OpenRaiser/Intern-Atlas huggingface dataset:
Designing a Recurrent Neural Network to Learn a Motion Planner for High-Dimensional Inputs
Venue: arXiv cs.RO 2022
Year: 2022

The use of machine learning in the self-driving industry has boosted a number of recent advancements. In particular, the usage of large deep learning models in the perception and prediction stack have proved quite successful, but there still lacks significant literature on the use of machine learning in the planning stack. The current state of the art in the planning stack often relies on fast constrained optimization or rule-based approaches. Both of these techniques fail to address a significant number of fundamental problems that would allow the vehicle to operate more similarly to that of human drivers. In this paper, we attempt to design a basic deep learning system to approach this problem. Furthermore, the main underlying goal of this paper is to demonstrate the potential uses of machine learning in the planning stack for autonomous vehicles (AV) and provide a baseline work for ongoing and future research.
Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques
Venue: arXiv cs.AI 2022
Year: 2022

Process mining is a subfield of process science that analyzes event data collected in databases called event logs. Recently, novel types of event data have become of interest due to the wide industrial application of process mining analyses. In this paper, we examine uncertain event data. Such data contain meta-attributes describing the amount of imprecision tied with attributes recorded in an event log. We provide examples of uncertain event data, present the state of the art in regard of uncertainty in process mining, and illustrate open challenges related to this research direction.
Programming Data Structures for Large-Scale Desktop Simulations of Complex Systems
Venue: arXiv cs.DS 2022
Year: 2022

The investigation of complex systems requires running large-scale simulations over many temporal iterations. It is therefore important to provide efficient implementations. The present study borrows philosophical concepts from Gilbert Simondon to identify data structures and algorithms that have the biggest impact on running time and memory usage. These are the entity $e$-tuple $\mathcal{E}$ and the intertwined update function $ϕ$. Focusing on implementing data structures in C#, $\mathcal{E}$ is implemented as a list of objects according to current software engineering practice and as an array of pointers according to theoretical considerations. Cellular automaton simulations with $10^9$ entities over one iteration reveal that the object-list with dynamic typing and multi-state readiness has a drastic effect on running time and memory usage, especially dynamic typing as it has a big impact on the evolution time. Pointer-arrays are possible to implement in C# and are more running time and memory efficient as compared to the object-list implementation, however, they are cumbersome to implement. In conclusion, avoiding dynamic typing in object-list based implementations or using pointer-arrays gives evolution times that are acceptable in practice, even on desktop computers.
Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System
Venue: arXiv cs.MA 2022
Year: 2022

Decentralized learning is an efficient emerging paradigm for boosting the computing capability of multiple bounded computing agents. In the big data era, performing inference within the distributed and federated learning (DL and FL) frameworks, the central server needs to process a large amount of data while relying on various agents to perform multiple distributed training tasks. Considering the decentralized computing topology, privacy has become a first-class concern. Moreover, assuming limited information processing capability for the agents calls for a sophisticated \textit{privacy-preserving decentralization} that ensures efficient computation. Towards this end, we study the \textit{privacy-aware server to multi-agent assignment} problem subject to information processing constraints associated with each agent, while maintaining the privacy and assuring learning informative messages received by agents about a global terminal through the distributed private federated learning (DPFL) approach. To find a decentralized scheme for a two-agent system, we formulate an optimization problem that balances privacy and accuracy, taking into account the quality of compression constraints associated with each agent. We propose an iterative converging algorithm by alternating over self-consistent equations. We also numerically evaluate the proposed solution to show the privacy-prediction trade-off and demonstrate the efficacy of the novel approach in ensuring privacy in DL and FL.
Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction
Venue: arXiv cs.LG 2022
Year: 2022

In the real world, long sequence time-series forecasting (LSTF) is needed in many cases, such as power consumption prediction and air quality prediction.Multi-dimensional long time series model has more strict requirements on the model, which not only needs to effectively capture the accurate long-term dependence between input and output, but also needs to capture the relationship between data of different dimensions.Recent research shows that the Informer model based on Transformer has achieved excellent performance in long time series prediction.However, this model still has some deficiencies in multidimensional prediction,it cannot capture the relationship between different dimensions well. We improved Informer to address its shortcomings in multidimensional forecasting. First,we introduce an adaptive graph neural network to capture hidden dimension dependencies in mostly time series prediction. Secondly,we integrate adaptive graph convolutional networks into various spatio-temporal series prediction models to solve the defect that they cannot capture the relationship between different dimensions. Thirdly,After experimental testing with multiple data sets, the accuracy of our framework improved by about 10\% after being introduced into the model.
Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models
Venue: arXiv cs.LG 2022
Year: 2022

