OpenRaiser/Intern-Atlas huggingface dataset:
Certify the Uncertified: Towards Assessment of Virtualization for Mixed-criticality in the Automotive Domain
Venue: arXiv cs.DC 2022
Year: 2022

Nowadays, a feature-rich automotive vehicle offers several technologies to assist the driver during his trip and guarantee an amusing infotainment system to the other passengers, too. Consolidating worlds at different criticalities is a welcomed challenge for car manufacturers that have recently tried to leverage virtualization technologies due to reduced maintenance, deployment, and shipping costs. For this reason, more and more mixed-criticality systems are emerging, trying to assure compliance with the ISO 26262 Road Vehicle Safety standard. In this short paper, we provide a preliminary investigation of the certification capabilities for Jailhouse, a popular open-source partitioning hypervisor. To this aim, we propose a testing methodology and showcase the results, pointing out when the software gets to a faulting state, deviating from its expected behavior. The ultimate goal is to picture the right direction for the hypervisor towards a potential certification process.
ORCA: Interpreting Prompted Language Models via Locating Supporting Data Evidence in the Ocean of Pretraining Data
Venue: arXiv cs.CL 2022
Year: 2022

Large pretrained language models have been performing increasingly well in a variety of downstream tasks via prompting. However, it remains unclear from where the model learns the task-specific knowledge, especially in a zero-shot setup. In this work, we want to find evidence of the model's task-specific competence from pretraining and are specifically interested in locating a very small subset of pretraining data that directly supports the model in the task. We call such a subset supporting data evidence and propose a novel method ORCA to effectively identify it, by iteratively using gradient information related to the downstream task. This supporting data evidence offers interesting insights about the prompted language models: in the tasks of sentiment analysis and textual entailment, BERT shows a substantial reliance on BookCorpus, the smaller corpus of BERT's two pretraining corpora, as well as on pretraining examples that mask out synonyms to the task verbalizers.
Are Large Pre-Trained Language Models Leaking Your Personal Information?
Venue: arXiv cs.CL 2022
Year: 2022

Are Large Pre-Trained Language Models Leaking Your Personal Information? In this paper, we analyze whether Pre-Trained Language Models (PLMs) are prone to leaking personal information. Specifically, we query PLMs for email addresses with contexts of the email address or prompts containing the owner's name. We find that PLMs do leak personal information due to memorization. However, since the models are weak at association, the risk of specific personal information being extracted by attackers is low. We hope this work could help the community to better understand the privacy risk of PLMs and bring new insights to make PLMs safe.
Multimodal Knowledge Alignment with Reinforcement Learning
Venue: arXiv cs.CL 2022
Year: 2022

Large language models readily adapt to novel settings, even without task-specific training data. Can their zero-shot capacity be extended to multimodal inputs? In this work, we propose ESPER which extends language-only zero-shot models to unseen multimodal tasks, like image and audio captioning. Our key novelty is to use reinforcement learning to align multimodal inputs to language model generations without direct supervision: for example, in the image case our reward optimization relies only on cosine similarity derived from CLIP, and thus requires no additional explicitly paired (image, caption) data. Because the parameters of the language model are left unchanged, the model maintains its capacity for zero-shot generalization. Experiments demonstrate that ESPER outperforms baselines and prior work on a variety of zero-shot tasks; these include a new benchmark we collect+release, ESP dataset, which tasks models with generating several diversely-styled captions for each image.
Asking the Right Questions in Low Resource Template Extraction
Venue: arXiv cs.CL 2022
Year: 2022

Information Extraction (IE) researchers are mapping tasks to Question Answering (QA) in order to leverage existing large QA resources, and thereby improve data efficiency. Especially in template extraction (TE), mapping an ontology to a set of questions can be more time-efficient than collecting labeled examples. We ask whether end users of TE systems can design these questions, and whether it is beneficial to involve an NLP practitioner in the process. We compare questions to other ways of phrasing natural language prompts for TE. We propose a novel model to perform TE with prompts, and find it benefits from questions over other styles of prompts, and that they do not require an NLP background to author.
Optimizing UAV Recharge Scheduling for Heterogeneous and Persistent Aerial Service
Venue: arXiv cs.RO 2022
Year: 2022

