OpenRaiser/Intern-Atlas huggingface dataset:
Private delegated computations using strong isolation
Venue: arXiv cs.CR 2022
Year: 2022

Sensitive computations are now routinely delegated to third-parties. In response, Confidential Computing technologies are being introduced to microprocessors, offering a protected processing environment, which we generically call an isolate, providing confidentiality and integrity guarantees to code and data hosted within -- even in the face of a privileged attacker. Isolates, with an attestation protocol, permit remote third-parties to establish a trusted "beachhead" containing known code and data on an otherwise untrusted machine. Yet, the rise of these technologies introduces many new problems, including: how to ease provisioning of computations safely into isolates; how to develop distributed systems spanning multiple classes of isolate; and what to do about the billions of "legacy" devices without support for Confidential Computing? Tackling the problems above, we introduce Veracruz, a framework that eases the design and implementation of complex privacy-preserving, collaborative, delegated computations among a group of mutually mistrusting principals. Veracruz supports multiple isolation technologies and provides a common programming model and attestation protocol across all of them, smoothing deployment of delegated computations over supported technologies. We demonstrate Veracruz in operation, on private in-cloud object detection on encrypted video streaming from a video camera. In addition to supporting hardware-backed isolates -- like AWS Nitro Enclaves and Arm Confidential Computing Architecture Realms -- Veracruz also provides pragmatic "software isolates" on Armv8-A devices without hardware Confidential Computing capability, using the high-assurance seL4 microkernel and our IceCap framework.
Leveraging strategic connection migration-powered traffic splitting for privacy
Venue: arXiv cs.CR 2022
Year: 2022

Network-level adversaries have developed increasingly sophisticated techniques to surveil and control users' network traffic. In this paper, we exploit our observation that many encrypted protocol connections are no longer tied to device IP address (e.g., the connection migration feature in QUIC, or IP roaming in WireGuard and Mosh), due to the need for performance in a mobile-first world. We design and implement a novel framework, Connection Migration Powered Splitting (CoMPS), that utilizes these performance features for enhancing user privacy. With CoMPS, we can split traffic mid-session across network paths and heterogeneous network protocols. Such traffic splitting mitigates the ability of a network-level adversary to perform traffic analysis attacks by limiting the amount of traffic they can observe. We use CoMPS to construct a website fingerprinting defense that is resilient against traffic analysis attacks by a powerful adaptive adversary in the open-world setting. We evaluate our system using both simulated splitting data and real-world traffic that is actively split using CoMPS. In our real-world experiments, CoMPS reduces the precision and recall of VarCNN to 29.9% and 36.7% respectively in the open-world setting with 100 monitored classes. CoMPS is not only immediately deployable with any unaltered server that supports connection migration, but also incurs little overhead, decreasing throughput by only 5-20%.
UAV-aided Wireless Node Localization Using Hybrid Radio Channel Models
Venue: arXiv cs.IT 2022
Year: 2022

This paper considers the problem of ground user localization based on received signal strength (RSS) measurements obtained by an unmanned aerial vehicle (UAV). We treat UAV-user link channel model parameters and antenna radiation pattern of the UAV as unknowns that need to be estimated. A hybrid channel model is proposed that consists of a traditional path loss model combined with a neural network approximating the UAV antenna gain function. With this model and a set of offline RSS measurements, the unknown parameters are estimated. We then employ the particle swarm optimization (PSO) technique which utilizes the learned hybrid channel model along with a 3D map of the environment to accurately localize the ground users. The performance of the developed algorithm is evaluated through simulations and also real-world experiments.
Age of Gossip in Ring Networks in the Presence of Jamming Attacks
Venue: arXiv cs.IT 2022
Year: 2022

