OpenRaiser/Intern-Atlas huggingface dataset:
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey
Venue: arXiv cs.CL 2022
Year: 2022

We present the results of the NLP Community Metasurvey. Run from May to June 2022, the survey elicited opinions on controversial issues, including industry influence in the field, concerns about AGI, and ethics. Our results put concrete numbers to several controversies: For example, respondents are split almost exactly in half on questions about the importance of artificial general intelligence, whether language models understand language, and the necessity of linguistic structure and inductive bias for solving NLP problems. In addition, the survey posed meta-questions, asking respondents to predict the distribution of survey responses. This allows us not only to gain insight on the spectrum of beliefs held by NLP researchers, but also to uncover false sociological beliefs where the community's predictions don't match reality. We find such mismatches on a wide range of issues. Among other results, the community greatly overestimates its own belief in the usefulness of benchmarks and the potential for scaling to solve real-world problems, while underestimating its own belief in the importance of linguistic structure, inductive bias, and interdisciplinary science.
A Note on Copy-Protection from Random Oracles
Venue: arXiv cs.CR 2022
Year: 2022

Quantum copy-protection, introduced by Aaronson (CCC'09), uses the no-cloning principle of quantum mechanics to protect software from being illegally distributed. Constructing copy-protection has been an important problem in quantum cryptography. Since copy-protection is shown to be impossible to achieve in the plain model, we investigate the question of constructing copy-protection for arbitrary classes of unlearnable functions in the random oracle model. We present an impossibility result that rules out a class of copy-protection schemes in the random oracle model assuming the existence of quantum fully homomorphic encryption and quantum hardness of learning with errors. En route, we prove the impossibility of approximately correct copy-protection in the plain model.
An Open-Source P416 Compiler Backend for Reconfigurable Match-Action Table Switches
Venue: arXiv cs.NI 2022
Year: 2022

The P4 language has become the dominant choice for programming the reconfigurable match-action table based programmable switches. V1Model architecture is the most widely available realization of this paradigm. The open-source compiler frontend developed by the P4 consortium can execute syntax analysis and derive a hardware-independent representation of a program written using the latest version of P4 (also known as P416 ). A compiler backend is required to map this intermediate representation to the hardware resources of a V1Model switch. However, there is no open-source compiler backend available to check the realizability of a P416 program over a V1Model switch. Proprietary tools provided by different hardware vendors are available for this purpose. However, they are closed source and do not provide access to the internal mapping mechanisms. Which inhibits experimenting with new mapping algorithms and innovative instruction sets for reconfigurable match-action table architecture. Moreover, the proprietary compiler backends are costly and come with various non-disclosure agreements. These factors pose serious challenges to programmable switch-related research. In this work, we present an open-source P416 compiler backend for the V1Model architecture-based programmable switches. It uses heuristic-based mapping algorithms to map a P416 program over the hardware resources of a V1Model switch. It allows developers to rapidly prototype different mapping algorithms. It also gives various resource usage statistics of a P416 program, enabling comparison among multiple P416 schemes.
RepParser: End-to-End Multiple Human Parsing with Representative Parts
Venue: arXiv cs.CV 2022
Year: 2022

Existing methods of multiple human parsing usually adopt a two-stage strategy (typically top-down and bottom-up), which suffers from either strong dependence on prior detection or highly computational redundancy during post-grouping. In this work, we present an end-to-end multiple human parsing framework using representative parts, termed RepParser. Different from mainstream methods, RepParser solves the multiple human parsing in a new single-stage manner without resorting to person detection or post-grouping.To this end, RepParser decouples the parsing pipeline into instance-aware kernel generation and part-aware human parsing, which are responsible for instance separation and instance-specific part segmentation, respectively. In particular, we empower the parsing pipeline by representative parts, since they are characterized by instance-aware keypoints and can be utilized to dynamically parse each person instance. Specifically, representative parts are obtained by jointly localizing centers of instances and estimating keypoints of body part regions. After that, we dynamically predict instance-aware convolution kernels through representative parts, thus encoding person-part context into each kernel responsible for casting an image feature as an instance-specific representation.Furthermore, a multi-branch structure is adopted to divide each instance-specific representation into several part-aware representations for separate part segmentation.In this way, RepParser accordingly focuses on person instances with the guidance of representative parts and directly outputs parsing results for each person instance, thus eliminating the requirement of the prior detection or post-grouping.Extensive experiments on two challenging benchmarks demonstrate that our proposed RepParser is a simple yet effective framework and achieves very competitive performance.
Robust 3D Vision for Autonomous Robots
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents a fault-tolerant 3D vision system for autonomous robotic operation. In particular, pose estimation of space objects is achieved using 3D vision data in an integrated Kalman filter (KF) and an Iterative Closest Point (ICP) algorithm in a closed-loop configuration. The initial guess for the internal ICP iteration is provided by the state estimate propagation of the Kalman filer. The Kalman filter is capable of not only estimating the target's states but also its inertial parameters. This allows the motion of the target to be predictable as soon as the filter converges. Consequently, the ICP can maintain pose tracking over a wider range of velocity due to the increased precision of ICP initialization. Furthermore, incorporation of the target's dynamics model in the estimation process allows the estimator continuously provide pose estimation even when the sensor temporally loses its signal namely due to obstruction. The capabilities of the pose estimation methodology is demonstrated by a ground testbed for Automated Rendezvous & Docking. In this experiment, Neptec's Laser Camera System (LCS) is used for real-time scanning of a satellite model attached to a manipulator arm, which is driven by a simulator according to orbital and attitude dynamics. The results showed that robust tracking of the free-floating tumbling satellite can be achieved only if the Kalman filter and ICP are in a closed-loop configuration.
Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
Venue: arXiv cs.LG 2022
Year: 2022

