OpenRaiser/Intern-Atlas huggingface dataset:
Measuring Alignment Bias in Neural Seq2Seq Semantic Parsers
Venue: arXiv cs.CL 2022
Year: 2022

Prior to deep learning the semantic parsing community has been interested in understanding and modeling the range of possible word alignments between natural language sentences and their corresponding meaning representations. Sequence-to-sequence models changed the research landscape suggesting that we no longer need to worry about alignments since they can be learned automatically by means of an attention mechanism. More recently, researchers have started to question such premise. In this work we investigate whether seq2seq models can handle both simple and complex alignments. To answer this question we augment the popular Geo semantic parsing dataset with alignment annotations and create Geo-Aligned. We then study the performance of standard seq2seq models on the examples that can be aligned monotonically versus examples that require more complex alignments. Our empirical study shows that performance is significantly better over monotonic alignments.
Literature Review to Collect Conceptual Variables of Scenario Methods for Establishing a Conceptual Scenario Framework
Venue: arXiv cs.SE 2022
Year: 2022

Over recent decades, scenarios and scenario-based software/system engineering have been actively employed as essential tools to handle intricate problems, validate requirements, and support stakeholders' communication. However, despite the widespread use of scenarios, there have been several challenges for engineers to more willingly utilize scenario-based engineering approaches (i.e., scenario methods) in their projects. First, the term scenario has numerous published definitions, thus lacking in a well-established shared understanding of scenarios and scenario methods. Second, the conceptual basis for engineers developing or employing scenarios is missing. To establish shared understanding and to find common denominators of scenario methods, this study leverages well-defined metamodeling and conceptualization that systematically investigate the concepts under analysis and define core entities and their relations. By conducting a semi-systematic literature review, conceptual variables are collected and conceptualized as a conceptual meta-model. As a result, this study introduces scenario variables (SVs) that represent constructs/semantics of scenario descriptions, according to 4 levels of constructs of a scenario method. To evaluate the comprehensibility and applicability of the defined variables, we analyze five existing scenario methods and their instances in automated driving system (ADS) domains. The results showed that our conceptual model and its constituent scenario variables adequately support the understanding of a scenario method and provide a means for comparative analysis between different scenario methods.
A compartmental model for cyber-epidemics
Venue: arXiv cs.CR 2022
Year: 2022

In our more and more interconnected world, a specific risk is that of a cyber-epidemic (or cyber-pandemic), produced either accidentally or intentionally, where a cyber virus propagates from device to device up to undermining the global Internet system with devastating consequences in terms of economic costs and societal harms related to the shutdown of essential services. We introduce a compartmental model for studying the spreading of a malware and of the awareness of its incidence through different waves which are evolving on top of the same graph structure (the global network of connected devices). This is realized by considering vectorial compartments made of two components, the first being descriptive of the state of the device with respect to the new malware's propagation, and the second accounting for the awareness of the device's user about the presence of the cyber threat. By introducing suitable transition rates between such compartments, one can then follow the evolution of a cyber-epidemic from the moment at which a new virus is seeded in the network, up to when a given user realizes that his/her device has suffered a damage and consequently starts a wave of awareness which eventually ends up with the development of a proper antivirus software. We then compare the overall damage that a malware is able to produce in Erdős-Rényi and scale-free network architectures for both the case in which the virus is causing a fixed damage on each device and the case where, instead, the virus is engineered to mutate while replicating from device to device. Our result constitute actually the attempt to build a specific compartmental model whose variables and parameters are entirely customized for describing cyber-epidemics.
Demystifying the Data Need of ML-surrogates for CFD Simulations
Venue: arXiv cs.LG 2022
Year: 2022

Computational fluid dynamics (CFD) simulations, a critical tool in various engineering applications, often require significant time and compute power to predict flow properties. The high computational cost associated with CFD simulations significantly restricts the scope of design space exploration and limits their use in planning and operational control. To address this issue, machine learning (ML) based surrogate models have been proposed as a computationally efficient tool to accelerate CFD simulations. However, a lack of clarity about CFD data requirements often challenges the widespread adoption of ML-based surrogates among design engineers and CFD practitioners. In this work, we propose an ML-based surrogate model to predict the temperature distribution inside the cabin of a passenger vehicle under various operating conditions and use it to demonstrate the trade-off between prediction performance and training dataset size. Our results show that the prediction accuracy is high and stable even when the training size is gradually reduced from 2000 to 200. The ML-based surrogates also reduce the compute time from ~30 minutes to around ~9 milliseconds. Moreover, even when only 50 CFD simulations are used for training, the temperature trend (e.g., locations of hot/cold regions) predicted by the ML-surrogate matches quite well with the results from CFD simulations.
Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis
Venue: arXiv cs.LG 2022
Year: 2022

