OpenRaiser/Intern-Atlas huggingface dataset:
Collusion-resistant fingerprinting of parallel content channels
Venue: arXiv cs.IT 2022
Year: 2022

The fingerprinting game is analysed when the coalition size $k$ is known to the tracer, but the colluders can distribute themselves across $L$ TV channels. The collusion channel is introduced and the extra degrees of freedom for the coalition are made manifest in our formulation. We introduce a payoff functional that is analogous to the single TV channel case, and is conjectured to be closely related to the fingerprinting capacity. For the binary alphabet case under the marking assumption, and the restriction of access to one TV channel per person per segment, we derive the asymptotic behavior of the payoff functional. We find that the value of the maximin game for our payoff is asymptotically equal to $L^2/k^2 2 \ln 2$, with optimal strategy for the tracer being the arcsine distribution, and for the coalition being the interleaving attack across all TV channels, as well as assigning an equal number of colluders across the $L$ TV channels.
Multimodal Proximity and Visuotactile Sensing With a Selectively Transmissive Soft Membrane
Venue: arXiv cs.RO 2022
Year: 2022

The most common sensing modalities found in a robot perception system are vision and touch, which together can provide global and highly localized data for manipulation. However, these sensing modalities often fail to adequately capture the behavior of target objects during the critical moments as they transition out of static, controlled contact with an end-effector to dynamic and uncontrolled motion. In this work, we present a novel multimodal visuotactile sensor that provides simultaneous visuotactile and proximity depth data. The sensor integrates an RGB camera and air pressure sensor to sense touch with an infrared time-of-flight (ToF) camera to sense proximity by leveraging a selectively transmissive soft membrane to enable the dual sensing modalities. We present the mechanical design, fabrication techniques, algorithm implementations, and evaluation of the sensor's tactile and proximity modalities. The sensor is demonstrated in three open-loop robotic tasks: approaching and contacting an object, catching, and throwing. The fusion of tactile and proximity data could be used to capture key information about a target object's transition behavior for sensor-based control in dynamic manipulation.
Context-Auditor: Context-sensitive Content Injection Mitigation
Venue: arXiv cs.CR 2022
Year: 2022

Cross-site scripting (XSS) is the most common vulnerability class in web applications over the last decade. Much research attention has focused on building exploit mitigation defenses for this problem, but no technique provides adequate protection in the face of advanced attacks. One technique that bypasses XSS mitigations is the scriptless attack: a content injection technique that uses (among other options) CSS and HTML injection to infiltrate data. In studying this technique and others, we realized that the common property among the exploitation of all content injection vulnerabilities, including not just XSS and scriptless attacks, but also command injections and several others, is an unintended context switch in the victim program's parsing engine that is caused by untrusted user input. In this paper, we propose Context-Auditor, a novel technique that leverages this insight to identify content injection vulnerabilities ranging from XSS to scriptless attacks and command injections. We implemented Context-Auditor as a general solution to content injection exploit detection problem in the form of a flexible, stand-alone detection module. We deployed instances of Context-Auditor as (1) a browser plugin, (2) a web proxy (3) a web server plugin, and (4) as a wrapper around potentially-injectable system endpoints. Because Context-Auditor targets the root cause of content injection exploitation (and, more specifically for the purpose of our prototype, XSS exploitation, scriptless exploitation, and command injection), our evaluation results demonstrate that Context-Auditor can identify and block content injection exploits that modern defenses cannot while maintaining low throughput overhead and avoiding false positives.
CorrGAN: Input Transformation Technique Against Natural Corruptions
Venue: arXiv cs.LG 2022
Year: 2022

Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial perturbations and corruptions. Specifically, natural corruptions like fog, blur, contrast etc can affect the prediction of DNN in an autonomous vehicle. In real time, these corruptions are needed to be detected and also the corrupted inputs are needed to be de-noised to be predicted correctly. In this work, we propose CorrGAN approach, which can generate benign input when a corrupted input is provided. In this framework, we train Generative Adversarial Network (GAN) with novel intermediate output-based loss function. The GAN can denoise the corrupted input and generate benign input. Through experimentation, we show that up to 75.2% of the corrupted misclassified inputs can be classified correctly by DNN using CorrGAN.
Self Supervised Adversarial Domain Adaptation for Cross-Corpus and Cross-Language Speech Emotion Recognition
Venue: arXiv cs.SD 2022
Year: 2022