We consider a system with a source which maintains the most current version of a file, and a ring network of $n$ user nodes that wish to acquire the latest version of the file. The source gets updated with newer file versions as a point process, and forwards them to the user nodes, which further forward them to their neighbors using a memoryless gossip protocol. We study the average version age of this network in the presence of $\tilde{n}$ jammers that disrupt inter-node communications. To this purpose, we construct an alternate system model of mini-rings and prove that the version age of the original model can be sandwiched between constant multiples of the version age of the alternate model. We show that when the number of jammers scales as a fractional power of the network size, i.e., $\tilde n= cn^α$, the version age scales as $\sqrt{n}$ when $α< \frac{1}{2}$, and as $n^α$ when $α\geq \frac{1}{2}$. As the version age of a ring network without any jammers scales as $\sqrt{n}$, our result implies that the version age with gossiping is robust against up to $\sqrt{n}$ jammers in a ring network.
Trainable Wavelet Neural Network for Non-Stationary Signals
Venue: arXiv cs.LG 2022
Year: 2022

This work introduces a wavelet neural network to learn a filter-bank specialized to fit non-stationary signals and improve interpretability and performance for digital signal processing. The network uses a wavelet transform as the first layer of a neural network where the convolution is a parameterized function of the complex Morlet wavelet. Experimental results, on both simplified data and atmospheric gravity waves, show the network is quick to converge, generalizes well on noisy data, and outperforms standard network architectures.
Psychologically-Inspired Music Recommendation System
Venue: arXiv cs.IR 2022
Year: 2022

In the last few years, automated recommendation systems have been a major focus in the music field, where companies such as Spotify, Amazon, and Apple are competing in the ability to generate the most personalized music suggestions for their users. One of the challenges developers still fail to tackle is taking into account the psychological and emotional aspects of the music. Our goal is to find a way to integrate users' personal traits and their current emotional state into a single music recommendation system with both collaborative and content-based filtering. We seek to relate the personality and the current emotional state of the listener to the audio features in order to build an emotion-aware MRS. We compare the results both quantitatively and qualitatively to the output of the traditional MRS based on the Spotify API data to understand if our advancements make a significant impact on the quality of music recommendations.
EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms
Venue: arXiv cs.CV 2022
Year: 2022

A new event camera dataset, EVIMO2, is introduced that improves on the popular EVIMO dataset by providing more data, from better cameras, in more complex scenarios. As with its predecessor, EVIMO2 provides labels in the form of per-pixel ground truth depth and segmentation as well as camera and object poses. All sequences use data from physical cameras and many sequences feature multiple independently moving objects. Typically, such labeled data is unavailable in physical event camera datasets. Thus, EVIMO2 will serve as a challenging benchmark for existing algorithms and rich training set for the development of new algorithms. In particular, EVIMO2 is suited for supporting research in motion and object segmentation, optical flow, structure from motion, and visual (inertial) odometry in both monocular or stereo configurations. EVIMO2 consists of 41 minutes of data from three 640$\times$480 event cameras, one 2080$\times$1552 classical color camera, inertial measurements from two six axis inertial measurement units, and millimeter accurate object poses from a Vicon motion capture system. The dataset's 173 sequences are arranged into three categories. 3.75 minutes of independently moving household objects, 22.55 minutes of static scenes, and 14.85 minutes of basic motions in shallow scenes. Some sequences were recorded in low-light conditions where conventional cameras fail. Depth and segmentation are provided at 60 Hz for the event cameras and 30 Hz for the classical camera. The masks can be regenerated using open-source code up to rates as high as 200 Hz. This technical report briefly describes EVIMO2. The full documentation is available online. Videos of individual sequences can be sampled on the download page.
The AI Index 2022 Annual Report
Venue: arXiv cs.AI 2022
Year: 2022

Welcome to the fifth edition of the AI Index Report! The latest edition includes data from a broad set of academic, private, and nonprofit organizations as well as more self-collected data and original analysis than any previous editions, including an expanded technical performance chapter, a new survey of robotics researchers around the world, data on global AI legislation records in 25 countries, and a new chapter with an in-depth analysis of technical AI ethics metrics. The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. The report aims to be the world's most credible and authoritative source for data and insights about AI.
Norm-Scaling for Out-of-Distribution Detection
Venue: arXiv cs.LG 2022
Year: 2022

