OpenRaiser/Intern-Atlas huggingface dataset:
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning
Venue: arXiv cs.LG 2021
Year: 2021

We propose Partially Interpretable Estimators (PIE) which attribute a prediction to individual features via an interpretable model, while a (possibly) small part of the PIE prediction is attributed to the interaction of features via a black-box model, with the goal to boost the predictive performance while maintaining interpretability. As such, the interpretable model captures the main contributions of features, and the black-box model attempts to complement the interpretable piece by capturing the "nuances" of feature interactions as a refinement. We design an iterative training algorithm to jointly train the two types of models. Experimental results show that PIE is highly competitive to black-box models while outperforming interpretable baselines. In addition, the understandability of PIE is comparable to simple linear models as validated via a human evaluation.
Parallelized sequential composition, pipelines, and hardware weak memory models
Venue: arXiv cs.LO 2021
Year: 2021

Since the introduction of the CDC 6600 in 1965 and its `scoreboarding' technique processors have not (necessarily) executed instructions in program order. Programmers of high-level code may sequence independent instructions in arbitrary order, and it is a matter of significant programming abstraction and computational efficiency that the processor can be relied upon to make sensible parallelizations/reorderings of low-level instructions to take advantage of, eg., multiple ALUs. At the architectural level such reordering is typically implemented via a per-processor pipeline, into which instructions are fetched in order, but possibly committed out of order depending on local considerations, provided any reordering preserves sequential semantics from that processor's perspective. However multicore architectures, where several pipelines run in parallel, can expose these processor-level reorderings as unexpected, or `weak', behaviours. Such weak behaviours are hard to reason about, and (via speculative execution) underlie at least one class of widespread security vulnerability. In this paper we introduce a novel program operator, \emph{parallelized sequential composition}, which can be instantiated with a function that controls the reordering of atomic instructions. It generalises both sequential and parallel composition, and when appropriately instantiated exhibits many of the weak behaviours of well-known hardware weak memory models. Our framework admits the application of established compositional techniques (eg. Owicki-Gries) for reasoning about weak behaviours, and is convenient for abstractly expressing properties from the literature. The semantics and theory is encoded and verified in a theorem prover, and we give an implementation of the pipeline semantics which we use to empirically show conformance against established models of ARM and RISC-V.
Learning Skeletal Articulations with Neural Blend Shapes
Venue: arXiv cs.GR 2021
Year: 2021

Animating a newly designed character using motion capture (mocap) data is a long standing problem in computer animation. A key consideration is the skeletal structure that should correspond to the available mocap data, and the shape deformation in the joint regions, which often requires a tailored, pose-specific refinement. In this work, we develop a neural technique for articulating 3D characters using enveloping with a pre-defined skeletal structure which produces high quality pose dependent deformations. Our framework learns to rig and skin characters with the same articulation structure (e.g., bipeds or quadrupeds), and builds the desired skeleton hierarchy into the network architecture. Furthermore, we propose neural blend shapes--a set of corrective pose-dependent shapes which improve the deformation quality in the joint regions in order to address the notorious artifacts resulting from standard rigging and skinning. Our system estimates neural blend shapes for input meshes with arbitrary connectivity, as well as weighting coefficients which are conditioned on the input joint rotations. Unlike recent deep learning techniques which supervise the network with ground-truth rigging and skinning parameters, our approach does not assume that the training data has a specific underlying deformation model. Instead, during training, the network observes deformed shapes and learns to infer the corresponding rig, skin and blend shapes using indirect supervision. During inference, we demonstrate that our network generalizes to unseen characters with arbitrary mesh connectivity, including unrigged characters built by 3D artists. Conforming to standard skeletal animation models enables direct plug-and-play in standard animation software, as well as game engines.
Remote Attestation: A Literature Review
Venue: arXiv cs.CR 2021
Year: 2021

With the rising number of IoT devices, the security of such devices becomes increasingly important. Remote attestation (RA) is a distinct security service that allows a remote verifer to reason about the state of an untrusted remote prover (device). Paradigms of remote attestation span from exclusively software, in software-based attestation, to exclusively hardware-based. In between the extremes are hybrid attestation that utilize the enhanced security of secure hardware components in combination with the lower cost of purely software-based implementations. Traditional remote attestation protocols are concerned with reasoning about the state of a prover. However, extensions to remote attestation also exist, such as code updates, device resets, erasure and attestation of the device's run-time state. Furthermore, as interconnected IoT devices are becoming increasingly more popular, so is the need for attestation of device swarms. We will describe and evaluate the state-of-the-art for remote attestation, which covers singular attestation of devices as well as newer research in the area of formally verified RA protocols, swarm attestation and control-flow attestation.
A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking
Venue: arXiv cs.CV 2021
Year: 2021

