OpenRaiser/Intern-Atlas huggingface dataset:
Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs
Venue: arXiv cs.CL 2021
Year: 2021

In this work, our aim is to provide a structured answer in natural language to a complex information need. Particularly, we envision using generative models from the perspective of data-to-text generation. We propose the use of a content selection and planning pipeline which aims at structuring the answer by generating intermediate plans. The experimental evaluation is performed using the TREC Complex Answer Retrieval (CAR) dataset. We evaluate both the generated answer and its corresponding structure and show the effectiveness of planning-based models in comparison to a text-to-text model.
On visual self-supervision and its effect on model robustness
Venue: arXiv cs.CV 2021
Year: 2021

Recent self-supervision methods have found success in learning feature representations that could rival ones from full supervision, and have been shown to be beneficial to the model in several ways: for example improving models robustness and out-of-distribution detection. In our paper, we conduct an empirical study to understand more precisely in what way can self-supervised learning - as a pre-training technique or part of adversarial training - affects model robustness to $l_2$ and $l_{\infty}$ adversarial perturbations and natural image corruptions. Self-supervision can indeed improve model robustness, however it turns out the devil is in the details. If one simply adds self-supervision loss in tandem with adversarial training, then one sees improvement in accuracy of the model when evaluated with adversarial perturbations smaller or comparable to the value of $ε_{train}$ that the robust model is trained with. However, if one observes the accuracy for $ε_{test} \ge ε_{train}$, the model accuracy drops. In fact, the larger the weight of the supervision loss, the larger the drop in performance, i.e. harming the robustness of the model. We identify primary ways in which self-supervision can be added to adversarial training, and observe that using a self-supervised loss to optimize both network parameters and find adversarial examples leads to the strongest improvement in model robustness, as this can be viewed as a form of ensemble adversarial training. Although self-supervised pre-training yields benefits in improving adversarial training as compared to random weight initialization, we observe no benefit in model robustness or accuracy if self-supervision is incorporated into adversarial training.
Stochastic Bounded Confidence Opinion Dynamics: How Far Apart Do Opinions Drift?
Venue: arXiv cs.SI 2021
Year: 2021

In this era of fast and large-scale opinion formation, a mathematical understanding of opinion evolution, a.k.a. opinion dynamics, is especially important. Linear graph-based dynamics and bounded confidence dynamics are the two most popular models for opinion dynamics in social networks. Recently, stochastic bounded confidence opinion dynamics were proposed as a general framework that incorporates both these dynamics as special cases and also captures the inherent stochasticity and noise (errors) in real-life social exchanges. Although these dynamics are quite general and realistic, their analysis is particularly challenging compared to other opinion dynamics models. This is because these dynamics are nonlinear and stochastic, and belong to the class of Markov processes that have asymptotically zero drift and unbounded jumps. The asymptotic behavior of these dynamics was characterized in prior works. However, they do not shed light on their finite-time behavior, which is often of interest in practice. We take a stride in this direction by analyzing the finite time behavior of a two-agent system, which is fundamental to the understanding of multi-agent dynamics. In particular, we show that the opinion difference between the two agents is well concentrated around zero under the conditions that lead to asymptotic stability of the dynamics.
Dependability Engineering in Isabelle
Venue: arXiv cs.SE 2021
Year: 2021

In this paper, we introduce a process of formal system development supported by interactive theorem proving in a dedicated Isabelle framework. This Isabelle Infrastructure framework implements specification and verification in a cyclic process supported by attack tree analysis closely inter-connected with formal refinement of the specification. The process is cyclic: in a repeated iteration the refinement adds more detail to the system specification. It is a known hard problem how to find the next refinement step: this problem is addressed by the attack based analysis using Kripke structures and CTL logic. We call this cyclic process the Refinement-Risk cycle (RR-cycle). It has been developed for security and privacy of IoT healthcare systems initially but is more generally applicable for safety as well, that is, dependability in general. In this paper, we present the extensions to the Isabelle Infrastructure framework implementing a formal notion of property preserving refinement interleaved with attack tree analysis for the RR-cycle. The process is illustrated on the specification development and privacy analysis of the mobile Corona-virus warning app.
Simple Fair Power Allocation for NOMA-Based Visible Light Communication Systems
Venue: arXiv cs.IT 2021
Year: 2021