Out-of-Distribution (OoD) inputs are examples that do not belong to the true underlying distribution of the dataset. Research has shown that deep neural nets make confident mispredictions on OoD inputs. Therefore, it is critical to identify OoD inputs for safe and reliable deployment of deep neural nets. Often a threshold is applied on a similarity score to detect OoD inputs. One such similarity is angular similarity which is the dot product of latent representation with the mean class representation. Angular similarity encodes uncertainty, for example, if the angular similarity is less, it is less certain that the input belongs to that class. However, we observe that, different classes have different distributions of angular similarity. Therefore, applying a single threshold for all classes is not ideal since the same similarity score represents different uncertainties for different classes. In this paper, we propose norm-scaling which normalizes the logits separately for each class. This ensures that a single value consistently represents similar uncertainty for various classes. We show that norm-scaling, when used with maximum softmax probability detector, achieves 9.78% improvement in AUROC, 5.99% improvement in AUPR and 33.19% reduction in FPR95 metrics over previous state-of-the-art methods.
Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance
Venue: arXiv cs.LG 2022
Year: 2022

Domain adaptation on time-series data is often encountered in the industry but received limited attention in academia. Most of the existing domain adaptation methods for time-series data borrow the ideas from the existing methods for non-time series data to extract the domain-invariant representation. However, two peculiar difficulties to time-series data have not been solved. 1) It is not a trivial task to model the domain-invariant and complex dependence among different timestamps. 2) The domain-variant information is important but how to leverage them is almost underexploited. Fortunately, the stableness of causal structures among different domains inspires us to explore the structures behind the time-series data. Based on this inspiration, we investigate the domain-invariant unweighted sparse associative structures and the domain-variant strengths of the structures. To achieve this, we propose Sparse Associative structure alignment by learning Invariance and Variance (SASA-IV in short), a model that simultaneously aligns the invariant unweighted spare associative structures and considers the variant information for time-series unsupervised domain adaptation. Technologically, we extract the domain-invariant unweighted sparse associative structures with a unidirectional alignment restriction and embed the domain-variant strengths via a well-designed autoregressive module. Experimental results not only testify that our model yields state-of-the-art performance on three real-world datasets but also provide some insightful discoveries on knowledge transfer.
Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning
Venue: arXiv cs.CV 2022
Year: 2022

The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience; while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision.
Bounds on the Total Coefficient Size of Nullstellensatz Proofs of the Pigeonhole Principle and the Ordering Principle
Venue: arXiv cs.CC 2022
Year: 2022

In this paper, we investigate the total coefficient size of Nullstellensatz proofs. We show that Nullstellensatz proofs of the pigeonhole principle on $n$ pigeons require total coefficient size $2^{Ω(n)}$ and that there exist Nullstellensatz proofs of the ordering principle on $n$ elements with total coefficient size $2^n - n$.
UniMorph 4.0: Universal Morphology
Venue: arXiv cs.CL 2022
Year: 2022

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements made on several fronts over the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g. missing gender and macron information. We have also amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.
Vector Representations of Idioms in Conversational Systems
Venue: arXiv cs.CL 2022
Year: 2022

We demonstrate, in this study, that an open-domain conversational system trained on idioms or figurative language generates more fitting responses to prompts containing idioms. Idioms are part of everyday speech in many languages, across many cultures, but they pose a great challenge for many Natural Language Processing (NLP) systems that involve tasks such as Information Retrieval (IR) and Machine Translation (MT), besides conversational AI. We utilize the Potential Idiomatic Expression (PIE)-English idioms corpus for the two tasks that we investigate: classification and conversation generation. We achieve state-of-the-art (SoTA) result of 98% macro F1 score on the classification task by using the SoTA T5 model. We experiment with three instances of the SoTA dialogue model, Dialogue Generative Pre-trained Transformer (DialoGPT), for conversation generation. Their performances are evaluated using the automatic metric perplexity and human evaluation. The results show that the model trained on the idiom corpus generates more fitting responses to prompts containing idioms 71.9% of the time, compared to a similar model not trained on the idioms corpus. We contribute the model checkpoint/demo and code on the HuggingFace hub for public access.
Timestamp-independent Haptic-Visual Synchronization
Venue: arXiv cs.MM 2022
Year: 2022