Siamese trackers are shown to be vulnerable to adversarial attacks recently. However, the existing attack methods craft the perturbations for each video independently, which comes at a non-negligible computational cost. In this paper, we show the existence of universal perturbations that can enable the targeted attack, e.g., forcing a tracker to follow the ground-truth trajectory with specified offsets, to be video-agnostic and free from inference in a network. Specifically, we attack a tracker by adding a universal imperceptible perturbation to the template image and adding a fake target, i.e., a small universal adversarial patch, into the search images adhering to the predefined trajectory, so that the tracker outputs the location and size of the fake target instead of the real target. Our approach allows perturbing a novel video to come at no additional cost except the mere addition operations -- and not require gradient optimization or network inference. Experimental results on several datasets demonstrate that our approach can effectively fool the Siamese trackers in a targeted attack manner. We show that the proposed perturbations are not only universal across videos, but also generalize well across different trackers. Such perturbations are therefore doubly universal, both with respect to the data and the network architectures. We will make our code publicly available.
A Unified Pre-training Framework for Conversational AI
Venue: arXiv cs.CL 2021
Year: 2021

In this work, we explore the application of PLATO-2 on various dialogue systems, including open-domain conversation, knowledge grounded dialogue, and task-oriented conversation. PLATO-2 is initially designed as an open-domain chatbot, trained via two-stage curriculum learning. In the first stage, a coarse-grained response generation model is learned to fit the simplified one-to-one mapping relationship. This model is applied to the task-oriented conversation, given that the semantic mappings tend to be deterministic in task completion. In the second stage, another fine-grained generation model and an evaluation model are further learned for diverse response generation and coherence estimation, respectively. With superior capability on capturing one-to-many mapping, such models are suitable for the open-domain conversation and knowledge grounded dialogue. For the comprehensive evaluation of PLATO-2, we have participated in multiple tasks of DSTC9, including interactive evaluation of open-domain conversation (Track3-task2), static evaluation of knowledge grounded dialogue (Track3-task1), and end-to-end task-oriented conversation (Track2-task1). PLATO-2 has obtained the 1st place in all three tasks, verifying its effectiveness as a unified framework for various dialogue systems.
Covering Convex Polygons by Two Congruent Disks
Venue: arXiv cs.CG 2021
Year: 2021

We consider the planar two-center problem for a convex polygon: given a convex polygon in the plane, find two congruent disks of minimum radius whose union contains the polygon. We present an $O(n\log n)$-time algorithm for the two-center problem for a convex polygon, where $n$ is the number of vertices of the polygon. This improves upon the previous best algorithm for the problem.
Minimizing costs of communication with random constant weight codes
Venue: arXiv cs.IT 2021
Year: 2021

We present a framework for minimizing costs in constant weight codes while maintaining a certain amount of differentiable codewords. Our calculations are based on a combinatorial view of constant weight codes and relay on simple approximations.
Distribution Awareness for AI System Testing
Venue: arXiv cs.SE 2021
Year: 2021

As Deep Learning (DL) is continuously adopted in many safety critical applications, its quality and reliability start to raise concerns. Similar to the traditional software development process, testing the DL software to uncover its defects at an early stage is an effective way to reduce risks after deployment. Although recent progress has been made in designing novel testing techniques for DL software, the distribution of generated test data is not taken into consideration. It is therefore hard to judge whether the identified errors are indeed meaningful errors to the DL application. Therefore, we propose a new OOD-guided testing technique which aims to generate new unseen test cases relevant to the underlying DL system task. Our results show that this technique is able to filter up to 55.44% of error test case on CIFAR-10 and is 10.05% more effective in enhancing robustness.
Assessing Dialogue Systems with Distribution Distances
Venue: arXiv cs.CL 2021
Year: 2021

An important aspect of developing dialogue systems is how to evaluate and compare the performance of different systems. Existing automatic evaluation metrics are based on turn-level quality evaluation and use average scores for system-level comparison. In this paper, we propose to measure the performance of a dialogue system by computing the distribution-wise distance between its generated conversations and real-world conversations. Specifically, two distribution-wise metrics, FBD and PRD, are developed and evaluated. Experiments on several dialogue corpora show that our proposed metrics correlate better with human judgments than existing metrics.
FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting
Venue: arXiv cs.LG 2021
Year: 2021

