OpenRaiser/Intern-Atlas huggingface dataset:
Datensouveränität für Verbraucher:innen: Technische Ansätze durch KI-basierte Transparenz und Auskunft im Kontext der DSGVO
Venue: arXiv cs.CY 2021
Year: 2021

A sufficient level of data sovereignty is extremely difficult for consumers in practice. The EU General Data Protection Regulation guarantees comprehensive data subject rights, which must be implemented by responsible controllers through technical and organizational measures. Traditional approaches, such as the provision of lengthy data protection declarations or the downloading of raw personal data without further assistance, do not meet the requirements of informational self-determination. The new technical approaches outlined below, in particular AI-based transparency and access modalities, demonstrate the practicability of effective and versatile mechanisms. For this purpose, the relevant transparency information is extracted in a semi-automated way, represented in a machine-readable format, and then played out via diverse channels such as virtual assistants or the enrichment of search results. --- Hinreichende Datensouveränität gestaltet sich für Verbraucher:innen in der Praxis als äußerst schwierig. Die Europäische Datenschutzgrundverordnung garantiert umfassende Betroffenenrechte, die von verantwortlichen Stellen durch technisch-organisatorische Maßnahmen umzusetzen sind. Traditionelle Vorgehensweisen wie die Bereitstellung länglicher Datenschutzerklärungen oder der ohne weitere Hilfestellungen angebotene Download von personenbezogenen Rohdaten werden dem Anspruch der informationellen Selbstbestimmung nicht gerecht. Die im Folgenden aufgezeigten neuen technischen Ansätze insbesondere KI-basierter Transparenz- und Auskunftsmodalitäten zeigen die Praktikabilität wirksamer und vielseitiger Mechanismen. Hierzu werden die relevanten Transparenzangaben teilautomatisiert extrahiert, maschinenlesbar repräsentiert und anschließend über diverse Kanäle wie virtuelle Assistenten oder die Anreicherung von Suchergebnissen ausgespielt.
RID-Noise: Towards Robust Inverse Design under Noisy Environments
Venue: arXiv cs.LG 2021
Year: 2021

From an engineering perspective, a design should not only perform well in an ideal condition, but should also resist noises. Such a design methodology, namely robust design, has been widely implemented in the industry for product quality control. However, classic robust design requires a lot of evaluations for a single design target, while the results of these evaluations could not be reused for a new target. To achieve a data-efficient robust design, we propose Robust Inverse Design under Noise (RID-Noise), which can utilize existing noisy data to train a conditional invertible neural network (cINN). Specifically, we estimate the robustness of a design parameter by its predictability, measured by the prediction error of a forward neural network. We also define a sample-wise weight, which can be used in the maximum weighted likelihood estimation of an inverse model based on a cINN. With the visual results from experiments, we clearly justify how RID-Noise works by learning the distribution and robustness from data. Further experiments on several real-world benchmark tasks with noises confirm that our method is more effective than other state-of-the-art inverse design methods. Code and supplementary is publicly available at https://github.com/ThyrixYang/rid-noise-aaai22
Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project
Venue: arXiv cs.LG 2021
Year: 2021

These two synthetic datasets comprise vital signs, laboratory test results, administered fluid boluses and vasopressors for 3,910 patients with acute hypotension and for 2,164 patients with sepsis in the Intensive Care Unit (ICU). The patient cohorts were built using previously published inclusion and exclusion criteria and the data were created using Generative Adversarial Networks (GANs) and the MIMIC-III Clinical Database. The risk of identity disclosure associated with the release of these data was estimated to be very low (0.045%). The datasets were generated and published as part of the Health Gym, a project aiming to publicly distribute synthetic longitudinal health data for developing machine learning algorithms (with a particular focus on offline reinforcement learning) and for educational purposes.
Evaluation of Static Vulnerability Detection Tools with Java Cryptographic API Benchmarks
Venue: arXiv cs.CR 2021
Year: 2021

