OpenRaiser/Intern-Atlas huggingface dataset:
Soft-input, soft-output joint detection and GRAND
Venue: arXiv cs.IT 2022
Year: 2022

Guessing random additive noise decoding (GRAND) is a maximum likelihood (ML) decoding method that identifies the noise effects corrupting code-words of arbitrary code-books. In a joint detection and decoding framework, this work demonstrates how GRAND can leverage crude soft information in received symbols and channel state information to generate, through guesswork, soft bit reliability outputs in log-likelihood ratios (LLRs). The LLRs are generated via successive computations of Euclidean-distance metrics corresponding to candidate noise-recovered words. Noting that the entropy of noise is much smaller than that of information bits, a small number of noise effect guesses generally suffices to hit a code-word, which allows generating LLRs for critical bits; LLR saturation is applied to the remaining bits. In an iterative (turbo) mode, the generated LLRs at a given soft-input, soft-output GRAND iteration serve as enhanced a priori information that adapts noise-sequence guess ordering in a subsequent iteration. Simulations demonstrate that a few turbo-GRAND iterations match the performance of ML-detection-based soft-GRAND in both AWGN and Rayleigh fading channels at a complexity cost that, on average, grows linearly (instead of exponentially) with the number of symbols.
An Ensemble Approach for Multiple Emotion Descriptors Estimation Using Multi-task Learning
Venue: arXiv cs.CV 2022
Year: 2022

This paper illustrates our submission method to the fourth Affective Behavior Analysis in-the-Wild (ABAW) Competition. The method is used for the Multi-Task Learning Challenge. Instead of using only face information, we employ full information from a provided dataset containing face and the context around the face. We utilized the InceptionNet V3 model to extract deep features then we applied the attention mechanism to refine the features. After that, we put those features into the transformer block and multi-layer perceptron networks to get the final multiple kinds of emotion. Our model predicts arousal and valence, classifies the emotional expression and estimates the action units simultaneously. The proposed system achieves the performance of 0.917 on the MTL Challenge validation dataset.
Cell-Free Massive MIMO with Multi-Antenna Users over Weichselberger Rician Channels
Venue: arXiv cs.IT 2022
Year: 2022

We consider a cell-free massive multiple-input multiple-output (MIMO) system with multi-antenna access points and user equipments (UEs) over Weichselberger Rician fading channels with random phase-shifts. More specifically, we investigate the uplink spectral efficiency (SE) for two pragmatic processing schemes: 1) the fully centralized processing scheme with global minimum mean square error (MMSE) or maximum ratio (MR) combining; 2) the large-scale fading decoding (LSFD) scheme with local MMSE or MR combining. To improve the system SE performance, we propose a practical uplink precoding scheme based on only the eigenbasis of the UE-side correlation matrices. Moreover, we derive novel closed-form SE expressions for characterizing the LSFD scheme with the MR combining. Numerical results validate the accuracy of our derived expressions and show that the proposed precoding scheme can significantly improve the SE performance compared with the scenario without any precoding scheme.
What's in the laundromat? Mapping and characterising offshore owned domestic property in London
Venue: arXiv cs.LG 2022
Year: 2022

The UK, particularly London, is a global hub for money laundering, a significant portion of which uses domestic property. However, understanding the distribution and characteristics of offshore domestic property in the UK is challenging due to data availability. This paper attempts to remedy that situation by enhancing a publicly available dataset of UK property owned by offshore companies. We create a data processing pipeline which draws on several datasets and machine learning techniques to create a parsed set of addresses classified into six use classes. The enhanced dataset contains 138,000 properties 44,000 more than the original dataset. The majority are domestic (95k), with a disproportionate amount of those in London (42k). The average offshore domestic property in London is worth 1.33 million GBP collectively this amounts to approximately 56 Billion GBP. We perform an in-depth analysis of the offshore domestic property in London, comparing the price, distribution and entropy/concentration with Airbnb property, low-use/empty property and conventional domestic property. We estimate that the total amount of offshore, low-use and airbnb property in London is between 144,000 and 164,000 and that they are collectively worth between 145-174 billion GBP. Furthermore, offshore domestic property is more expensive and has higher entropy/concentration than all other property types. In addition, we identify two different types of offshore property, nested and individual, which have different price and distribution characteristics. Finally, we release the enhanced offshore property dataset, the complete low-use London dataset and the pipeline for creating the enhanced dataset to reduce the barriers to studying this topic.
Physics-informed convolutional neural network with bicubic spline interpolation for sound field estimation
Venue: arXiv cs.SD 2022
Year: 2022