With the goal of predicting the future rainfall intensity in a local region over a relatively short period time, precipitation nowcasting has been a long-time scientific challenge with great social and economic impact. The radar echo extrapolation approaches for precipitation nowcasting take radar echo images as input, aiming to generate future radar echo images by learning from the historical images. To effectively handle complex and high non-stationary evolution of radar echoes, we propose to decompose the movement into optical flow field motion and morphologic deformation. Following this idea, we introduce Flow-Deformation Network (FDNet), a neural network that models flow and deformation in two parallel cross pathways. The flow encoder captures the optical flow field motion between consecutive images and the deformation encoder distinguishes the change of shape from the translational motion of radar echoes. We evaluate the proposed network architecture on two real-world radar echo datasets. Our model achieves state-of-the-art prediction results compared with recent approaches. To the best of our knowledge, this is the first network architecture with flow and deformation separation to model the evolution of radar echoes for precipitation nowcasting. We believe that the general idea of this work could not only inspire much more effective approaches but also be applied to other similar spatiotemporal prediction tasks
Scaling up Memory-Efficient Formal Verification Tools for Tree Ensembles
Venue: arXiv cs.LG 2021
Year: 2021

To guarantee that machine learning models yield outputs that are not only accurate, but also robust, recent works propose formally verifying robustness properties of machine learning models. To be applicable to realistic safety-critical systems, the used verification algorithms need to manage the combinatorial explosion resulting from vast variations in the input domain, and be able to verify correctness properties derived from versatile and domain-specific requirements. In this paper, we formalise the VoTE algorithm presented earlier as a tool description, and extend the tool set with mechanisms for systematic scalability studies. In particular, we show a) how the separation of property checking from the core verification engine enables verification of versatile requirements, b) the scalability of the tool, both in terms of time taken for verification and use of memory, and c) that the algorithm has attractive properties that lend themselves well for massive parallelisation. We demonstrate the application of the tool in two case studies, namely digit recognition and aircraft collision avoidance, where the first case study serves to assess the resource utilisation of the tool, and the second to assess the ability to verify versatile correctness properties.
Analysis and Improvement of Heterogeneous Hardware Support in Docker Images
Venue: arXiv cs.CR 2021
Year: 2021

Docker images are used to distribute and deploy cloud-native applications in containerised form. A container engine runs them with separated privileges according to namespaces. Recent studies have investigated security vulnerabilities and runtime characteristics of Docker images. In contrast, little is known about the extent of hardware-dependent features in them such as processor-specific trusted execution environments, graphics acceleration or extension boards. This problem can be generalised to missing knowledge about the extent of any hardware-bound instructions within the images that may require elevated privileges. We first conduct a systematic one-year evolution analysis of a sample of Docker images concerning their use of hardware-specific features. To improve the state of technology, we contribute novel tools to manage such images. Our heuristic hardware dependency detector and a hardware-aware Docker executor give early warnings upon missing dependencies instead of leading to silent or untimely failures. Our dataset and tools are released to the research community.
Development and Application of Sentiment Analysis Tools in Software Engineering: A Systematic Literature Review
Venue: arXiv cs.SE 2021
Year: 2021

Software development is a collaborative task and, hence, involves different persons. Research has shown the relevance of social aspects in the development team for a successful and satisfying project closure. Especially the mood of a team has been proven to be of particular importance. Thus, project managers or project leaders want to be aware of situations in which negative mood is present to allow for interventions. So-called sentiment analysis tools offer a way to determine the mood based on text-based communication. In this paper, we present the results of a systematic literature review of sentiment analysis tools developed for or applied in the context of software engineering. Our results summarize insights from 80 papers with respect to (1) the application domain, (2) the purpose, (3) the used data sets, (4) the approaches for developing sentiment analysis tools and (5) the difficulties researchers face when applying sentiment analysis in the context of software projects. According to our results, sentiment analysis is frequently applied to open-source software projects, and most tools are based on support-vector machines. Despite the frequent use of sentiment analysis in software engineering, there are open issues, e.g., regarding the identification of irony or sarcasm, pointing to future research directions.
Current State of IPv6 Security in IoT
Venue: arXiv cs.NI 2021
Year: 2021

This report presents the current state of security in IPv6 for IoT devices. In this research conducted from May 2020 to July 2020, we explored the global growth of IPv6 and compared it with the real growth of IPv6 in a medium size network. If IPv6 is already being used, are attackers already attacking using this protocol? To answer this question we look at the current vulnerabilities, attacks, and malware leveraging IPv6. Our research showed that while IPv6 adoption is growing, we are years away of a full adoption. The current global adoption is of 35\%, however there are countries rapidly adopting IPv6, such as India with 60\% of IPv6 enabled in the country. IPv6 brings new challenges for both attackers and defenders. With a larger address space, the activity of device discovery will force attackers to devise new techniques and tools. Defenders will also have to adapt their tools and monitoring technology to be able to work with IPv6. There are currently more than 16 million devices exposed on the internet on IPv6, however malware authors seem to be still focused mainly on IPv4. There is to date, one malware capable of attacking IPv6 networks. This may give an edge to defenders, who have now the opportunity to give the first step ahead of attackers.
Deep Weighted Consensus: Dense correspondence confidence maps for 3D shape registration
Venue: arXiv cs.CV 2021
Year: 2021