Several studies showed that misuses of cryptographic APIs are common in real-world code (e.g., Apache projects and Android apps). There exist several open-sourced and commercial security tools that automatically screen Java programs to detect misuses. To compare their accuracy and security guarantees, we develop two comprehensive benchmarks named CryptoAPI-Bench and ApacheCryptoAPI-Bench. CryptoAPI-Bench consists of 181 unit test cases that cover basic cases, as well as complex cases, including interprocedural, field sensitive, multiple class test cases, and path sensitive data flow of misuse cases. The benchmark also includes correct cases for testing false-positive rates. The ApacheCryptoAPI-Bench consists of 121 cryptographic cases from 10 Apache projects. We evaluate four tools, namely, SpotBugs, CryptoGuard, CrySL, and Coverity using both benchmarks. We present their performance and comparative analysis. The ApacheCryptoAPI-Bench also examines the scalability of the tools. Our benchmarks are useful for advancing state-of-the-art solutions in the space of misuse detection.
A QoT Estimation Method using EGN-assisted Machine Learning for Network Planning Applications
Venue: arXiv cs.NI 2021
Year: 2021

An ML model based on precomputed per-channel SCI is proposed. Due to its superior accuracy over closed-form GN, an average SNR gain of 1.1 dB in an end-to-end link optimization and a 40% reduction in required lightpaths to meet traffic requests in a network planning scenario are shown.
Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study
Venue: arXiv cs.CV 2021
Year: 2021

With the rapid development of intelligent vehicles and Advanced Driver-Assistance Systems (ADAS), a new trend is that mixed levels of human driver engagements will be involved in the transportation system. Therefore, necessary visual guidance for drivers is vitally important under this situation to prevent potential risks. To advance the development of visual guidance systems, we introduce a novel vision-cloud data fusion methodology, integrating camera image and Digital Twin information from the cloud to help intelligent vehicles make better decisions. Target vehicle bounding box is drawn and matched with the help of the object detector (running on the ego-vehicle) and position information (received from the cloud). The best matching result, a 79.2% accuracy under 0.7 intersection over union threshold, is obtained with depth images served as an additional feature source. A case study on lane change prediction is conducted to show the effectiveness of the proposed data fusion methodology. In the case study, a multi-layer perceptron algorithm is proposed with modified lane change prediction approaches. Human-in-the-loop simulation results obtained from the Unity game engine reveal that the proposed model can improve highway driving performance significantly in terms of safety, comfort, and environmental sustainability.
Learning to Localize, Grasp, and Hand Over Unmodified Surgical Needles
Venue: arXiv cs.RO 2021
Year: 2021

Robotic Surgical Assistants (RSAs) are commonly used to perform minimally invasive surgeries by expert surgeons. However, long procedures filled with tedious and repetitive tasks such as suturing can lead to surgeon fatigue, motivating the automation of suturing. As visual tracking of a thin reflective needle is extremely challenging, prior work has modified the needle with nonreflective contrasting paint. As a step towards automation of a suturing subtask without modifying the needle, we propose HOUSTON: Handoff of Unmodified, Surgical, Tool-Obstructed Needles, a problem and algorithm that uses a learned active sensing policy with a stereo camera to localize and align the needle into a visible and accessible pose for the other arm. To compensate for robot positioning and needle perception errors, the algorithm then executes a high-precision grasping motion that uses multiple cameras. In physical experiments using the da Vinci Research Kit (dVRK), HOUSTON successfully passes unmodified surgical needles with a success rate of 96.7% and is able to perform handover sequentially between the arms 32.4 times on average before failure. On needles unseen in training, HOUSTON achieves a success rate of 75 - 92.9%. To our knowledge, this work is the first to study handover of unmodified surgical needles. See https://tinyurl.com/houston-surgery for additional materials.
Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study
Venue: arXiv cs.IR 2021
Year: 2021

