OpenRaiser/Intern-Atlas huggingface dataset:
Combining Vision and Tactile Sensation for Video Prediction
Venue: arXiv cs.RO 2023
Year: 2023

In this paper, we explore the impact of adding tactile sensation to video prediction models for physical robot interactions. Predicting the impact of robotic actions on the environment is a fundamental challenge in robotics. Current methods leverage visual and robot action data to generate video predictions over a given time period, which can then be used to adjust robot actions. However, humans rely on both visual and tactile feedback to develop and maintain a mental model of their physical surroundings. In this paper, we investigate the impact of integrating tactile feedback into video prediction models for physical robot interactions. We propose three multi-modal integration approaches and compare the performance of these tactile-enhanced video prediction models. Additionally, we introduce two new datasets of robot pushing that use a magnetic-based tactile sensor for unsupervised learning. The first dataset contains visually identical objects with different physical properties, while the second dataset mimics existing robot-pushing datasets of household object clusters. Our results demonstrate that incorporating tactile feedback into video prediction models improves scene prediction accuracy and enhances the agent's perception of physical interactions and understanding of cause-effect relationships during physical robot interactions.
EdgeRIC: Empowering Realtime Intelligent Optimization and Control in NextG Networks
Venue: arXiv cs.NI 2023
Year: 2023

Radio Access Networks (RAN) are increasingly softwarized and accessible via data-collection and control interfaces. RAN intelligent control (RIC) is an approach to manage these interfaces at different timescales. In this paper, we develop a RIC platform called RICworld, consisting of (i) EdgeRIC, which is colocated, but decoupled from the RAN stack, and can access RAN and application-level information to execute AI-optimized and other policies in realtime (sub-millisecond) and (ii) DigitalTwin, a full-stack, trace-driven emulator for training AI-based policies offline. We demonstrate that realtime EdgeRIC operates as if embedded within the RAN stack and significantly outperforms a cloud-based near-realtime RIC (> 15 ms latency) in terms of attained throughput. We train AI-based polices on DigitalTwin, execute them on EdgeRIC, and show that these policies are robust to channel dynamics, and outperform queueing-model based policies by 5% to 25% on throughput and application-level benchmarks in a variety of mobile environments.
ChatGPT: More than a Weapon of Mass Deception, Ethical challenges and responses from the Human-Centered Artificial Intelligence (HCAI) perspective
Venue: arXiv cs.CY 2023
Year: 2023

This article explores the ethical problems arising from the use of ChatGPT as a kind of generative AI and suggests responses based on the Human-Centered Artificial Intelligence (HCAI) framework. The HCAI framework is appropriate because it understands technology above all as a tool to empower, augment, and enhance human agency while referring to human wellbeing as a grand challenge, thus perfectly aligning itself with ethics, the science of human flourishing. Further, HCAI provides objectives, principles, procedures, and structures for reliable, safe, and trustworthy AI which we apply to our ChatGPT assessments. The main danger ChatGPT presents is the propensity to be used as a weapon of mass deception (WMD) and an enabler of criminal activities involving deceit. We review technical specifications to better comprehend its potentials and limitations. We then suggest both technical (watermarking, styleme, detectors, and fact-checkers) and non-technical measures (terms of use, transparency, educator considerations, HITL) to mitigate ChatGPT misuse or abuse and recommend best uses (creative writing, non-creative writing, teaching and learning). We conclude with considerations regarding the role of humans in ensuring the proper use of ChatGPT for individual and social wellbeing.
Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK
Venue: arXiv cs.CY 2023
Year: 2023

Public attention towards explainability of artificial intelligence (AI) systems has been rising in recent years to offer methodologies for human oversight. This has translated into the proliferation of research outputs, such as from Explainable AI, to enhance transparency and control for system debugging and monitoring, and intelligibility of system process and output for user services. Yet, such outputs are difficult to adopt on a practical level due to a lack of a common regulatory baseline, and the contextual nature of explanations. Governmental policies are now attempting to tackle such exigence, however it remains unclear to what extent published communications, regulations, and standards adopt an informed perspective to support research, industry, and civil interests. In this study, we perform the first thematic and gap analysis of this plethora of policies and standards on explainability in the EU, US, and UK. Through a rigorous survey of policy documents, we first contribute an overview of governmental regulatory trajectories within AI explainability and its sociotechnical impacts. We find that policies are often informed by coarse notions and requirements for explanations. This might be due to the willingness to conciliate explanations foremost as a risk management tool for AI oversight, but also due to the lack of a consensus on what constitutes a valid algorithmic explanation, and how feasible the implementation and deployment of such explanations are across stakeholders of an organization. Informed by AI explainability research, we conduct a gap analysis of existing policies, leading us to formulate a set of recommendations on how to address explainability in regulations for AI systems, especially discussing the definition, feasibility, and usability of explanations, as well as allocating accountability to explanation providers.
LightningSim: Fast and Accurate Trace-Based Simulation for High-Level Synthesis
Venue: arXiv cs.PF 2023
Year: 2023

