OpenRaiser/Intern-Atlas huggingface dataset:
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Despite great progress achieved by transformer in various vision tasks, it is still underexplored for skeleton-based action recognition with only a few attempts. Besides, these methods directly calculate the pair-wise global self-attention equally for all the joints in both the spatial and temporal dimensions, undervaluing the effect of discriminative local joints and the short-range temporal dynamics. In this work, we propose a novel Focal and Global Spatial-Temporal Transformer network (FG-STFormer), that is equipped with two key components: (1) FG-SFormer: focal joints and global parts coupling spatial transformer. It forces the network to focus on modelling correlations for both the learned discriminative spatial joints and human body parts respectively. The selective focal joints eliminate the negative effect of non-informative ones during accumulating the correlations. Meanwhile, the interactions between the focal joints and body parts are incorporated to enhance the spatial dependencies via mutual cross-attention. (2) FG-TFormer: focal and global temporal transformer. Dilated temporal convolution is integrated into the global self-attention mechanism to explicitly capture the local temporal motion patterns of joints or body parts, which is found to be vital important to make temporal transformer work. Extensive experimental results on three benchmarks, namely NTU-60, NTU-120 and NW-UCLA, show our FG-STFormer surpasses all existing transformer-based methods, and compares favourably with state-of-the art GCN-based methods.
Exploiting NOMA and RIS in Integrated Sensing and Communication
Venue: arXiv cs.IT 2022
Year: 2022

A novel integrated sensing and communication (ISAC) system is proposed, where a dual-functional base station is utilized to transmit the superimposed non-orthogonal multiple access (NOMA) communication signal for serving communication users and sensing targets simultaneously. Furthermore, a new reconfigurable intelligent surface (RIS)-aided-sensing structure is also proposed to address the significant path loss or blockage of LoS links for the sensing task. Based on this setup, the beampattern gain at the RIS for the radar target is derived and adopted as a sensing metric. The objective of this paper is to maximize the minimum beampattern gain by jointly optimizing active beamforming, power allocation coefficients and passive beamforming. To tackle the non-convexity of the formulated optimization problem, the beampattern gain and constraints are first transformed into more tractable forms. Then, an iterative block coordinate descent (IBCD) algorithm is proposed by employing successive convex approximation (SCA), Schur complement, semidefinite relaxation (SDR) and sequential rank-one constraint relaxation (SRCR) methods. To reduce the complexity of the proposed IBCD algorithm, a low-complexity iterative alternating optimization (IAO) algorithm is proposed. Particularly, the active beamforming is optimized by solving a semidefinite programming (SDP) problem and the closed-form solutions of the power allocation coefficients are derived. Numerical results show that: i) the proposed RIS-NOMA-ISAC system always outperforms the RIS-ISAC system without NOMA in beampattern gain and illumination power; ii) the low-complexity IAO algorithm achieves a comparable performance to that achieved by the IBCD algorithm. iii) high beampattern gain can be achieved by the proposed joint optimization algorithms in underloaded and overloaded communication scenarios.
CLAD: A Contrastive Learning based Approach for Background Debiasing
Venue: arXiv cs.CV 2022
Year: 2022

Convolutional neural networks (CNNs) have achieved superhuman performance in multiple vision tasks, especially image classification. However, unlike humans, CNNs leverage spurious features, such as background information to make decisions. This tendency creates different problems in terms of robustness or weak generalization performance. Through our work, we introduce a contrastive learning-based approach (CLAD) to mitigate the background bias in CNNs. CLAD encourages semantic focus on object foregrounds and penalizes learning features from irrelavant backgrounds. Our method also introduces an efficient way of sampling negative samples. We achieve state-of-the-art results on the Background Challenge dataset, outperforming the previous benchmark with a margin of 4.1\%. Our paper shows how CLAD serves as a proof of concept for debiasing of spurious features, such as background and texture (in supplementary material).
Artificial virtuous agents in a multiagent tragedy of the commons
Venue: arXiv cs.AI 2022
Year: 2022

Although virtue ethics has repeatedly been proposed as a suitable framework for the development of artificial moral agents (AMAs), it has been proven difficult to approach from a computational perspective. In this work, we present the first technical implementation of artificial virtuous agents (AVAs) in moral simulations. First, we review previous conceptual and technical work in artificial virtue ethics and describe a functionalistic path to AVAs based on dispositional virtues, bottom-up learning, and top-down eudaimonic reward. We then provide the details of a technical implementation in a moral simulation based on a tragedy of the commons scenario. The experimental results show how the AVAs learn to tackle cooperation problems while exhibiting core features of their theoretical counterpart, including moral character, dispositional virtues, learning from experience, and the pursuit of eudaimonia. Ultimately, we argue that virtue ethics provides a compelling path toward morally excellent machines and that our work provides an important starting point for such endeavors.
KnowledgeShovel: An AI-in-the-Loop Document Annotation System for Scientific Knowledge Base Construction
Venue: arXiv cs.DL 2022
Year: 2022