Despite the recent advancement in speech emotion recognition (SER) within a single corpus setting, the performance of these SER systems degrades significantly for cross-corpus and cross-language scenarios. The key reason is the lack of generalisation in SER systems towards unseen conditions, which causes them to perform poorly in cross-corpus and cross-language settings. Recent studies focus on utilising adversarial methods to learn domain generalised representation for improving cross-corpus and cross-language SER to address this issue. However, many of these methods only focus on cross-corpus SER without addressing the cross-language SER performance degradation due to a larger domain gap between source and target language data. This contribution proposes an adversarial dual discriminator (ADDi) network that uses the three-players adversarial game to learn generalised representations without requiring any target data labels. We also introduce a self-supervised ADDi (sADDi) network that utilises self-supervised pre-training with unlabelled data. We propose synthetic data generation as a pretext task in sADDi, enabling the network to produce emotionally discriminative and domain invariant representations and providing complementary synthetic data to augment the system. The proposed model is rigorously evaluated using five publicly available datasets in three languages and compared with multiple studies on cross-corpus and cross-language SER. Experimental results demonstrate that the proposed model achieves improved performance compared to the state-of-the-art methods.
On The Cross-Modal Transfer from Natural Language to Code through Adapter Modules
Venue: arXiv cs.SE 2022
Year: 2022

Pre-trained neural Language Models (PTLM), such as CodeBERT, are recently used in software engineering as models pre-trained on large source code corpora. Their knowledge is transferred to downstream tasks (e.g. code clone detection) via fine-tuning. In natural language processing (NLP), other alternatives for transferring the knowledge of PTLMs are explored through using adapters, compact, parameter efficient modules inserted in the layers of the PTLM. Although adapters are known to facilitate adapting to many downstream tasks compared to fine-tuning the model that require retraining all of the models' parameters -- which owes to the adapters' plug and play nature and being parameter efficient -- their usage in software engineering is not explored. Here, we explore the knowledge transfer using adapters and based on the Naturalness Hypothesis proposed by Hindle et. al \cite{hindle2016naturalness}. Thus, studying the bimodality of adapters for two tasks of cloze test and code clone detection, compared to their benchmarks from the CodeXGLUE platform. These adapters are trained using programming languages and are inserted in a PTLM that is pre-trained on English corpora (N-PTLM). Three programming languages, C/C++, Python, and Java, are studied along with extensive experiments on the best setup used for adapters. Improving the results of the N-PTLM confirms the success of the adapters in knowledge transfer to software engineering, which sometimes are in par with or exceed the results of a PTLM trained on source code; while being more efficient in terms of the number of parameters, memory usage, and inference time. Our results can open new directions to build smaller models for more software engineering tasks. We open source all the scripts and the trained adapters.
Basilic: Resilient Optimal Consensus Protocols With Benign and Deceitful Faults
Venue: arXiv cs.DC 2022
Year: 2022

The problem of Byzantine consensus has been key to designing secure distributed systems. However, it is particularly difficult, mainly due to the presence of Byzantine processes that act arbitrarily and the unknown message delays in general networks. Although it is well known that both safety and liveness are at risk as soon as $n/3$ Byzantine processes fail, very few works attempted to characterize precisely the faults that produce safety violations from the faults that produce termination violations. In this paper, we present a new lower bound on the solvability of the consensus problem by distinguishing deceitful faults violating safety and benign faults violating termination from the more general Byzantine faults, in what we call the Byzantine-deceitful-benign fault model. We show that one cannot solve consensus if $n\leq 3t+d+2q$ with $t$ Byzantine processes, $d$ deceitful processes, and $q$ benign processes. In addition, we show that this bound is tight by presenting the Basilic class of consensus protocols that solve consensus when $n > 3t+d+2q$. These protocols differ in the number of processes from which they wait to receive messages before progressing. Each of these protocols is thus better suited for some applications depending on the predominance of benign or deceitful faults. Finally, we study the fault tolerance of the Basilic class of consensus protocols in the context of blockchains that need to solve the weaker problem of eventual consensus. We demonstrate that Basilic solves this problem with only $n > 2t+d+q$, hence demonstrating how it can strengthen blockchain security.
Quantum-Secured Space-Air-Ground Integrated Networks: Concept, Framework, and Case Study
Venue: arXiv cs.CR 2022
Year: 2022

In the upcoming 6G era, existing terrestrial networks have evolved toward space-air-ground integrated networks (SAGIN), providing ultra-high data rates, seamless network coverage, and ubiquitous intelligence for communications of applications and services. However, conventional communications in SAGIN still face data confidentiality issues. Fortunately, the concept of Quantum Key Distribution (QKD) over SAGIN is able to provide information-theoretic security for secure communications in SAGIN with quantum cryptography. Therefore, in this paper, we propose the quantum-secured SAGIN which is feasible to achieve proven secure communications using quantum mechanics to protect data channels between space, air, and ground nodes. Moreover, we propose a universal QKD service provisioning framework to minimize the cost of QKD services under the uncertainty and dynamics of communications in quantum-secured SAGIN. In this framework, fiber-based QKD services are deployed in passive optical networks with the advantages of low loss and high stability. Moreover, the widely covered and flexible satellite- and UAV-based QKD services are provisioned as a supplement during the real-time data transmission phase. Finally, to examine the effectiveness of the proposed concept and framework, a case study of quantum-secured SAGIN in the Metaverse is conducted where uncertain and dynamic factors of the secure communications in Metaverse applications are effectively resolved in the proposed framework.
On the Use of Causal Graphical Models for Designing Experiments in the Automotive Domain
Venue: arXiv cs.SE 2022
Year: 2022

