OpenRaiser/Intern-Atlas huggingface dataset:
Tuple Packing: Efficient Batching of Small Graphs in Graph Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

When processing a batch of graphs in machine learning models such as Graph Neural Networks (GNN), it is common to combine several small graphs into one overall graph to accelerate processing and remove or reduce the overhead of padding. This is for example supported in the PyG library. However, the sizes of small graphs can vary substantially with respect to the number of nodes and edges, and hence the size of the combined graph can still vary considerably, especially for small batch sizes. Therefore, the costs of excessive padding and wasted compute are still incurred when working with static shapes, which are preferred for maximum acceleration. This paper proposes a new hardware agnostic approach -- tuple packing -- for generating batches that cause minimal overhead. The algorithm extends recently introduced sequence packing approaches to work on the 2D tuples of (|nodes|, |edges|). A monotone heuristic is applied to the 2D histogram of tuple values to define a priority for packing histogram bins together with the objective to reach a limit on the number of nodes as well as the number of edges. Experiments verify the effectiveness of the algorithm on multiple datasets.
Federated Learning based on Defending Against Data Poisoning Attacks in IoT
Venue: arXiv cs.CR 2022
Year: 2022

The rapidly expanding number of Internet of Things (IoT) devices is generating huge quantities of data, but the data privacy and security exposure in IoT devices, especially in the automatic driving system. Federated learning (FL) is a paradigm that addresses data privacy, security, access rights, and access to heterogeneous message issues by integrating a global model based on distributed nodes. However, data poisoning attacks on FL can undermine the benefits, destroying the global model's availability and disrupting model training. To avoid the above issues, we build up a hierarchical defense data poisoning (HDDP) system framework to defend against data poisoning attacks in FL, which monitors each local model of individual nodes via abnormal detection to remove the malicious clients. Whether the poisoning defense server has a trusted test dataset, we design the \underline{l}ocal \underline{m}odel \underline{t}est \underline{v}oting (LMTV) and \underline{k}ullback-\underline{l}eibler divergence \underline{a}nomaly parameters \underline{d}etection (KLAD) algorithms to defend against label-flipping poisoning attacks. Specifically, the trusted test dataset is utilized to obtain the evaluation results for each classification to recognize the malicious clients in LMTV. More importantly, we adopt the kullback leibler divergence to measure the similarity between local models without the trusted test dataset in KLAD. Finally, through extensive evaluations and against the various label-flipping poisoning attacks, LMTV and KLAD algorithms could achieve the $100\%$ and $40\%$ to $85\%$ successful defense ratios under different detection situations.
Reflectance-Guided, Contrast-Accumulated Histogram Equalization
Venue: arXiv cs.CV 2022
Year: 2022

Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously. To address this problem, we propose a histogram equalization-based method that adapts to the data-dependent requirements of brightness enhancement and improves the visibility of details without losing the global contrast. This method incorporates the spatial information provided by image context in density estimation for discriminative histogram equalization. To minimize the adverse effect of non-uniform illumination, we propose defining spatial information on the basis of image reflectance estimated with edge preserving smoothing. Our method works particularly well for determining how the background brightness should be adaptively adjusted and for revealing useful image details hidden in the dark.
Frequency Reversal Alamouti Code-Based FBMC with Resilience to Inter-Antenna Frequency Offsets
Venue: arXiv cs.IT 2022
Year: 2022

Transmit diversity schemes for filter bank multicarrier (FBMC) are known to be challenging. No existing schemes have considered the presence of inter-antenna frequency offset (IAFO), which will result in performance degradation. In this letter, a new transmit scheme based on the frequency reversal Alamouti code (FRAC)-based structure to address the issue of IAFO is proposed and is proven to inherently cancel the inter-antenna inter-carrier interference (ICI) while preserving spatial diversity. Moreover, the proposed FRAC structure is applicable in frequency-selective channels. Numerical results show that the proposed scheme undergoes negligible bit error rate (BER) degradation even with considerable IAFOs.
Multi-Dimensional Unlimited Sampling and Robust Reconstruction
Venue: arXiv cs.IT 2022
Year: 2022