High-Level Synthesis allows hardware designers to create complex RTL designs using C/C++. The traditional HLS workflow involves iterations of C/C++ simulation for partial functional verification and HLS synthesis for coarse timing estimates. However, neither C/C++ simulation nor HLS synthesis estimates can account for complex behaviors like FIFO interactions and pipeline stalls, thereby obscuring problems like deadlocks and latency overheads. Such problems are revealed only through C/RTL co-simulation, which is typically orders of magnitude slower than either C/C++ simulation or HLS synthesis, far too slow to integrate into the edit-run development cycle. Addressing this, we propose LightningSim, a fast simulation tool for HLS that combines the speed of native C/C++ with the accuracy of C/RTL co-simulation. LightningSim directly operates on the LLVM intermediate representation (IR) code and accurately simulates a hardware design's dynamic behavior. First, it traces LLVM IR execution to capture the run-time information; second, it maps the static HLS scheduling information to the trace to simulate the dynamic behavior; third, it calculates stalls and deadlocks from inter-function interactions to get precise cycle counts. Evaluated on 33 benchmarks, LightningSim produces 99.9%-accurate timing estimates up to 95x faster than RTL simulation. Our code is publicly available on GitHub.
Stochastic MPC Based Attacks on Object Tracking in Autonomous Driving Systems
Venue: arXiv cs.RO 2023
Year: 2023

Decision making in advanced driver assistance systems involves in general the estimated trajectories of the surrounding objects. Multiple object tracking refers to the process of estimating in real time these trajectories, leveraging for this purpose sensors to detect the objects. This paper deals with devising attacks on object tracking in automated vehicles. The vehicle is assumed to have a detection-based object tracking system that relies on multiple sensors and uses an estimator such as a Kalman filter for sensor fusion and state estimation. The attack goal is to modify the object's state estimated by the victim vehicle to put the vehicle in an unsafe situation. This goal is achieved by judiciously perturbing some or all of the sensor outputs corresponding to the object of interest over a desired horizon. A stochastic model predictive control (SMPC) problem is formulated to compute the sequence of perturbations, whereby hard constraints on the perturbations and probabilistic chance constraints on the object's state are imposed. The chance constraints ensure that some desired conditions for a successful attack are satisfied with a prespecified probability. Reasonable assumptions are then made to obtain a computationally tractable linear SMPC program. The approach is demonstrated on an adaptive cruise control system in a simulation environment, where successful sequential attacks are generated, leading the victim vehicle into dangerous driving situations including collisions.
Euclidean Capacitated Vehicle Routing in Random Setting: A $1.55$-Approximation Algorithm
Venue: arXiv cs.DS 2023
Year: 2023

We study the unit-demand capacitated vehicle routing problem in the random setting of the Euclidean plane. The objective is to visit $n$ random terminals in a square using a set of tours of minimum total length, such that each tour visits the depot and at most $k$ terminals. We design an elegant algorithm combining the classical sweep heuristic and Arora's framework for the Euclidean traveling salesman problem [Journal of the ACM 1998]. We show that our algorithm is a polynomial-time approximation of ratio at most $1.55$ asymptotically almost surely. This improves on previous approximation ratios of $1.995$ due to Bompadre, Dror, and Orlin [Journal of Applied Probability 2007] and $1.915$ due to Mathieu and Zhou [Random Structures and Algorithms 2022]. In addition, we conjecture that, for any $\varepsilon>0$, our algorithm is a $(1+\varepsilon)$-approximation asymptotically almost surely.
Dis/Immersion in Mindfulness Meditation with a Wandering Voice Assistant
Venue: arXiv cs.HC 2023
Year: 2023

Mindfulness meditation is a validated means of helping people manage stress. Voice-based virtual assistants (VAs) in smart speakers, smartphones, and smart environments can assist people in carrying out mindfulness meditation through guided experiences. However, the common fixed location embodiment of VAs makes it difficult to provide intuitive support. In this work, we explored the novel embodiment of a "wandering voice" that is co-located with the user and "moves" with the task. We developed a multi-speaker VA embedded in a yoga mat that changes location along the body according to the meditation experience. We conducted a qualitative user study in two sessions, comparing a typical fixed smart speaker to the wandering VA embodiment. Thick descriptions from interviews with twelve people revealed sometimes simultaneous experiences of immersion and dis-immersion. We offer design implications for "wandering voices" and a new paradigm for VA embodiment that may extend to guidance tasks in other contexts.
Nonverbal Cues in Human-Robot Interaction: A Communication Studies Perspective
Venue: arXiv cs.RO 2023
Year: 2023