A sound field estimation method based on a physics-informed convolutional neural network (PICNN) using spline interpolation is proposed. Most of the sound field estimation methods are based on wavefunction expansion, making the estimated function satisfy the Helmholtz equation. However, these methods rely only on physical properties; thus, they suffer from a significant deterioration of accuracy when the number of measurements is small. Recent learning-based methods based on neural networks have advantages in estimating from sparse measurements when training data are available. However, since physical properties are not taken into consideration, the estimated function can be a physically infeasible solution. We propose the application of PICNN to the sound field estimation problem by using a loss function that penalizes deviation from the Helmholtz equation. Since the output of CNN is a spatially discretized pressure distribution, it is difficult to directly evaluate the Helmholtz-equation loss function. Therefore, we incorporate bicubic spline interpolation in the PICNN framework. Experimental results indicated that accurate and physically feasible estimation from sparse measurements can be achieved with the proposed method.
Hyper-Representations for Pre-Training and Transfer Learning
Venue: arXiv cs.LG 2022
Year: 2022

Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation. Recently, an autoencoder trained on a model zoo was able to learn a hyper-representation, which captures intrinsic and extrinsic properties of the models in the zoo. In this work, we extend hyper-representations for generative use to sample new model weights as pre-training. We propose layer-wise loss normalization which we demonstrate is key to generate high-performing models and a sampling method based on the empirical density of hyper-representations. The models generated using our methods are diverse, performant and capable to outperform conventional baselines for transfer learning. Our results indicate the potential of knowledge aggregation from model zoos to new models via hyper-representations thereby paving the avenue for novel research directions.
Visible and Near Infrared Image Fusion Based on Texture Information
Venue: arXiv cs.CV 2022
Year: 2022

Multi-sensor fusion is widely used in the environment perception system of the autonomous vehicle. It solves the interference caused by environmental changes and makes the whole driving system safer and more reliable. In this paper, a novel visible and near-infrared fusion method based on texture information is proposed to enhance unstructured environmental images. It aims at the problems of artifact, information loss and noise in traditional visible and near infrared image fusion methods. Firstly, the structure information of the visible image (RGB) and the near infrared image (NIR) after texture removal is obtained by relative total variation (RTV) calculation as the base layer of the fused image; secondly, a Bayesian classification model is established to calculate the noise weight and the noise information and the noise information in the visible image is adaptively filtered by joint bilateral filter; finally, the fused image is acquired by color space conversion. The experimental results demonstrate that the proposed algorithm can preserve the spectral characteristics and the unique information of visible and near-infrared images without artifacts and color distortion, and has good robustness as well as preserving the unique texture.
Applying Machine Learning on RSRP-based Features for False Base Station Detection
Venue: arXiv cs.CR 2022
Year: 2022

False base stations -- IMSI catchers, Stingrays -- are devices that impersonate legitimate base stations, as a part of malicious activities like unauthorized surveillance or communication sabotage. Detecting them on the network side using 3GPP standardized measurement reports is a promising technique. While applying predetermined detection rules works well when an attacker operates a false base station with an illegitimate Physical Cell Identifiers (PCI), the detection will produce false negatives when a more resourceful attacker operates the false base station with one of the legitimate PCIs obtained by scanning the neighborhood first. In this paper, we show how Machine Learning (ML) can be applied to alleviate such false negatives. We demonstrate our approach by conducting experiments in a simulation setup using the ns-3 LTE module. We propose three robust ML features (COL, DIST, XY) based on Reference Signal Received Power (RSRP) contained in measurement reports and cell locations. We evaluate four ML models (Regression Clustering, Anomaly Detection Forest, Autoencoder, and RCGAN) and show that several of them have a high precision in detection even when the false base station is using a legitimate PCI. In our experiments with a layout of 12 cells, where one cell acts as a moving false cell, between 75-95\% of the false positions are detected by the best model at a cost of 0.5\% false positives.
Layer-Wise Partitioning and Merging for Efficient and Scalable Deep Learning
Venue: arXiv cs.DC 2022
Year: 2022

Deep Neural Network (DNN) models are usually trained sequentially from one layer to another, which causes forward, backward and update locking's problems, leading to poor performance in terms of training time. The existing parallel strategies to mitigate these problems provide suboptimal runtime performance. In this work, we have proposed a novel layer-wise partitioning and merging, forward and backward pass parallel framework to provide better training performance. The novelty of the proposed work consists of 1) a layer-wise partition and merging model which can minimise communication overhead between devices without the memory cost of existing strategies during the training process; 2) a forward pass and backward pass parallelisation and optimisation to address the update locking problem and minimise the total training cost. The experimental evaluation on real use cases shows that the proposed method outperforms the state-of-the-art approaches in terms of training speed; and achieves almost linear speedup without compromising the accuracy performance of the non-parallel approach.
Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction
Venue: arXiv cs.LG 2022
Year: 2022