We present a new paradigm for rigid alignment between point clouds based on learnable weighted consensus which is robust to noise as well as the full spectrum of the rotation group. Current models, learnable or axiomatic, work well for constrained orientations and limited noise levels, usually by an end-to-end learner or an iterative scheme. However, real-world tasks require us to deal with large rotations as well as outliers and all known models fail to deliver. Here we present a different direction. We claim that we can align point clouds out of sampled matched points according to confidence level derived from a dense, soft alignment map. The pipeline is differentiable, and converges under large rotations in the full spectrum of SO(3), even with high noise levels. We compared the network to recently presented methods such as DCP, PointNetLK, RPM-Net, PRnet, and axiomatic methods such as ICP and Go-ICP. We report here a fundamental boost in performance.
Robust Analog Beamforming for Periodic Broadcast V2V Communication
Venue: arXiv cs.IT 2021
Year: 2021

We generalize an existing low-cost analog signal processing concept that takes advantage of the periodicity of vehicle-to-vehicle broadcast service to the transmitter side. In particular, we propose to process multiple antennas using either an analog beamforming network (ABN) of phase shifters, or an an antenna switching network (ASN) that periodically alternate between the available antennas, to transmit periodic messages to receivers that have an analog combining network (ACN) of phase shifters, which has been proposed in earlier work. To guarantee robustness, we aim to minimize the burst error probability for the worst receiving vehicular user, in a scenario of bad propagation condition that is modeled by a single dominant path between the communicating vehicles. In absence of any form of channel knowledge, we analytically derive the optimal parameters of both ABN and ASN. The ABN beamforming vector is found to be optimal for all users and not only for the worst receiving user. Further, we demonstrate that Alamouti scheme for the special case of two transmit antennas yields similar performance to ABN and ASN. At last, we show that the derived parameters of the two proposed transmission strategies are also optimal when hybrid ACN-maximal ratio combining is used at the receiver.
Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet
Venue: arXiv cs.CV 2021
Year: 2021

The strong performance of vision transformers on image classification and other vision tasks is often attributed to the design of their multi-head attention layers. However, the extent to which attention is responsible for this strong performance remains unclear. In this short report, we ask: is the attention layer even necessary? Specifically, we replace the attention layer in a vision transformer with a feed-forward layer applied over the patch dimension. The resulting architecture is simply a series of feed-forward layers applied over the patch and feature dimensions in an alternating fashion. In experiments on ImageNet, this architecture performs surprisingly well: a ViT/DeiT-base-sized model obtains 74.9\% top-1 accuracy, compared to 77.9\% and 79.9\% for ViT and DeiT respectively. These results indicate that aspects of vision transformers other than attention, such as the patch embedding, may be more responsible for their strong performance than previously thought. We hope these results prompt the community to spend more time trying to understand why our current models are as effective as they are.
Solve routing problems with a residual edge-graph attention neural network
Venue: arXiv cs.LG 2021
Year: 2021

For NP-hard combinatorial optimization problems, it is usually difficult to find high-quality solutions in polynomial time. The design of either an exact algorithm or an approximate algorithm for these problems often requires significantly specialized knowledge. Recently, deep learning methods provide new directions to solve such problems. In this paper, an end-to-end deep reinforcement learning framework is proposed to solve this type of combinatorial optimization problems. This framework can be applied to different problems with only slight changes of input (for example, for a traveling salesman problem (TSP), the input is the two-dimensional coordinates of nodes; while for a capacity-constrained vehicle routing problem (CVRP), the input is simply changed to three-dimensional vectors including the two-dimensional coordinates and the customer demands of nodes), masks and decoder context vectors. The proposed framework is aiming to improve the models in literacy in terms of the neural network model and the training algorithm. The solution quality of TSP and the CVRP up to 100 nodes are significantly improved via our framework. Specifically, the average optimality gap is reduced from 4.53\% (reported best \cite{R22}) to 3.67\% for TSP with 100 nodes and from 7.34\% (reported best \cite{R22}) to 6.68\% for CVRP with 100 nodes when using the greedy decoding strategy. Furthermore, our framework uses about 1/3$\sim$3/4 training samples compared with other existing learning methods while achieving better results. The results performed on randomly generated instances and the benchmark instances from TSPLIB and CVRPLIB confirm that our framework has a linear running time on the problem size (number of nodes) during the testing phase, and has a good generalization performance from random instance training to real-world instance testing.