OpenRaiser/Intern-Atlas huggingface dataset:
A Real-Time Millimeter Wave V2V Channel Sounder
Venue: arXiv cs.IT 2022
Year: 2022

Wireless communication in millimeter wave spectrum is poised to provide the latency and bandwidth needed for advanced use cases unfeasible at lower frequencies. Despite the market potential of vehicular communication networks, investigations into the millimeter wave vehicular channel are lacking. In this paper, we present a detailed overview of a novel 1 GHz wide, multi-antenna vehicle to vehicle directional channel sounding and measurement platform operating at 28 GHz. The channel sounder uses two 256-element phased arrays at the transmitter vehicle and four 64-element arrays at the receiver vehicle, with the receiver measuring 116 different directional beams in less than 1 millisecond. By measuring the full multi-beam channel impulse response at large bandwidths, our system provides unprecedented insight in instantaneous mobile vehicle to vehicle channels. The system also uses centimeter-level global position tracking and 360 degree video capture to provide additional contextual information for joint communication and sensing applications. An initial measurement campaign was conducted on highway and surface streets in Austin, Texas. We show example data that highlights the sensing capability of the system. Preliminary results from the measurement campaign show that bumper mounted mmWave arrays provide rich scattering in traffic as well a provide significant directional diversity aiding towards high reliability vehicular communication. Additionally, potential waveguide effects from high traffic in lanes can also extend the range of mmWave signals significantly.
Confidence Dimension for Deep Learning based on Hoeffding Inequality and Relative Evaluation
Venue: arXiv cs.LG 2022
Year: 2022

Research on the generalization ability of deep neural networks (DNNs) has recently attracted a great deal of attention. However, due to their complex architectures and large numbers of parameters, measuring the generalization ability of specific DNN models remains an open challenge. In this paper, we propose to use multiple factors to measure and rank the relative generalization of DNNs based on a new concept of confidence dimension (CD). Furthermore, we provide a feasible framework in our CD to theoretically calculate the upper bound of generalization based on the conventional Vapnik-Chervonenk dimension (VC-dimension) and Hoeffding's inequality. Experimental results on image classification and object detection demonstrate that our CD can reflect the relative generalization ability for different DNNs. In addition to full-precision DNNs, we also analyze the generalization ability of binary neural networks (BNNs), whose generalization ability remains an unsolved problem. Our CD yields a consistent and reliable measure and ranking for both full-precision DNNs and BNNs on all the tasks.
Deep Point Cloud Simplification for High-quality Surface Reconstruction
Venue: arXiv cs.CV 2022
Year: 2022

The growing size of point clouds enlarges consumptions of storage, transmission, and computation of 3D scenes. Raw data is redundant, noisy, and non-uniform. Therefore, simplifying point clouds for achieving compact, clean, and uniform points is becoming increasingly important for 3D vision and graphics tasks. Previous learning based methods aim to generate fewer points for scene understanding, regardless of the quality of surface reconstruction, leading to results with low reconstruction accuracy and bad point distribution. In this paper, we propose a novel point cloud simplification network (PCS-Net) dedicated to high-quality surface mesh reconstruction while maintaining geometric fidelity. We first learn a sampling matrix in a feature-aware simplification module to reduce the number of points. Then we propose a novel double-scale resampling module to refine the positions of the sampled points, to achieve a uniform distribution. To further retain important shape features, an adaptive sampling strategy with a novel saliency loss is designed. With our PCS-Net, the input non-uniform and noisy point cloud can be simplified in a feature-aware manner, i.e., points near salient features are consolidated but still with uniform distribution locally. Experiments demonstrate the effectiveness of our method and show that we outperform previous simplification or reconstruction-oriented upsampling methods.
An Empirical Study of Bugs in Eclipse Stable Internal Interfaces
Venue: arXiv cs.SE 2022
Year: 2022

TThe Eclipse framework is a popular and widely used framework that has been evolving for over a decade. The framework provides both stable interfaces (APIs) and unstable interfaces (non-APIs). Despite being discouraged by Eclipse, application developers often use non-APIs which cause their systems to fail when ported to new framework releases. Previous studies showed that applications using relatively old non-APIs are more likely to be compatible with new releases compared to the ones that used newly introduced non-APIs. Furthermore, from our previous study about the stability of Eclipse internal interfaces, we discovered that there exist 327K stable non-API methods as the Eclipse framework evolves. In the same study, we recommended that 327K stable non-API methods can be used by Eclipse interface providers as possible candidates for promotion to stable interfaces. However, since non-APIs are unsupported and considered to be immature i.e., can contain bugs, to this end there exist a need to first investigate the stable non-APIs for possible bugs before they can be promoted to APIs. In this study, we empirically investigated the stable non-API for possible bugs using Sonarqube software quality tool. We discovered that over 79.8% classes containing old stable non-APIs methods have zero bugs. Results from this study can be used by both interface providers and users as a starting point to analyze which interfaces are well tested and also estimate how much work could be involved when performing bug fixing for a given eclipse release.
Type-Driven Multi-Turn Corrections for Grammatical Error Correction
Venue: arXiv cs.CL 2022
Year: 2022