In this paper we introduce a new sampling and reconstruction approach for multi-dimensional analog signals. Building on top of the Unlimited Sensing Framework (USF), we present a new folded sampling operator called the multi-dimensional modulo-hysteresis that is also backwards compatible with the existing one-dimensional modulo operator. Unlike previous approaches, the proposed model is specifically tailored to multi-dimensional signals. In particular, the model uses certain redundancy in dimensions 2 and above, which is exploited for input recovery with robustness. We prove that the new operator is well-defined and its outputs have a bounded dynamic range. For the noiseless case, we derive a theoretically guaranteed input reconstruction approach. When the input is corrupted by Gaussian noise, we exploit redundancy in higher dimensions to provide a bound on the error probability and show this drops to 0 for high enough sampling rates leading to new theoretical guarantees for the noisy case. Our numerical examples corroborate the theoretical results and show that the proposed approach can handle a significantly larger amount of noise compared to USF.
A Hybrid Deep Learning Model-based Remaining Useful Life Estimation for Reed Relay with Degradation Pattern Clustering
Venue: arXiv cs.LG 2022
Year: 2022

Reed relay serves as the fundamental component of functional testing, which closely relates to the successful quality inspection of electronics. To provide accurate remaining useful life (RUL) estimation for reed relay, a hybrid deep learning network with degradation pattern clustering is proposed based on the following three considerations. First, multiple degradation behaviors are observed for reed relay, and hence a dynamic time wrapping-based $K$-means clustering is offered to distinguish degradation patterns from each other. Second, although proper selections of features are of great significance, few studies are available to guide the selection. The proposed method recommends operational rules for easy implementation purposes. Third, a neural network for remaining useful life estimation (RULNet) is proposed to address the weakness of the convolutional neural network (CNN) in capturing temporal information of sequential data, which incorporates temporal correlation ability after high-level feature representation of convolutional operation. In this way, three variants of RULNet are constructed with health indicators, features with self-organizing map, or features with curve fitting. Ultimately, the proposed hybrid model is compared with the typical baseline models, including CNN and long short-term memory network (LSTM), through a practical reed relay dataset with two distinct degradation manners. The results from both degradation cases demonstrate that the proposed method outperforms CNN and LSTM regarding the index root mean squared error.
Real-world Video Anomaly Detection by Extracting Salient Features in Videos
Venue: arXiv cs.CV 2022
Year: 2022

We propose a lightweight and accurate method for detecting anomalies in videos. Existing methods used multiple-instance learning (MIL) to determine the normal/abnormal status of each segment of the video. Recent successful researches argue that it is important to learn the temporal relationships among segments to achieve high accuracy, instead of focusing on only a single segment. Therefore we analyzed the existing methods that have been successful in recent years, and found that while it is indeed important to learn all segments together, the temporal orders among them are irrelevant to achieving high accuracy. Based on this finding, we do not use the MIL framework, but instead propose a lightweight model with a self-attention mechanism to automatically extract features that are important for determining normal/abnormal from all input segments. As a result, our neural network model has 1.3\% of the number of parameters of the existing method. We evaluated the frame-level detection accuracy of our method on three benchmark datasets (UCF-Crime, ShanghaiTech, and XD-Violence) and demonstrate that our method can achieve the comparable or better accuracy than state-of-the-art methods.
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers
Venue: arXiv cs.CL 2022
Year: 2022

This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncertainty perspective, it is indisputable that a single SQL can be learned from multiple semantically-equivalent questions.Different from previous methods that are limited to one-to-one mapping, we propose a data uncertainty constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions (many-to-one) and learn the robust feature representations with reduced spurious associations. In this way, we can reduce the sensitivity of the learned representations and improve the robustness of the parser. From the model uncertainty perspective, there is often structural information (dependence) among the weights of neural networks. To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms strong competitors and achieves new state-of-the-art results. For reproducibility, we release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/sunsql.
Security of Virtual Reality Authentication Methods in Metaverse: An Overview
Venue: arXiv cs.CR 2022
Year: 2022

The metaverse is said to be the future Internet and will consist of several worlds called verses. This concept is being discussed a lot lately, however, the security issues of these virtual worlds are not discussed enough. This study first discusses the privacy and security concerns of the metaverse. Virtual reality headsets are the main devices used to access the Metaverse. The user needs to verify their identity to log in to the metaverse platforms, and the security of this phase becomes vital. This paper aims to compare the security of the main authentication methods that are used in virtual reality environments. Information-based, biometric, and multi-model methods are compared and analyzed in terms of security. These methods aim to verify the user with different data types such as 3D patterns, PIN systems, or biometric data. The pros and cons are discussed. The paper also concludes with what work can be done to improve the safety of these authentication methods and future work.
Personalized Emotion Detection using IoT and Machine Learning
Venue: arXiv cs.LG 2022
Year: 2022