Non-orthogonal multiple access (NOMA) in the power-domain has been recognized as a promising technique to overcome the bandwidth limitations of current visible light communication (VLC) systems. In this letter, we investigate the power allocation (PA) problem in an NOMA-VLC system under high signal-to-noise-ratio (SNR) regime. A simple fair power allocation strategy (SFPA) is proposed to ensure equitable allocation of transmission resources in a multi-user scenario. SFPA requires minimal channel state information (CSI), making it less prone to channel estimation errors. Results show that NOMA with SFPA provides fairer and higher achievable rates per user (up to 79.5\% higher in the studied setup), without significantly compromising the overall system performance.
Audio-Visual Synchronisation in the wild
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we consider the problem of audio-visual synchronisation applied to videos `in-the-wild' (ie of general classes beyond speech). As a new task, we identify and curate a test set with high audio-visual correlation, namely VGG-Sound Sync. We compare a number of transformer-based architectural variants specifically designed to model audio and visual signals of arbitrary length, while significantly reducing memory requirements during training. We further conduct an in-depth analysis on the curated dataset and define an evaluation metric for open domain audio-visual synchronisation. We apply our method on standard lip reading speech benchmarks, LRS2 and LRS3, with ablations on various aspects. Finally, we set the first benchmark for general audio-visual synchronisation with over 160 diverse classes in the new VGG-Sound Sync video dataset. In all cases, our proposed model outperforms the previous state-of-the-art by a significant margin.
Canonical Representations for Direct Generation of Strategies in High-level Petri Games
Venue: arXiv cs.GT 2021
Year: 2021

Petri games are a multi-player game model for the synthesis of distributed systems with multiple concurrent processes based on Petri nets. The processes are the players in the game represented by the token of the net. The players are divided into two teams: the controllable system and the uncontrollable environment. An individual controller is synthesized for each process based only on their locally available causality-based information. For one environment player and a bounded number of system players, the problem of solving Petri games can be reduced to that of solving Büchi games. High-level Petri games are a concise representation of ordinary Petri games. Symmetries, derived from a high-level representation, can be exploited to significantly reduce the state space in the corresponding Büchi game. We present a new construction for solving high-level Petri games. It involves the definition of a unique, canonical representation of the reduced Büchi game. This allows us to translate a strategy in the Büchi game directly into a strategy in the Petri game. An implementation applied on six structurally different benchmark families shows in almost all cases a performance increase for larger state spaces.
Daily peak electrical load forecasting with a multi-resolution approach
Venue: arXiv cs.LG 2021
Year: 2021

In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders of the energy industry. An understanding of peak magnitude and timing is paramount for the implementation of smart grid strategies such as peak shaving. The modelling approach proposed in this paper leverages high-resolution and low-resolution information to forecast daily peak demand size and timing. The resulting multi-resolution modelling framework can be adapted to different model classes. The key contributions of this paper are a) a general and formal introduction to the multi-resolution modelling approach, b) a discussion on modelling approaches at different resolutions implemented via Generalised Additive Models and Neural Networks and c) experimental results on real data from the UK electricity market. The results confirm that the predictive performance of the proposed modelling approach is competitive with that of low- and high-resolution alternatives.
SIRfyN: Single Image Relighting from your Neighbors
Venue: arXiv cs.CV 2021
Year: 2021

We show how to relight a scene, depicted in a single image, such that (a) the overall shading has changed and (b) the resulting image looks like a natural image of that scene. Applications for such a procedure include generating training data and building authoring environments. Naive methods for doing this fail. One reason is that shading and albedo are quite strongly related; for example, sharp boundaries in shading tend to appear at depth discontinuities, which usually apparent in albedo. The same scene can be lit in different ways, and established theory shows the different lightings form a cone (the illumination cone). Novel theory shows that one can use similar scenes to estimate the different lightings that apply to a given scene, with bounded expected error. Our method exploits this theory to estimate a representation of the available lighting fields in the form of imputed generators of the illumination cone. Our procedure does not require expensive "inverse graphics" datasets, and sees no ground truth data of any kind. Qualitative evaluation suggests the method can erase and restore soft indoor shadows, and can "steer" light around a scene. We offer a summary quantitative evaluation of the method with a novel application of the FID. An extension of the FID allows per-generated-image evaluation. Furthermore, we offer qualitative evaluation with a user study, and show that our method produces images that can successfully be used for data augmentation.
InvGAN: Invertible GANs
Venue: arXiv cs.CV 2021
Year: 2021