This work proposes a Stochastic Variational Deep Kernel Learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The framework is composed of an encoder that compresses high-dimensional measurements into low-dimensional state variables, and a latent dynamical model for the state variables that predicts the system evolution over time. The training of the proposed model is carried out in an unsupervised manner, i.e., not relying on labeled data. Our learning method is evaluated on the motion of a pendulum -- a well studied baseline for nonlinear model identification and control with continuous states and control inputs -- measured via high-dimensional noisy RGB images. Results show that the method can effectively denoise measurements, learn compact state representations and latent dynamical models, as well as identify and quantify modeling uncertainties.
Extra DoF of Near-Field Holographic MIMOCommunications Leveraging Evanescent Waves
Venue: arXiv cs.IT 2022
Year: 2022

In this letter, we consider transceivers with spatially-constrained antenna apertures of rectangular symmetry, and aim to improve of spatial degrees of freedom (DoF) and channel capacity leveraging evanescent waves for information transmission in near-field scenarios based on the Fourier plane-wave series expansion. The treatment is limited to an isotropic scattering environment but can be extended to the non-isotropic case through the linear-system theoretic interpretation of plane-wave propagation. Numerical results show that evanescent waves have the significant potential to provide additional DoF and capacity in the near-field region.
Object Goal Navigation using Data Regularized Q-Learning
Venue: arXiv cs.RO 2022
Year: 2022

Object Goal Navigation requires a robot to find and navigate to an instance of a target object class in a previously unseen environment. Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal ('where to go') based on the semantic map to locate the target object instance. Long-term goal selection is formulated as a vision-based deep reinforcement learning problem. Specifically, an Encoder Network is trained to extract high-level features from a semantic map and select a long-term goal. In addition, we incorporate data augmentation and Q-function regularization to make the long-term goal selection more effective. We report experimental results using the photo-realistic Gibson benchmark dataset in the AI Habitat 3D simulation environment to demonstrate substantial performance improvement on standard measures in comparison with a state of the art data-driven baseline.
Information Security Management in High Quality IS Journals: A Review and Research Agenda
Venue: arXiv cs.CR 2022
Year: 2022

In the digital age, the protection of information resources is critical to the viability of organizations. Information Security Management (ISM) is a protective function that preserves the confidentiality, integrity and availability of information resources in organizations operating in a complex and evolving security threat landscape. This paper analyses ISM research themes, methods, and theories in high quality IS journals over a period of 30 years (up to the end of 2017). Although our review found that less than 1 percent of papers to be in the area of ISM, there has been a dramatic increase in the number of ISM publications as well as new emerging themes in the past decade. Further, past trends towards subjective-argumentative papers have reversed in favour of empirically validated research. Our analysis of research methods and approaches found ISM studies to be dominated by one-time surveys rather than case studies and action research. The findings suggest that although ISM research has improved its empirical backing over the years, it remains relatively disengaged from organisational practice.
An Access Control Method with Secret Key for Semantic Segmentation Models
Venue: arXiv cs.CV 2022
Year: 2022

A novel method for access control with a secret key is proposed to protect models from unauthorized access in this paper. We focus on semantic segmentation models with the vision transformer (ViT), called segmentation transformer (SETR). Most existing access control methods focus on image classification tasks, or they are limited to CNNs. By using a patch embedding structure that ViT has, trained models and test images can be efficiently encrypted with a secret key, and then semantic segmentation tasks are carried out in the encrypted domain. In an experiment, the method is confirmed to provide the same accuracy as that of using plain images without any encryption to authorized users with a correct key and also to provide an extremely degraded accuracy to unauthorized users.
Reshape: Adaptive Result-aware Skew Handling for Exploratory Analysis on Big Data
Venue: arXiv cs.DC 2022
Year: 2022

