OpenRaiser/Intern-Atlas huggingface dataset:
Deep Learning-Based Discrete Calibrated Survival Prediction
Venue: arXiv cs.LG 2022
Year: 2022

Deep neural networks for survival prediction outper-form classical approaches in discrimination, which is the ordering of patients according to their time-of-event. Conversely, classical approaches like the Cox Proportional Hazards model display much better calibration, the correct temporal prediction of events of the underlying distribution. Especially in the medical domain, where it is critical to predict the survival of a single patient, both discrimination and calibration are important performance metrics. Here we present Discrete Calibrated Survival (DCS), a novel deep neural network for discriminated and calibrated survival prediction that outperforms competing survival models in discrimination on three medical datasets, while achieving best calibration among all discrete time models. The enhanced performance of DCS can be attributed to two novel features, the variable temporal output node spacing and the novel loss term that optimizes the use of uncensored and censored patient data. We believe that DCS is an important step towards clinical application of deep-learning-based survival prediction with state-of-the-art discrimination and good calibration.
Assurance Cases as Foundation Stone for Auditing AI-enabled and Autonomous Systems: Workshop Results and Political Recommendations for Action from the ExamAI Project
Venue: arXiv cs.SE 2022
Year: 2022

The European Machinery Directive and related harmonized standards do consider that software is used to generate safety-relevant behavior of the machinery but do not consider all kinds of software. In particular, software based on machine learning (ML) are not considered for the realization of safety-relevant behavior. This limits the introduction of suitable safety concepts for autonomous mobile robots and other autonomous machinery, which commonly depend on ML-based functions. We investigated this issue and the way safety standards define safety measures to be implemented against software faults. Functional safety standards use Safety Integrity Levels (SILs) to define which safety measures shall be implemented. They provide rules for determining the SIL and rules for selecting safety measures depending on the SIL. In this paper, we argue that this approach can hardly be adopted with respect to ML and other kinds of Artificial Intelligence (AI). Instead of simple rules for determining an SIL and applying related measures against faults, we propose the use of assurance cases to argue that the individually selected and applied measures are sufficient in the given case. To get a first rating regarding the feasibility and usefulness of our proposal, we presented and discussed it in a workshop with experts from industry, German statutory accident insurance companies, work safety and standardization commissions, and representatives from various national, European, and international working groups dealing with safety and AI. In this paper, we summarize the proposal and the workshop discussion. Moreover, we check to which extent our proposal is in line with the European AI Act proposal and current safety standardization initiatives addressing AI and Autonomous Systems
Understanding Long Documents with Different Position-Aware Attentions
Venue: arXiv cs.CL 2022
Year: 2022

Despite several successes in document understanding, the practical task for long document understanding is largely under-explored due to several challenges in computation and how to efficiently absorb long multimodal input. Most current transformer-based approaches only deal with short documents and employ solely textual information for attention due to its prohibitive computation and memory limit. To address those issues in long document understanding, we explore different approaches in handling 1D and new 2D position-aware attention with essentially shortened context. Experimental results show that our proposed models have advantages for this task based on various evaluation metrics. Furthermore, our model makes changes only to the attention and thus can be easily adapted to any transformer-based architecture.
MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation
Venue: arXiv cs.LG 2022
Year: 2022

Large language models have demonstrated the ability to generate both natural language and programming language text. Such models open up the possibility of multi-language code generation: could code generation models generalize knowledge from one language to another? Although contemporary code generation models can generate semantically correct Python code, little is known about their abilities with other languages. We propose MultiPL-E, a system for translating unit test-driven code generation benchmarks to new languages. We create the first massively multilingual code generation benchmark by using MultiPL-E to translate two popular Python code generation benchmarks to 18 additional programming languages. We use MultiPL-E to extend the HumanEval benchmark and MBPP benchmark to 18 languages that encompass a range of programming paradigms and popularity. Using these new parallel benchmarks, we evaluate the multi-language performance of three state-of-the-art code generation models: Codex, CodeGen, and InCoder. We find that Codex matches or even exceeds its performance on Python for several other languages. The range of programming languages represented in MultiPL-E allow us to explore the impact of language frequency and language features on model performance. Finally, the MultiPL-E approach of compiling code generation benchmarks to new programming languages is both scalable and extensible, making it straightforward to evaluate new models, benchmarks, and languages.
Input Repair via Synthesis and Lightweight Error Feedback
Venue: arXiv cs.SE 2022
Year: 2022

