OpenRaiser/Intern-Atlas huggingface dataset:
Gesture2Path: Imitation Learning for Gesture-aware Navigation
Venue: arXiv cs.RO 2022
Year: 2022

As robots increasingly enter human-centered environments, they must not only be able to navigate safely around humans, but also adhere to complex social norms. Humans often rely on non-verbal communication through gestures and facial expressions when navigating around other people, especially in densely occupied spaces. Consequently, robots also need to be able to interpret gestures as part of solving social navigation tasks. To this end, we present Gesture2Path, a novel social navigation approach that combines image-based imitation learning with model-predictive control. Gestures are interpreted based on a neural network that operates on streams of images, while we use a state-of-the-art model predictive control algorithm to solve point-to-point navigation tasks. We deploy our method on real robots and showcase the effectiveness of our approach for the four gestures-navigation scenarios: left/right, follow me, and make a circle. Our experiments indicate that our method is able to successfully interpret complex human gestures and to use them as a signal to generate socially compliant trajectories for navigation tasks. We validated our method based on in-situ ratings of participants interacting with the robots.
State-driven Implicit Modeling for Sparsity and Robustness in Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

Implicit models are a general class of learning models that forgo the hierarchical layer structure typical in neural networks and instead define the internal states based on an ``equilibrium'' equation, offering competitive performance and reduced memory consumption. However, training such models usually relies on expensive implicit differentiation for backward propagation. In this work, we present a new approach to training implicit models, called State-driven Implicit Modeling (SIM), where we constrain the internal states and outputs to match that of a baseline model, circumventing costly backward computations. The training problem becomes convex by construction and can be solved in a parallel fashion, thanks to its decomposable structure. We demonstrate how the SIM approach can be applied to significantly improve sparsity (parameter reduction) and robustness of baseline models trained on FashionMNIST and CIFAR-100 datasets.
Partial sequence labeling with structured Gaussian Processes
Venue: arXiv cs.LG 2022
Year: 2022

Existing partial sequence labeling models mainly focus on max-margin framework which fails to provide an uncertainty estimation of the prediction. Further, the unique ground truth disambiguation strategy employed by these models may include wrong label information for parameter learning. In this paper, we propose structured Gaussian Processes for partial sequence labeling (SGPPSL), which encodes uncertainty in the prediction and does not need extra effort for model selection and hyperparameter learning. The model employs factor-as-piece approximation that divides the linear-chain graph structure into the set of pieces, which preserves the basic Markov Random Field structure and effectively avoids handling large number of candidate output sequences generated by partially annotated data. Then confidence measure is introduced in the model to address different contributions of candidate labels, which enables the ground-truth label information to be utilized in parameter learning. Based on the derived lower bound of the variational lower bound of the proposed model, variational parameters and confidence measures are estimated in the framework of alternating optimization. Moreover, weighted Viterbi algorithm is proposed to incorporate confidence measure to sequence prediction, which considers label ambiguity arose from multiple annotations in the training data and thus helps improve the performance. SGPPSL is evaluated on several sequence labeling tasks and the experimental results show the effectiveness of the proposed model.
Task-Agnostic Adaptation for Safe Human-Robot Handover
Venue: arXiv cs.RO 2022
Year: 2022

Human-robot interaction (HRI) is an important component to improve the flexibility of modern production lines. However, in real-world applications, the task (\ie the conditions that the robot needs to operate on, such as the environmental lighting condition, the human subjects to interact with, and the hardware platforms) may vary and it remains challenging to optimally and efficiently configure and adapt the robotic system under these changing tasks. To address the challenge, this paper proposes a task-agnostic adaptable controller that can 1) adapt to different lighting conditions, 2) adapt to individual behaviors and ensure safety when interacting with different humans, and 3) enable easy transfer across robot platforms with different control interfaces. The proposed framework is tested on a human-robot handover task using the FANUC LR Mate 200id/7L robot and the Kinova Gen3 robot. Experiments show that the proposed task-agnostic controller can achieve consistent performance across different tasks.
BP-Im2col: Implicit Im2col Supporting AI Backpropagation on Systolic Arrays
Venue: arXiv cs.AR 2022
Year: 2022