Traffic prediction plays an important role in the realization of traffic control and scheduling tasks in intelligent transportation systems. With the diversification of data sources, reasonably using rich traffic data to model the complex spatial-temporal dependence and nonlinear characteristics in traffic flow are the key challenge for intelligent transportation system. In addition, clearly evaluating the importance of spatial-temporal features extracted from different data becomes a challenge. A Double Layer - Spatial Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The lower layer of DL-STFEE is spatial-temporal feature extraction layer. The spatial and temporal features in traffic data are extracted by multi-graph graph convolution and attention mechanism, and different combinations of spatial and temporal features are generated. The upper layer of DL-STFEE is the spatial-temporal feature evaluation layer. Through the attention score matrix generated by the high-dimensional self-attention mechanism, the spatial-temporal features combinations are fused and evaluated, so as to get the impact of different combinations on prediction effect. Three sets of experiments are performed on actual traffic datasets to show that DL-STFEE can effectively capture the spatial-temporal features and evaluate the importance of different spatial-temporal feature combinations.
New Decoding of Reed-Solomon Codes Based on FFT and Modular Approach
Venue: arXiv cs.IT 2022
Year: 2022

Decoding algorithms for Reed--Solomon (RS) codes are of great interest for both practical and theoretical reasons. In this paper, an efficient algorithm, called the modular approach (MA), is devised for solving the Welch--Berlekamp (WB) key equation. By taking the MA as the key equation solver, we propose a new decoding algorithm for systematic RS codes. For $(n,k)$ RS codes, where $n$ is the code length and $k$ is the code dimension, the proposed decoding algorithm has both the best asymptotic computational complexity $O(n\log(n-k) + (n-k)\log^2(n-k))$ and the smallest constant factor achieved to date. By comparing the number of field operations required, we show that when decoding practical RS codes, the new algorithm is significantly superior to the existing methods in terms of computational complexity. When decoding the $(4096, 3584)$ RS code defined over $\mathbb{F}_{2^{12}}$, the new algorithm is 10 times faster than a conventional syndrome-based method. Furthermore, the new algorithm has a regular architecture and is thus suitable for hardware implementation.
Do Artificial Intelligence Systems Understand?
Venue: arXiv cs.AI 2022
Year: 2022

Are intelligent machines really intelligent? Is the underlying philosophical concept of intelligence satisfactory for describing how the present systems work? Is understanding a necessary and sufficient condition for intelligence? If a machine could understand, should we attribute subjectivity to it? This paper addresses the problem of deciding whether the so-called "intelligent machines" are capable of understanding, instead of merely processing signs. It deals with the relationship between syntaxis and semantics. The main thesis concerns the inevitability of semantics for any discussion about the possibility of building conscious machines, condensed into the following two tenets: "If a machine is capable of understanding (in the strong sense), then it must be capable of combining rules and intuitions"; "If semantics cannot be reduced to syntaxis, then a machine cannot understand." Our conclusion states that it is not necessary to attribute understanding to a machine in order to explain its exhibited "intelligent" behavior; a merely syntactic and mechanistic approach to intelligence as a task-solving tool suffices to justify the range of operations that it can display in the current state of technological development.
METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping
Venue: arXiv cs.CV 2022
Year: 2022

Reducing methane emissions is essential for mitigating global warming. To attribute methane emissions to their sources, a comprehensive dataset of methane source infrastructure is necessary. Recent advancements with deep learning on remotely sensed imagery have the potential to identify the locations and characteristics of methane sources, but there is a substantial lack of publicly available data to enable machine learning researchers and practitioners to build automated mapping approaches. To help fill this gap, we construct a multi-sensor dataset called METER-ML containing 86,599 georeferenced NAIP, Sentinel-1, and Sentinel-2 images in the U.S. labeled for the presence or absence of methane source facilities including concentrated animal feeding operations, coal mines, landfills, natural gas processing plants, oil refineries and petroleum terminals, and wastewater treatment plants. We experiment with a variety of models that leverage different spatial resolutions, spatial footprints, image products, and spectral bands. We find that our best model achieves an area under the precision recall curve of 0.915 for identifying concentrated animal feeding operations and 0.821 for oil refineries and petroleum terminals on an expert-labeled test set, suggesting the potential for large-scale mapping. We make METER-ML freely available at https://stanfordmlgroup.github.io/projects/meter-ml/ to support future work on automated methane source mapping.
Silent Spring: Prototype Pollution Leads to Remote Code Execution in Node.js
Venue: arXiv cs.CR 2022
Year: 2022