Often times, input data may ostensibly conform to a given input format, but cannot be parsed by a conforming program, for instance, due to human error or data corruption. In such cases, a data engineer is tasked with input repair, i.e., she has to manually repair the corrupt data such that it follows a given format, and hence can be processed by the conforming program. Such manual repair can be time-consuming and error-prone. In particular, input repair is challenging without an input specification (e.g., input grammar) or program analysis. In this work, we show that incorporating lightweight failure feedback (e.g., input incompleteness) to parsers is sufficient to repair any corrupt input data with maximal closeness to the semantics of the input data. We propose an approach (called FSYNTH) that leverages lightweight error-feedback and input synthesis to repair invalid inputs. FSYNTH is grammar-agnostic, and it does not require program analysis. Given a conforming program, and any invalid input, FSYNTH provides a set of repairs prioritized by the distance of the repair from the original input. We evaluate FSYNTH on 806 (real-world) invalid inputs using four well-known input formats, namely INI, TinyC, SExp, and cJSON. In our evaluation, we found that FSYNTH recovers 91% of valid input data. FSYNTH is also highly effective and efficient in input repair: It repairs 77% of invalid inputs within four minutes. It is up to 35% more effective than DDMax, the previously best-known approach. Overall, our approach addresses several limitations of DDMax, both in terms of what it can repair, as well as in terms of the set of repairs offered.
Prediction of Oral Food Challenge Outcomes via Ensemble Learning
Venue: arXiv cs.LG 2022
Year: 2022

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values 89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study.
Providing High Capacity for AR/VR traffic in 5G Systems with Multi-Connectivity
Venue: arXiv cs.NI 2022
Year: 2022

Augmented and Virtual Reality (AR/VR) is often called a "killer" application of 5G systems because it imposes very strict Quality of Service (QoS) requirements related to throughput, latency, and reliability. A high-resolution AR/VR flow requires a bandwidth of dozens of MHz. Since the existing low-frequency bands (i.e., below 6 GHz) have limited bandwidth and are overpopulated, one of the ways to satisfy high AR/VR demands is to use wide frequency channels available in the millimeter-Wave (mmWave) band. However, transmission in the mmWave band suffers from high throughput fluctuation and even blockage, which leads to violation of strict AR/VR latency and reliability requirements. To address this problem, 5G specifications introduce a Multi-Connectivity (MC) feature that allows a mobile user to connect simultaneously to several base stations. The paper considers a scenario with two base stations: the first base station operates in the low-frequency band to provide reliable data delivery, while the second one operates in the mmWave band and offers high data rates when the channel conditions are favorable. An open question that falls out of the scope of specifications is how to balance AR/VR traffic between two links with different characteristics. The paper proposes a Delay-Based Traffic Balancing (DBTB) algorithm that minimizes resource consumption of the low-frequency link while satisfying strict AR/VR QoS requirements. With extensive simulations, DBTB is shown to double the network capacity for AR/VR traffic compared with the state-of-the-art traffic balancing algorithms.
Error Parity Fairness: Testing for Group Fairness in Regression Tasks
Venue: arXiv cs.LG 2022
Year: 2022

The applications of Artificial Intelligence (AI) surround decisions on increasingly many aspects of human lives. Society responds by imposing legal and social expectations for the accountability of such automated decision systems (ADSs). Fairness, a fundamental constituent of AI accountability, is concerned with just treatment of individuals and sensitive groups (e.g., based on sex, race). While many studies focus on fair learning and fairness testing for the classification tasks, the literature is rather limited on how to examine fairness in regression tasks. This work presents error parity as a regression fairness notion and introduces a testing methodology to assess group fairness based on a statistical hypothesis testing procedure. The error parity test checks whether prediction errors are distributed similarly across sensitive groups to determine if an ADS is fair. It is followed by a suitable permutation test to compare groups on several statistics to explore disparities and identify impacted groups. The usefulness and applicability of the proposed methodology are demonstrated via a case study on COVID-19 projections in the US at the county level, which revealed race-based differences in forecast errors. Overall, the proposed regression fairness testing methodology fills a gap in the fair machine learning literature and may serve as a part of larger accountability assessments and algorithm audits.
The proper conflict-free $k$-coloring problem and the odd $k$-coloring problem are NP-complete on bipartite graphs
Venue: arXiv cs.CC 2022
Year: 2022

