OpenRaiser/Intern-Atlas huggingface dataset:
Simulation platform for pattern recognition based on reservoir computing with memristor networks
Venue: arXiv cs.ET 2022
Year: 2022

Memristive systems and devices are potentially available for implementing reservoir computing (RC) systems applied to pattern recognition. However, the computational ability of memristive RC systems depends on intertwined factors such as system architectures and physical properties of memristive elements, which complicates identifying the key factor for system performance. Here we develop a simulation platform for RC with memristor device networks, which enables testing different system designs for performance improvement. Numerical simulations show that the memristor-network-based RC systems can yield high computational performance comparable to that of state-of-the-art methods in three time series classification tasks. We demonstrate that the excellent and robust computation under device-to-device variability can be achieved by appropriately setting network structures, nonlinearity of memristors, and pre/post-processing, which increases the potential for reliable computation with unreliable component devices. Our results contribute to an establishment of a design guide for memristive reservoirs toward a realization of energy-efficient machine learning hardware.
Automatic travel pattern extraction from visa page stamps using CNN models
Venue: arXiv cs.CV 2022
Year: 2022

Manual travel pattern inference from visa page stamps is a time consuming activity and constitutes an important bottleneck in the efficiency of traveler inspection at border crossings. Despite efforts to digitize and record the border crossing information into databases, travel pattern inference from stamps will remain a problem until every country in the world is incorporated into such a unified system. This could take decades. We propose an automated document analysis system that processes scanned visa pages and automatically extracts the travel pattern from detected stamps. The system processes the page via the following pipeline: stamp detection in the visa page; general stamp country and entry/exit recognition; Schengen area stamp country and entry/exit recognition; Schengen area stamp date extraction. For each stage of the proposed pipeline we construct neural network models and train then on a mixture of real and synthetic data. We integrated Schengen area stamp detection and date, country, entry/exit recognition models together with a graphical user interface into a prototype of an automatic travel pattern extraction tool. We find that by combining simple neural network models into our proposed pipeline a useful tool can be created which can speed up the travel pattern extraction significantly.
Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance
Venue: arXiv cs.SE 2022
Year: 2022

The increasing use of Machine Learning (ML) components embedded in autonomous systems -- so-called Learning-Enabled Systems (LESs) -- has resulted in the pressing need to assure their functional safety. As for traditional functional safety, the emerging consensus within both, industry and academia, is to use assurance cases for this purpose. Typically assurance cases support claims of reliability in support of safety, and can be viewed as a structured way of organising arguments and evidence generated from safety analysis and reliability modelling activities. While such assurance activities are traditionally guided by consensus-based standards developed from vast engineering experience, LESs pose new challenges in safety-critical application due to the characteristics and design of ML models. In this article, we first present an overall assurance framework for LESs with an emphasis on quantitative aspects, e.g., breaking down system-level safety targets to component-level requirements and supporting claims stated in reliability metrics. We then introduce a novel model-agnostic Reliability Assessment Model (RAM) for ML classifiers that utilises the operational profile and robustness verification evidence. We discuss the model assumptions and the inherent challenges of assessing ML reliability uncovered by our RAM and propose solutions to practical use. Probabilistic safety argument templates at the lower ML component-level are also developed based on the RAM. Finally, to evaluate and demonstrate our methods, we not only conduct experiments on synthetic/benchmark datasets but also scope our methods with case studies on simulated Autonomous Underwater Vehicles and physical Unmanned Ground Vehicles.
Iterative Saliency Enhancement using Superpixel Similarity
Venue: arXiv cs.CV 2022
Year: 2022

Saliency Object Detection (SOD) has several applications in image analysis. The methods have evolved from image-intrinsic to object-inspired (deep-learning-based) models. When a model fail, however, there is no alternative to enhance its saliency map. We fill this gap by introducing a hybrid approach, named \textit{Iterative Saliency Enhancement over Superpixel Similarity} (ISESS), that iteratively generates enhanced saliency maps by executing two operations alternately: object-based superpixel segmentation and superpixel-based saliency estimation -- cycling operations never exploited. ISESS estimates seeds for superpixel delineation from a given saliency map and defines superpixel queries in the foreground and background. A new saliency map results from color similarities between queries and superpixels at each iteration. The process repeats and, after a given number of iterations, the generated saliency maps are combined into one by cellular automata. Finally, the resulting map is merged with the initial one by the maximum bewteen their average values per superpixel. We demonstrate that our hybrid model can consistently outperform three state-of-the-art deep-learning-based methods on five image datasets.
Ideals, Determinants, and Straightening: Proving and Using Lower Bounds for Polynomial Ideals
Venue: arXiv cs.CC 2022
Year: 2022