Generation of photo-realistic images, semantic editing and representation learning are a few of many potential applications of high resolution generative models. Recent progress in GANs have established them as an excellent choice for such tasks. However, since they do not provide an inference model, image editing or downstream tasks such as classification can not be done on real images using the GAN latent space. Despite numerous efforts to train an inference model or design an iterative method to invert a pre-trained generator, previous methods are dataset (e.g. human face images) and architecture (e.g. StyleGAN) specific. These methods are nontrivial to extend to novel datasets or architectures. We propose a general framework that is agnostic to architecture and datasets. Our key insight is that, by training the inference and the generative model together, we allow them to adapt to each other and to converge to a better quality model. Our \textbf{InvGAN}, short for Invertible GAN, successfully embeds real images to the latent space of a high quality generative model. This allows us to perform image inpainting, merging, interpolation and online data augmentation. We demonstrate this with extensive qualitative and quantitative experiments.
Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Monocular 3D object detection aims to localize 3D bounding boxes in an input single 2D image. It is a highly challenging problem and remains open, especially when no extra information (e.g., depth, lidar and/or multi-frames) can be leveraged in training and/or inference. This paper proposes a simple yet effective formulation for monocular 3D object detection without exploiting any extra information. It presents the MonoCon method which learns Monocular Contexts, as auxiliary tasks in training, to help monocular 3D object detection. The key idea is that with the annotated 3D bounding boxes of objects in an image, there is a rich set of well-posed projected 2D supervision signals available in training, such as the projected corner keypoints and their associated offset vectors with respect to the center of 2D bounding box, which should be exploited as auxiliary tasks in training. The proposed MonoCon is motivated by the Cramer-Wold theorem in measure theory at a high level. In implementation, it utilizes a very simple end-to-end design to justify the effectiveness of learning auxiliary monocular contexts, which consists of three components: a Deep Neural Network (DNN) based feature backbone, a number of regression head branches for learning the essential parameters used in the 3D bounding box prediction, and a number of regression head branches for learning auxiliary contexts. After training, the auxiliary context regression branches are discarded for better inference efficiency. In experiments, the proposed MonoCon is tested in the KITTI benchmark (car, pedestrain and cyclist). It outperforms all prior arts in the leaderboard on car category and obtains comparable performance on pedestrian and cyclist in terms of accuracy. Thanks to the simple design, the proposed MonoCon method obtains the fastest inference speed with 38.7 fps in comparisons
Process Mining-Driven Analysis of the COVID19 Impact on the Vaccinations of Victorian Patients
Venue: arXiv cs.DB 2021
Year: 2021

Process mining is a discipline sitting between data mining and process science, whose goal is to provide theoretical methods and software tools to analyse process execution data, known as event logs. Although process mining was originally conceived to facilitate business process management activities, research studies have shown the benefit of leveraging process mining tools in different contexts, including healthcare. However, applying process mining tools to analyse healthcare process execution data is not straightforward. In this paper, we report the analysis of an event log recording more than 30 million events capturing the general practice healthcare processes of more than one million patients in Victoria--Australia--over five years. Our analysis allowed us to understand benefits and limitations of the state-of-the-art process mining techniques when dealing with highly variable processes and large data-sets. While we provide solutions to the identified limitations, the overarching goal of this study was to detect differences between the patients` health services utilization pattern observed in 2020--during the COVID-19 pandemic and mandatory lock-downs --and the one observed in the prior four years, 2016 to 2019. By using a combination of process mining techniques and traditional data mining, we were able to demonstrate that vaccinations in Victoria did not drop drastically--as other interactions did. On the contrary, we observed a surge of influenza and pneumococcus vaccinations in 2020, contradicting research findings of similar studies conducted in different geographical areas.
Kinematic Modeling of Handed Shearing Auxetics via Piecewise Constant Curvature
Venue: arXiv cs.RO 2021
Year: 2021

Handed Shearing Auxetics (HSA) are a promising technique for making motor-driven, soft, continuum robots. Many potential applications from inspection tasks to solar tracking require accurate kinematic models to predict the position and orientation of these structures. Currently there are no models for HSA based continuum platforms. To address this gap we propose to adapt Piecewise Constant Curvature (PCC) Models using a length change coupling matrix. This models the interaction of HSA structures in a 2x2 array. The coupling matrix maps the change in motor angles to length changes and defines the configuration space in our modified PCC Model. We evaluate our model on a composite movement encompassing bending, extension and compression behavior. Our model achieves a positional accuracy with mean error of 5.5mm or 4.5% body length and standard deviation of 1.72mm. Further, we achieve an angular accuracy with mean error of -2.8$^\circ$ and standard deviation of 1.9$^\circ$.
Adaptive List Decoder with Flip Operations for Polar Codes
Venue: arXiv cs.IT 2021
Year: 2021