Prototype pollution is a dangerous vulnerability affecting prototype-based languages like JavaScript and the Node.js platform. It refers to the ability of an attacker to inject properties into an object's root prototype at runtime and subsequently trigger the execution of legitimate code gadgets that access these properties on the object's prototype, leading to attacks such as Denial of Service (DoS), privilege escalation, and Remote Code Execution (RCE). While there is anecdotal evidence that prototype pollution leads to RCE, current research does not tackle the challenge of gadget detection, thus only showing feasibility of DoS attacks, mainly against Node.js libraries. In this paper, we set out to study the problem in a holistic way, from the detection of prototype pollution to detection of gadgets, with the ambitious goal of finding end-to-end exploits beyond DoS, in full-fledged Node.js applications. We build the first multi-staged framework that uses multi-label static taint analysis to identify prototype pollution in Node.js libraries and applications, as well as a hybrid approach to detect universal gadgets, notably, by analyzing the Node.js source code. We implement our framework on top of GitHub's static analysis framework CodeQL to find 11 universal gadgets in core Node.js APIs, leading to code execution. Furthermore, we use our methodology in a study of 15 popular Node.js applications to identify prototype pollutions and gadgets. We manually exploit eight RCE vulnerabilities in three high-profile applications such as NPM CLI, Parse Server, and Rocket.Chat. Our results provide alarming evidence that prototype pollution in combination with powerful universal gadgets lead to RCE in Node.js.
Explaining Dynamic Graph Neural Networks via Relevance Back-propagation
Venue: arXiv cs.LG 2022
Year: 2022

Graph Neural Networks (GNNs) have shown remarkable effectiveness in capturing abundant information in graph-structured data. However, the black-box nature of GNNs hinders users from understanding and trusting the models, thus leading to difficulties in their applications. While recent years witness the prosperity of the studies on explaining GNNs, most of them focus on static graphs, leaving the explanation of dynamic GNNs nearly unexplored. It is challenging to explain dynamic GNNs, due to their unique characteristic of time-varying graph structures. Directly using existing models designed for static graphs on dynamic graphs is not feasible because they ignore temporal dependencies among the snapshots. In this work, we propose DGExplainer to provide reliable explanation on dynamic GNNs. DGExplainer redistributes the output activation score of a dynamic GNN to the relevances of the neurons of its previous layer, which iterates until the relevance scores of the input neuron are obtained. We conduct quantitative and qualitative experiments on real-world datasets to demonstrate the effectiveness of the proposed framework for identifying important nodes for link prediction and node regression for dynamic GNNs.
Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation
Venue: arXiv cs.CV 2022
Year: 2022

Many successful methods developed for medical image analysis that are based on machine learning use supervised learning approaches, which often require large datasets annotated by experts to achieve high accuracy. However, medical data annotation is time-consuming and expensive, especially for segmentation tasks. To solve the problem of learning with limited labeled medical image data, an alternative deep learning training strategy based on self-supervised pretraining on unlabeled MRI scans is proposed in this work. Our pretraining approach first, randomly applies different distortions to random areas of unlabeled images and then predicts the type of distortions and loss of information. To this aim, an improved version of Mask-RCNN architecture has been adapted to localize the distortion location and recover the original image pixels. The effectiveness of the proposed method for segmentation tasks in different pre-training and fine-tuning scenarios is evaluated based on the Osteoarthritis Initiative dataset. Using this self-supervised pretraining method improved the Dice score by 20% compared to training from scratch. The proposed self-supervised learning is simple, effective, and suitable for different ranges of medical image analysis tasks including anomaly detection, segmentation, and classification.
Target-Driven Structured Transformer Planner for Vision-Language Navigation
Venue: arXiv cs.CV 2022
Year: 2022

Vision-language navigation is the task of directing an embodied agent to navigate in 3D scenes with natural language instructions. For the agent, inferring the long-term navigation target from visual-linguistic clues is crucial for reliable path planning, which, however, has rarely been studied before in literature. In this article, we propose a Target-Driven Structured Transformer Planner (TD-STP) for long-horizon goal-guided and room layout-aware navigation. Specifically, we devise an Imaginary Scene Tokenization mechanism for explicit estimation of the long-term target (even located in unexplored environments). In addition, we design a Structured Transformer Planner which elegantly incorporates the explored room layout into a neural attention architecture for structured and global planning. Experimental results demonstrate that our TD-STP substantially improves previous best methods' success rate by 2% and 5% on the test set of R2R and REVERIE benchmarks, respectively. Our code is available at https://github.com/YushengZhao/TD-STP .
Flow Moods: Recommending Music by Moods on Deezer
Venue: arXiv cs.IR 2022
Year: 2022

The music streaming service Deezer extensively relies on its Flow algorithm, which generates personalized radio-style playlists of songs, to help users discover musical content. Nonetheless, despite promising results over the past years, Flow used to ignore the moods of users when providing recommendations. In this paper, we present Flow Moods, an improved version of Flow that addresses this limitation. Flow Moods leverages collaborative filtering, audio content analysis, and mood annotations from professional music curators to generate personalized mood-specific playlists at scale. We detail the motivations, the development, and the deployment of this system on Deezer. Since its release in 2021, Flow Moods has been recommending music by moods to millions of users every day.