OpenRaiser/Intern-Atlas huggingface dataset:
Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcare
Venue: arXiv cs.HC 2022
Year: 2022

An important goal in the field of human-AI interaction is to help users more appropriately trust AI systems' decisions. A situation in which the user may particularly benefit from more appropriate trust is when the AI receives anomalous input or provides anomalous output. To the best of our knowledge, this is the first work towards understanding how anomaly alerts may contribute to appropriate trust of AI. In a formative mixed-methods study with 4 radiologists and 4 other physicians, we explore how AI alerts for anomalous input, very high and low confidence, and anomalous saliency-map explanations affect users' experience with mockups of an AI clinical decision support system (CDSS) for evaluating chest x-rays for pneumonia. We find evidence suggesting that the four anomaly alerts are desired by non-radiologists, and the high-confidence alerts are desired by both radiologists and non-radiologists. In a follow-up user study, we investigate how high- and low-confidence alerts affect the accuracy and thus appropriate trust of 33 radiologists working with AI CDSS mockups. We observe that these alerts do not improve users' accuracy or experience and discuss potential reasons why.
Stream Iterative Distributed Coded Computing for Learning Applications in Heterogeneous Systems
Venue: arXiv cs.DC 2022
Year: 2022

To improve the utility of learning applications and render machine learning solutions feasible for complex applications, a substantial amount of heavy computations is needed. Thus, it is essential to delegate the computations among several workers, which brings up the major challenge of coping with delays and failures caused by the system's heterogeneity and uncertainties. In particular, minimizing the end-to-end job in-order execution delay, from arrival to delivery, is of great importance for real-world delay-sensitive applications. In this paper, for computation of each job iteration in a stochastic heterogeneous distributed system where the workers vary in their computing and communicating powers, we present a novel joint scheduling-coding framework that optimally split the coded computational load among the workers. This closes the gap between the workers' response time, and is critical to maximize the resource utilization. To further reduce the in-order execution delay, we also incorporate redundant computations in each iteration of a distributed computational job. Our simulation results demonstrate that the delay obtained using the proposed solution is dramatically lower than the uniform split which is oblivious to the system's heterogeneity and, in fact, is very close to an ideal lower bound just by introducing a small percentage of redundant computations.
Adversarial Fine-tune with Dynamically Regulated Adversary
Venue: arXiv cs.LG 2022
Year: 2022

Adversarial training is an effective method to boost model robustness to malicious, adversarial attacks. However, such improvement in model robustness often leads to a significant sacrifice of standard performance on clean images. In many real-world applications such as health diagnosis and autonomous surgical robotics, the standard performance is more valued over model robustness against such extremely malicious attacks. This leads to the question: To what extent we can boost model robustness without sacrificing standard performance? This work tackles this problem and proposes a simple yet effective transfer learning-based adversarial training strategy that disentangles the negative effects of adversarial samples on model's standard performance. In addition, we introduce a training-friendly adversarial attack algorithm, which facilitates the boost of adversarial robustness without introducing significant training complexity. Extensive experimentation indicates that the proposed method outperforms previous adversarial training algorithms towards the target: to improve model robustness while preserving model's standard performance on clean data.
Benchmarking the Hooke-Jeeves Method, MTS-LS1, and BSrr on the Large-scale BBOB Function Set
Venue: arXiv cs.NE 2022
Year: 2022

This paper investigates the performance of three black-box optimizers exploiting separability on the 24 large-scale BBOB functions, including the Hooke-Jeeves method, MTS-LS1, and BSrr. Although BSrr was not specially designed for large-scale optimization, the results show that BSrr has a state-of-the-art performance on the five separable large-scale BBOB functions. The results show that the asymmetry significantly influences the performance of MTS-LS1. The results also show that the Hooke-Jeeves method performs better than MTS-LS1 on unimodal separable BBOB functions.
Robots: the Century Past and the Century Ahead
Venue: arXiv cs.RO 2022
Year: 2022

Let us reflect on the state of robotics. This year marks the $101$-st anniversary of R.U.R., a play by the writer Karel Čapek, often credited with introducing the word "robot". The word used to refer to feudal forced labourers in Slavic languages. Indeed, it points to one key characteristic of robotic systems: they are mere slaves, have no rights, and execute our wills instruction by instruction, without asking anything in return. The relationship with us humans is commensalism; in biology, commensalism subsists between two symbiotic species when one species benefits from it (robots boost productivity for humans), while the other species neither benefits nor is harmed (can you really argue that robots benefit from simply functioning?). We then distinguish robots from "living machines", that is, machines infused with life. If living machines should ever become a reality, we would need to shift our relationship with them from commensalism to mutualism. The distinction is not subtle: we experience it every day with domesticated animals, that exchange serfdom for forage and protection. This is because life has evolved to resist any attempt at enslaving it; it is stubborn. In the path towards living machines, let us ask: what has been achieved by robotics in the last $100$ years? What is left to accomplish in the next $100$ years? For us, the answers boil down to three words: juice, need (or death), and embodiment, as we shall see in the following.
A Closer Look at Branch Classifiers of Multi-exit Architectures
Venue: arXiv cs.CV 2022
Year: 2022