The adoption of UAVs in communication networks is becoming reality thanks to the deployment of advanced solutions for connecting UAVs and using them as communication relays. However, the use of UAVs introduces novel energy constraints and scheduling challenges in the dynamic management of network devices, due to the need to call back and recharge, or substitute, UAVs that run out of energy. In this paper, we design UAV recharging schemes under realistic assumptions on limited flight times and time consuming charging operations. Such schemes are designed to minimize the size of the fleet to be devoted to a persistent service of a set of aerial locations, hence its cost. We consider a fleet of homogeneous UAVs both under homogeneous and heterogeneous service topologies. For UAVs serving aerial locations with homogeneous distances to a recharge station, we design a simple scheduling, that we name HORR, which we prove to be feasible and optimal, in the sense that it uses the minimum possible number of UAVs to guarantee the coverage of the aerial service locations. For the case of non-evenly distributed aerial locations, we demonstrate that the problem becomes NP-hard, and design a lightweight recharging scheduling scheme, PHERR, that extends the operation of HORR to the heterogeneous case, leveraging the partitioning of the set of service locations. We show that PHERR is near-optimal because it approaches the performance limits identified through a lower bound that we formulate on the total fleet size.
Deterministic Massively Parallel Algorithms for Ruling Sets
Venue: arXiv cs.DS 2022
Year: 2022

In this paper we present a deterministic $O(\log\log n)$-round algorithm for the 2-ruling set problem in the Massively Parallel Computation model with $\tilde{O}(n)$ memory; this algorithm also runs in $O(\log\log n)$ rounds in the Congested Clique model. This is exponentially faster than the fastest known deterministic 2-ruling set algorithm for these models, which is simply the $O(\log Δ)$-round deterministic Maximal Independent Set algorithm due to Czumaj, Davies, and Parter (SPAA 2020). Our result is obtained by derandomizing the 2-ruling set algorithm of Kothapalli and Pemmaraju (FSTTCS 2012).
Service Discovery in Social Internet of Things using Graph Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

Internet-of-Things (IoT) networks intelligently connect thousands of physical entities to provide various services for the community. It is witnessing an exponential expansion, which is complicating the process of discovering IoT devices existing in the network and requesting corresponding services from them. As the highly dynamic nature of the IoT environment hinders the use of traditional solutions of service discovery, we aim, in this paper, to address this issue by proposing a scalable resource allocation neural model adequate for heterogeneous large-scale IoT networks. We devise a Graph Neural Network (GNN) approach that utilizes the social relationships formed between the devices in the IoT network to reduce the search space of any entity lookup and acquire a service from another device in the network. This proposed resource allocation approach surpasses standardization issues and embeds the structure and characteristics of the social IoT graph, by the means of GNNs, for eventual clustering analysis process. Simulation results applied on a real-world dataset illustrate the performance of this solution and its significant efficiency to operate on large-scale IoT networks.
Uncertainty Quantification for Transport in Porous media using Parameterized Physics Informed neural Networks
Venue: arXiv cs.CE 2022
Year: 2022

We present a Parametrization of the Physics Informed Neural Network (P-PINN) approach to tackle the problem of uncertainty quantification in reservoir engineering problems. We demonstrate the approach with the immiscible two phase flow displacement (Buckley-Leverett problem) in heterogeneous porous medium. The reservoir properties (porosity, permeability) are treated as random variables. The distribution of these properties can affect dynamic properties such as the fluids saturation, front propagation speed or breakthrough time. We explore and use to our advantage the ability of networks to interpolate complex high dimensional functions. We observe that the additional dimensions resulting from a stochastic treatment of the partial differential equations tend to produce smoother solutions on quantities of interest (distributions parameters) which is shown to improve the performance of PINNS. We show that provided a proper parameterization of the uncertainty space, PINN can produce solutions that match closely both the ensemble realizations and the stochastic moments. We demonstrate applications for both homogeneous and heterogeneous fields of properties. We are able to solve problems that can be challenging for classical methods. This approach gives rise to trained models that are both more robust to variations in the input space and can compete in performance with traditional stochastic sampling methods.
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
Venue: arXiv cs.LG 2022
Year: 2022

Multitask learning assumes that models capable of learning from multiple tasks can achieve better quality and efficiency via knowledge transfer, a key feature of human learning. Though, state of the art ML models rely on high customization for each task and leverage size and data scale rather than scaling the number of tasks. Also, continual learning, that adds the temporal aspect to multitask, is often focused to the study of common pitfalls such as catastrophic forgetting instead of being studied at a large scale as a critical component to build the next generation artificial intelligence.We propose an evolutionary method capable of generating large scale multitask models that support the dynamic addition of new tasks. The generated multitask models are sparsely activated and integrates a task-based routing that guarantees bounded compute cost and fewer added parameters per task as the model expands.The proposed method relies on a knowledge compartmentalization technique to achieve immunity against catastrophic forgetting and other common pitfalls such as gradient interference and negative transfer. We demonstrate empirically that the proposed method can jointly solve and achieve competitive results on 69public image classification tasks, for example improving the state of the art on a competitive benchmark such as cifar10 by achieving a 15% relative error reduction compared to the best model trained on public data.
Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors
Venue: arXiv cs.LG 2022
Year: 2022

