COVID-19 pandemic is quite challenging and important as there is a huge difference in-terms of people's and vehicle's travel behavioural patterns. In this paper, a case study is conducted to understand the variations in spatio-temporal traffic patterns during COVID-19. We apply nonnegative matrix factorization (NMF) to elicit patterns. The NMF model outputs are analysed based on the spatio-temporal pattern behaviours observed during the year 2019 and 2020, which is before pandemic and during pandemic situations respectively, in Great Britain. The outputs of the analysed spatio-temporal traffic pattern variation behaviours will be useful in the fields of traffic management in Intelligent Transportation System and management in various stages of pandemic or unavoidable scenarios in-relation to road traffic.
Phish What You Wish
Venue: arXiv cs.CR 2021
Year: 2021

IT professionals have no simple tool to create phishing websites and raise the awareness of users. We developed a prototype that can dynamically mimic websites by using enriched screenshots, which requires no additional programming experience and is simple to set up. The generated websites are functional and remain up-to-date. We found that 98% of the hyperlinks in mimicked websites are functional with our tool, compared to 43% with the best competitor, and only two participants suspected phishing attempts at the time they were performing tasks with our prototype. This work intends to raise awareness for phishing attempts especially with local websites by providing an easy to use prototype to set up such phishing sites.
Grounded Graph Decoding Improves Compositional Generalization in Question Answering
Venue: arXiv cs.CL 2021
Year: 2021

Question answering models struggle to generalize to novel compositions of training patterns, such to longer sequences or more complex test structures. Current end-to-end models learn a flat input embedding which can lose input syntax context. Prior approaches improve generalization by learning permutation invariant models, but these methods do not scale to more complex train-test splits. We propose Grounded Graph Decoding, a method to improve compositional generalization of language representations by grounding structured predictions with an attention mechanism. Grounding enables the model to retain syntax information from the input in thereby significantly improving generalization over complex inputs. By predicting a structured graph containing conjunctions of query clauses, we learn a group invariant representation without making assumptions on the target domain. Our model significantly outperforms state-of-the-art baselines on the Compositional Freebase Questions (CFQ) dataset, a challenging benchmark for compositional generalization in question answering. Moreover, we effectively solve the MCD1 split with 98% accuracy.
Holodeck: Immersive 3D Displays Using Swarms of Flying Light Specks
Venue: arXiv cs.HC 2021
Year: 2021

Unmanned Aerial Vehicles (UAVs) have moved beyond a platform for hobbyists to enable environmental monitoring, journalism, film industry, search and rescue, package delivery, and entertainment. This paper describes 3D displays using swarms of flying light specks, FLSs. An FLS is a small (hundreds of micrometers in size) UAV with one or more light sources to generate different colors and textures with adjustable brightness. A synchronized swarm of FLSs renders an illumination in a pre-specified 3D volume, an FLS display. An FLS display provides true depth, enabling a user to perceive a scene more completely by analyzing its illumination from different angles. An FLS display may either be non-immersive or immersive. Both will support 3D acoustics. Non-immersive FLS displays may be the size of a 1980's computer monitor, enabling a surgical team to observe and control micro robots performing heart surgery inside a patient's body. Immersive FLS displays may be the size of a room, enabling users to interact with objects, e.g., a rock, a teapot. An object with behavior will be constructed using FLS-matters. FLS-matter will enable a user to touch and manipulate an object, e.g., a user may pick up a teapot or throw a rock. An immersive and interactive FLS display will approximate Star Trek's Holodeck. A successful realization of the research ideas presented in this paper will provide fundamental insights into implementing a Holodeck using swarms of FLSs. A Holodeck will transform the future of human communication and perception, and how we interact with information and data. It will revolutionize the future of how we work, learn, play and entertain, receive medical care, and socialize.
Reconstructing Training Data from Diverse ML Models by Ensemble Inversion
Venue: arXiv cs.LG 2021
Year: 2021

Model Inversion (MI), in which an adversary abuses access to a trained Machine Learning (ML) model attempting to infer sensitive information about its original training data, has attracted increasing research attention. During MI, the trained model under attack (MUA) is usually frozen and used to guide the training of a generator, such as a Generative Adversarial Network (GAN), to reconstruct the distribution of the original training data of that model. This might cause leakage of original training samples, and if successful, the privacy of dataset subjects will be at risk if the training data contains Personally Identifiable Information (PII). Therefore, an in-depth investigation of the potentials of MI techniques is crucial for the development of corresponding defense techniques. High-quality reconstruction of training data based on a single model is challenging. However, existing MI literature does not explore targeting multiple models jointly, which may provide additional information and diverse perspectives to the adversary. We propose the ensemble inversion technique that estimates the distribution of original training data by training a generator constrained by an ensemble (or set) of trained models with shared subjects or entities. This technique leads to noticeable improvements of the quality of the generated samples with distinguishable features of the dataset entities compared to MI of a single ML model. We achieve high quality results without any dataset and show how utilizing an auxiliary dataset that's similar to the presumed training data improves the results. The impact of model diversity in the ensemble is thoroughly investigated and additional constraints are utilized to encourage sharp predictions and high activations for the reconstructed samples, leading to more accurate reconstruction of training images.
Exception Handling on Multiparty Interactions
Venue: arXiv cs.PL 2021
Year: 2021