Analog hardware has become a popular choice for machine learning on resource-constrained devices recently due to its fast execution and energy efficiency. However, the inherent presence of noise in analog hardware and the negative impact of the noise on deployed deep neural network (DNN) models limit their usage. The degradation in performance due to the noise calls for the novel design of DNN models that have excellent noiseresistant property, leveraging the properties of the fundamental building block of DNN models. In this work, the use of L1 or TopK BatchNorm type, a fundamental DNN model building block, in designing DNN models with excellent noise-resistant property is proposed. Specifically, a systematic study has been carried out by training DNN models with L1/TopK BatchNorm type, and the performance is compared with DNN models with L2 BatchNorm types. The resulting model noise-resistant property is tested by injecting additive noise to the model weights and evaluating the new model inference accuracy due to the noise. The results show that L1 and TopK BatchNorm type has excellent noise-resistant property, and there is no sacrifice in performance due to the change in the BatchNorm type from L2 to L1/TopK BatchNorm type.
GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data
Venue: arXiv cs.LG 2022
Year: 2022

Electronic health records (EHRs) are usually highly dimensional, heterogeneous, and multimodal. Besides, the random recording of clinical variables results in high missing rates and uneven time intervals between adjacent records in the multivariate clinical time-series data extracted from EHRs. Current works using clinical time-series data for patient representation regard the patients' physiological status as a discrete process described by sporadically collected records. However, changes in the patient's physiological condition are continuous and dynamic processes. The perception of time and velocity of change is crucial for patient representation learning. In this study, we propose a time- and velocity-aware gated recurrent unit model (GRU-TV) for patient representation learning of clinical multivariate time-series data in a time-continuous manner. The neural ordinary differential equations (ODEs) and velocity perception mechanism are applied to perceive the time interval between adjacent records and changing rate of the patient's physiological status, respectively. Our experiments on two real clinical EHR datasets (PhysioNet2012, MIMIC-III) establish that GRU-TV is a robust model on computer-aided diagnosis (CAD) tasks, especially on sequences with high-variance time intervals.
Evaluating the Impact of Tiled User-Adaptive Real-Time Point Cloud Streaming on VR Remote Communication
Venue: arXiv cs.MM 2022
Year: 2022

Remote communication has rapidly become a part of everyday life in both professional and personal contexts. However, popular video conferencing applications present limitations in terms of quality of communication, immersion and social meaning. VR remote communication applications offer a greater sense of co-presence and mutual sensing of emotions between remote users. Previous research on these applications has shown that realistic point cloud user reconstructions offer better immersion and communication as compared to synthetic user avatars. However, photorealistic point clouds require a large volume of data per frame and are challenging to transmit over bandwidth-limited networks. Recent research has demonstrated significant improvements to perceived quality by optimizing the usage of bandwidth based on the position and orientation of the user's viewport with user-adaptive streaming. In this work, we developed a real-time VR communication application with an adaptation engine that features tiled user-adaptive streaming based on user behaviour. The application also supports traditional network adaptive streaming. The contribution of this work is to evaluate the impact of tiled user-adaptive streaming on quality of communication, visual quality, system performance and task completion in a functional live VR remote communication system. We perform a subjective evaluation with 33 users to compare the different streaming conditions with a neck exercise training task. As a baseline, we use uncompressed streaming requiring ca. 300Mbps and our solution achieves similar visual quality with tiled adaptive streaming at 14Mbps. We also demonstrate statistically significant gains to the quality of interaction and improvements to system performance and CPU consumption with tiled adaptive streaming as compared to the more traditional network adaptive streaming.
Reasoning in the Description Logic ALC under Category Semantics
Venue: arXiv cs.LO 2022
Year: 2022

We present in this paper a reformulation of the usual set-theoretical semantics of the description logic $\mathcal{ALC}$ with general TBoxes by using categorical language. In this setting, $\mathcal{ALC}$ concepts are represented as objects, concept subsumptions as arrows, and memberships as logical quantifiers over objects and arrows of categories. Such a category-based semantics provides a more modular representation of the semantics of $\mathcal{ALC}$. This feature allows us to define a sublogic of $\mathcal{ALC}$ by dropping the interaction between existential and universal restrictions, which would be responsible for an exponential complexity in space. Such a sublogic is undefinable in the usual set-theoretical semantics, We show that this sublogic is {\sc{PSPACE}} by proposing a deterministic algorithm for checking concept satisfiability which runs in polynomial space.
Gamified Speaker Comparison by Listening
Venue: arXiv cs.SD 2022
Year: 2022