Constructing a comprehensive, accurate, and useful scientific knowledge base is crucial for human researchers synthesizing scientific knowledge and for enabling Al-driven scientific discovery. However, the current process is difficult, error-prone, and laborious due to (1) the enormous amount of scientific literature available; (2) the highly-specialized scientific domains; (3) the diverse modalities of information (text, figure, table); and, (4) the silos of scientific knowledge in different publications with inconsistent formats and structures. Informed by a formative study and iterated with participatory design workshops, we designed and developed KnowledgeShovel, an Al-in-the-Loop document annotation system for researchers to construct scientific knowledge bases. The design of KnowledgeShovel introduces a multi-step multi-modal human-AI collaboration pipeline that aligns with users' existing workflows to improve data accuracy while reducing the human burden. A follow-up user evaluation with 7 geoscience researchers shows that KnowledgeShovel can enable efficient construction of scientific knowledge bases with satisfactory accuracy.
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet Detection
Venue: arXiv cs.CR 2022
Year: 2022

Botnet detectors based on machine learning are potential targets for adversarial evasion attacks. Several research works employ adversarial training with samples generated from generative adversarial nets (GANs) to make the botnet detectors adept at recognising adversarial evasions. However, the synthetic evasions may not follow the original semantics of the input samples. This paper proposes a novel GAN model leveraged with deep reinforcement learning (DRL) to explore semantic aware samples and simultaneously harden its detection. A DRL agent is used to attack the discriminator of the GAN that acts as a botnet detector. The discriminator is trained on the crafted perturbations by the agent during the GAN training, which helps the GAN generator converge earlier than the case without DRL. We name this model RELEVAGAN, i.e. ["relive a GAN" or deep REinforcement Learning-based Evasion Generative Adversarial Network] because, with the help of DRL, it minimises the GAN's job by letting its generator explore the evasion samples within the semantic limits. During the GAN training, the attacks are conducted to adjust the discriminator weights for learning crafted perturbations by the agent. RELEVAGAN does not require adversarial training for the ML classifiers since it can act as an adversarial semantic-aware botnet detection model. Code will be available at https://github.com/rhr407/RELEVAGAN.
DQC$^2$O: Distributed Quantum Computing for Collaborative Optimization in Future Networks
Venue: arXiv cs.DC 2022
Year: 2022

With the advantages of high-speed parallel processing, quantum computers can efficiently solve large-scale complex optimization problems in future networks. However, due to the uncertain qubit fidelity and quantum channel noise, distributed quantum computing which relies on quantum networks connected through entanglement faces a lot of challenges for exchanging information across quantum computers. In this paper, we propose an adaptive distributed quantum computing approach to manage quantum computers and quantum channels for solving optimization tasks in future networks. Firstly, we describe the fundamentals of quantum computing and its distributed concept in quantum networks. Secondly, to address the uncertainty of future demands of collaborative optimization tasks and instability over quantum networks, we propose a quantum resource allocation scheme based on stochastic programming for minimizing quantum resource consumption. Finally, based on the proposed approach, we discuss the potential applications for collaborative optimization in future networks, such as smart grid management, IoT cooperation, and UAV trajectory planning. Promising research directions that can lead to the design and implementation of future distributed quantum computing frameworks are also highlighted.
On Explaining Confounding Bias
Venue: arXiv cs.DB 2022
Year: 2022

When analyzing large datasets, analysts are often interested in the explanations for surprising or unexpected results produced by their queries. In this work, we focus on aggregate SQL queries that expose correlations in the data. A major challenge that hinders the interpretation of such queries is confounding bias, which can lead to an unexpected correlation. We generate explanations in terms of a set of confounding variables that explain the unexpected correlation observed in a query. We propose to mine candidate confounding variables from external sources since, in many real-life scenarios, the explanations are not solely contained in the input data. We present an efficient algorithm that finds the optimal subset of attributes (mined from external sources and the input dataset) that explain the unexpected correlation. This algorithm is embodied in a system called MESA. We demonstrate experimentally over multiple real-life datasets and through a user study that our approach generates insightful explanations, outperforming existing methods that search for explanations only in the input data. We further demonstrate the robustness of our system to missing data and the ability of MESA to handle input datasets containing millions of tuples and an extensive search space of candidate confounding attributes.
GNSS/MEMS-INS Integration for Drone Navigation using EKF on Lie Groups
Venue: arXiv cs.RO 2022
Year: 2022