The process of data analysis, especially in GUI-based analytics systems, is highly exploratory. The user iteratively refines a workflow multiple times before arriving at the final workflow. In such an exploratory setting, it is valuable to the user if the initial results of the workflow are representative of the final answers so that the user can refine the workflow without waiting for the completion of its execution. Partitioning skew may lead to the production of misleading initial results during the execution. In this paper, we explore skew and its mitigation strategies from the perspective of the results shown to the user. We present a novel framework called Reshape that can adaptively handle partitioning skew in pipelined execution. Reshape employs a two-phase approach that transfers load in a fine-tuned manner to mitigate skew iteratively during execution, thus enabling it to handle changes in input-data distribution. Reshape has the ability to adaptively adjust skew-handling parameters, which reduces the technical burden on the users. Reshape supports a variety of operators such as HashJoin, Group-by, and Sort. We implemented Reshape on top of two big data engines, namely Amber and Flink, to demonstrate its generality and efficiency, and report an experimental evaluation using real and synthetic datasets.
Influence Maximization (IM) in Complex Networks with Limited Visibility Using Statistical Methods
Venue: arXiv cs.SI 2022
Year: 2022

A social network (SN) is a social structure consisting of a group representing the interaction between them. SNs have recently been widely used and, subsequently, have become suitable and popular platforms for product promotion and information diffusion. People in an SN directly influence each other's interests and behavior. One of the most important problems in SNs is to find people who can have the maximum influence on other nodes in the network in a cascade manner if they are chosen as the seed nodes of a network diffusion scenario. Influential diffusers are people who, if they are chosen as the seed set in a publishing issue in the network, that network will have the most people who have learned about that diffused entity. This is a well-known problem in literature known as influence maximization (IM) problem. Although it has been proven that this is an NP-complete problem and does not have a solution in polynomial time, it has been argued that it has the properties of sub modular functions and, therefore, can be solved using a greedy algorithm. Most of the methods proposed to improve this complexity are based on the assumption that the entire graph is visible. However, this assumption does not hold for many real-world graphs. This study is conducted to extend current maximization methods with link prediction techniques to pseudo-visibility graphs. To this end, a graph generation method called the exponential random graph model (ERGM) is used for link prediction. The proposed method is tested using the data from the Snap dataset of Stanford University. According to the experimental tests, the proposed method is efficient on real-world graphs.
Towards Real-World Video Deblurring by Exploring Blur Formation Process
Venue: arXiv cs.CV 2022
Year: 2022

This paper aims at exploring how to synthesize close-to-real blurs that existing video deblurring models trained on them can generalize well to real-world blurry videos. In recent years, deep learning-based approaches have achieved promising success on video deblurring task. However, the models trained on existing synthetic datasets still suffer from generalization problems over real-world blurry scenarios with undesired artifacts. The factors accounting for the failure remain unknown. Therefore, we revisit the classical blur synthesis pipeline and figure out the possible reasons, including shooting parameters, blur formation space, and image signal processor~(ISP). To analyze the effects of these potential factors, we first collect an ultra-high frame-rate (940 FPS) RAW video dataset as the data basis to synthesize various kinds of blurs. Then we propose a novel realistic blur synthesis pipeline termed as RAW-Blur by leveraging blur formation cues. Through numerous experiments, we demonstrate that synthesizing blurs in the RAW space and adopting the same ISP as the real-world testing data can effectively eliminate the negative effects of synthetic data. Furthermore, the shooting parameters of the synthesized blurry video, e.g., exposure time and frame-rate play significant roles in improving the performance of deblurring models. Impressively, the models trained on the blurry data synthesized by the proposed RAW-Blur pipeline can obtain more than 5dB PSNR gain against those trained on the existing synthetic blur datasets. We believe the novel realistic synthesis pipeline and the corresponding RAW video dataset can help the community to easily construct customized blur datasets to improve real-world video deblurring performance largely, instead of laboriously collecting real data pairs.
MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities
Venue: arXiv cs.SE 2022
Year: 2022