The Medical Internet of Things, a recent technological advancement in medicine, is incredibly helpful in providing real-time monitoring of health metrics. This paper presents a non-invasive IoT system that tracks patients' emotions, especially those with autism spectrum disorder. With a few affordable sensors and cloud computing services, the individual's heart rates are monitored and analyzed to study the effects of changes in sweat and heartbeats per minute for different emotions. Under normal resting conditions of the individual, the proposed system could detect the right emotion using machine learning algorithms with a performance of up to 92% accuracy. The result of the proposed approach is comparable with the state-of-the-art solutions in medical IoT.
The Embeddings World and Artificial General Intelligence
Venue: arXiv cs.AI 2022
Year: 2022

From early days, a key and controversial question inside the artificial intelligence community was whether Artificial General Intelligence (AGI) is achievable. AGI is the ability of machines and computer programs to achieve human-level intelligence and do all tasks that a human being can. While there exist a number of systems in the literature claiming they realize AGI, several other researchers argue that it is impossible to achieve it. In this paper, we take a different view to the problem. First, we discuss that in order to realize AGI, along with building intelligent machines and programs, an intelligent world should also be constructed which is on the one hand, an accurate approximation of our world and on the other hand, a significant part of reasoning of intelligent machines is already embedded in this world. Then we discuss that AGI is not a product or algorithm, rather it is a continuous process which will become more and more mature over time (like human civilization and wisdom). Then, we argue that pre-trained embeddings play a key role in building this intelligent world and as a result, realizing AGI. We discuss how pre-trained embeddings facilitate achieving several characteristics of human-level intelligence, such as embodiment, common sense knowledge, unconscious knowledge and continuality of learning, by machines.
One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text
Venue: arXiv cs.CV 2022
Year: 2022

Active consumption of digital documents has yielded scope for research in various applications, including search. Traditionally, searching within a document has been cast as a text matching problem ignoring the rich layout and visual cues commonly present in structured documents, forms, etc. To that end, we ask a mostly unexplored question: "Can we search for other similar snippets present in a target document page given a single query instance of a document snippet?". We propose MONOMER to solve this as a one-shot snippet detection task. MONOMER fuses context from visual, textual, and spatial modalities of snippets and documents to find query snippet in target documents. We conduct extensive ablations and experiments showing MONOMER outperforms several baselines from one-shot object detection (BHRL), template matching, and document understanding (LayoutLMv3). Due to the scarcity of relevant data for the task at hand, we train MONOMER on programmatically generated data having many visually similar query snippets and target document pairs from two datasets - Flamingo Forms and PubLayNet. We also do a human study to validate the generated data.
Distributed Multi-Robot Obstacle Avoidance via Logarithmic Map-based Deep Reinforcement Learning
Venue: arXiv cs.RO 2022
Year: 2022

Developing a safe, stable, and efficient obstacle avoidance policy in crowded and narrow scenarios for multiple robots is challenging. Most existing studies either use centralized control or need communication with other robots. In this paper, we propose a novel logarithmic map-based deep reinforcement learning method for obstacle avoidance in complex and communication-free multi-robot scenarios. In particular, our method converts laser information into a logarithmic map. As a step toward improving training speed and generalization performance, our policies will be trained in two specially designed multi-robot scenarios. Compared to other methods, the logarithmic map can represent obstacles more accurately and improve the success rate of obstacle avoidance. We finally evaluate our approach under a variety of simulation and real-world scenarios. The results show that our method provides a more stable and effective navigation solution for robots in complex multi-robot scenarios and pedestrian scenarios. Videos are available at https://youtu.be/r0EsUXe6MZE.
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs
Venue: arXiv cs.LG 2022
Year: 2022

Single-cell RNA-seq datasets are growing in size and complexity, enabling the study of cellular composition changes in various biological/clinical contexts. Scalable dimensionality reduction techniques are in need to disentangle biological variation in them, while accounting for technical and biological confounders. In this work, we extend a popular approach for probabilistic non-linear dimensionality reduction, the Gaussian process latent variable model, to scale to massive single-cell datasets while explicitly accounting for technical and biological confounders. The key idea is to use an augmented kernel which preserves the factorisability of the lower bound allowing for fast stochastic variational inference. We demonstrate its ability to reconstruct latent signatures of innate immunity recovered in Kumasaka et al. (2021) with 9x lower training time. We further analyze a COVID dataset and demonstrate across a cohort of 130 individuals, that this framework enables data integration while capturing interpretable signatures of infection. Specifically, we explore COVID severity as a latent dimension to refine patient stratification and capture disease-specific gene expression.
On Language Clustering: A Non-parametric Statistical Approach
Venue: arXiv cs.CL 2022
Year: 2022