Multi-exit architectures consist of a backbone and branch classifiers that offer shortened inference pathways to reduce the run-time of deep neural networks. In this paper, we analyze different branching patterns that vary in their allocation of computational complexity for the branch classifiers. Constant-complexity branching keeps all branches the same, while complexity-increasing and complexity-decreasing branching place more complex branches later or earlier in the backbone respectively. Through extensive experimentation on multiple backbones and datasets, we find that complexity-decreasing branches are more effective than constant-complexity or complexity-increasing branches, which achieve the best accuracy-cost trade-off. We investigate a cause by using knowledge consistency to probe the effect of adding branches onto a backbone. Our findings show that complexity-decreasing branching yields the least disruption to the feature abstraction hierarchy of the backbone, which explains the effectiveness of the branching patterns.
Information Inequalities via Submodularity and a Problem in Extremal Graph Theory
Venue: arXiv cs.IT 2022
Year: 2022

The present paper offers, in its first part, a unified approach for the derivation of families of inequalities for set functions which satisfy sub/supermodularity properties. It applies this approach for the derivation of information inequalities with Shannon information measures. Connections of the considered approach to a generalized version of Shearer's lemma, and other related results in the literature are considered. Some of the derived information inequalities are new, and also known results (such as a generalized version of Han's inequality) are reproduced in a simple and unified way. In its second part, this paper applies the generalized Han's inequality to analyze a problem in extremal graph theory. This problem is motivated and analyzed from the perspective of information theory, and the analysis leads to generalized and refined bounds. The two parts of this paper are meant to be independently accessible to the reader.
AE-NeRF: Auto-Encoding Neural Radiance Fields for 3D-Aware Object Manipulation
Venue: arXiv cs.CV 2022
Year: 2022

We propose a novel framework for 3D-aware object manipulation, called Auto-Encoding Neural Radiance Fields (AE-NeRF). Our model, which is formulated in an auto-encoder architecture, extracts disentangled 3D attributes such as 3D shape, appearance, and camera pose from an image, and a high-quality image is rendered from the attributes through disentangled generative Neural Radiance Fields (NeRF). To improve the disentanglement ability, we present two losses, global-local attribute consistency loss defined between input and output, and swapped-attribute classification loss. Since training such auto-encoding networks from scratch without ground-truth shape and appearance information is non-trivial, we present a stage-wise training scheme, which dramatically helps to boost the performance. We conduct experiments to demonstrate the effectiveness of the proposed model over the latest methods and provide extensive ablation studies.
Regotron: Regularizing the Tacotron2 architecture via monotonic alignment loss
Venue: arXiv cs.SD 2022
Year: 2022

Recent deep learning Text-to-Speech (TTS) systems have achieved impressive performance by generating speech close to human parity. However, they suffer from training stability issues as well as incorrect alignment of the intermediate acoustic representation with the input text sequence. In this work, we introduce Regotron, a regularized version of Tacotron2 which aims to alleviate the training issues and at the same time produce monotonic alignments. Our method augments the vanilla Tacotron2 objective function with an additional term, which penalizes non-monotonic alignments in the location-sensitive attention mechanism. By properly adjusting this regularization term we show that the loss curves become smoother, and at the same time Regotron consistently produces monotonic alignments in unseen examples even at an early stage (13\% of the total number of epochs) of its training process, whereas the fully converged Tacotron2 fails to do so. Moreover, our proposed regularization method has no additional computational overhead, while reducing common TTS mistakes and achieving slighlty improved speech naturalness according to subjective mean opinion scores (MOS) collected from 50 evaluators.
Weighted Packet Selection for Rechargeable Links: Complexity and Approximation
Venue: arXiv cs.DS 2022
Year: 2022