Learning heterogeneous graphs consisting of different types of nodes and edges enhances the results of homogeneous graph techniques. An interesting example of such graphs is control-flow graphs representing possible software code execution flows. As such graphs represent more semantic information of code, developing techniques and tools for such graphs can be highly beneficial for detecting vulnerabilities in software for its reliability. However, existing heterogeneous graph techniques are still insufficient in handling complex graphs where the number of different types of nodes and edges is large and variable. This paper concentrates on the Ethereum smart contracts as a sample of software codes represented by heterogeneous contract graphs built upon both control-flow graphs and call graphs containing different types of nodes and links. We propose MANDO, a new heterogeneous graph representation to learn such heterogeneous contract graphs' structures. MANDO extracts customized metapaths, which compose relational connections between different types of nodes and their neighbors. Moreover, it develops a multi-metapath heterogeneous graph attention network to learn multi-level embeddings of different types of nodes and their metapaths in the heterogeneous contract graphs, which can capture the code semantics of smart contracts more accurately and facilitate both fine-grained line-level and coarse-grained contract-level vulnerability detection. Our extensive evaluation of large smart contract datasets shows that MANDO improves the vulnerability detection results of other techniques at the coarse-grained contract level. More importantly, it is the first learning-based approach capable of identifying vulnerabilities at the fine-grained line-level, and significantly improves the traditional code analysis-based vulnerability detection approaches by 11.35% to 70.81% in terms of F1-score.
Bayesian Neural Network Language Modeling for Speech Recognition
Venue: arXiv cs.CL 2022
Year: 2022

State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. They are prone to overfitting and poor generalization when given limited training data. To this end, an overarching full Bayesian learning framework encompassing three methods is proposed in this paper to account for the underlying uncertainty in LSTM-RNN and Transformer LMs. The uncertainty over their model parameters, choice of neural activations and hidden output representations are modeled using Bayesian, Gaussian Process and variational LSTM-RNN or Transformer LMs respectively. Efficient inference approaches were used to automatically select the optimal network internal components to be Bayesian learned using neural architecture search. A minimal number of Monte Carlo parameter samples as low as one was also used. These allow the computational costs incurred in Bayesian NNLM training and evaluation to be minimized. Experiments are conducted on two tasks: AMI meeting transcription and Oxford-BBC LipReading Sentences 2 (LRS2) overlapped speech recognition using state-of-the-art LF-MMI trained factored TDNN systems featuring data augmentation, speaker adaptation and audio-visual multi-channel beamforming for overlapped speech. Consistent performance improvements over the baseline LSTM-RNN and Transformer LMs with point estimated model parameters and drop-out regularization were obtained across both tasks in terms of perplexity and word error rate (WER). In particular, on the LRS2 data, statistically significant WER reductions up to 1.3% and 1.2% absolute (12.1% and 11.3% relative) were obtained over the baseline LSTM-RNN and Transformer LMs respectively after model combination between Bayesian NNLMs and their respective baselines.
Bipolar Almost Equiangular Tight Frames for NOMA Systems
Venue: arXiv cs.IT 2022
Year: 2022

Non-Orthogonal Multiple Access (NOMA) is a concept which is gaining a big popularity in multiuser networks. It's due to its advantages in sense of total network throughput. It becomes especially significant in large networks such as Internet of Things (IoT) networks or 5G networks. One of the known NOMA techniques is DS-CDMA NOMA, which make use of non-orthogonal coding schemes to optimize capacity at multiuser networks. Equiangular Tight Frames (ETF) are known to be an optimal sequences' sets (in sense of capacity) for this technique. Unfortunately, ETFs are limited to very specific pairs of users' number and sequence lengths which put undesirable constraints on practical systems. In this paper our goal is to break those constraints by proposing alternative family of non-orthogonal sequences which on the one hand, possess similar properties to those of ETFs (for that reason we'll denote them as Almost ETFs) and on the other hand, doesn't have those limitation on users' number and sequence length. We're basing our approach by starting with known technique of building standard ETFs, and extending it by slight modifications to technique of building AETFs. In this paper we'll concentrate on bipolar (+/-1 valued) and relatively short (up to length of 100) sequences, since we're interested in sequences which will be of practical value in real systems.
Learning Stable Robotic Skills on Riemannian Manifolds
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, we propose an approach to learn stable dynamical systems evolving on Riemannian manifolds. The approach leverages a data-efficient procedure to learn a diffeomorphic transformation that maps simple stable dynamical systems onto complex robotic skills. By exploiting mathematical tools from differential geometry, the method ensures that the learned skills fulfill the geometric constraints imposed by the underlying manifolds, such as unit quaternion (UQ) for orientation and symmetric positive definite (SPD) matrices for impedance, while preserving the convergence to a given target. The proposed approach is firstly tested in simulation on a public benchmark, obtained by projecting Cartesian data into UQ and SPD manifolds, and compared with existing approaches. Apart from evaluating the approach on a public benchmark, several experiments were performed on a real robot performing bottle stacking in different conditions and a drilling task in cooperation with a human operator. The evaluation shows promising results in terms of learning accuracy and task adaptation capabilities.