Mapping X-ray images, radiology reports, and other medical data as binary codes in the common space, which can assist clinicians to retrieve pathology-related data from heterogeneous modalities (i.e., hashing-based cross-modal medical data retrieval), provides a new view to promot computeraided diagnosis. Nevertheless, there remains a barrier to boost medical retrieval accuracy: how to reveal the ambiguous semantics of medical data without the distraction of superfluous information. To circumvent this drawback, we propose Deep Supervised Information Bottleneck Hashing (DSIBH), which effectively strengthens the discriminability of hash codes. Specifically, the Deep Deterministic Information Bottleneck (Yu, Yu, and Principe 2021) for single modality is extended to the cross-modal scenario. Benefiting from this, the superfluous information is reduced, which facilitates the discriminability of hash codes. Experimental results demonstrate the superior accuracy of the proposed DSIBH compared with state-of-the-arts in cross-modal medical data retrieval tasks.
Individualized Risk Assessment of Preoperative Opioid Use by Interpretable Neural Network Regression
Venue: arXiv cs.LG 2022
Year: 2022

Preoperative opioid use has been reported to be associated with higher preoperative opioid demand, worse postoperative outcomes, and increased postoperative healthcare utilization and expenditures. Understanding the risk of preoperative opioid use helps establish patient-centered pain management. In the field of machine learning, deep neural network (DNN) has emerged as a powerful means for risk assessment because of its superb prediction power; however, the blackbox algorithms may make the results less interpretable than statistical models. Bridging the gap between the statistical and machine learning fields, we propose a novel Interpretable Neural Network Regression (INNER), which combines the strengths of statistical and DNN models. We use the proposed INNER to conduct individualized risk assessment of preoperative opioid use. Intensive simulations and an analysis of 34,186 patients expecting surgery in the Analgesic Outcomes Study (AOS) show that the proposed INNER not only can accurately predict the preoperative opioid use using preoperative characteristics as DNN, but also can estimate the patient specific odds of opioid use without pain and the odds ratio of opioid use for a unit increase in the reported overall body pain, leading to more straightforward interpretations of the tendency to use opioids than DNN. Our results identify the patient characteristics that are strongly associated with opioid use and is largely consistent with the previous findings, providing evidence that INNER is a useful tool for individualized risk assessment of preoperative opioid use.
Full Duplex Massive MIMO Architectures: Recent Advances, Applications, and Future Directions
Venue: arXiv cs.IT 2022
Year: 2022

The increasingly demanding objectives for next generation wireless communications have spurred recent research activities on multi-antenna transceiver hardware architectures and relevant intelligent communication schemes. Among them belong the Full Duplex (FD) Multiple-Input Multiple-Output (MIMO) architectures, which offer the potential for simultaneous uplink and downlink operations in the entire frequency band. However, as the number of antenna elements increases, the interference signal leaking from the transmitter of the FD radio to its receiver becomes more severe. In this article, we present a unified FD massive MIMO architecture comprising analog and digital transmit and receive BeamForming (BF), as well as analog and digital SI cancellation, which can be jointly optimized for various performance objectives and complexity requirements. Performance evaluation results for applications of the proposed architecture to fully digital and hybrid analog and digital BF operations using recent algorithmic designs, as well as simultaneous communication of data and control signals are presented. It is shown that the proposed architecture, for both small and large numbers of antennas, enables improved spectral efficiency FD communications with fewer analog cancellation elements compared to various benchmark schemes. The article is concluded with a list of open challenges and research directions for future FD massive MIMO communication systems and their promising applications.
From mean-field to complex topologies: network effects on the algorithmic bias model
Venue: arXiv cs.SI 2022
Year: 2022

Nowadays, we live in a society where people often form their opinion by accessing and discussing contents shared on social networking websites. While these platforms have fostered information access and diffusion, they represent optimal environments for the proliferation of polluted contents, which is argued to be one of the co-causes of polarization/radicalization. Moreover, recommendation algorithms - intended to enhance platform usage - are likely to augment such phenomena, generating the so called Algorithmic Bias. In this work, we study the impact that different network topologies have on the formation and evolution of opinion in the context of a recent opinion dynamic model which includes bounded confidence and algorithmic bias. Mean-field, scale-free and random topologies, as well as networks generated by the Lancichinetti-Fortunato-Radicchi benchmark, are compared in terms of opinion fragmentation/polarization and time to convergence.
An Application of a Multivariate Estimation of Distribution Algorithm to Cancer Chemotherapy
Venue: arXiv cs.AI 2022
Year: 2022