Building upon the theory of Kalman Filtering on Lie Groups, this paper describes an Extended Kalman Filter and Smoother for Loosely Coupled Integration of GNSS/INS tailored for post-processing applications. The approach employs a dynamic model on a matrix Lie Group that aggregates position, velocity, attitude, and the IMU biases as a single element of a Lie group. The development was motivated by a drone-borne Differential Interferometric SAR (DinSAR) application, which requires high-precision navigation information for short-flight missions using low-cost MEMS sensors. The filter and the Rauch-Tung-Striebel (RTS) smoother are both implemented and validated. The paper also presents a novel algorithm to initialize the heading value as an alternative to gyro-compassing or magnetometer-based alignments. The Mahalanobis Distance and the $χ^2$-test are employed during the filter update step to address the practical issue of outlier rejection for the GNSS measurements. The paper uses synthetic data to compare classic navigation schemes based on multiplicative quaternions and Euler angles. Finally, real data experiments demonstrate that the Kalman Filter based on Lie Groups performs better DinSAR processing than state-of-the-art commercial software.
What Do I Need to Design for Co-Design? Supporting Co-design as a Designerly Practice
Venue: arXiv cs.HC 2022
Year: 2022

Co-design practices have been used for decades to support participatory engagement in design work. However, despite a wide range of materials that describe the design and commitments of numerous co-design experiences, few descriptions of the knowledge that guides designers when creating these experiences exist. Thus, we ask: What kind of knowledge do designers need to design co-design experiences? What form(s) could intermediate-level knowledge for co-design take? To answer these questions, we adopted a co/auto-ethnographic and Research-through-Design approach to reflexively engage with our design decisions, outcomes, and challenges related to two virtual co-design workshops. We constructed a set of four multi-dimensional facets(Rhythms of Engagement, Material Engagement, Ludic Engagement, and Conceptual Achievement) and three roles (designer, researcher, facilitator) to consider when creating co-design experiences. We illustrate these facets and roles through examples, building new \textit{intermediate-level knowledge} to support future co-design research and design, framing co-design as a designerly practice.
Practitioner Trajectories of Engagement with Ethics-Focused Method Creation
Venue: arXiv cs.HC 2022
Year: 2022

Design and technology practitioners are increasingly aware of the ethical impact of their work practices, desiring tools to support their ethical awareness across a range of contexts. In this paper, we report on findings from a series of co-design workshops with technology and design practitioners that supported their creation of a bespoke ethics-focused action plan. Using a qualitative content analysis and thematic analysis approach, we identified a range of roles and process moves that practitioners employed and illustrate the interplay of these elements of practitioners' instrumental judgment through a series of three cases, which includes evolution of the action plan itself, the ethical dilemmas or areas of support the action plan was intended to support, and how the action plan represents resonance for the practitioner that created it. We conclude with implications for supporting ethics in socio-technical practice and opportunities for the further development of ethics-focused methods that are resonant with the realities of practice.
Comparative Analysis and Calibration of Low Cost Resistive and Capacitive Soil Moisture Sensor
Venue: arXiv cs.NI 2022
Year: 2022

Soil moisture is an essential parameter in agriculture. It determines several environmental and agricultural activities such as climate change, drought prediction, irrigation, etc. Smart irrigation management requires continuous soil moisture monitoring to reduce unnecessary water usage. In recent times, the use of low-cost sensors is becoming popular among farmers for soil moisture monitoring. In this paper, a comparison of low-cost resistive and capacitive soil moisture sensors is demonstrated in two ways. One way is the calibration of the sensors in gravimetric and volumetric water content, and the other is the sensors' response analysis when different quantities of water are added to the same amount of soil. The analysis shown in this work is essential before choosing cost-effective sensors for any soil moisture monitoring platform.
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!
Venue: arXiv cs.CV 2022
Year: 2022

We introduce a formalization and benchmark for the unsupervised anomaly detection task in the distribution-shift scenario. Our work builds upon the iWildCam dataset, and, to the best of our knowledge, we are the first to propose such an approach for visual data. We empirically validate that environment-aware methods perform better in such cases when compared with the basic Empirical Risk Minimization (ERM). We next propose an extension for generating positive samples for contrastive methods that considers the environment labels when training, improving the ERM baseline score by 8.7%.
Gastrointestinal Disorder Detection with a Transformer Based Approach
Venue: arXiv cs.CV 2022
Year: 2022

Accurate disease categorization using endoscopic images is a significant problem in Gastroenterology. This paper describes a technique for assisting medical diagnosis procedures and identifying gastrointestinal tract disorders based on the categorization of characteristics taken from endoscopic pictures using a vision transformer and transfer learning model. Vision transformer has shown very promising results on difficult image classification tasks. In this paper, we have suggested a vision transformer based approach to detect gastrointestianl diseases from wireless capsule endoscopy (WCE) curated images of colon with an accuracy of 95.63\%. We have compared this transformer based approach with pretrained convolutional neural network (CNN) model DenseNet201 and demonstrated that vision transformer surpassed DenseNet201 in various quantitative performance evaluation metrics.
CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration
Venue: arXiv cs.RO 2022
Year: 2022