Grammatical Error Correction (GEC) aims to automatically detect and correct grammatical errors. In this aspect, dominant models are trained by one-iteration learning while performing multiple iterations of corrections during inference. Previous studies mainly focus on the data augmentation approach to combat the exposure bias, which suffers from two drawbacks. First, they simply mix additionally-constructed training instances and original ones to train models, which fails to help models be explicitly aware of the procedure of gradual corrections. Second, they ignore the interdependence between different types of corrections. In this paper, we propose a Type-Driven Multi-Turn Corrections approach for GEC. Using this approach, from each training instance, we additionally construct multiple training instances, each of which involves the correction of a specific type of errors. Then, we use these additionally-constructed training instances and the original one to train the model in turn. Experimental results and in-depth analysis show that our approach significantly benefits the model training. Particularly, our enhanced model achieves state-of-the-art single-model performance on English GEC benchmarks. We release our code at Github.
Active Visuo-Haptic Object Shape Completion
Venue: arXiv cs.RO 2022
Year: 2022

Recent advancements in object shape completion have enabled impressive object reconstructions using only visual input. However, due to self-occlusion, the reconstructions have high uncertainty in the occluded object parts, which negatively impacts the performance of downstream robotic tasks such as grasping. In this work, we propose an active visuo-haptic shape completion method called Act-VH that actively computes where to touch the objects based on the reconstruction uncertainty. Act-VH reconstructs objects from point clouds and calculates the reconstruction uncertainty using IGR, a recent state-of-the-art implicit surface deep neural network. We experimentally evaluate the reconstruction accuracy of Act-VH against five baselines in simulation and in the real world. We also propose a new simulation environment for this purpose. The results show that Act-VH outperforms all baselines and that an uncertainty-driven haptic exploration policy leads to higher reconstruction accuracy than a random policy and a policy driven by Gaussian Process Implicit Surfaces. As a final experiment, we evaluate Act-VH and the best reconstruction baseline on grasping 10 novel objects. The results show that Act-VH reaches a significantly higher grasp success rate than the baseline on all objects. Together, this work opens up the door for using active visuo-haptic shape completion in more complex cluttered scenes.
Optimal Rejection Function Meets Character Recognition Tasks
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we propose an optimal rejection method for rejecting ambiguous samples by a rejection function. This rejection function is trained together with a classification function under the framework of Learning-with-Rejection (LwR). The highlights of LwR are: (1) the rejection strategy is not heuristic but has a strong background from a machine learning theory, and (2) the rejection function can be trained on an arbitrary feature space which is different from the feature space for classification. The latter suggests we can choose a feature space that is more suitable for rejection. Although the past research on LwR focused only on its theoretical aspect, we propose to utilize LwR for practical pattern classification tasks. Moreover, we propose to use features from different CNN layers for classification and rejection. Our extensive experiments of notMNIST classification and character/non-character classification demonstrate that the proposed method achieves better performance than traditional rejection strategies.
EMAKG: An Enhanced Version Of The Microsoft Academic Knowledge Graph
Venue: arXiv cs.DL 2022
Year: 2022

Scholarly knowledge graphs are valuable sources of information in several research fields. Despite the number of existing datasets related to publications and researchers, resource quality, coverage and accessibility are still limited. This article presents the Enhanced Microsoft Academic Knowledge Graph, a large dataset of information about scientific publications and involved entities, and the methods developed to build it. Data includes geographical information, researchers' collaborative networks and movements between institutions, academic-related metrics, and linguistic features. The dataset merges information from several data sources and has high temporal and spatial 7 coverage, allowing several use cases.
Nearest Neighbor Classifier with Margin Penalty for Active Learning
Venue: arXiv cs.IR 2022
Year: 2022

As deep learning becomes the mainstream in the field of natural language processing, the need for suitable active learning method are becoming unprecedented urgent. Active Learning (AL) methods based on nearest neighbor classifier are proposed and demonstrated superior results. However, existing nearest neighbor classifier are not suitable for classifying mutual exclusive classes because inter-class discrepancy cannot be assured by nearest neighbor classifiers. As a result, informative samples in the margin area can not be discovered and AL performance are damaged. To this end, we propose a novel Nearest neighbor Classifier with Margin penalty for Active Learning(NCMAL). Firstly, mandatory margin penalty are added between classes, therefore both inter-class discrepancy and intra-class compactness are both assured. Secondly, a novel sample selection strategy are proposed to discover informative samples within the margin area. To demonstrate the effectiveness of the methods, we conduct extensive experiments on for datasets with other state-of-the-art methods. The experimental results demonstrate that our method achieves better results with fewer annotated samples than all baseline methods.
Abstract Interpretation on E-Graphs
Venue: arXiv cs.LO 2022
Year: 2022