Randomized field experiments are the gold standard for evaluating the impact of software changes on customers. In the online domain, randomization has been the main tool to ensure exchangeability. However, due to the different deployment conditions and the high dependence on the surrounding environment, designing experiments for automotive software needs to consider a higher number of restricted variables to ensure conditional exchangeability. In this paper, we show how at Volvo Cars we utilize causal graphical models to design experiments and explicitly communicate the assumptions of experiments. These graphical models are used to further assess the experiment validity, compute direct and indirect causal effects, and reason on the transportability of the causal conclusions.
Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity
Venue: arXiv cs.CV 2022
Year: 2022

We propose a simple yet effective proposal-free architecture for lidar panoptic segmentation. We jointly optimize both semantic segmentation and class-agnostic instance classification in a single network using a pillar-based bird's-eye view representation. The instance classification head learns pairwise affinity between pillars to determine whether the pillars belong to the same instance or not. We further propose a local clustering algorithm to propagate instance ids by merging semantic segmentation and affinity predictions. Our experiments on nuScenes dataset show that our approach outperforms previous proposal-free methods and is comparable to proposal-based methods which requires extra annotation from object detection.
Edge-enhanced Feature Distillation Network for Efficient Super-Resolution
Venue: arXiv cs.CV 2022
Year: 2022

With the recently massive development in convolution neural networks, numerous lightweight CNN-based image super-resolution methods have been proposed for practical deployments on edge devices. However, most existing methods focus on one specific aspect: network or loss design, which leads to the difficulty of minimizing the model size. To address the issue, we conclude block devising, architecture searching, and loss design to obtain a more efficient SR structure. In this paper, we proposed an edge-enhanced feature distillation network, named EFDN, to preserve the high-frequency information under constrained resources. In detail, we build an edge-enhanced convolution block based on the existing reparameterization methods. Meanwhile, we propose edge-enhanced gradient loss to calibrate the reparameterized path training. Experimental results show that our edge-enhanced strategies preserve the edge and significantly improve the final restoration quality. Code is available at https://github.com/icandle/EFDN.
Where Was COVID-19 First Discovered? Designing a Question-Answering System for Pandemic Situations
Venue: arXiv cs.CL 2022
Year: 2022

The COVID-19 pandemic is accompanied by a massive "infodemic" that makes it hard to identify concise and credible information for COVID-19-related questions, like incubation time, infection rates, or the effectiveness of vaccines. As a novel solution, our paper is concerned with designing a question-answering system based on modern technologies from natural language processing to overcome information overload and misinformation in pandemic situations. To carry out our research, we followed a design science research approach and applied Ingwersen's cognitive model of information retrieval interaction to inform our design process from a socio-technical lens. On this basis, we derived prescriptive design knowledge in terms of design requirements and design principles, which we translated into the construction of a prototypical instantiation. Our implementation is based on the comprehensive CORD-19 dataset, and we demonstrate our artifact's usefulness by evaluating its answer quality based on a sample of COVID-19 questions labeled by biomedical experts.
Understanding Toxicity Triggers on Reddit in the Context of Singapore
Venue: arXiv cs.CY 2022
Year: 2022

While the contagious nature of online toxicity sparked increasing interest in its early detection and prevention, most of the literature focuses on the Western world. In this work, we demonstrate that 1) it is possible to detect toxicity triggers in an Asian online community, and 2) toxicity triggers can be strikingly different between Western and Eastern contexts.
RNNCTPs: A Neural Symbolic Reasoning Method Using Dynamic Knowledge Partitioning Technology
Venue: arXiv cs.AI 2022
Year: 2022

Although traditional symbolic reasoning methods are highly interpretable, their application in knowledge graph link prediction is limited due to their low computational efficiency. In this paper, we propose a new neural symbolic reasoning method: RNNCTPs, which improves computational efficiency by re-filtering the knowledge selection of Conditional Theorem Provers (CTPs), and is less sensitive to the embedding size parameter. RNNCTPs are divided into relation selectors and predictors. The relation selectors are trained efficiently and interpretably, so that the whole model can dynamically generate knowledge for the inference of the predictor. In all four datasets, the method shows competitive performance against traditional methods on the link prediction task, and can have higher applicability to the selection of datasets relative to CTPs.
A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization
Venue: arXiv cs.SD 2022
Year: 2022