State-of-the-art systolic array-based accelerators adopt the traditional im2col algorithm to accelerate the inference of convolutional layers. However, traditional im2col cannot efficiently support AI backpropagation. Backpropagation in convolutional layers involves performing transposed convolution and dilated convolution, which usually introduces plenty of zero-spaces into the feature map or kernel. The zero-space data reorganization interfere with the continuity of training and incur additional and non-negligible overhead in terms of off- and on-chip storage, access and performance. Since countermeasures for backpropagation are rarely proposed, we propose BP-im2col, a novel im2col algorithm for AI backpropagation, and implement it in RTL on a TPU-like accelerator. Experiments on TPU-like accelerator indicate that BP-im2col reduces the backpropagation runtime by 34.9% on average, and reduces the bandwidth of off-chip memory and on-chip buffers by at least 22.7% and 70.6% respectively, over a baseline accelerator adopting the traditional im2col. It further reduces the additional storage overhead in the backpropagation process by at least 74.78%.
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes
Venue: arXiv cs.LG 2022
Year: 2022

The spread of COVID-19 revealed that transmission risk patterns are not homogenous across different cities and communities, and various heterogeneous features can influence the spread trajectories. Hence, for predictive pandemic monitoring, it is essential to explore latent heterogeneous features in cities and communities that distinguish their specific pandemic spread trajectories. To this end, this study creates a network embedding model capturing cross-county visitation networks, as well as heterogeneous features to uncover clusters of counties in the United States based on their pandemic spread transmission trajectories. We collected and computed location intelligence features from 2,787 counties from March 3 to June 29, 2020 (initial wave). Second, we constructed a human visitation network, which incorporated county features as node attributes, and visits between counties as network edges. Our attributed network embeddings approach integrates both typological characteristics of the cross-county visitation network, as well as heterogeneous features. We conducted clustering analysis on the attributed network embeddings to reveal four archetypes of spread risk trajectories corresponding to four clusters of counties. Subsequently, we identified four features as important features underlying the distinctive transmission risk patterns among the archetypes. The attributed network embedding approach and the findings identify and explain the non-homogenous pandemic risk trajectories across counties for predictive pandemic monitoring. The study also contributes to data-driven and deep learning-based approaches for pandemic analytics to complement the standard epidemiological models for policy analysis in pandemics.
Data-Centric AI Paradigm Based on Application-Driven Fine-Grained Dataset Design
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning has a wide range of applications in industrial scenario, but reducing false alarm (FA) remains a major difficulty. Optimizing network architecture or network parameters is used to tackle this challenge in academic circles, while ignoring the essential characteristics of data in application scenarios, which often results in increased FA in new scenarios. In this paper, we propose a novel paradigm for fine-grained design of datasets, driven by industrial applications. We flexibly select positive and negative sample sets according to the essential features of the data and application requirements, and add the remaining samples to the training set as uncertainty classes. We collect more than 10,000 mask-wearing recognition samples covering various application scenarios as our experimental data. Compared with the traditional data design methods, our method achieves better results and effectively reduces FA. We make all contributions available to the research community for broader use. The contributions will be available at https://github.com/huh30/OpenDatasets.
Deep Learning-Based Signal Detection for Dual-Mode Index Modulation 3D-OFDM
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we propose a deep learning-based signal detector called DuaIM-3DNet for dual-mode index modulation-based three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM). Herein, DM-IM-3D- OFDM is a subcarrier index modulation scheme which conveys data bits via both dual-mode 3D constellation symbols and indices of active subcarriers. Thus, this scheme obtains better error performance than the existing IM schemes when using the conventional maximum likelihood (ML) detector, which, however, suffers from high computational complexity, especially when the system parameters increase. In order to address this fundamental issue, we propose the usage of a deep neural network (DNN) at the receiver to jointly and reliably detect both symbols and index bits of DM-IM-3D-OFDM under Rayleigh fading channels in a data-driven manner. Simulation results demonstrate that our proposed DNN detector achieves near-optimal performance at significantly lower runtime complexity compared to the ML detector.
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation
Venue: arXiv cs.LG 2022
Year: 2022

Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we show that these approaches fail to approximate the classification probability. On the contrary, we propose a scalable and intuitive framework to calibrate ensembles of deep learning models to produce uncertainty quantification measurements that approximate the classification probability. On unseen test data, we demonstrate improved calibration, sensitivity (in two out of three cases) and precision when being compared with the standard approaches. We further motivate the usage of our method in active learning, creating pseudo-labels to learn from unlabeled images and human-machine collaboration.
Synapse: Interactive Guidance by Demonstration with Trial-and-Error Support for Older Adults to Use Smartphone Apps
Venue: arXiv cs.HC 2022
Year: 2022

As smartphones are widely adopted, mobile applications (apps) are emerging to provide critical services such as food delivery and telemedicine. While bring convenience to everyday life, this trend may create barriers for older adults who tend to be less tech-savvy than young people. In-person or screen sharing support is helpful but limited by the help-givers' availability. Video tutorials can be useful but require users to switch contexts between watching the tutorial and performing the corresponding actions in the app, which is cumbersome to do on a mobile phone. Although interactive tutorials have been shown to be promising, none was designed for older adults. Furthermore, the trial-and-error approach has been shown to be beneficial for older adults, but they often lack support to use the approach. Inspired by both interactive tutorials and trial-and-error approach, we designed an app-independent mobile service, \textit{Synapse}, for help-givers to create a multimodal interactive tutorial on a smartphone and for help-receivers (e.g., older adults) to receive interactive guidance with trial-and-error support when they work on the same task. We conducted a user study with 18 older adults who were 60 and over. Our quantitative and qualitative results show that Synapse provided better support than the traditional video approach and enabled participants to feel more confident and motivated. Lastly, we present further design considerations to better support older adults with trial-and-error on smartphones.
A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges
Venue: arXiv cs.NI 2022
Year: 2022

Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements.
A Reference Model for Common Understanding of Capabilities and Skills in Manufacturing
Venue: arXiv cs.AI 2022
Year: 2022

In manufacturing, many use cases of Industry 4.0 require vendor-neutral and machine-readable information models to describe, implement and execute resource functions. Such models have been researched under the terms capabilities and skills. Standardization of such models is required, but currently not available. This paper presents a reference model developed jointly by members of various organizations in a working group of the Plattform Industrie 4.0. This model covers definitions of most important aspects of capabilities and skills. It can be seen as a basis for further standardization efforts.
The BUCEA Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2022
Venue: arXiv cs.SD 2022
Year: 2022

This paper describes the BUCEA speaker diarization system for the 2022 VoxCeleb Speaker Recognition Challenge. Voxsrc-22 provides the development set and test set of VoxConverse, and we mainly use the test set of VoxConverse for parameter adjustment. Our system consists of several modules, including speech activity detection (VAD), speaker embedding extractor, clustering methods, overlapping speech detection (OSD), and result fusion. Without considering overlap, the Dover-LAP (short for Diarization Output Voting Error Reduction) method was applied to system fusion, and overlapping speech detection and processing were finally carried out. Our best system achieves a diarization error rate (DER) of 5.48% and a Jaccard error rate (JER) of 32.1% on the VoxSRC 2022 evaluation set respectively.
Explainable Clustering via Exemplars: Complexity and Efficient Approximation Algorithms
Venue: arXiv cs.AI 2022
Year: 2022

Explainable AI (XAI) is an important developing area but remains relatively understudied for clustering. We propose an explainable-by-design clustering approach that not only finds clusters but also exemplars to explain each cluster. The use of exemplars for understanding is supported by the exemplar-based school of concept definition in psychology. We show that finding a small set of exemplars to explain even a single cluster is computationally intractable; hence, the overall problem is challenging. We develop an approximation algorithm that provides provable performance guarantees with respect to clustering quality as well as the number of exemplars used. This basic algorithm explains all the instances in every cluster whilst another approximation algorithm uses a bounded number of exemplars to allow simpler explanations and provably covers a large fraction of all the instances. Experimental results show that our work is useful in domains involving difficult to understand deep embeddings of images and text.
Encoding inductive invariants as barrier certificates: synthesis via difference-of-convex programming
Venue: arXiv cs.LO 2022
Year: 2022

