OpenRaiser/Intern-Atlas huggingface dataset:
Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations
Venue: arXiv cs.LG 2022
Year: 2022

We introduce a sampling based machine learning approach, Monte Carlo physics informed neural networks (MC-PINNs), for solving forward and inverse fractional partial differential equations (FPDEs). As a generalization of physics informed neural networks (PINNs), our method relies on deep neural network surrogates in addition to a stochastic approximation strategy for computing the fractional derivatives of the DNN outputs. A key ingredient in our MC-PINNs is to construct an unbiased estimation of the physical soft constraints in the loss function. Our directly sampling approach can yield less overall computational cost compared to fPINNs proposed in \cite{pang2019fpinns} and thus provide an opportunity for solving high dimensional fractional PDEs. We validate the performance of MC-PINNs method via several examples that include high dimensional integral fractional Laplacian equations, parametric identification of time-space fractional PDEs, and fractional diffusion equation with random inputs. The results show that MC-PINNs is flexible and promising to tackle high-dimensional FPDEs.
A Survey of Historical Document Image Datasets
Venue: arXiv cs.CV 2022
Year: 2022

This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 65 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or content analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies.
LEVEN: A Large-Scale Chinese Legal Event Detection Dataset
Venue: arXiv cs.CL 2022
Year: 2022

Recognizing facts is the most fundamental step in making judgments, hence detecting events in the legal documents is important to legal case analysis tasks. However, existing Legal Event Detection (LED) datasets only concern incomprehensive event types and have limited annotated data, which restricts the development of LED methods and their downstream applications. To alleviate these issues, we present LEVEN a large-scale Chinese LEgal eVENt detection dataset, with 8,116 legal documents and 150,977 human-annotated event mentions in 108 event types. Not only charge-related events, LEVEN also covers general events, which are critical for legal case understanding but neglected in existing LED datasets. To our knowledge, LEVEN is the largest LED dataset and has dozens of times the data scale of others, which shall significantly promote the training and evaluation of LED methods. The results of extensive experiments indicate that LED is challenging and needs further effort. Moreover, we simply utilize legal events as side information to promote downstream applications. The method achieves improvements of average 2.2 points precision in low-resource judgment prediction, and 1.5 points mean average precision in unsupervised case retrieval, which suggests the fundamentality of LED. The source code and dataset can be obtained from https://github.com/thunlp/LEVEN.
Undersmoothing Causal Estimators with Generative Trees
Venue: arXiv cs.LG 2022
Year: 2022

Inferring individualised treatment effects from observational data can unlock the potential for targeted interventions. It is, however, hard to infer these effects from observational data. One major problem that can arise is covariate shift where the data (outcome) conditional distribution remains the same but the covariate (input) distribution changes between the training and test set. In an observational data setting, this problem is materialised in control and treated units coming from different distributions. A common solution is to augment learning methods through reweighing schemes (e.g. propensity scores). These are needed due to model misspecification, but might hurt performance in the individual case. In this paper, we explore a novel generative tree based approach that tackles model misspecification directly, helping downstream estimators achieve better robustness. We show empirically that the choice of model class can indeed significantly affect the final performance and that reweighing methods can struggle in individualised effect estimation. Our proposed approach is competitive with reweighing methods on average treatment effects while performing significantly better on individualised treatment effects.
Maintainable Log Datasets for Evaluation of Intrusion Detection Systems
Venue: arXiv cs.CR 2022
Year: 2022

Intrusion detection systems (IDS) monitor system logs and network traffic to recognize malicious activities in computer networks. Evaluating and comparing IDSs with respect to their detection accuracies is thereby essential for their selection in specific use-cases. Despite a great need, hardly any labeled intrusion detection datasets are publicly available. As a consequence, evaluations are often carried out on datasets from real infrastructures, where analysts cannot control system parameters or generate a reliable ground truth, or private datasets that prevent reproducibility of results. As a solution, we present a collection of maintainable log datasets collected in a testbed representing a small enterprise. Thereby, we employ extensive state machines to simulate normal user behavior and inject a multi-step attack. For scalable testbed deployment, we use concepts from model-driven engineering that enable automatic generation and labeling of an arbitrary number of datasets that comprise repetitions of attack executions with variations of parameters. In total, we provide 8 datasets containing 20 distinct types of log files, of which we label 8 files for 10 unique attack steps. We publish the labeled log datasets and code for testbed setup and simulation online as open-source to enable others to reproduce and extend our results.
Towards a Roadmap on Software Engineering for Responsible AI
Venue: arXiv cs.SE 2022
Year: 2022