We address speaker comparison by listening in a game-like environment, hypothesized to make the task more motivating for naive listeners. We present the same 30 trials selected with the help of an x-vector speaker recognition system from VoxCeleb to a total of 150 crowdworkers recruited through Amazon's Mechanical Turk. They are divided into cohorts of 50, each using one of three alternative interface designs: (i) a traditional (nongamified) design; (ii) a gamified design with feedback on decisions, along with points, game level indications, and possibility for interface customization; (iii) another gamified design with an additional constraint of maximum of 5 'lives' consumed by wrong answers. We analyze the impact of these interface designs to listener error rates (both misses and false alarms), probability calibration, time of quitting, along with survey questionnaire. The results indicate improved performance from (i) to (ii) and (iii), particularly in terms of balancing the two types of detection errors.
Proactive Traffic Offloading in Dynamic Integrated Multi-Satellite Terrestrial Networks
Venue: arXiv cs.NI 2022
Year: 2022

The integration between the satellite network and the terrestrial network will play a key role in the upcoming sixth-generation (6G) of mobile cellular networks thanks to the wide coverage and bandwidth offered by satellite networks. To leverage this integration, we propose a proactive traffic offloading scheme in an integrated multi-satellite terrestrial network (IMSTN) that considers the future networks' heterogeneity and predicts their variability. Our proposed offloading scheme hinges on traffic prediction to answer the stringent requirements of data-rate, latency and reliability imposed by heterogeneous and coexisting services and traffic namely enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable low latency communication (URLLC). However, the fulfilment of these requirements during offloading in dynamic IMSTN comes at the expense of significant energy consumption and introduces inherently supplementary latency. Therefore, our offloading scheme aims to balance the fundamental trade-offs first between energy consumption and the achievable data-rate and second between energy consumption and latency while meeting the respective needs of the present traffic. Our findings prove the importance of the cooperation between the multi-satellite network and the terrestrial network conditioned by traffic prediction to enhance the performance of IMTSN in terms of latency and energy consumption.
Improving Emergency Training for Earthquakes Through Immersive Virtual Environments and Anxiety Tests: A Case Study
Venue: arXiv cs.HC 2022
Year: 2022

Because of the occurrence of severe and large magnitude earthquakes each year, earthquake-prone countries suffer considerable financial damage and loss of life. Teaching essential safety measures will lead to a generation that can perform basic procedures during an earthquake, which is an important and effective solution in preventing the loss of life in this natural disaster. In recent years, virtual reality technology is a tool that has been used to educate people on safety matters. This paper evaluates the effect of education and premonition on the incorrect decision-making of residents under the stressful conditions of an earthquake. For this purpose, a virtual model has been designed and built from a proposed classroom in a school of the city of Tehran. Accordingly, two educational scenarios, presented in reality and the virtual model respectively, were conducted on a statistical sample of 20 students within the range of 20 to 25 years of age. Within the mentioned sample, the first group of 10 students were taught safety measures in a traditional classroom. The second group of 10 students participated in a virtual classroom. Evaluation tests on safety measures against earthquakes were distributed after two weeks. Furthermore, two self-reporting tests of Depression, anxiety, stress test (DASS), and Beck Anxiety Inventory (BAI) were assigned to the second group to evaluate the effect of foresight under two different scenarios. The results show that educating through virtual reality technology yields a higher performance level relative to the traditional approach to education. Additionally, the ability to detect earthquakes ahead of time is an influential factor in controlling stress and determining the right decisions should the event occur.
Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory
Venue: arXiv cs.HC 2022
Year: 2022

Understanding how ML models work is a prerequisite for responsibly designing, deploying, and using ML-based systems. With interpretability approaches, ML can now offer explanations for its outputs to aid human understanding. Though these approaches rely on guidelines for how humans explain things to each other, they ultimately solve for improving the artifact -- an explanation. In this paper, we propose an alternate framework for interpretability grounded in Weick's sensemaking theory, which focuses on who the explanation is intended for. Recent work has advocated for the importance of understanding stakeholders' needs -- we build on this by providing concrete properties (e.g., identity, social context, environmental cues, etc.) that shape human understanding. We use an application of sensemaking in organizations as a template for discussing design guidelines for Sensible AI, AI that factors in the nuances of human cognition when trying to explain itself.
Extracting Latent Steering Vectors from Pretrained Language Models
Venue: arXiv cs.CL 2022
Year: 2022