Screening or assessing studies is critical to the quality and outcomes of a systematic review. Typically, a Boolean query retrieves the set of studies to screen. As the set of studies retrieved is unordered, screening all retrieved studies is usually required for high-quality systematic reviews. Screening prioritisation, or in other words, ranking the set of studies, enables downstream activities of a systematic review to begin in parallel. We investigate a method that exploits seed studies -- potentially relevant studies used to seed the query formulation process -- for screening prioritisation. Our investigation aims to reproduce this method to determine if it is generalisable on recently published datasets and determine the impact of using multiple seed studies on effectiveness.We show that while we could reproduce the original methods, we could not replicate their results exactly. However, we believe this is due to minor differences in document pre-processing, not deficiencies with the original methodology. Our results also indicate that our reproduced screening prioritisation method, (1) is generalisable across datasets of similar and different topicality compared to the original implementation, (2) that when using multiple seed studies, the effectiveness of the method increases using our techniques to enable this, (3) and that the use of multiple seed studies produces more stable rankings compared to single seed studies. Finally, we make our implementation and results publicly available at the following URL: https://github.com/ielab/sdr
A Preamble Based MAC Mechanism in Ad-Hoc Network
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we propose a preamble based medium access control (P-MAC) mechanism in Ad-Hoc network. Different from traditional carrier sense multiple access (CSMA) in Ad-Hoc network, P-MAC uses much shorter preamble to establish the network. First, we propose the P-MAC mechanism to shorten the time of establishing the Ad-Hoc network. Based on the P-MAC, we propose a more efficient way to maintain the network. Next, focusing on the power line communication (PLC) network which is a kind of Ad-Hoc network, we propose a frequency division power line communication (FD-PLC) network architecture to obtain the best communication frequency. To obtain the best frequency, i.e., highest SNR, we design the frequency sweeping mechanism which can determine the frequency of uplink and downlink communication before the transmitter and receiver communicate. Due to the large-scale networks in industry, P-MAC can be exploited to speed up the establishment of the Ad-Hoc PLC network. Finally, we compare our mechanism with CSMA. Numerical results indicate that our strategy greatly shortens the time of establishing the Ad-Hoc network.
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives
Venue: arXiv cs.LG 2021
Year: 2021

We present ShinRL, an open-source library specialized for the evaluation of reinforcement learning (RL) algorithms from both theoretical and practical perspectives. Existing RL libraries typically allow users to evaluate practical performances of deep RL algorithms through returns. Nevertheless, these libraries are not necessarily useful for analyzing if the algorithms perform as theoretically expected, such as if Q learning really achieves the optimal Q function. In contrast, ShinRL provides an RL environment interface that can compute metrics for delving into the behaviors of RL algorithms, such as the gap between learned and the optimal Q values and state visitation frequencies. In addition, we introduce a flexible solver interface for evaluating both theoretically justified algorithms (e.g., dynamic programming and tabular RL) and practically effective ones (i.e., deep RL, typically with some additional extensions and regularizations) in a consistent fashion. As a case study, we show that how combining these two features of ShinRL makes it easier to analyze the behavior of deep Q learning. Furthermore, we demonstrate that ShinRL can be used to empirically validate recent theoretical findings such as the effect of KL regularization for value iteration and for deep Q learning, and the robustness of entropy-regularized policies to adversarial rewards. The source code for ShinRL is available on GitHub: https://github.com/omron-sinicx/ShinRL.
SeaPlace: Process Variation Aware Placement for Reliable Combinational Circuits against SETs and METs
Venue: arXiv cs.AR 2021
Year: 2021