A proper coloring of a graph is \emph{proper conflict-free} if every non-isolated vertex $v$ has a neighbor whose color is unique in the neighborhood of $v$. A proper coloring of a graph is \emph{odd} if for every non-isolated vertex $v$, there is a color appearing an odd number of times in the neighborhood of $v$. For an integer $k$, the \textsc{PCF $k$-Coloring} problem asks whether an input graph admits a proper conflict-free $k$-coloring and the \textsc{Odd $k$-Coloring} asks whether an input graph admits an odd $k$-coloring. We show that for every integer $k\geq3$, both problems are NP-complete, even if the input graph is bipartite. Furthermore, we show that the \textsc{PCF $4$-Coloring} problem is NP-complete when the input graph is planar.
Neural Embeddings for Text
Venue: arXiv cs.CL 2022
Year: 2022

We propose a new kind of embedding for natural language text that deeply represents semantic meaning. Standard text embeddings use the outputs from hidden layers of a pretrained language model. In our method, we let a language model learn from the text and then literally pick its brain, taking the actual weights of the model's neurons to generate a vector. We call this representation of the text a neural embedding. We confirm the ability of this representation to reflect semantics of the text by an analysis of its behavior on several datasets, and by a comparison of neural embedding with state of the art sentence embeddings.
MoCapDeform: Monocular 3D Human Motion Capture in Deformable Scenes
Venue: arXiv cs.CV 2022
Year: 2022

3D human motion capture from monocular RGB images respecting interactions of a subject with complex and possibly deformable environments is a very challenging, ill-posed and under-explored problem. Existing methods address it only weakly and do not model possible surface deformations often occurring when humans interact with scene surfaces. In contrast, this paper proposes MoCapDeform, i.e., a new framework for monocular 3D human motion capture that is the first to explicitly model non-rigid deformations of a 3D scene for improved 3D human pose estimation and deformable environment reconstruction. MoCapDeform accepts a monocular RGB video and a 3D scene mesh aligned in the camera space. It first localises a subject in the input monocular video along with dense contact labels using a new raycasting based strategy. Next, our human-environment interaction constraints are leveraged to jointly optimise global 3D human poses and non-rigid surface deformations. MoCapDeform achieves superior accuracy than competing methods on several datasets, including our newly recorded one with deforming background scenes.
Analyzing Robustness of End-to-End Neural Models for Automatic Speech Recognition
Venue: arXiv cs.CL 2022
Year: 2022

We investigate robustness properties of pre-trained neural models for automatic speech recognition. Real life data in machine learning is usually very noisy and almost never clean, which can be attributed to various factors depending on the domain, e.g. outliers, random noise and adversarial noise. Therefore, the models we develop for various tasks should be robust to such kinds of noisy data, which led to the thriving field of robust machine learning. We consider this important issue in the setting of automatic speech recognition. With the increasing popularity of pre-trained models, it's an important question to analyze and understand the robustness of such models to noise. In this work, we perform a robustness analysis of the pre-trained neural models wav2vec2, HuBERT and DistilHuBERT on the LibriSpeech and TIMIT datasets. We use different kinds of noising mechanisms and measure the model performances as quantified by the inference time and the standard Word Error Rate metric. We also do an in-depth layer-wise analysis of the wav2vec2 model when injecting noise in between layers, enabling us to predict at a high level what each layer learns. Finally for this model, we visualize the propagation of errors across the layers and compare how it behaves on clean versus noisy data. Our experiments conform the predictions of Pasad et al. [2021] and also raise interesting directions for future work.
DF-Captcha: A Deepfake Captcha for Preventing Fake Calls
Venue: arXiv cs.CR 2022
Year: 2022

Social engineering (SE) is a form of deception that aims to trick people into giving access to data, information, networks and even money. For decades SE has been a key method for attackers to gain access to an organization, virtually skipping all lines of defense. Attackers also regularly use SE to scam innocent people by making threatening phone calls which impersonate an authority or by sending infected emails which look like they have been sent from a loved one. SE attacks will likely remain a top attack vector for criminals because humans are the weakest link in cyber security. Unfortunately, the threat will only get worse now that a new technology called deepfakes as arrived. A deepfake is believable media (e.g., videos) created by an AI. Although the technology has mostly been used to swap the faces of celebrities, it can also be used to `puppet' different personas. Recently, researchers have shown how this technology can be deployed in real-time to clone someone's voice in a phone call or reenact a face in a video call. Given that any novice user can download this technology to use it, it is no surprise that criminals have already begun to monetize it to perpetrate their SE attacks. In this paper, we propose a lightweight application which can protect organizations and individuals from deepfake SE attacks. Through a challenge and response approach, we leverage the technical and theoretical limitations of deepfake technologies to expose the attacker. Existing defence solutions are too heavy as an end-point solution and can be evaded by a dynamic attacker. In contrast, our approach is lightweight and breaks the reactive arms race, putting the attacker at a disadvantage.
Survey of Methods for Solving Systems of Nonlinear Equations, Part I: Root-finding Approaches
Venue: arXiv cs.MS 2022
Year: 2022