Performance-score synchronization is an integral task in signal processing, which entails generating an accurate mapping between an audio recording of a performance and the corresponding musical score. Traditional synchronization methods compute alignment using knowledge-driven and stochastic approaches, and are typically unable to generalize well to different domains and modalities. We present a novel data-driven method for structure-aware performance-score synchronization. We propose a convolutional-attentional architecture trained with a custom loss based on time-series divergence. We conduct experiments for the audio-to-MIDI and audio-to-image alignment tasks pertained to different score modalities. We validate the effectiveness of our method via ablation studies and comparisons with state-of-the-art alignment approaches. We demonstrate that our approach outperforms previous synchronization methods for a variety of test settings across score modalities and acoustic conditions. Our method is also robust to structural differences between the performance and score sequences, which is a common limitation of standard alignment approaches.
Detect-and-describe: Joint learning framework for detection and description of objects
Venue: arXiv cs.CV 2022
Year: 2022

Traditional object detection answers two questions; "what" (what the object is?) and "where" (where the object is?). "what" part of the object detection can be fine-grained further i.e. "what type", "what shape" and "what material" etc. This results in the shifting of the object detection tasks to the object description paradigm. Describing an object provides additional detail that enables us to understand the characteristics and attributes of the object ("plastic boat" not just boat, "glass bottle" not just bottle). This additional information can implicitly be used to gain insight into unseen objects (e.g. unknown object is "metallic", "has wheels"), which is not possible in traditional object detection. In this paper, we present a new approach to simultaneously detect objects and infer their attributes, we call it Detect and Describe (DaD) framework. DaD is a deep learning-based approach that extends object detection to object attribute prediction as well. We train our model on aPascal train set and evaluate our approach on aPascal test set. We achieve 97.0% in Area Under the Receiver Operating Characteristic Curve (AUC) for object attributes prediction on aPascal test set. We also show qualitative results for object attribute prediction on unseen objects, which demonstrate the effectiveness of our approach for describing unknown objects.
Differentiating Network Flows for Priority-Aware Scheduling of Incoming Packets in Real-Time IoT Systems
Venue: arXiv cs.NI 2022
Year: 2022

When IP-packet processing is unconditionally carried out on behalf of an operating system kernel thread, processing systems can experience overload in high incoming traffic scenarios. This is especially worrying for embedded real-time devices controlling their physical environment in industrial IoT scenarios and automotive systems. We propose an embedded real-time aware IP stack adaption with an early demultiplexing scheme for incoming packets and subsequent per-flow aperiodic scheduling. By instrumenting existing embedded IP stacks, rigid prioritization with minimal latency is deployed without the need of further task resources. Simple mitigation techniques can be applied to individual flows, causing hardly measurable overhead while at the same time protecting the system from overload conditions. Our IP stack adaption is able to reduce the low-priority packet processing time by over 86% compared to an unmodified stack. The network subsystem can thereby remain active at a 7x higher general traffic load before disabling the receive IRQ as a last resort to assure deadlines.
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
Venue: arXiv cs.CL 2022
Year: 2022

As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing. We find that 1) Semantic role labeling (SRL) and dependency parsing (DP), would bring more performance gain than other tasks e.g. MT and summarization in the text-to-AMR transition even with much less data. 2) To make a better fit for AMR, data from auxiliary tasks should be properly "AMRized" to PseudoAMR before training. Knowledge from shallow level parsing tasks can be better transferred to AMR Parsing with structure transform. 3) Intermediate-task learning is a better paradigm to introduce auxiliary tasks to AMR parsing, compared to multitask learning. From an empirical perspective, we propose a principled method to involve auxiliary tasks to boost AMR parsing. Extensive experiments show that our method achieves new state-of-the-art performance on different benchmarks especially in topology-related scores.
A survey on improving NLP models with human explanations
Venue: arXiv cs.CL 2022
Year: 2022

Training a model with access to human explanations can improve data efficiency and model performance on in- and out-of-domain data. Adding to these empirical findings, similarity with the process of human learning makes learning from explanations a promising way to establish a fruitful human-machine interaction. Several methods have been proposed for improving natural language processing (NLP) models with human explanations, that rely on different explanation types and mechanism for integrating these explanations into the learning process. These methods are rarely compared with each other, making it hard for practitioners to choose the best combination of explanation type and integration mechanism for a specific use-case. In this paper, we give an overview of different methods for learning from human explanations, and discuss different factors that can inform the decision of which method to choose for a specific use-case.