Chemotherapy treatment for cancer is a complex optimisation problem with a large number of interacting variables and constraints. A number of different probabilistic algorithms have been applied to it with varying success. In this paper we expand on this by applying two estimation of distribution algorithms to the problem. One is UMDA, which uses a univariate probabilistic model similar to previously applied EDAs. The other is hBOA, the first EDA using a multivariate probabilistic model to be applied to the chemotherapy problem. While instinct would lead us to predict that the more sophisticated algorithm would yield better performance on a complex problem like this, we show that it is outperformed by the algorithms using the simpler univariate model. We hypothesise that this is caused by the more sophisticated algorithm being impeded by the large number of interactions in the problem which are unnecessary for its solution.
Data-driven Driver Model for Speed Advisory Systems in Partially Automated Vehicles
Venue: arXiv cs.RO 2022
Year: 2022

Vehicle control algorithms exploiting connectivity and automation, such as Connected and Automated Vehicles (CAVs) or Advanced Driver Assistance Systems (ADAS), have the opportunity to improve energy savings. However, lower levels of automation involve a human-machine interaction stage, where the presence of a human driver affects the performance of the control algorithm in closed loop. This occurs for instance in the case of Eco-Driving control algorithms implemented as a velocity advisory system, where the driver is displayed an optimal speed trajectory to follow to reduce energy consumption. Achieving the control objectives relies on the human driver perfectly following the recommended speed. If the driver is unable to follow the recommended speed, a decline in energy savings and poor vehicle performance may occur. This warrants the creation of methods to model and forecast the response of a human driver when operating in the loop with a speed advisory system. This work focuses on developing a sequence to sequence long-short term memory (LSTM)-based driver behavior model that models the interaction of the human driver to a suggested desired vehicle speed trajectory in real-world conditions. A driving simulator is used for data collection and training the driver model, which is then compared against the driving data and a deterministic model. Results show close proximity of the LSTM-based model with the driving data, demonstrating that the model can be adopted as a tool to design human-centered speed advisory systems.
Control Interface Remapping for Bias-Aware Assistive Teleoperation
Venue: arXiv cs.HC 2022
Year: 2022

Users of assistive devices vary in their extent of motor impairment, and hence their physical interaction with control interfaces can differ. There is the potential for improved utility if control interface actuation is mapped to assistive device control signals in a manner customized to each user. In this paper, we present (1) a method for creating a custom interface to assistive device control mapping based on the design of a user's bias profile, (2) a procedure and virtual task for gathering interface actuation data from which to build the bias profile and map, and (3) an evaluation of our method on 6 participants with upper limb motor impairments. Our results show that custom interface remapping based on user bias profiles shows promise in providing assistance via an improvement in the reachability of the device control space. This effect was especially pronounced for individuals who had a more limited reachable space.
Susceptibility of Age of Gossip to Timestomping
Venue: arXiv cs.IT 2022
Year: 2022

We consider a fully connected network consisting of a source that maintains the current version of a file, $n$ nodes that use asynchronous gossip mechanisms to disseminate fresh information in the network, and an adversary who infects the packets at a target node through data timestamp manipulation, with the intent to replace circulation of fresh packets with outdated packets in the network. We show that a single infected node increases the expected age of a fully connected network from $O(\log n)$ to $O(n)$. Further, we show that the optimal behavior for an adversary is to reset the timestamps of all outgoing packets to the current time and of all incoming packets to an outdated time. Additionally, if the adversary allows the infected node to accept a small fraction of incoming packets from the network, then a large network can manage to curb the spread of stale files coming from the infected node and pull the network age back to $O(\log n)$. Lastly, we show that if an infected node contacts only a single node instead of all nodes of the network, the system age can still be degraded to $O(n)$. These show that fully connected nature of a network can be both a benefit and a detriment for information freshness; full connectivity, while enabling fast dissemination of information, also enables fast dissipation of adversarial inputs.
Static analysis tools in the era of cloud-native systems
Venue: arXiv cs.SE 2022
Year: 2022

Microservices fuel cloud-native systems with small service sets developed and deployed independently. The independent nature of this modular architecture also leads to challenges and gaps. The intended system design might deviate far from what is eventually produced and maintained as the architecture tends to degrade over time. This paper challenges the audience on how static analysis could contribute to microservice system development and management, particularly managing architectural degradation. It elaborates on challenges and needed changes in the traditional code analysis to better fit these systems and discusses implications for practitioners once robust static analysis tools become available
AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars
Venue: arXiv cs.CV 2022
Year: 2022