Human Activity Recognition (HAR) is a relevant inference task in many mobile applications. State-of-the-art HAR at the edge is typically achieved with lightweight machine learning models such as decision trees and Random Forests (RFs), whereas deep learning is less common due to its high computational complexity. In this work, we propose a novel implementation of HAR based on deep neural networks, and precisely on Binary Neural Networks (BNNs), targeting low-power general purpose processors with a RISC-V instruction set. BNNs yield very small memory footprints and low inference complexity, thanks to the replacement of arithmetic operations with bit-wise ones. However, existing BNN implementations on general purpose processors impose constraints tailored to complex computer vision tasks, which result in over-parametrized models for simpler problems like HAR. Therefore, we also introduce a new BNN inference library, which targets ultra-compact models explicitly. With experiments on a single-core RISC-V processor, we show that BNNs trained on two HAR datasets obtain higher classification accuracy compared to a state-of-the-art baseline based on RFs. Furthermore, our BNN reaches the same accuracy of a RF with either less memory (up to 91%) or more energy-efficiency (up to 70%), depending on the complexity of the features extracted by the RF.
Reasoning over Logically Interacted Conditions for Question Answering
Venue: arXiv cs.CL 2022
Year: 2022

Some questions have multiple answers that are not equally correct, i.e. answers are different under different conditions. Conditions are used to distinguish answers as well as to provide additional information to support them. In this paper, we study a more challenging task where answers are constrained by a list of conditions that logically interact, which requires performing logical reasoning over the conditions to determine the correctness of the answers. Even more challenging, we only provide evidences for a subset of the conditions, so some questions may not have deterministic answers. In such cases, models are asked to find probable answers and identify conditions that need to be satisfied to make the answers correct. We propose a new model, TReasoner, for this challenging reasoning task. TReasoner consists of an entailment module, a reasoning module, and a generation module (if the answers are free-form text spans). TReasoner achieves state-of-the-art performance on two benchmark conditional QA datasets, outperforming the previous state-of-the-art by 3-10 points.
Joint Beam Management and Power Allocation in THz-NOMA Networks
Venue: arXiv cs.IT 2022
Year: 2022

This paper investigates how to apply non-orthogonal multiple access (NOMA) as an add-on in terahertz (THz) networks. In particular, prior to the implementation of NOMA, it is assumed that there exists a legacy THz system, where spatial beams have already been configured to serve legacy primary users. The aim of this paper is to study how these pre-configured spatial beams can be used as a type of bandwidth resources, on which additional secondary users are served without degrading the performance of the legacy primary users. A joint beam management and power allocation problem is first formulated as a mixed combinatorial non-convex optimization problem, and then solved by two methods with different performance-complexity tradeoffs, one based on the branch and bound method and the other based on successive convex approximation. Both analytical and simulation results are presented to illustrate the new features of beam-based resource allocation in THz-NOMA networks and also demonstrate that those pre-configured spatial beams can be employed to improve the system throughput and connectivity in a spectrally efficient manner.
Wireless Deep Video Semantic Transmission
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed methods exploit nonlinear transform and conditional coding architecture to adaptively extract semantic features across video frames, and transmit semantic feature domain representations over wireless channels via deep joint source-channel coding. Our framework is collected under the name deep video semantic transmission (DVST). In particular, benefiting from the strong temporal prior provided by the feature domain context, the learned nonlinear transform function becomes temporally adaptive, resulting in a richer and more accurate entropy model guiding the transmission of current frame. Accordingly, a novel rate adaptive transmission mechanism is developed to customize deep joint source-channel coding for video sources. It learns to allocate the limited channel bandwidth within and among video frames to maximize the overall transmission performance. The whole DVST design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under perceptual quality metrics or machine vision task performance metrics. Across standard video source test sequences and various communication scenarios, experiments show that our DVST can generally surpass traditional wireless video coded transmission schemes. The proposed DVST framework can well support future semantic communications due to its video content-aware and machine vision task integration abilities.
Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification
Venue: arXiv cs.CL 2022
Year: 2022