Nowadays nanoscale combinational circuits are facing significant reliability challenges including soft errors and process variations. This paper presents novel process variation-aware placement strategies that include two algorithms to increase the reliability of combinational circuits against both Single Event Transients (SETs) and Multiple Event Transients (METs). The first proposed algorithm is a global placement method (called SeaPlace-G) that places the cells for hardening the circuit against SETs by solving a quadratic formulation. Afterwards, a detailed placement algorithm (named SeaPlace-D) is proposed to increase the circuit reliability against METs by solving a linear programming optimization problem. Experimental results show that SeaPlace-G and SeaPlace-D averagely achieve 41.78% and 32.04% soft error reliability improvement against SET and MET, respectively. Moreover, when SeaPlace-D is followed by SeaPlace-G, MET reduction can be improved by up to 53.3%.
Equity Promotion in Online Resource Allocation
Venue: arXiv cs.GT 2021
Year: 2021

We consider online resource allocation under a typical non-profit setting, where limited or even scarce resources are administered by a not-for-profit organization like a government. We focus on the internal-equity by assuming that arriving requesters are homogeneous in terms of their external factors like demands but heterogeneous for their internal attributes like demographics. Specifically, we associate each arriving requester with one or several groups based on their demographics (i.e., race, gender, and age), and we aim to design an equitable distributing strategy such that every group of requesters can receive a fair share of resources proportional to a preset target ratio. We present two LP-based sampling algorithms and investigate them both theoretically (in terms of competitive-ratio analysis) and experimentally based on real COVID-19 vaccination data maintained by the Minnesota Department of Health. Both theoretical and numerical results show that our LP-based sampling strategies can effectively promote equity, especially when the arrival population is disproportionately represented, as observed in the early stage of the COVID-19 vaccine rollout.
Survey of charging scheduling, fleet management, and location planning of charging stations for electrified demand-responsive transport systems: methodologies and recent developments
Venue: arXiv cs.CE 2021
Year: 2021

The accelerated electrification of transport systems with EVs has brought new challenges for charging scheduling, fleet management, and charging infrastructure location and configuration planning. In this review, we have provided a systematic review of the recent development in strategic, tactical, and operational decisions for demand responsive transport system planning using electric vehicles (EV-DRT). We have summarized recent developments in mathematical modeling approaches and identified future research directions. A list of existing open-access datasets, numerical test instances, and software are provided for future research in EV-DRT and related problems.
Feature matching for multi-epoch historical aerial images
Venue: arXiv cs.CV 2021
Year: 2021

Historical imagery is characterized by high spatial resolution and stereo-scopic acquisitions, providing a valuable resource for recovering 3D land-cover information. Accurate geo-referencing of diachronic historical images by means of self-calibration remains a bottleneck because of the difficulty to find sufficient amount of feature correspondences under evolving landscapes. In this research, we present a fully automatic approach to detecting feature correspondences between historical images taken at different times (i.e., inter-epoch), without auxiliary data required. Based on relative orientations computed within the same epoch (i.e., intra-epoch), we obtain DSMs (Digital Surface Model) and incorporate them in a rough-to-precise matching. The method consists of: (1) an inter-epoch DSMs matching to roughly co-register the orientations and DSMs (i.e, the 3D Helmert transformation), followed by (2) a precise inter-epoch feature matching using the original RGB images. The innate ambiguity of the latter is largely alleviated by narrowing down the search space using the co-registered data. With the inter-epoch features, we refine the image orientations and quantitatively evaluate the results (1) with DoD (Difference of DSMs), (2) with ground check points, and (3) by quantifying ground displacement due to an earthquake. We demonstrate that our method: (1) can automatically georeference diachronic historical images; (2) can effectively mitigate systematic errors induced by poorly estimated camera parameters; (3) is robust to drastic scene changes. Compared to the state-of-the-art, our method improves the image georeferencing accuracy by a factor of 2. The proposed methods are implemented in MicMac, a free, open-source photogrammetric software.
DMRVisNet: Deep Multi-head Regression Network for Pixel-wise Visibility Estimation Under Foggy Weather
Venue: arXiv cs.CV 2021
Year: 2021