This paper presents a comprehensive survey of methods which can be utilized to search for solutions to systems of nonlinear equations (SNEs). Our objectives with this survey are to synthesize pertinent literature in this field by presenting a thorough description and analysis of the known methods capable of finding one or many solutions to SNEs, and to assist interested readers seeking to identify solution techniques which are well suited for solving the various classes of SNEs which one may encounter in real world applications. To accomplish these objectives, we present a multi-part survey. In part one, we focus on root-finding approaches which can be used to search for solutions to a SNE without transforming it into an optimization problem. In part two, we will introduce the various transformations which have been utilized to transform a SNE into an optimization problem, and we discuss optimization algorithms which can then be used to search for solutions. In part three, we will present a robust quantitative comparative analysis of methods capable of searching for solutions to SNEs.
Rethinking the Performance of ISAC System: From Efficiency and Utility Perspectives
Venue: arXiv cs.IT 2022
Year: 2022

Integrated sensing and communications (ISAC) is an essential technology for the 6G communication system, which enables the conventional wireless communication network capable of sensing targets around. The shared use of pilots is a promising strategy to achieve ISAC. It brings a trade-off between communication and sensing, which is still unclear under the imperfect channel estimation condition. To provide some insights, the trade-off between ergodic capacity with imperfect channel estimation and ergodic Cramer-Rao bound (CRB) of range sensing is investigated. Firstly, the closedform expressions of ergodic capacity and ergodic range CRB are derived, which are associated with the number of pilots. Secondly, two novel metrics named efficiency and utility are firstly proposed to evaluate the joint performance of capacity and range sensing error. Specifically, efficiency is used to evaluate the achievable capacity per unit of the sensing error, and utility is designed to evaluate the utilization degree of ISAC. Moreover, an algorithm of pilot length optimization is designed to achieve the best efficiency. Finally, simulation results are given to verify the accuracy of analytical results, and provide some insights on designing the slot structure.
On the implementation of an ETSI MEC using open source solutions
Venue: arXiv cs.NI 2022
Year: 2022

For autonomous vehicles to be fully aware of its environment, it needs to collect data consistently from other vehicles and Road Side Units (RSU) in the surroundings. This heavy exchange increases latency and cybersecurity threats. This paper introduces Multi-Access Edge Computing (MEC), a 5G specification, as a promising solution to this significant issue. It proposes the adoption of a MEC platform, at the autonomous car premises capable of providing the environment required for running vehicular MEC applications, therefore reducing latency and decentralizing data treatment. For that purpose, the standard established by the European Telecommunications Standards Institute (ETSI) for the MEC framework is adopted. This work introduces few open-source solutions while analyzing how strongly it respects the ETSI standard, and it supports mobility in a Cooperative, Connected, and Automated Mobility (CCAM) context. It also recommends possible MEC security solutions.
Steering Action-aware Adaptive Cruise Control for Teleoperated Driving
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, a steering action-aware Adaptive Cruise Control (ACC) approach for teleoperated road vehicles is proposed. In order to keep the vehicle in a safe state, the ACC approach can override the human operator's velocity control commands. The safe state is defined as a state from which the vehicle can be stopped safely, no matter which steering actions are applied by the operator. This is achieved by first sampling various potential future trajectories. In a second stage, assuming the trajectory with the highest risk, a safe and comfortable velocity profile is optimized. This yields a safe velocity control command for the vehicle. In simulations, the characteristics of the approach are compared to a Model Predictive Control-based approach that is capable of overriding both, the commanded steering angle as well as the velocity. Furthermore, in teleoperation experiments with a 1:10-scale vehicle testbed, it is demonstrated that the proposed ACC approach keeps the vehicle safe, even if the control commands from the operator would have resulted in a collision.