Although AI is transforming the world, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and frameworks for responsible AI have been issued recently. However, they are high level and difficult to put into practice. On the other hand, most AI researchers focus on algorithmic solutions, while the responsible AI challenges actually crosscut the entire engineering lifecycle and components of AI systems. To close the gap in operationalizing responsible AI, this paper aims to develop a roadmap on software engineering for responsible AI. The roadmap focuses on (i) establishing multi-level governance for responsible AI systems, (ii) setting up the development processes incorporating process-oriented practices for responsible AI systems, and (iii) building responsible-AI-by-design into AI systems through system-level architectural style, patterns and techniques.
Extended vehicle energy dataset (eVED): an enhanced large-scale dataset for deep learning on vehicle trip energy consumption
Venue: arXiv cs.LG 2022
Year: 2022

This work presents an extended version of the Vehicle Energy Dataset (VED), which is a openly released large-scale dataset for vehicle energy consumption analysis. Compared with its original version, the extended VED (eVED) dataset is enhanced with accurate vehicle trip GPS coordinates, serving as a basis to associate the VED trip records with external information, e.g., road speed limit and intersections, from accessible map services to accumulate attributes that is essential in analyzing vehicle energy consumption. In particularly, we calibrate all the GPS trace records in the original VED data, upon which we associated the VED data with attributes extracted from the Geographic Information System (QGIS), the Overpass API, the Open Street Map API, and Google Maps API. The associated attributes include 12,609,170 records of road elevation, 12,203,044 of speed limit, 12,281,719 of speed limit with direction (in case the road is bi-directional), 584,551 of intersections, 429,638 of bus stop, 312,196 of crossings, 195,856 of traffic signals, 29,397 of stop signs, 5,848 of turning loops, 4,053 of railway crossings (level crossing), 3,554 of turning circles, and 2,938 of motorway junctions. With the accurate GPS coordinates and enriched features of the vehicle trip record, the obtained eVED dataset can provide a precise and abundant medium to feed a learning engine, especially a deep learning engine that is more demanding on data sufficiency and richness. Moreover, our software work for data calibration and enrichment can be reused to generate further vehicle trip datasets for specific user cases, contributing to deep insights into vehicle behaviors and traffic dynamics analyses. We anticipate that the eVED dataset and our data enrichment software can serve the academic and industrial automotive section as apparatus in developing future technologies.
Adversarial Learned Fair Representations using Dampening and Stacking
Venue: arXiv cs.LG 2022
Year: 2022

As more decisions in our daily life become automated, the need to have machine learning algorithms that make fair decisions increases. In fair representation learning we are tasked with finding a suitable representation of the data in which a sensitive variable is censored. Recent work aims to learn fair representations through adversarial learning. This paper builds upon this work by introducing a novel algorithm which uses dampening and stacking to learn adversarial fair representations. Results show that that our algorithm improves upon earlier work in both censoring and reconstruction.
Understanding motivations and characteristics of financially-motivated cybercriminals
Venue: arXiv cs.CR 2022
Year: 2022

Background: Cyber offences, such as hacking, malware creation and distribution, and online fraud, present a substantial threat to organizations attempting to safeguard their data and information. By understanding the evolving characteristics and motivations of individuals involved in these activities, and the threats that they may pose, cyber security practitioners will be better placed to understand and assess current threats to their systems and the range of socio-technical mitigations that may best reduce these. Aim: The reported work-in-progress aims to explore the extent to which findings from prior academic literature regarding the characteristics and motivations of offenders engaging in financially-motivated, cyber-dependent crime are supported by the contemporary experiences and perspectives of practitioners currently working in the cyber crime field. Method: A targeted, online survey was developed consisting of both closed and open-ended questions relating to current cyber threats and the characteristics and motivations of offenders engaged in these activities. Sixteen practitioners working in law enforcement-related domains in the cyber crime field completed the survey, providing a combination of qualitative and quantitative data for analysis.
Unraveled Multilevel Transformation Networks for Predicting Sparsely-Observed Spatiotemporal Dynamics
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we address the problem of predicting complex, nonlinear spatiotemporal dynamics when available data is recorded at irregularly-spaced sparse spatial locations. Most of the existing deep learning models for modeling spatiotemporal dynamics are either designed for data in a regular grid or struggle to uncover the spatial relations from sparse and irregularly-spaced data sites. We propose a deep learning model that learns to predict unknown spatiotemporal dynamics using data from sparsely-distributed data sites. We base our approach on Radial Basis Function (RBF) collocation method which is often used for meshfree solution of partial differential equations (PDEs). The RBF framework allows us to unravel the observed spatiotemporal function and learn the spatial interactions among data sites on the RBF-space. The learned spatial features are then used to compose multilevel transformations of the raw observations and predict its evolution in future time steps. We demonstrate the advantage of our approach using both synthetic and real-world climate data.
Turning Stocks into Memes: A Dataset for Understanding How Social Communities Can Drive Wall Street
Venue: arXiv cs.CL 2022
Year: 2022