Any approach aimed at pasteurizing and quantifying a particular phenomenon must include the use of robust statistical methodologies for data analysis. With this in mind, the purpose of this study is to present statistical approaches that may be employed in nonparametric nonhomogeneous data frameworks, as well as to examine their application in the field of natural language processing and language clustering. Furthermore, this paper discusses the many uses of nonparametric approaches in linguistic data mining and processing. The data depth idea allows for the centre-outward ordering of points in any dimension, resulting in a new nonparametric multivariate statistical analysis that does not require any distributional assumptions. The concept of hierarchy is used in historical language categorisation and structuring, and it aims to organise and cluster languages into subfamilies using the same premise. In this regard, the current study presents a novel approach to language family structuring based on non-parametric approaches produced from a typological structure of words in various languages, which is then converted into a Cartesian framework using MDS. This statistical-depth-based architecture allows for the use of data-depth-based methodologies for robust outlier detection, which is extremely useful in understanding the categorization of diverse borderline languages and allows for the re-evaluation of existing classification systems. Other depth-based approaches are also applied to processes such as unsupervised and supervised clustering. This paper therefore provides an overview of procedures that can be applied to nonhomogeneous language classification systems in a nonparametric framework.
Asynchronous Functional Sessions: Cyclic and Concurrent
Venue: arXiv cs.LO 2022
Year: 2022

We present Concurrent GV (CGV), a functional calculus with message-passing concurrency governed by session types. With respect to prior calculi, CGV has increased support for concurrent evaluation and for cyclic network topologies. The design of CGV draws on APCP, a session-typed asynchronous pi-calculus developed in prior work. Technical contributions are (i) the syntax, semantics, and type system of CGV; (ii) a correct translation of CGV into APCP; (iii) a technique for establishing deadlock-free CGV programs, by resorting to APCP's priority-based type system.
A Model Drift Detection and Adaptation Framework for 5G Core Networks
Venue: arXiv cs.NI 2022
Year: 2022

The advent of Fifth Generation (5G) and beyond 5G networks (5G+) has revolutionized the way network operators consider the management and orchestration of their networks. With an increased focus on intelligence and automation through core network functions such as the NWDAF, service providers are tasked with integrating machine learning models and artificial intelligence systems into their existing network operation practices. Due to the dynamic nature of next-generation networks and their supported use cases and applications, model drift is a serious concern, which can deteriorate the performance of intelligent models deployed throughout the network. The work presented in this paper introduces a model drift detection and adaptation module for 5G core networks. Using a functional prototype of a 5G core network, a drift in user behaviour is emulated, and the proposed framework is deployed and tested. The results of this work demonstrate the ability of the drift detection module to accurately characterize a drifted concept as well as the ability of the drift adaptation module to begin the necessary remediation efforts to restore system performance.
Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications
Venue: arXiv cs.CV 2022
Year: 2022

As input distributions evolve over a mission lifetime, maintaining performance of learning-based models becomes challenging. This paper presents a framework to incrementally retrain a model by selecting a subset of test inputs to label, which allows the model to adapt to changing input distributions. Algorithms within this framework are evaluated based on (1) model performance throughout mission lifetime and (2) cumulative costs associated with labeling and model retraining. We provide an open-source benchmark of a satellite pose estimation model trained on images of a satellite in space and deployed in novel scenarios (e.g., different backgrounds or misbehaving pixels), where algorithms are evaluated on their ability to maintain high performance by retraining on a subset of inputs. We also propose a novel algorithm to select a diverse subset of inputs for labeling, by characterizing the information gain from an input using Bayesian uncertainty quantification and choosing a subset that maximizes collective information gain using concepts from batch active learning. We show that our algorithm outperforms others on the benchmark, e.g., achieves comparable performance to an algorithm that labels 100% of inputs, while only labeling 50% of inputs, resulting in low costs and high performance over the mission lifetime.
Landmark-free Statistical Shape Modeling via Neural Flow Deformations
Venue: arXiv cs.CV 2022
Year: 2022

Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shape reconstruction and image segmentation, but also shape generation and classification. Existing shape priors either require dense correspondence between training examples or lack robustness and topological guarantees. We present FlowSSM, a novel shape modeling approach that learns shape variability without requiring dense correspondence between training instances. It relies on a hierarchy of continuous deformation flows, which are parametrized by a neural network. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior for distal femur and liver. We show that the emerging latent representation is discriminative by separating healthy from pathological shapes. Ultimately, we demonstrate its effectiveness on two shape reconstruction tasks from partial data. Our source code is publicly available (https://github.com/davecasp/flowssm).