Class imbalance naturally exists when train and test models in different domains. Unsupervised domain adaptation (UDA) augments model performance with only accessible annotations from the source domain and unlabeled data from the target domain. However, existing state-of-the-art UDA models learn domain-invariant representations and evaluate primarily on class-balanced data across domains. In this work, we propose an unsupervised domain adaptation approach via reinforcement learning that jointly leverages feature variants and imbalanced labels across domains. We experiment with the text classification task for its easily accessible datasets and compare the proposed method with five baselines. Experiments on three datasets prove that our proposed method can effectively learn robust domain-invariant representations and successfully adapt text classifiers on imbalanced classes over domains. The code is available at https://github.com/woqingdoua/ImbalanceClass.
Grasping as Inference: Reactive Grasping in Heavily Cluttered Environment
Venue: arXiv cs.RO 2022
Year: 2022

Although, in the task of grasping via a data-driven method, closed-loop feedback and predicting 6 degrees of freedom (DoF) grasp rather than conventionally used 4DoF top-down grasp are demonstrated to improve performance individually, few systems have both. Moreover, the sequential property of that task is hardly dealt with, while the approaching motion necessarily generates a series of observations. Therefore, this paper synthesizes three approaches and suggests a closed-loop framework that can predict the 6DoF grasp in a heavily cluttered environment from continuously received vision observations. This can be realized by formulating the grasping problem as Hidden Markov Model and applying a particle filter to infer grasp. Additionally, we introduce a novel lightweight Convolutional Neural Network (CNN) model that evaluates and initializes grasp samples in real-time, making the particle filter process possible. The experiments, which are conducted on a real robot with a heavily cluttered environment, show that our framework not only quantitatively improves the grasping success rate significantly compared to the baseline algorithms, but also qualitatively reacts to a dynamic change in the environment and cleans up the table.
Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional Networks
Venue: arXiv cs.CL 2022
Year: 2022

The last few years have witnessed an exponential rise in the propagation of offensive text on social media. Identification of this text with high precision is crucial for the well-being of society. Most of the existing approaches tend to give high toxicity scores to innocuous statements (e.g., "I am a gay man"). These false positives result from over-generalization on the training data where specific terms in the statement may have been used in a pejorative sense (e.g., "gay"). Emphasis on such words alone can lead to discrimination against the classes these systems are designed to protect. In this paper, we address the problem of offensive language detection on Twitter, while also detecting the type and the target of the offence. We propose a novel approach called SyLSTM, which integrates syntactic features in the form of the dependency parse tree of a sentence and semantic features in the form of word embeddings into a deep learning architecture using a Graph Convolutional Network. Results show that the proposed approach significantly outperforms the state-of-the-art BERT model with orders of magnitude fewer number of parameters.
Revisiting the Efficiency of Asynchronous Multi Party Computation Against General Adversaries
Venue: arXiv cs.CR 2022
Year: 2022

In this paper, we design secure multi-party computation (MPC) protocols in the asynchronous communication setting with optimal resilience. Our protocols are secure against a computationally-unbounded malicious adversary, characterized by an adversary structure $\mathcal{Z}$, which enumerates all possible subsets of potentially corrupt parties. Our protocols incur a communication of $\mathcal{O}(|\mathcal{Z}|^2)$ and $\mathcal{O}(|\mathcal{Z}|)$ bits per multiplication for perfect and statistical security respectively. These are the first protocols with this communication complexity, as such protocols were known only in the synchronous communication setting (Hirt and Tschudi, ASIACRYPT 2013).
Prototyping Next-Generation O-RAN Research Testbeds with SDRs
Venue: arXiv cs.NI 2022
Year: 2022

Open RAN (O-RAN) defines an emerging cellular radio access network (RAN) architecture for future 6G wireless networks, emphasizing openness and intelligence which are considered the foundations of future 6G wireless networks. While the inherent complexity and flexibility of the RAN give rise to many new research problems, progress in developing solutions is hampered due to the lack of end-to-end, fully developed platforms that can help in pursuing use cases in realistic environments. This has motivated the formation of open-source frameworks available to the wireless community. However, the rapid evolution of dedicated platforms and solutions utilizing various software-based technologies often leaves questions regarding the interoperability and interactions between the components in the framework. This article shows how to build a software-defined radio testbed featuring an open-source 5G system that can interact with the near-real-time (near-RT) RAN intelligent controller (RIC) of the O-RAN architecture through standard interfaces. We focus on the O-RAN E2 interface interactions and outline the procedure to enable a RAN system with E2 capabilities. We demonstrate the working of two xApps on the testbed with detailed E2 message exchange procedures and their role in controlling next-generation RANs.