Successive cancellation list decoders with flip operations (SCL-Flip) can utilize re-decoding attempts to significantly improve the error-correction performance of polar codes. However, these re-decoding attempts result in extra computation complexity, which thus leads to increased energy consumption and decoding latency to the communication system adopting SCL-Flip decoders. To significantly reduce the computation complexity of current SCL-Flip decoders, we design a new adaptive SCL-Flip (AD-SCLF) decoder, which can be easily implemented based on existing SCL-Flip techniques. Simulation results showed that the AD-SCLF can reduce up to 80.85\% of the computational complexity of a current SCL-Flip decoder at a matched $FER=10^{-3}$. The result implies our decoder can significantly reduce the energy consumption caused by redundant re-decoding attempts from the SCL-Flip decoder.
Scheduling Algorithms for Hierarchical Fog Networks
Venue: arXiv cs.DC 2021
Year: 2021

Fog computing brings the functionality of the cloud near the edge of the network with the help of fog devices/micro data centers ($mdcs$). Job scheduling in such systems is a complex problem due to the hierarchical and geo-distributed nature of fog devices. We propose two fog scheduling algorithms, named $FiFSA$ (Hierarchical $Fi$rst $F$og $S$cheduling $A$lgorithm) and $EFSA$ ( Hierarchical $E$lected $F$og $S$cheduling $A$lgorithm). We consider a hierarchical model of fog devices, where the computation power of fog devices present in higher tiers is greater than those present in lower tiers. However, the higher tier fog devices are located at greater physical distance from data generation sources as compared to lower tier fog devices. Jobs with varying granularity and cpu requirements have been considered. In general, jobs with modest cpu requirements are scheduled on lower tier fog devices, and jobs with larger cpu requirements are scheduled on higher tier fog devices or the cloud data center $(cdc)$. The performance of $FiFSA$ and $EFSA$ has been evaluated using a real life workload trace on various simulated fog hierarchies as well as on a prototype testbed. Employing $FiFSA$ offers an average improvement of 27% and 57.9% in total completion time and an improvement of 32% and 61% in cost as compared to Longest Time First ($LTF$) and cloud-only ($cdc-only$) scheduling algorithms, respectively. Employing $EFSA$ offers an average improvement of 48% and 70% in total completion time and an improvement of 52% and 72% in cost as compared to $LTF$ and $cdc-only$ respectively.
OPTT: Optimal Piecewise Transformation Technique for Analyzing Numerical Data under Local Differential Privacy
Venue: arXiv cs.CR 2021
Year: 2021

Privacy preserving data analysis (PPDA) has received increasing attention due to a great variety of applications. Local differential privacy (LDP), as an emerging standard that is suitable for PPDA, has been widely deployed into various real-world scenarios to analyze massive data while protecting against many forms of privacy breach. In this study, we are mainly concerned with piecewise transformation technique (PTT) for analyzing numerical data under local differential privacy. We provide a principled framework for PTT in the context of LDP, based on which PTT is studied systematically. As a result, we show that (1) many members in PTTs are asymptotically optimal when used to obtain an unbiased estimator for mean of numerical data, and (2) for a given privacy budget, there is PTT that reaches the theoretical low bound with respect to variance. Next, we prove by studying two classes of PTTs in detail that (1) there do not exist optimal PTTs compared to the well-used technique, i.e., Duchi's scheme, in terms of the consistency noisy variance, (2) on the other hand, one has the ability to find a great number of PTTs that are consistently more optimal than the latter with regard to the worst-case noisy variance, which is never reported so far. When we are restricted to consider only the high privacy level, enough PTTs turn out to be optimal than the well-known Laplace mechanism. Lastly, we prove that for a family of PTTs, the correspondingly theoretical low bound of noisy variance follows $O(ε^{-2})$ when considering the high privacy level.
GPU backed Data Mining on Android Devices
Venue: arXiv cs.DC 2021
Year: 2021

Choosing an appropriate programming paradigm for high-performance computing on low-power devices can be useful to speed up calculations. Many Android devices have an integrated GPU and - although not officially supported - the OpenCL framework can be used on Android devices for addressing these GPUs. OpenCL supports thread and data parallelism. Applications that use the GPU must account for the fact that they can be suspended by the user or the Android operating system at any moment. We have created a wrapper library that allows to use OpenCL on Android devices. Already written OpenCL programs can be executed with almost no modification. We have used this library to compare the performance of the DBSCAN and Kmeans algorithms on an integrated GPU of an Arm-v7 tablet with other single and multithreaded implementations on the same device. We have investigated which programming paradigm and language allows the best tradeoff between execution speed and energy consumption. Using the GPU for HPC on Android devices can help to carry out computationally intensive machine learning or data mining tasks in remote areas, under harsh environmental conditions and in areas where energy supply is an issue.