We consider a natural problem dealing with weighted packet selection across a rechargeable link, which e.g., finds applications in cryptocurrency networks. The capacity of a link $(u,v)$ is determined by how much players $u$ and $v$ allocate for this link. Specifically, the input is a finite ordered sequence of packets that arrive in both directions along a link. Given $(u, v)$ and a packet of weight $x$ going from $u$ to $v$, player $u$ can either accept or reject the packet. If player $u$ accepts the packet, their capacity on link $(u,v)$ decreases by $x$. Correspondingly, player $v$ capacity on $(u,v)$ increases by $x$. If a player rejects the packet, this will entail a cost linear in the weight of the packet. A link is "rechargeable" in the sense that the total capacity of the link has to remain constant, but the allocation of capacity at the ends of the link can depend arbitrarily on players' decisions. The goal is to minimise the sum of the capacity injected into the link and the cost of rejecting packets. We show the problem is NP-hard, but can be approximated efficiently with a ratio of $(1+ \varepsilon)\cdot (1+\sqrt{3})$ for some arbitrary $\varepsilon >0$.
Stay Safe under Panic: Affine Rust Programming with Multiparty Session Types
Venue: arXiv cs.PL 2022
Year: 2022

Communicating systems comprise diverse software components across networks. To ensure their robustness, modern programming languages such as Rust provide both strongly typed channels, whose usage is guaranteed to be affine (at most once), and cancellation operations over binary channels. For coordinating components to correctly communicate and synchronise with each other, we use the structuring mechanism from multiparty session types, extending it with affine communication channels and implicit/explicit cancellation mechanisms. This new typing discipline, affine multiparty session types (AMPST), ensures cancellation termination of multiple, independently running components and guarantees that communication will not get stuck due to error or abrupt termination. Guided by AMPST, we implemented an automated generation tool (MultiCrusty) of Rust APIs associated with cancellation termination algorithms, by which the Rust compiler auto-detects unsafe programs. Our evaluation shows that MultiCrusty provides an efficient mechanism for communication, synchronisation and propagation of the notifications of cancellation for arbitrary processes. We have implemented several usecases, including popular application protocols (OAuth, SMTP), and protocols with exception handling patterns (circuit breaker, distributed logging).
The Value of Measuring Trust in AI - A Socio-Technical System Perspective
Venue: arXiv cs.HC 2022
Year: 2022

Building trust in AI-based systems is deemed critical for their adoption and appropriate use. Recent research has thus attempted to evaluate how various attributes of these systems affect user trust. However, limitations regarding the definition and measurement of trust in AI have hampered progress in the field, leading to results that are inconsistent or difficult to compare. In this work, we provide an overview of the main limitations in defining and measuring trust in AI. We focus on the attempt of giving trust in AI a numerical value and its utility in informing the design of real-world human-AI interactions. Taking a socio-technical system perspective on AI, we explore two distinct approaches to tackle these challenges. We provide actionable recommendations on how these approaches can be implemented in practice and inform the design of human-AI interactions. We thereby aim to provide a starting point for researchers and designers to re-evaluate the current focus on trust in AI, improving the alignment between what empirical research paradigms may offer and the expectations of real-world human-AI interactions.
Death By A Thousand COTS: Disrupting Satellite Communications using Low Earth Orbit Constellations
Venue: arXiv cs.CR 2022
Year: 2022

Satellites in Geostationary Orbit (GEO) provide a number of commercial, government, and military services around the world, offering everything from surveillance and monitoring to video calls and internet access. However a dramatic lowering of the cost-per-kilogram to space has led to a recent explosion in real and planned constellations in Low Earth Orbit (LEO) of smaller satellites. These constellations are managed remotely and it is important to consider a scenario in which an attacker gains control over the constituent satellites. In this paper we aim to understand what damage this attacker could cause, using the satellites to generate interference. To ground our analysis, we simulate a number of existing and planned LEO constellations against an example GEO constellation, and evaluate the relative effectiveness of each. Our model shows that with conservative power estimates, both current and planned constellations could disrupt GEO satellite services at every groundstation considered, with effectiveness varying considerably between locations. We analyse different patterns of interference, how they reflect the structures of the constellations creating them, and how effective they might be against a number of legitimate services. We found that real-time usage (e.g. calls, streaming) would be most affected, with 3 constellation designs able to generate thousands of outages of 30 seconds or longer over the course of the day across all groundstations.
Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes
Venue: arXiv cs.LG 2022
Year: 2022

Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records are valuable resources capturing unique information, but are seldom used to their full potential. We explore conventional and deep machine learning methods to assess violence risk in psychiatric patients using practitioner notes. The performance of our best models is comparable to the currently used questionnaire-based method, with an area under the Receiver Operating Characteristic curve of approximately 0.8. We find that the deep-learning model BERTje performs worse than conventional machine learning methods. We also evaluate our data and our classifiers to understand the performance of our models better. This is particularly important for the applicability of evaluated classifiers to new data, and is also of great interest to practitioners, due to the increased availability of new data in electronic format.
Algorithmic QUBO Formulations for k-SAT and Hamiltonian Cycles
Venue: arXiv cs.DS 2022
Year: 2022