Recent e-graph applications have typically considered concrete semantics of expressions, where the notion of equivalence stems from concrete interpretation of expressions. However, equivalences that hold over one interpretation may not hold in an alternative interpretation. Such an observation can be exploited. We consider the application of abstract interpretation to e-graphs, and show that within an e-graph, the lattice meet operation associated with the abstract domain has a natural interpretation for an e-class, leading to improved precision in over-approximation. In this extended abstract, we use Interval Arithmetic (IA) to illustrate this point.
A domain specific language for data-centric infographics: technical report
Venue: arXiv cs.SE 2022
Year: 2022

The production process of data-centric infographics entails problems related to a disconnection between the supporting software environments. We investigate those problems and redesigns this process following the model-driven paradigm. We have designed a domain-specific language to model infographics, and implemented an interpreter that generates the infographics automatically. To inform the engineering of the DSL and interpreter, we have analysed 58 infographics reporting on ethical, social and environmental accounting, and 10 infographic design tools. We then have tested our proposal by means of modelling a sample of 10 infographics and generating them. We have also conducted a controlled experiment to assess the extent to which the generated infograhics are as visually attractive as the original ones and whether the participants can identify whether the infographic they see is original or generated. This report provides the details of this research project: conceptual model, collected data, analysis, and supporting instruments.
Non-commutative propositional logic with short-circuited biconditional and NAND
Venue: arXiv cs.LO 2022
Year: 2022

Short-circuit evaluation denotes the semantics of propositional connectives in which the second argument is evaluated only if the first argument does not suffice to determine the value of the expression. In programming, short-circuit evaluation is widely used, with left-sequential conjunction and disjunction as primitive connectives. We consider left-sequential, non-commutative propositional logic, also known as MSCL (memorising short-circuit logic), and start from a previously published, equational axiomatisation. First, we extend this logic with a left-sequential version of the biconditional connective, which allows for an elegant axiomatisation of MSCL. Next, we consider a left-sequential version of the NAND operator (the Sheffer stroke) and again give a complete, equational axiomatisation of the corresponding variant of MSCL. Finally, we consider these logical systems in a three-valued setting with a constant for `undefined', and again provide completeness results.
Application of Data Collected by Endpoint Detection and Response Systems for Implementation of a Network Security System based on Zero Trust Principles and the EigenTrust Algorithm
Venue: arXiv cs.CR 2022
Year: 2022

Traditionally, security systems for enterprises have implicit access based on strong cryptography, authentication and key sharing, wherein access control is based on Role Based Access Control (RBAC), in which roles such as manager, accountant and so on provide a way of deciding a subject's authority. However, years of post-attack analysis on enterprise networks has shown that a majority of times, security breaches occur intentionally or accidently due to implicitly trusted people of an enterprise itself. Zero Trust Architecture works on the principle of never granting trust implicitly, but rather continuously evaluating the trust parameters for each resource access request and has a strict, but not rigid, set of protocols for access control of a subject to resources. Endpoint Detection and Response (EDR) systems are tools that collect a large number of attributes in and around machines within an enterprise network to have close visibility into sophisticated intrusion. In our work, we seek to deploy EDR systems and build trust algorithms using tactical provenance analysis, threshold cryptography and reputation management to continuously record data, evaluate trust of a subject, and simultaneously analyze them against a database of known threat vectors to provide conditional access control. However, EDR tools generate a high volume of data that leads to false alarms, misdetections and correspondingly a high backlog of tasks that makes it infeasible, which is addressed using tactical provenance analysis and information theory.
Semi-Markov Offline Reinforcement Learning for Healthcare
Venue: arXiv cs.LG 2022
Year: 2022

Reinforcement learning (RL) tasks are typically framed as Markov Decision Processes (MDPs), assuming that decisions are made at fixed time intervals. However, many applications of great importance, including healthcare, do not satisfy this assumption, yet they are commonly modelled as MDPs after an artificial reshaping of the data. In addition, most healthcare (and similar) problems are offline by nature, allowing for only retrospective studies. To address both challenges, we begin by discussing the Semi-MDP (SMDP) framework, which formally handles actions of variable timings. We next present a formal way to apply SMDP modifications to nearly any given value-based offline RL method. We use this theory to introduce three SMDP-based offline RL algorithms, namely, SDQN, SDDQN, and SBCQ. We then experimentally demonstrate that only these SMDP-based algorithms learn the optimal policy in variable-time environments, whereas their MDP counterparts do not. Finally, we apply our new algorithms to a real-world offline dataset pertaining to warfarin dosing for stroke prevention and demonstrate similar results.
Admission Control and Resource Reservation for Prioritized Slice Requests with Guaranteed SLA under Uncertainties
Venue: arXiv cs.NI 2022
Year: 2022