We show that any nonzero polynomial in the ideal generated by the $r \times r$ minors of an $n \times n$ matrix $X$ can be used to efficiently approximate the determinant. For any nonzero polynomial $f$ in this ideal, we construct a small depth-three $f$-oracle circuit that approximates the determinant of size $Θ(r^{1/3})$ in the sense of border complexity. For many classes of algebraic circuits, this implies that every nonzero polynomial in the ideal generated by $r \times r$ minors is at least as hard to approximately compute as the determinant of size $Θ(r^{1/3})$. We also prove an analogous result for the Pfaffian of a $2n \times 2n$ skew-symmetric matrix and the ideal generated by Pfaffians of $2r \times 2r$ principal submatrices. This answers a recent question of Grochow about complexity in polynomial ideals in the setting of border complexity. We give several applications of our result, two of which are highlighted below. $\bullet$ We prove super-polynomial lower bounds for Ideal Proof System refutations computed by low-depth circuits. This extends the recent breakthrough low-depth circuit lower bounds of Limaye, Srinivasan, and Tavenas to the setting of proof complexity. For many natural circuit classes, we show that the approximative proof complexity of our hard instance is governed by the approximative circuit complexity of the determinant. $\bullet$ We construct new hitting set generators for polynomial-size low-depth circuits. For any $\varepsilon > 0$, we construct generators with seed length $O(n^\varepsilon)$ that attain a near-optimal tradeoff between their seed length and degree, and are computable by low-depth circuits of near-linear size (with respect to the size of their output). This matches the seed length of the generators recently obtained by Limaye, Srinivasan, and Tavenas, but improves on the generator's degree and circuit complexity.
Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

News recommendation is a widely adopted technique to provide personalized news feeds for the user. Recently, pre-trained language models (PLMs) have demonstrated the great capability of natural language understanding and benefited news recommendation via improving news modeling. However, most existing works simply finetune the PLM with the news recommendation task, which may suffer from the known domain shift problem between the pre-training corpus and downstream news texts. Moreover, PLMs usually contain a large volume of parameters and have high computational overhead, which imposes a great burden on low-latency online services. In this paper, we propose Tiny-NewsRec, which can improve both the effectiveness and the efficiency of PLM-based news recommendation. We first design a self-supervised domain-specific post-training method to better adapt the general PLM to the news domain with a contrastive matching task between news titles and news bodies. We further propose a two-stage knowledge distillation method to improve the efficiency of the large PLM-based news recommendation model while maintaining its performance. Multiple teacher models originated from different time steps of our post-training procedure are used to transfer comprehensive knowledge to the student in both its post-training and finetuning stage. Extensive experiments on two real-world datasets validate the effectiveness and efficiency of our method.
Towards Personalization of User Preferences in Partially Observable Smart Home Environments
Venue: arXiv cs.AI 2022
Year: 2022

The technologies used in smart homes have recently improved to learn the user preferences from feedback in order to enhance the user convenience and quality of experience. Most smart homes learn a uniform model to represent the thermal preferences of users, which generally fails when the pool of occupants includes people with different sensitivities to temperature, for instance due to age and physiological factors. Thus, a smart home with a single optimal policy may fail to provide comfort when a new user with a different preference is integrated into the home. In this paper, we propose a Bayesian Reinforcement learning framework that can approximate the current occupant state in a partially observable smart home environment using its thermal preference, and then identify the occupant as a new user or someone is already known to the system. Our proposed framework can be used to identify users based on the temperature and humidity preferences of the occupant when performing different activities to enable personalization and improve comfort. We then compare the proposed framework with a baseline long short-term memory learner that learns the thermal preference of the user from the sequence of actions which it takes. We perform these experiments with up to 5 simulated human models each based on hierarchical reinforcement learning. The results show that our framework can approximate the belief state of the current user just by its temperature and humidity preferences across different activities with a high degree of accuracy.
Contrastive Cross-domain Recommendation in Matching
Venue: arXiv cs.IR 2022
Year: 2022

Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-world systems. However, CDR in the matching (i.e., candidate generation) module struggles with the data sparsity and popularity bias issues in both representation learning and knowledge transfer. In this work, we propose a novel Contrastive Cross-Domain Recommendation (CCDR) framework for CDR in matching. Specifically, we build a huge diversified preference network to capture multiple information reflecting user diverse interests, and design an intra-domain contrastive learning (intra-CL) and three inter-domain contrastive learning (inter-CL) tasks for better representation learning and knowledge transfer. The intra-CL enables more effective and balanced training inside the target domain via a graph augmentation, while the inter-CL builds different types of cross-domain interactions from user, taxonomy, and neighbor aspects. In experiments, CCDR achieves significant improvements on both offline and online evaluations in a real-world system. Currently, we have deployed our CCDR on WeChat Top Stories, affecting plenty of users. The source code is in https://github.com/lqfarmer/CCDR.
Distributed Control for a Robotic Swarm to Pass through a Curve Virtual Tube
Venue: arXiv cs.RO 2022
Year: 2022

Robotic swarm systems are now becoming increasingly attractive for many challenging applications. The main task for any robot is to reach the destination while keeping a safe separation from other robots and obstacles. In many scenarios, robots need to move within a narrow corridor, through a window or a doorframe. In order to guide all robots to move in a cluttered environment, a curve virtual tube with no obstacle inside is carefully designed in this paper. There is no obstacle inside the tube, namely the area inside the tube can be seen as a safety zone. Then, a distributed swarm controller is proposed with three elaborate control terms: a line approaching term, a robot avoidance term and a tube keeping term. Formal analysis and proofs are made to show that the curve virtual tube passing problem can be solved in a finite time. For the convenience in practical use, a modified controller with an approximate control performance is put forward. Finally, the effectiveness of the proposed method is validated by numerical simulations and real experiments. To show the advantages of the proposed method, the comparison between our method and the control barrier function method is also presented in terms of calculation speed.
Situation-Aware Environment Perception Using a Multi-Layer Attention Map
Venue: arXiv cs.RO 2022
Year: 2022

Within the field of automated driving, a clear trend in environment perception tends towards more sensors, higher redundancy, and overall increase in computational power. This is mainly driven by the paradigm to perceive the entire environment as best as possible at all times. However, due to the ongoing rise in functional complexity, compromises have to be considered to ensure real-time capabilities of the perception system. In this work, we introduce a concept for situation-aware environment perception to control the resource allocation towards processing relevant areas within the data as well as towards employing only a subset of functional modules for environment perception, if sufficient for the current driving task. Specifically, we propose to evaluate the context of an automated vehicle to derive a multi-layer attention map (MLAM) that defines relevant areas. Using this MLAM, the optimum of active functional modules is dynamically configured and intra-module processing of only relevant data is enforced. We outline the feasibility of application of our concept using real-world data in a straight-forward implementation for our system at hand. While retaining overall functionality, we achieve a reduction of accumulated processing time of 59%.
Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

The neural network-based approach to solving partial differential equations has attracted considerable attention due to its simplicity and flexibility in representing the solution of the partial differential equation. In training a neural network, the network learns global features corresponding to low-frequency components while high-frequency components are approximated at a much slower rate. For a class of equations in which the solution contains a wide range of scales, the network training process can suffer from slow convergence and low accuracy due to its inability to capture the high-frequency components. In this work, we propose a hierarchical approach to improve the convergence rate and accuracy of the neural network solution to partial differential equations. The proposed method comprises multi-training levels in which a newly introduced neural network is guided to learn the residual of the previous level approximation. By the nature of neural networks' training process, the high-level correction is inclined to capture the high-frequency components. We validate the efficiency and robustness of the proposed hierarchical approach through a suite of linear and nonlinear partial differential equations.
Deep residential representations: Using unsupervised learning to unlock elevation data for geo-demographic prediction
Venue: arXiv cs.LG 2022
Year: 2022

LiDAR (short for "Light Detection And Ranging" or "Laser Imaging, Detection, And Ranging") technology can be used to provide detailed three-dimensional elevation maps of urban and rural landscapes. To date, airborne LiDAR imaging has been predominantly confined to the environmental and archaeological domains. However, the geographically granular and open-source nature of this data also lends itself to an array of societal, organizational and business applications where geo-demographic type data is utilised. Arguably, the complexity involved in processing this multi-dimensional data has thus far restricted its broader adoption. In this paper, we propose a series of convenient task-agnostic tile elevation embeddings to address this challenge, using recent advances from unsupervised Deep Learning. We test the potential of our embeddings by predicting seven English indices of deprivation (2019) for small geographies in the Greater London area. These indices cover a range of socio-economic outcomes and serve as a proxy for a wide variety of downstream tasks to which the embeddings can be applied. We consider the suitability of this data not just on its own but also as an auxiliary source of data in combination with demographic features, thus providing a realistic use case for the embeddings. Having trialled various model/embedding configurations, we find that our best performing embeddings lead to Root-Mean-Squared-Error (RMSE) improvements of up to 21% over using standard demographic features alone. We also demonstrate how our embedding pipeline, using Deep Learning combined with K-means clustering, produces coherent tile segments which allow the latent embedding features to be interpreted.
InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation
Venue: arXiv cs.CL 2022
Year: 2022