Scene perception is essential for driving decision-making and traffic safety. However, fog, as a kind of common weather, frequently appears in the real world, especially in the mountain areas, making it difficult to accurately observe the surrounding environments. Therefore, precisely estimating the visibility under foggy weather can significantly benefit traffic management and safety. To address this, most current methods use professional instruments outfitted at fixed locations on the roads to perform the visibility measurement; these methods are expensive and less flexible. In this paper, we propose an innovative end-to-end convolutional neural network framework to estimate the visibility leveraging Koschmieder's law exclusively using the image data. The proposed method estimates the visibility by integrating the physical model into the proposed framework, instead of directly predicting the visibility value via the convolutional neural work. Moreover, we estimate the visibility as a pixel-wise visibility map against those of previous visibility measurement methods which solely predict a single value for an entire image. Thus, the estimated result of our method is more informative, particularly in uneven fog scenarios, which can benefit to developing a more precise early warning system for foggy weather, thereby better protecting the intelligent transportation infrastructure systems and promoting its development. To validate the proposed framework, a virtual dataset, FACI, containing 3,000 foggy images in different concentrations, is collected using the AirSim platform. Detailed experiments show that the proposed method achieves performance competitive to those of state-of-the-art methods.
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning models have been widely used for anomaly detection in surveillance videos. Typical models are equipped with the capability to reconstruct normal videos and evaluate the reconstruction errors on anomalous videos to indicate the extent of abnormalities. However, existing approaches suffer from two disadvantages. Firstly, they can only encode the movements of each identity independently, without considering the interactions among identities which may also indicate anomalies. Secondly, they leverage inflexible models whose structures are fixed under different scenes, this configuration disables the understanding of scenes. In this paper, we propose a Hierarchical Spatio-Temporal Graph Convolutional Neural Network (HSTGCNN) to address these problems, the HSTGCNN is composed of multiple branches that correspond to different levels of graph representations. High-level graph representations encode the trajectories of people and the interactions among multiple identities while low-level graph representations encode the local body postures of each person. Furthermore, we propose to weightedly combine multiple branches that are better at different scenes. An improvement over single-level graph representations is achieved in this way. An understanding of scenes is achieved and serves anomaly detection. High-level graph representations are assigned higher weights to encode moving speed and directions of people in low-resolution videos while low-level graph representations are assigned higher weights to encode human skeletons in high-resolution videos. Experimental results show that the proposed HSTGCNN significantly outperforms current state-of-the-art models on four benchmark datasets (UCSD Pedestrian, ShanghaiTech, CUHK Avenue and IITB-Corridor) by using much less learnable parameters.
Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy Detection
Venue: arXiv cs.CV 2021
Year: 2021

Copy detection, which is a task to determine whether an image is a modified copy of any image in a database, is an unsolved problem. Thus, we addressed copy detection by training convolutional neural networks (CNNs) with contrastive learning. Training with a large memory-bank and hard data augmentation enables the CNNs to obtain more discriminative representation. Our proposed negative embedding subtraction further boosts the copy detection accuracy. Using our methods, we achieved 1st place in the Facebook AI Image Similarity Challenge: Descriptor Track. Our code is publicly available here: \url{https://github.com/lyakaap/ISC21-Descriptor-Track-1st}
A PTAS for the Min-Max Euclidean Multiple TSP
Venue: arXiv cs.DS 2021
Year: 2021

We present a polynomial-time approximation scheme (PTAS) for the min-max multiple TSP problem in Euclidean space, where multiple traveling salesmen are tasked with visiting a set of $n$ points and the objective is to minimize the maximum tour length. For an arbitrary $\varepsilon > 0$, our PTAS achieves a $(1 + \varepsilon)$-approximation in time $O \big(n ((1/\varepsilon) \log (n/\varepsilon))^{O(1/\varepsilon)} \big)$. Our approach extends Sanjeev Arora's dynamic-programming (DP) PTAS for the Euclidean TSP (https://doi.org/10.1145/290179.290180). Our algorithm introduces a rounding process to balance the allocation of path lengths among the multiple salesman. We analyze the accumulation of error in the DP to prove that the solution is a $(1 + \varepsilon)$-approximation.