Quadratic unconstrained binary optimization (QUBO) can be seen as a generic language for optimization problems. QUBOs attract particular attention since they can be solved with quantum hardware, like quantum annealers or quantum gate computers running QAOA. In this paper, we present two novel QUBO formulations for $k$-SAT and Hamiltonian Cycles that scale significantly better than existing approaches. For $k$-SAT we reduce the growth of the QUBO matrix from $O(k)$ to $O(log(k))$. For Hamiltonian Cycles the matrix no longer grows quadratically in the number of nodes, as currently, but linearly in the number of edges and logarithmically in the number of nodes. We present these two formulations not as mathematical expressions, as most QUBO formulations are, but as meta-algorithms that facilitate the design of more complex QUBO formulations and allow easy reuse in larger and more complex QUBO formulations.
Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos
Venue: arXiv cs.CV 2022
Year: 2022

In videos that contain actions performed unintentionally, agents do not achieve their desired goals. In such videos, it is challenging for computer vision systems to understand high-level concepts such as goal-directed behavior, an ability present in humans from a very early age. Inculcating this ability in artificially intelligent agents would make them better social learners by allowing them to evaluate human action under a teleological lens. To validate the ability of deep learning models to perform this task, we curate the W-Oops dataset, built upon the Oops dataset [15]. W-Oops consists of 2,100 unintentional human action videos, with 44 goal-directed and 30 unintentional video-level activity labels collected through human annotations. Due to the expensive segment annotation procedure, we propose a weakly supervised algorithm for localizing the goal-directed as well as unintentional temporal regions in the video leveraging solely video-level labels. In particular, we employ an attention mechanism-based strategy that predicts the temporal regions which contribute the most to a classification task. Meanwhile, our designed overlap regularization allows the model to focus on distinct portions of the video for inferring the goal-directed and unintentional activity while guaranteeing their temporal ordering. Extensive quantitative experiments verify the validity of our localization method. We further conduct a video captioning experiment which demonstrates that the proposed localization module does indeed assist teleological action understanding.
A Close Look into Human Activity Recognition Models using Deep Learning
Venue: arXiv cs.CV 2022
Year: 2022

Human activity recognition using deep learning techniques has become increasing popular because of its high effectivity with recognizing complex tasks, as well as being relatively low in costs compared to more traditional machine learning techniques. This paper surveys some state-of-the-art human activity recognition models that are based on deep learning architecture and has layers containing Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), or a mix of more than one type for a hybrid system. The analysis outlines how the models are implemented to maximize its effectivity and some of the potential limitations it faces.
Emotion Recognition In Persian Speech Using Deep Neural Networks
Venue: arXiv cs.SD 2022
Year: 2022

Speech Emotion Recognition (SER) is of great importance in Human-Computer Interaction (HCI), as it provides a deeper understanding of the situation and results in better interaction. In recent years, various machine learning and Deep Learning (DL) algorithms have been developed to improve SER techniques. Recognition of the spoken emotions depends on the type of expression that varies between different languages. In this paper, to further study important factors in the Farsi language, we examine various DL techniques on a Farsi/Persian dataset, Sharif Emotional Speech Database (ShEMO), which was released in 2018. Using signal features in low- and high-level descriptions and different deep neural networks and machine learning techniques, Unweighted Accuracy (UA) of 65.20% and Weighted Accuracy (WA) of 78.29% are achieved.
Unified Simulation, Perception, and Generation of Human Behavior
Venue: arXiv cs.CV 2022
Year: 2022

Understanding and modeling human behavior is fundamental to almost any computer vision and robotics applications that involve humans. In this thesis, we take a holistic approach to human behavior modeling and tackle its three essential aspects -- simulation, perception, and generation. Throughout the thesis, we show how the three aspects are deeply connected and how utilizing and improving one aspect can greatly benefit the other aspects. We also discuss the lessons learned and our vision for what is next for human behavior modeling.
NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures
Venue: arXiv cs.CL 2022
Year: 2022

Being able to rank the similarity of short text segments is an interesting bonus feature of neural machine translation. Translation-based similarity measures include direct and pivot translation probability, as well as translation cross-likelihood, which has not been studied so far. We analyze these measures in the common framework of multilingual NMT, releasing the NMTScore library (available at https://github.com/ZurichNLP/nmtscore). Compared to baselines such as sentence embeddings, translation-based measures prove competitive in paraphrase identification and are more robust against adversarial or multilingual input, especially if proper normalization is applied. When used for reference-based evaluation of data-to-text generation in 2 tasks and 17 languages, translation-based measures show a relatively high correlation to human judgments.