A barrier certificate often serves as an inductive invariant that isolates an unsafe region from the reachable set of states, and hence is widely used in proving safety of hybrid systems possibly over an infinite time horizon. We present a novel condition on barrier certificates, termed the invariant barrier-certificate condition, that witnesses unbounded-time safety of differential dynamical systems. The proposed condition is the weakest possible one to attain inductive invariance. We show that discharging the invariant barrier-certificate condition -- thereby synthesizing invariant barrier certificates -- can be encoded as solving an optimization problem subject to bilinear matrix inequalities (BMIs). We further propose a synthesis algorithm based on difference-of-convex programming, which approaches a local optimum of the BMI problem via solving a series of convex optimization problems. This algorithm is incorporated in a branch-and-bound framework that searches for the global optimum in a divide-and-conquer fashion. We present a weak completeness result of our method, namely, a barrier certificate is guaranteed to be found (under some mild assumptions) whenever there exists an inductive invariant (in the form of a given template) that suffices to certify safety of the system. Experimental results on benchmarks demonstrate the effectiveness and efficiency of our approach.
Advanced Data Protection Control (ADPC): An Interdisciplinary Overview
Venue: arXiv cs.NI 2022
Year: 2022

The Advanced Data Protection Control (ADPC) is a technical specification - and a set of sociotechnical mechanisms surrounding it - that can change the current practice of Internet-based personal data protection and consenting by providing novel and standardized means for the communication of privacy and consenting data, meta-data, information, requests, preferences, and decisions. The ADPC supports humans in practicing their rights to privacy and agency by giving them more human-centric control over the processing of their personal data and consent. It helps the data controllers to improve their users' experiences and provides them with easy-to-adopt means to comply with the relevant legal and ethical requirements and expectations.
Storage Management with Multi-Version Partitioned B-Trees
Venue: arXiv cs.DB 2022
Year: 2022

Database Management Systems and K/V-Stores operate on updatable datasets -- massively exceeding the size of available main memory. Tree-based K/V storage management structures became particularly popular in storage engines. B+ Trees allow constant search performance, however write-heavy workloads yield in inefficient write patterns to secondary storage devices and poor performance characteristics. LSM-Trees overcome this issue by horizontal partitioning fractions of data - small enough to fully reside in main memory, but require frequent maintenance to sustain search performance. Firstly, we propose Multi-Version Partitioned BTrees (MV-PBT) as sole storage and index management structure in key-sorted storage engines like K/V-Stores. Secondly, we compare MV-PBT against LSM-Trees. The logical horizontal partitioning in MV-PBT allows leveraging recent advances in modern B$^+$-Tree techniques in a small transparent and memory resident portion of the structure. Structural properties sustain steady read performance, yielding efficient write patterns and reducing write amplification. We integrated MV-PBT in the WiredTiger KV storage engine. MV-PBT offers an up to 2x increased steady throughput in comparison to LSM-Trees and several orders of magnitude in comparison to B+ Trees in a YCSB workload.
Low latency communication over commercially available LTE and remote driving
Venue: arXiv cs.NI 2022
Year: 2022

In addition to autonomous car operation, in many cases it is desirable to let a human drive the vehicle remotely. To make remote operation possible, it is very critical to have a low and predictable latency to transmit video from the car cameras and receive control commands back. In this paper, we analyze the problem and present a communication and video streaming system that addresses the latency challenges and enables teleoperation of a real car over commercially available LTE network; demonstrating sub-50ms roundtrip latencies for 720p, 60FPS video, with average PSNR 36db.
Assisted Specification of Code Using Search
Venue: arXiv cs.SE 2022
Year: 2022

We describe an intelligent assistant based on mining existing software repositories to help the developer interactively create checkable specifications of code. To be most useful we apply this at the subsystem level, that is chunks of code of 1000-10000 lines that can be standalone or integrated into an existing application to provide additional functionality or capabilities. The resultant specifications include both a syntactic description of what should be written and a semantic specification of what it should do, initially in the form of test cases. The generated specification is designed to be used for automatic code generation using various technologies that have been proposed including machine learning, code search, and program synthesis. Our research goal is to enable these technologies to be used effectively for creating subsystems without requiring the developer to write detailed specifications from scratch.