Prior work on controllable text generation has focused on learning how to control language models through trainable decoding, smart-prompt design, or fine-tuning based on a desired objective. We hypothesize that the information needed to steer the model to generate a target sentence is already encoded within the model. Accordingly, we explore a different approach altogether: extracting latent vectors directly from pretrained language model decoders without fine-tuning. Experiments show that there exist steering vectors, which, when added to the hidden states of the language model, generate a target sentence nearly perfectly (> 99 BLEU) for English sentences from a variety of domains. We show that vector arithmetic can be used for unsupervised sentiment transfer on the Yelp sentiment benchmark, with performance comparable to models tailored to this task. We find that distances between steering vectors reflect sentence similarity when evaluated on a textual similarity benchmark (STS-B), outperforming pooled hidden states of models. Finally, we present an analysis of the intrinsic properties of the steering vectors. Taken together, our results suggest that frozen LMs can be effectively controlled through their latent steering space.
Sibylvariant Transformations for Robust Text Classification
Venue: arXiv cs.CL 2022
Year: 2022

The vast majority of text transformation techniques in NLP are inherently limited in their ability to expand input space coverage due to an implicit constraint to preserve the original class label. In this work, we propose the notion of sibylvariance (SIB) to describe the broader set of transforms that relax the label-preserving constraint, knowably vary the expected class, and lead to significantly more diverse input distributions. We offer a unified framework to organize all data transformations, including two types of SIB: (1) Transmutations convert one discrete kind into another, (2) Mixture Mutations blend two or more classes together. To explore the role of sibylvariance within NLP, we implemented 41 text transformations, including several novel techniques like Concept2Sentence and SentMix. Sibylvariance also enables a unique form of adaptive training that generates new input mixtures for the most confused class pairs, challenging the learner to differentiate with greater nuance. Our experiments on six benchmark datasets strongly support the efficacy of sibylvariance for generalization performance, defect detection, and adversarial robustness.
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling
Venue: arXiv cs.CV 2022
Year: 2022

Every day, a new method is published to tackle Few-Shot Image Classification, showing better and better performances on academic benchmarks. Nevertheless, we observe that these current benchmarks do not accurately represent the real industrial use cases that we encountered. In this work, through both qualitative and quantitative studies, we expose that the widely used benchmark tieredImageNet is strongly biased towards tasks composed of very semantically dissimilar classes e.g. bathtub, cabbage, pizza, schipperke, and cardoon. This makes tieredImageNet (and similar benchmarks) irrelevant to evaluate the ability of a model to solve real-life use cases usually involving more fine-grained classification. We mitigate this bias using semantic information about the classes of tieredImageNet and generate an improved, balanced benchmark. Going further, we also introduce a new benchmark for Few-Shot Image Classification using the Danish Fungi 2020 dataset. This benchmark proposes a wide variety of evaluation tasks with various fine-graininess. Moreover, this benchmark includes many-way tasks (e.g. composed of 100 classes), which is a challenging setting yet very common in industrial applications. Our experiments bring out the correlation between the difficulty of a task and the semantic similarity between its classes, as well as a heavy performance drop of state-of-the-art methods on many-way few-shot classification, raising questions about the scaling abilities of these methods. We hope that our work will encourage the community to further question the quality of standard evaluation processes and their relevance to real-life applications.
Soft Robotic Mannequin: Design and Algorithm for Deformation Control
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents a novel soft robotic system for a deformable mannequin that can be employed to physically realize the 3D geometry of different human bodies. The soft membrane on a mannequin is deformed by inflating several curved chambers using pneumatic actuation. Controlling the freeform surface of a soft membrane by adjusting the pneumatic actuation in different chambers is challenging as the membrane's shape is commonly determined by the interaction between all chambers. Using vision feedback provided by a structured-light based 3D scanner, we developed an efficient algorithm to compute the optimized actuation of all chambers which could drive the soft membrane to deform into the best approximation of different target shapes. Our algorithm converges quickly by including pose estimation in the loop of optimization. The time-consuming step of evaluating derivatives on the deformable membrane is avoided by using the Broyden update when possible. The effectiveness of our soft robotic mannequin with controlled deformation has been verified in experiments.
A Gödel Calculus for Linear Temporal Logic
Venue: arXiv cs.LO 2022
Year: 2022

We consider Gödel temporal logic ($\sf GTL$), a variant of linear temporal logic based on Gödel--Dummett propositional logic. In recent work, we have shown this logic to enjoy natural semantics both as a fuzzy logic and as a superintuitionistic logic. Using semantical methods, the logic was shown to be {\sc pspace}-complete. In this paper we provide a deductive calculus for $\sf GTL$, and show this calculus to be sound and complete for the above-mentioned semantics.