3D avatar creation plays a crucial role in the digital age. However, the whole production process is prohibitively time-consuming and labor-intensive. To democratize this technology to a larger audience, we propose AvatarCLIP, a zero-shot text-driven framework for 3D avatar generation and animation. Unlike professional software that requires expert knowledge, AvatarCLIP empowers layman users to customize a 3D avatar with the desired shape and texture, and drive the avatar with the described motions using solely natural languages. Our key insight is to take advantage of the powerful vision-language model CLIP for supervising neural human generation, in terms of 3D geometry, texture and animation. Specifically, driven by natural language descriptions, we initialize 3D human geometry generation with a shape VAE network. Based on the generated 3D human shapes, a volume rendering model is utilized to further facilitate geometry sculpting and texture generation. Moreover, by leveraging the priors learned in the motion VAE, a CLIP-guided reference-based motion synthesis method is proposed for the animation of the generated 3D avatar. Extensive qualitative and quantitative experiments validate the effectiveness and generalizability of AvatarCLIP on a wide range of avatars. Remarkably, AvatarCLIP can generate unseen 3D avatars with novel animations, achieving superior zero-shot capability.
Strategizing against Learners in Bayesian Games
Venue: arXiv cs.LG 2022
Year: 2022

We study repeated two-player games where one of the players, the learner, employs a no-regret learning strategy, while the other, the optimizer, is a rational utility maximizer. We consider general Bayesian games, where the payoffs of both the optimizer and the learner could depend on the type, which is drawn from a publicly known distribution, but revealed privately to the learner. We address the following questions: (a) what is the bare minimum that the optimizer can guarantee to obtain regardless of the no-regret learning algorithm employed by the learner? (b) are there learning algorithms that cap the optimizer payoff at this minimum? (c) can these algorithms be implemented efficiently? While building this theory of optimizer-learner interactions, we define a new combinatorial notion of regret called polytope swap regret, that could be of independent interest in other settings.
Text Detection & Recognition in the Wild for Robot Localization
Venue: arXiv cs.CV 2022
Year: 2022

Signage is everywhere and a robot should be able to take advantage of signs to help it localize (including Visual Place Recognition (VPR)) and map. Robust text detection & recognition in the wild is challenging due to such factors as pose, irregular text, illumination, and occlusion. We propose an end-to-end scene text spotting model that simultaneously outputs the text string and bounding boxes. This model is more suitable for VPR. Our central contribution is introducing utilizing an end-to-end scene text spotting framework to adequately capture the irregular and occluded text regions in different challenging places. To evaluate our proposed architecture's performance for VPR, we conducted several experiments on the challenging Self-Collected Text Place (SCTP) benchmark dataset. The initial experimental results show that the proposed method outperforms the SOTA methods in terms of precision and recall when tested on this benchmark.
Building Information Modeling Using Constraint Logic Programming
Venue: arXiv cs.LO 2022
Year: 2022

Building Information Modeling (BIM) produces three-dimensional models of buildings combining the geometrical information with a wide range of properties. BIM is slowly but inevitably revolutionizing the architecture, engineering, and construction (AEC) industry. Buildings need to be compliant with regulations about stability, safety, and environmental impact. Manual compliance checking is tedious and error-prone, and amending flaws discovered only at construction time causes huge additional costs and delays. Several tools can check BIM models for conformance with rules/guidelines. For example, Singapore's CORENET e-Submission System checks fire safety. But since the current BIM exchange format only contains basic information of building objects, a separate, ad-hoc model pre-processing is required to determine, e.g., evacuation routes. Moreover, they face difficulties in adapting existing built-in rules and/or adding new ones (to cater for building regulations, that can vary not only among countries but also among parts of the same city), if at all possible. We propose the use of logic-based executable formalisms (CLP and Constraint ASP) to couple BIM models with advanced knowledge representation and reasoning capabilities. Previous experience shows that such formalisms can be used to uniformly capture and reason with knowledge (including ambiguity) in a large variety of domains. Additionally, incorporating checking within design tools makes it possible to ensure that models are rule-compliant at every step. This also prevents erroneous designs from having to be (partially) redone, which is also costly and burdensome. To validate our proposal, we implemented a preliminary reasoner under CLP(Q/R) and ASP with constraints and evaluated it with several BIM models. Under consideration for acceptance in Theory and Practice of Logic Programming (TPLP).