Who actually expresses an intent to buy GameStop shares on Reddit? What convinces people to buy stocks? Are people convinced to support a coordinated plan to adversely impact Wall Street investors? Existing literature on understanding intent has mainly relied on surveys and self reporting; however there are limitations to these methodologies. Hence, in this paper, we develop an annotated dataset of communications centered on the GameStop phenomenon to analyze the subscriber intentions behaviors within the r/WallStreetBets community to buy (or not buy) stocks. Likewise, we curate a dataset to better understand how intent interacts with a user's general support towards the coordinated actions of the community for GameStop. Overall, our dataset can provide insight to social scientists on the persuasive power to buy into social movements online by adopting common language and narrative. WARNING: This paper contains offensive language that commonly appears on Reddit's r/WallStreetBets subreddit.
DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation
Venue: arXiv cs.CV 2022
Year: 2022

This paper proposes a simple baseline framework for video-based 2D/3D human pose estimation that can achieve 10 times efficiency improvement over existing works without any performance degradation, named DeciWatch. Unlike current solutions that estimate each frame in a video, DeciWatch introduces a simple yet effective sample-denoise-recover framework that only watches sparsely sampled frames, taking advantage of the continuity of human motions and the lightweight pose representation. Specifically, DeciWatch uniformly samples less than 10% video frames for detailed estimation, denoises the estimated 2D/3D poses with an efficient Transformer architecture, and then accurately recovers the rest of the frames using another Transformer-based network. Comprehensive experimental results on three video-based human pose estimation and body mesh recovery tasks with four datasets validate the efficiency and effectiveness of DeciWatch. Code is available at https://github.com/cure-lab/DeciWatch.
Input Influence Matrix Design for MIMO Discrete-Time Ultra-Local Model
Venue: arXiv cs.RO 2022
Year: 2022

Ultra-Local Models (ULM) have been applied to perform model-free control of nonlinear systems with unknown or partially known dynamics. Unfortunately, extending these methods to MIMO systems requires designing a dense input influence matrix which is challenging. This paper presents guidelines for designing an input influence matrix for discrete-time, control-affine MIMO systems using an ULM-based controller. This paper analyzes the case that uses ULM and a model-free control scheme: the Hölder-continuous Finite-Time Stable (FTS) control. By comparing the ULM with the actual system dynamics, the paper describes how to extract the input-dependent part from the lumped ULM dynamics and finds that the tracking and state estimation error are coupled. The stability of the ULM-FTS error dynamics is affected by the eigenvalues of the difference (defined by matrix multiplication) between the actual and designed input influence matrix. Finally, the paper shows that a wide range of input influence matrix designs can keep the ULM-FTS error dynamics (at least locally) asymptotically stable. A numerical simulation is included to verify the result. The analysis can also be extended to other ULM-based controllers.
Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey
Venue: arXiv cs.AR 2022
Year: 2022

Deep Neural Networks (DNNs) are very popular because of their high performance in various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have brought beyond human accuracy in many tasks, but at the cost of high computational complexity. To enable efficient execution of DNN inference, more and more research works, therefore, exploit the inherent error resilience of DNNs and employ Approximate Computing (AC) principles to address the elevated energy demands of DNN accelerators. This article provides a comprehensive survey and analysis of hardware approximation techniques for DNN accelerators. First, we analyze the state of the art and by identifying approximation families, we cluster the respective works with respect to the approximation type. Next, we analyze the complexity of the performed evaluations (with respect to the dataset and DNN size) to assess the efficiency, the potential, and limitations of approximate DNN accelerators. Moreover, a broad discussion is provided, regarding error metrics that are more suitable for designing approximate units for DNN accelerators as well as accuracy recovery approaches that are tailored to DNN inference. Finally, we present how Approximate Computing for DNN accelerators can go beyond energy efficiency and address reliability and security issues, as well.
UnseenNet: Fast Training Detector for Any Unseen Concept
Venue: arXiv cs.CV 2022
Year: 2022