Assessing the quality of natural language generation systems through human annotation is very expensive. Additionally, human annotation campaigns are time-consuming and include non-reusable human labour. In practice, researchers rely on automatic metrics as a proxy of quality. In the last decade, many string-based metrics (e.g., BLEU) have been introduced. However, such metrics usually rely on exact matches and thus, do not robustly handle synonyms. In this paper, we introduce InfoLM a family of untrained metrics that can be viewed as a string-based metric that addresses the aforementioned flaws thanks to a pre-trained masked language model. This family of metrics also makes use of information measures allowing the adaptation of InfoLM to various evaluation criteria. Using direct assessment, we demonstrate that InfoLM achieves statistically significant improvement and over $10$ points of correlation gains in many configurations on both summarization and data2text generation.
Understanding Performance Problems in Deep Learning Systems
Venue: arXiv cs.SE 2022
Year: 2022

Deep learning (DL) has been widely applied to many domains. Unique challenges in engineering DL systems are posed by the programming paradigm shift from traditional systems to DL systems, and performance is one of the challenges. Performance problems (PPs) in DL systems can cause severe consequences such as excessive resource consumption and financial loss. While bugs in DL systems have been extensively investigated, PPs in DL systems have hardly been explored. To bridge this gap, we present the first comprehensive study to i) characterize symptoms, root causes, and introducing and exposing stages of PPs in DL systems developed in TensorFLow and Keras, with 224 PPs collected from 210 StackOverflow posts, and to ii) assess the capability of existing performance analysis approaches in tackling PPs, with a constructed benchmark of 58 PPs in DL systems. Our findings shed light on the implications on developing high-performance DL systems, and detecting and localizing PPs in DL systems. To demonstrate the usefulness of our findings, we develop a static checker Deep-Perf to detect three types of PPs. It has detected 488 new PPs in 130 GitHub projects. 105 and 27 PPs have been confirmed and fixed.
Semantic Segmentation of Legal Documents via Rhetorical Roles
Venue: arXiv cs.CL 2022
Year: 2022

Legal documents are unstructured, use legal jargon, and have considerable length, making them difficult to process automatically via conventional text processing techniques. A legal document processing system would benefit substantially if the documents could be segmented into coherent information units. This paper proposes a new corpus of legal documents annotated (with the help of legal experts) with a set of 13 semantically coherent units labels (referred to as Rhetorical Roles), e.g., facts, arguments, statute, issue, precedent, ruling, and ratio. We perform a thorough analysis of the corpus and the annotations. For automatically segmenting the legal documents, we experiment with the task of rhetorical role prediction: given a document, predict the text segments corresponding to various roles. Using the created corpus, we experiment extensively with various deep learning-based baseline models for the task. Further, we develop a multitask learning (MTL) based deep model with document rhetorical role label shift as an auxiliary task for segmenting a legal document. The proposed model shows superior performance over the existing models. We also experiment with model performance in the case of domain transfer and model distillation techniques to see the model performance in limited data conditions.
A Study of the Impact of the Contention Window on the Performance of IEEE 802.11bd Networks with Channel Bonding
Venue: arXiv cs.NI 2022
Year: 2022

Nowadays, Vehicle-To-Everything (V2X) networks are actively developing. Most of the already deployed V2X networks are based on the IEEE 802.11p standard. However, these networks can provide only basic V2X applications and will unlikely fulfill stringent requirements of modern V2X applications. Thus, the IEEE has launched a new IEEE 802.11bd standard. A significant novelty of this standard is channel bonding. IEEE 802.11bd describes two channel bonding techniques, which differ from the legacy one used in modern Wi-Fi networks. Our study performs a comparative analysis of the various channel bonding techniques and a single-channel access method from IEEE 802.11p via simulation. We compare them under different contention window sizes and demonstrate that the legacy technique provides the best quality of service in terms of frame transmission delays and packet loss ratio. Moreover, we have found a quasi-optimal contention window size for the legacy technique.