Network slicing has emerged as a key concept in 5G systems, allowing Mobile Network Operators (MNOs) to build isolated logical networks (slices) on top of shared infrastructure networks managed by Infrastructure Providers (InP). Network slicing requires the assignment of infrastructure network resources to virtual network components at slice activation time and the adjustment of resources for slices under operation. Performing these operations just-in-time, on a best-effort basis, comes with no guarantee on the availability of enough infrastructure resources to meet slice requirements. This paper proposes a prioritized admission control mechanism for concurrent slices based on an infrastructure resource reservation approach. The reservation accounts for the dynamic nature of slice requests while being robust to uncertainties in slice resource demands. Adopting the perspective of an InP, reservation schemes are proposed that maximize the number of slices for which infrastructure resources can be granted while minimizing the costs charged to the MNOs. This requires the solution of a max-min optimization problem with a non-linear cost function and non-linear constraints induced by the robustness to uncertainties of demands and the limitation of the impact of reservation on background services. The cost and the constraints are linearized and several reduced-complexity strategies are proposed to solve the slice admission control and resource reservation problem. Simulations show that the proportion of admitted slices of different priority levels can be adjusted by a differentiated selection of the delay between the reception and the processing instants of a slice resource request.
A Systematic Study of Android Non-SDK (Hidden) Service API Security
Venue: arXiv cs.CR 2022
Year: 2022

Android allows apps to communicate with its system services via system service helpers so that these apps can use various functions provided by the system services. Meanwhile, the system services rely on their service helpers to enforce security checks for protection. Unfortunately, the security checks in the service helpers may be bypassed via directly exploiting the non-SDK (hidden) APIs, degrading the stability and posing severe security threats such as privilege escalation, automatic function execution without users' interactions, crashes, and DoS attacks. Google has proposed various approaches to address this problem, e.g., case-by-case fixing the bugs or even proposing a blacklist to block all the non-SDK APIs. However, the developers can still figure out new ways of exploiting these hidden APIs to evade the non-SDKs restrictions. In this paper, we systematically study the vulnerabilities due to the hidden API exploitation and analyze the effectiveness of Google's countermeasures. We aim to answer if there are still vulnerable hidden APIs that can be exploited in the newest Android 12. We develop a static analysis tool called ServiceAudit to automatically mine the inconsistent security enforcement between service helper classes and the hidden service APIs. We apply ServiceAudit to Android 6~12. Our tool discovers 112 vulnerabilities in Android 6 with higher precision than existing approaches. Moreover, in Android 11 and 12, we identify more than 25 hidden APIs with inconsistent protections; however, only one of the vulnerable APIs can lead to severe security problems in Android 11, and none of them work on Android 12.
Continual Learning Based on OOD Detection and Task Masking
Venue: arXiv cs.CV 2022
Year: 2022

Existing continual learning techniques focus on either task incremental learning (TIL) or class incremental learning (CIL) problem, but not both. CIL and TIL differ mainly in that the task-id is provided for each test sample during testing for TIL, but not provided for CIL. Continual learning methods intended for one problem have limitations on the other problem. This paper proposes a novel unified approach based on out-of-distribution (OOD) detection and task masking, called CLOM, to solve both problems. The key novelty is that each task is trained as an OOD detection model rather than a traditional supervised learning model, and a task mask is trained to protect each task to prevent forgetting. Our evaluation shows that CLOM outperforms existing state-of-the-art baselines by large margins. The average TIL/CIL accuracy of CLOM over six experiments is 87.6/67.9% while that of the best baselines is only 82.4/55.0%.
Diffusion Probabilistic Modeling for Video Generation
Venue: arXiv cs.CV 2022
Year: 2022

Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation. This paper showcases their ability to sequentially generate video, surpassing prior methods in perceptual and probabilistic forecasting metrics. We propose an autoregressive, end-to-end optimized video diffusion model inspired by recent advances in neural video compression. The model successively generates future frames by correcting a deterministic next-frame prediction using a stochastic residual generated by an inverse diffusion process. We compare this approach against five baselines on four datasets involving natural and simulation-based videos. We find significant improvements in terms of perceptual quality for all datasets. Furthermore, by introducing a scalable version of the Continuous Ranked Probability Score (CRPS) applicable to video, we show that our model also outperforms existing approaches in their probabilistic frame forecasting ability.