Training of object detection models using less data is currently the focus of existing N-shot learning models in computer vision. Such methods use object-level labels and takes hours to train on unseen classes. There are many cases where we have large amount of image-level labels available for training but cannot be utilized by few shot object detection models for training. There is a need for a machine learning framework that can be used for training any unseen class and can become useful in real-time situations. In this paper, we proposed an "Unseen Class Detector" that can be trained within a very short time for any possible unseen class without bounding boxes with competitive accuracy. We build our approach on "Strong" and "Weak" baseline detectors, which we trained on existing object detection and image classification datasets, respectively. Unseen concepts are fine-tuned on the strong baseline detector using only image-level labels and further adapted by transferring the classifier-detector knowledge between baselines. We use semantic as well as visual similarities to identify the source class (i.e. Sheep) for the fine-tuning and adaptation of unseen class (i.e. Goat). Our model (UnseenNet) is trained on the ImageNet classification dataset for unseen classes and tested on an object detection dataset (OpenImages). UnseenNet improves the mean average precision (mAP) by 10% to 30% over existing baselines (semi-supervised and few-shot) of object detection on different unseen class splits. Moreover, training time of our model is <10 min for each unseen class. Qualitative results demonstrate that UnseenNet is suitable not only for few classes of Pascal VOC but for unseen classes of any dataset or web. Code is available at https://github.com/Asra-Aslam/UnseenNet.
GLASS: A Citizen-Centric Distributed Data-Sharing Model within an e-Governance Architecture
Venue: arXiv cs.CR 2022
Year: 2022

E-governance is a process that aims to enhance a government's ability to simplify all the processes that may involve government, citizens, businesses, and so on. The rapid evolution of digital technologies has often created the necessity for the establishment of an e-Governance model. There is often a need for an inclusive e-governance model with integrated multiactor governance services and where a single market approach can be adopted. e-Governance often aims to minimise bureaucratic processes, while at the same time including a digital-by-default approach to public services. This aims at administrative efficiency and the reduction of bureaucratic processes. It can also improve government capabilities, and enhances trust and security, which brings confidence in governmental transactions. However, solid implementations of a distributed data sharing model within an e-governance architecture is far from a reality; hence, citizens of European countries often go through the tedious process of having their confidential information verified. This paper focuses on the sinGLe sign-on e-GovernAnce Paradigm based on a distributed file-exchange network for security, transparency, cost-effectiveness and trust (GLASS) model, which aims to ensure that a citizen can control their relationship with governmental agencies. The paper thus proposes an approach that integrates a permissioned blockchain with the InterPlanetary File System (IPFS). This method demonstrates how we may encrypt and store verifiable credentials of the GLASS ecosystem, such as academic awards, ID documents and so on, within IPFS in a secure manner and thus only allow trusted users to read a blockchain record, and obtain the encryption key. This allows for the decryption of a given verifiable credential that stored on IPFS. This paper outlines the creation of a demonstrator that proves the principles of the GLASS approach.
Neural-Network-Directed Genetic Programmer for Discovery of Governing Equations
Venue: arXiv cs.NE 2022
Year: 2022

We develop a symbolic regression framework for extracting the governing mathematical expressions from observed data. The evolutionary approach, faiGP, is designed to leverage the properties of a function algebra that have been encoded into a grammar, providing a theoretical guarantee of universal approximation and a way to minimize bloat. In this framework, the choice of operators of the grammar may be informed by a physical theory or symmetry considerations. Since there is currently no theory that can derive the 'constants of nature', an empirical investigation on extracting these coefficients from an evolutionary process is of methodological interest. We quantify the impact of different types of regularizers, including a diversity metric adapted from studies of the transcriptome and a complexity measure, on the performance of the framework. Our implementation, which leverages neural networks and a genetic programmer, generates non-trivial symbolically equivalent expressions ("Ramanujan expressions") or approximations with potentially interesting numerical applications. To illustrate the framework, a model of ligand-receptor binding kinetics, including an account of gene regulation by transcription factors, and a model of the regulatory range of the cistrome from omics data are presented. This study has important implications on the development of data-driven methodologies for the discovery of governing equations in experimental data derived from new sensing systems and high-throughput screening technologies.