Robot grasping is an actively studied area in robotics, mainly focusing on the quality of generated grasps for object manipulation. However, despite advancements, these methods do not consider the human-robot collaboration settings where robots and humans will have to grasp the same objects concurrently. Therefore, generating robot grasps compatible with human preferences of simultaneously holding an object becomes necessary to ensure a safe and natural collaboration experience. In this paper, we propose a novel, deep neural network-based method called CoGrasp that generates human-aware robot grasps by contextualizing human preference models of object grasping into the robot grasp selection process. We validate our approach against existing state-of-the-art robot grasping methods through simulated and real-robot experiments and user studies. In real robot experiments, our method achieves about 88\% success rate in producing stable grasps that also allow humans to interact and grasp objects simultaneously in a socially compliant manner. Furthermore, our user study with 10 independent participants indicated our approach enables a safe, natural, and socially-aware human-robot objects' co-grasping experience compared to a standard robot grasping technique.
Beyond the shortest path: the path length index as a distribution
Venue: arXiv cs.DM 2022
Year: 2022

The traditional complex network approach considers only the shortest paths from one node to another, not taking into account several other possible paths. This limitation is significant, for example, in urban mobility studies. In this short report, as the first steps, we present an exhaustive approach to address that problem and show we can go beyond the shortest path, but we do not need to go so far: we present an interactive procedure and an early stop possibility. After presenting some fundamental concepts in graph theory, we presented an analytical solution for the problem of counting the number of possible paths between two nodes in complete graphs, and a depth-limited approach to get all possible paths between each pair of nodes in a general graph (an NP-hard problem). We do not collapse the distribution of path lengths between a pair of nodes into a scalar number, we look at the distribution itself - taking all paths up to a pre-defined path length (considering a truncated distribution), and show the impact of that approach on the most straightforward distance-based graph index: the walk/path length.
HealthE: Classifying Entities in Online Textual Health Advice
Venue: arXiv cs.CL 2022
Year: 2022

The processing of entities in natural language is essential to many medical NLP systems. Unfortunately, existing datasets vastly under-represent the entities required to model public health relevant texts such as health advice often found on sites like WebMD. People rely on such information for personal health management and clinically relevant decision making. In this work, we release a new annotated dataset, HealthE, consisting of 6,756 health advice. HealthE has a more granular label space compared to existing medical NER corpora and contains annotation for diverse health phrases. Additionally, we introduce a new health entity classification model, EP S-BERT, which leverages textual context patterns in the classification of entity classes. EP S-BERT provides a 4-point increase in F1 score over the nearest baseline and a 34-point increase in F1 when compared to off-the-shelf medical NER tools trained to extract disease and medication mentions from clinical texts. All code and data are publicly available on Github.
Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation
Venue: arXiv cs.CL 2022
Year: 2022

Negation is poorly captured by current language models, although the extent of this problem is not widely understood. We introduce a natural language inference (NLI) test suite to enable probing the capabilities of NLP methods, with the aim of understanding sub-clausal negation. The test suite contains premise--hypothesis pairs where the premise contains sub-clausal negation and the hypothesis is constructed by making minimal modifications to the premise in order to reflect different possible interpretations. Aside from adopting standard NLI labels, our test suite is systematically constructed under a rigorous linguistic framework. It includes annotation of negation types and constructions grounded in linguistic theory, as well as the operations used to construct hypotheses. This facilitates fine-grained analysis of model performance. We conduct experiments using pre-trained language models to demonstrate that our test suite is more challenging than existing benchmarks focused on negation, and show how our annotation supports a deeper understanding of the current NLI capabilities in terms of negation and quantification.
Considerations for Task Allocation in Human-Robot Teams
Venue: arXiv cs.RO 2022
Year: 2022

In human-robot teams where agents collaborate together, there needs to be a clear allocation of tasks to agents. Task allocation can aid in achieving the presumed benefits of human-robot teams, such as improved team performance. Many task allocation methods have been proposed that include factors such as agent capability, availability, workload, fatigue, and task and domain-specific parameters. In this paper, selected work on task allocation is reviewed. In addition, some areas for continued and further consideration in task allocation are discussed. These areas include level of collaboration, novel tasks, unknown and dynamic agent capabilities, negotiation and fairness, and ethics. Where applicable, we also mention some of our work on task allocation. Through continued efforts and considerations in task allocation, human-robot teaming can be improved.