In designing distributed and parallel systems there are several approaches for programming interactions in a multiprocess environment. Usually, these approaches take care only of synchronization or communication in two-party interactions. This paper is concerned with a more general concept: multiparty interactions. In a multiparty interaction, several executing threads somehow "come together" to produce an intermediate and temporary combined state, use this state as a well-defined starting point for some joint activity, and then leave this interaction and continue their separate execution. The concept of multiparty interactions has been investigated by several researchers, but to the best of our knowledge, none have considered how faults in one or more participants of the multiparty interaction can best be dealt with. The goal of this paper is twofold: to show how an existing specification language can be extended in order to allow dependable multiparty interactions (DMIs) to be declared and to present an object-oriented framework for implementing DMIs in distributed systems. To show how our scheme can be used to program a system in which multiparty interactions are more than simple synchronizations or communications, we use a case study based on an industrial production cell model developed by Forschungszentrum Informatik, Karlsruhe, Germany.
An Adaptive Honeypot Configuration, Deployment and Maintenance Strategy
Venue: arXiv cs.CR 2021
Year: 2021

Since honeypots first appeared as an advanced network security concept they suffer from poor deployment and maintenance strategies. State-of-the-Art deployment is a manual process in which the honeypot needs to be configured and maintained by a network administrator. In this paper we present a method for a dynamic honeypot configuration, deployment and maintenance strategy based on machine learning techniques. Our method features an identification mechanism for machines and devices in a network. These entities are analysed and clustered. Based on the clusters, honeypots are intelligently deployed in the network. The proposed method needs no configuration and maintenance and is therefore a major advantage for the honeypot technology in modern network security.
Automatic Program Repair with OpenAI's Codex: Evaluating QuixBugs
Venue: arXiv cs.SE 2021
Year: 2021

OpenAI's Codex, a GPT-3 like model trained on a large code corpus, has made headlines in and outside of academia. Given a short user-provided description, it is capable of synthesizing code snippets that are syntactically and semantically valid in most cases. In this work, we want to investigate whether Codex is able to localize and fix bugs, a task of central interest in the field of automated program repair. Our initial evaluation uses the multi-language QuixBugs benchmark (40 bugs in both Python and Java). We find that, despite not being trained for APR, Codex is surprisingly effective, and competitive with recent state of the art techniques. Our results also show that Codex is slightly more successful at repairing Python than Java.
Space-Time Block Coded Spatial Modulation for Indoor Visible Light Communications
Venue: arXiv cs.IT 2021
Year: 2021

Visible light communication (VLC) has been recognized as a promising technology for handling the continuously increasing quality of service and connectivity requirements in modern wireless communications, particularly in indoor scenarios. In this context, the present work considers the integration of two distinct modulation schemes, namely spatial modulation (SM) with space time block codes (STBCs), aiming at improving the overall VLC system reliability. Based on this and in order to further enhance the achievable transmission data rate, we integrate quasi-orthogonal STBC (QOSTBC) with SM, since relaxing the orthogonality condition of OSTBC ultimately provides a higher coding rate. Then, we generalize the developed results to any number of active light-emitting diodes (LEDs) and any M-ary pulse amplitude modulation size. Furthermore, we derive a tight and tractable upper bound for the corresponding bit error rate (BER) by considering a simple two-step decoding procedure to detect the indices of the transmitting LEDs and then decode the signal domain symbols. Notably, the obtained results demonstrate that QOSTBC with SM enhances the achievable BER compared to SM with repetition coding (RC-SM). Finally, we compare STBC-SM with both multiple active SM (MASM) and RC-SM in terms of the achievable BER and overall data rate, which further justifies the usefulness of the proposed scheme.
Convolutional Gated MLP: Combining Convolutions & gMLP
Venue: arXiv cs.CV 2021
Year: 2021

To the best of our knowledge, this is the first paper to introduce Convolutions to Gated MultiLayer Perceptron and contributes an implementation of this novel Deep Learning architecture. Google Brain introduced the gMLP in May 2021. Microsoft introduced Convolutions in Vision Transformer in Mar 2021. Inspired by both gMLP and CvT, we introduce convolutional layers in gMLP. CvT combined the power of Convolutions and Attention. Our implementation combines the best of Convolutional learning along with spatial gated MLP. Further, the paper visualizes how CgMLP learns. Visualizations show how CgMLP learns from features such as outline of a car. While Attention was the basis of much of recent progress in Deep Learning, gMLP proposed an approach that doesn't use Attention computation. In Transformer based approaches, a whole lot of Attention matrixes need to be learnt using vast amount of training data. In gMLP, the fine tunning for new tasks can be challenging by transfer learning with smaller datasets. We implement CgMLP and compares it with gMLP on CIFAR dataset. Experimental results explore the power of generaliza-tion of CgMLP, while gMLP tend to drastically overfit the training data. To summarize, the paper contributes a novel Deep Learning architecture and demonstrates the learning mechanism of CgMLP through visualizations, for the first time in literature.