The booming haptic data significantly improves the users'immersion during multimedia interaction. As a result, the study of Haptic,Audio-Visual Environment(HAVE)has attracted attentions of multimedia community. To realize such a system, a challenging tack is the synchronization of multiple sensorial signals that is critical to user experience. Despite of audio-visual synchronization efforts, there is still a lack of haptic-aware multimedia synchronization model. In this work, we propose a timestamp-independent synchronization for haptic-visual signal transmission. First, we exploit the sequential correlations during delivery and playback of a haptic-visual communication system. Second, we develop a key sample extraction of haptic signals based on the force feedback characteristics, and a key frame extraction of visual signals based on deep object detection. Third, we combine the key samples and frames to synchronize the corresponding haptic-visual signals. Without timestamps in signal flow, the proposed method is still effective and more robust to complicated network conditions. Subjective evaluation also shows a significant improvement of user experience with the proposed method.
AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney Injury
Venue: arXiv cs.CL 2022
Year: 2022

Acute kidney injury (AKI) is a common clinical syndrome characterized by a sudden episode of kidney failure or kidney damage within a few hours or a few days. Accurate early prediction of AKI for patients in ICU who are more likely than others to have AKI can enable timely interventions, and reduce the complications of AKI. Much of the clinical information relevant to AKI is captured in clinical notes that are largely unstructured text and requires advanced natural language processing (NLP) for useful information extraction. On the other hand, pre-trained contextual language models such as Bidirectional Encoder Representations from Transformers (BERT) have improved performances for many NLP tasks in general domain recently. However, few have explored BERT on disease-specific medical domain tasks such as AKI early prediction. In this paper, we try to apply BERT to specific diseases and present an AKI domain-specific pre-trained language model based on BERT (AKI-BERT) that could be used to mine the clinical notes for early prediction of AKI. AKI-BERT is a BERT model pre-trained on the clinical notes of patients having risks for AKI. Our experiments on Medical Information Mart for Intensive Care III (MIMIC-III) dataset demonstrate that AKI-BERT can yield performance improvements for early AKI prediction, thus expanding the utility of the BERT model from general clinical domain to disease-specific domain.
Math-KG: Construction and Applications of Mathematical Knowledge Graph
Venue: arXiv cs.CL 2022
Year: 2022

Recently, the explosion of online education platforms makes a success in encouraging us to easily access online education resources. However, most of them ignore the integration of massive unstructured information, which inevitably brings the problem of \textit{information overload} and \textit{knowledge trek}. In this paper, we proposed a mathematical knowledge graph named Math-KG, which automatically constructed by the pipeline method with the natural language processing technology to integrate the resources of the mathematics. It is built from the corpora of Baidu Baike, Wikipedia. We implement a simple application system to validate the proposed Math-KG can make contributions on a series of scenes, including faults analysis and semantic search. The system is publicly available at GitHub \footnote{\url{https://github.com/wjn1996/Mathematical-Knowledge-Entity-Recognition}.}.
Communication Compression for Decentralized Learning with Operator Splitting Methods
Venue: arXiv cs.LG 2022
Year: 2022

In decentralized learning, operator splitting methods using a primal-dual formulation (e.g., the Edge-Consensus Learning (ECL)) has been shown to be robust to heterogeneous data and has attracted significant attention in recent years. However, in the ECL, a node needs to exchange dual variables with its neighbors. These exchanges incur significant communication costs. For the Gossip-based algorithms, many compression methods have been proposed, but these Gossip-based algorithm do not perform well when the data distribution held by each node is statistically heterogeneous. In this work, we propose the novel framework of the compression methods for the ECL, called the Communication Compressed ECL (C-ECL). Specifically, we reformulate the update formulas of the ECL, and propose to compress the update values of the dual variables. We demonstrate experimentally that the C-ECL can achieve a nearly equivalent performance with fewer parameter exchanges than the ECL. Moreover, we demonstrate that the C-ECL is more robust to heterogeneous data than the Gossip-based algorithms.