Communication between people is characterized by a broad range of nonverbal cues. Transferring these cues into the design of robots and other artificial agents that interact with people may foster more natural, inviting, and accessible experiences. In this position paper, we offer a series of definitive nonverbal codes for human-robot interaction (HRI) that address the five human sensory systems (visual, auditory, haptic, olfactory, gustatory) drawn from the field of communication studies. We discuss how these codes can be translated into design patterns for HRI using a curated sample of the communication studies and HRI literatures. As nonverbal codes are an essential mode in human communication, we argue that integrating robotic nonverbal codes in HRI will afford robots a feeling of "aliveness" or "social agency" that would otherwise be missing. We end with suggestions for research directions to stimulate work on nonverbal communication within the field of HRI and improve communication between human and robots.
BiTrackGAN: Cascaded CycleGANs to Constraint Face Aging
Venue: arXiv cs.CV 2023
Year: 2023

With the increased accuracy of modern computer vision technology, many access control systems are equipped with face recognition functions for faster identification. In order to maintain high recognition accuracy, it is necessary to keep the face database up-to-date. However, it is impractical to collect the latest facial picture of the system's user through human effort. Thus, we propose a bottom-up training method for our proposed network to address this challenge. Essentially, our proposed network is a translation pipeline that cascades two CycleGAN blocks (a widely used unpaired image-to-image translation generative adversarial network) called BiTrackGAN. By bottom-up training, it induces an ideal intermediate state between these two CycleGAN blocks, namely the constraint mechanism. Experimental results show that BiTrackGAN achieves more reasonable and diverse cross-age facial synthesis than other CycleGAN-related methods. As far as we know, it is a novel and effective constraint mechanism for more reason and accurate aging synthesis through the CycleGAN approach.
Self-supervised Learning by View Synthesis
Venue: arXiv cs.CV 2023
Year: 2023

We present view-synthesis autoencoders (VSA) in this paper, which is a self-supervised learning framework designed for vision transformers. Different from traditional 2D pretraining methods, VSA can be pre-trained with multi-view data. In each iteration, the input to VSA is one view (or multiple views) of a 3D object and the output is a synthesized image in another target pose. The decoder of VSA has several cross-attention blocks, which use the source view as value, source pose as key, and target pose as query. They achieve cross-attention to synthesize the target view. This simple approach realizes large-angle view synthesis and learns spatial invariant representation, where the latter is decent initialization for transformers on downstream tasks, such as 3D classification on ModelNet40, ShapeNet Core55, and ScanObjectNN. VSA outperforms existing methods significantly for linear probing and is competitive for fine-tuning. The code will be made publicly available.
Semantic Specialization for Knowledge-based Word Sense Disambiguation
Venue: arXiv cs.CL 2023
Year: 2023

A promising approach for knowledge-based Word Sense Disambiguation (WSD) is to select the sense whose contextualized embeddings computed for its definition sentence are closest to those computed for a target word in a given sentence. This approach relies on the similarity of the \textit{sense} and \textit{context} embeddings computed by a pre-trained language model. We propose a semantic specialization for WSD where contextualized embeddings are adapted to the WSD task using solely lexical knowledge. The key idea is, for a given sense, to bring semantically related senses and contexts closer and send different/unrelated senses farther away. We realize this idea as the joint optimization of the Attract-Repel objective for sense pairs and the self-training objective for context-sense pairs while controlling deviations from the original embeddings. The proposed method outperformed previous studies that adapt contextualized embeddings. It achieved state-of-the-art performance on knowledge-based WSD when combined with the reranking heuristic that uses the sense inventory. We found that the similarity characteristics of specialized embeddings conform to the key idea. We also found that the (dis)similarity of embeddings between the related/different/unrelated senses correlates well with the performance of WSD.
Towards Carbon-Neutral Edge Computing: Greening Edge AI by Harnessing Spot and Future Carbon Markets
Venue: arXiv cs.LG 2023
Year: 2023

Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among accuracy loss, carbon emission, and unknown future costs. Besides, many governments are launching carbon emission rights (CER) for operators to reduce carbon emissions further to reverse climate change. Facing these challenges, to achieve carbon-aware ML task offloading under limited carbon emission rights thus to achieve green edge AI, we establish a joint ML task offloading and CER purchasing problem, intending to minimize the accuracy loss under the long-term time-averaged cost budget of purchasing the required CER. However, considering the uncertainty of the resource prices, the CER purchasing prices, the carbon intensity of sites, and ML tasks' arrivals, it is hard to decide the optimal policy online over a long-running period time. To overcome this difficulty, we leverage the two-timescale Lyapunov optimization technique, of which the $T$-slot drift-plus-penalty methodology inspires us to propose an online algorithm that purchases CER in multiple timescales (on-preserved in carbon future market and on-demanded in the carbon spot market) and makes decisions about where to offload ML tasks. Considering the NP-hardness of the $T$-slot problems, we further propose the resource-restricted randomized dependent rounding algorithm to help to gain the near-optimal solution with no help of any future information. Our theoretical analysis and extensive simulation results driven by the real carbon intensity trace show the superior performance of the proposed algorithms.
WiThRay: A Versatile Ray-Tracing Simulator for Smart Wireless Environments
Venue: arXiv cs.IT 2023
Year: 2023

This paper presents the development and evaluation of WiThRay, a new wireless three-dimensional ray-tracing (RT) simulator. RT-based simulators are widely used for generating realistic channel data by combining RT methodology to get signal trajectories and electromagnetic (EM) equations, resulting in generalized channel impulse responses (CIRs). This paper first provides a comprehensive comparison on methodologies of existing RT-based simulators. We then introduce WiThRay, which can evaluate the performance of various wireless communication techniques such as channel estimation/tracking, beamforming, and localization in realistic EM wave propagation. WiThRay implements its own RT methodology, the bypassing on edge (BE) algorithm, that follows the Fermat's principle and has low computational complexity. The scattering ray calibration in WiThRay also provides a precise solution in the analysis of EM propagation. Different from most of the previous RT-based simulators, WiThRay incorporates reconfigurable intelligent surfaces (RIS), which will be a key component of future wireless communications. We thoroughly show that the channel data from WiThRay match sufficiently well with the fundamental theory of wireless channels. The virtue of WiThRay lies in its feature of not making any assumption about the channel, like being slow/fast fading or frequency selective. A realistic wireless environment, which can be conveniently simulated via WiThRay, naturally defines the physical properties of the wireless channels. WiThRay is open to the public, and anyone can exploit this versatile simulator to develop and test their communications and signal processing techniques.
Lightweight Toxicity Detection in Spoken Language: A Transformer-based Approach for Edge Devices
Venue: arXiv cs.SD 2023
Year: 2023

Toxicity is a prevalent social behavior that involves the use of hate speech, offensive language, bullying, and abusive speech. While text-based approaches for toxicity detection are common, there is limited research on processing speech signals in the physical world. Detecting toxicity in the physical world is challenging due to the difficulty of integrating AI-capable computers into the environment. We propose a lightweight transformer model based on wav2vec2.0 and optimize it using techniques such as quantization and knowledge distillation. Our model uses multitask learning and achieves an average macro F1-score of 90.3\% and a weighted accuracy of 88\%, outperforming state-of-the-art methods on DeToxy-B and a public dataset. Our results show that quantization reduces the model size by almost 4 times and RAM usage by 3.3\%, with only a 1\% F1 score decrease. Knowledge distillation reduces the model size by 3.7 times, RAM usage by 1.9, and inference time by 2 times, but decreases accuracy by 8\%. Combining both techniques reduces the model size by 14.6 times and RAM usage by around 4.3 times, with a two-fold inference time improvement. Our compact model is the first end-to-end speech-based toxicity detection model based on a lightweight transformer model suitable for deployment in physical spaces. The results show its feasibility for toxicity detection on edge devices in real-world environments.
A bounded rationality account of dependency length minimization in Hindi
Venue: arXiv cs.CL 2023
Year: 2023

The principle of DEPENDENCY LENGTH MINIMIZATION, which seeks to keep syntactically related words close in a sentence, is thought to universally shape the structure of human languages for effective communication. However, the extent to which dependency length minimization is applied in human language systems is not yet fully understood. Preverbally, the placement of long-before-short constituents and postverbally, short-before-long constituents are known to minimize overall dependency length of a sentence. In this study, we test the hypothesis that placing only the shortest preverbal constituent next to the main-verb explains word order preferences in Hindi (a SOV language) as opposed to the global minimization of dependency length. We characterize this approach as a least-effort strategy because it is a cost-effective way to shorten all dependencies between the verb and its preverbal dependencies. As such, this approach is consistent with the bounded-rationality perspective according to which decision making is governed by "fast but frugal" heuristics rather than by a search for optimal solutions. Consistent with this idea, our results indicate that actual corpus sentences in the Hindi-Urdu Treebank corpus are better explained by the least effort strategy than by global minimization of dependency lengths. Additionally, for the task of distinguishing corpus sentences from counterfactual variants, we find that the dependency length and constituent length of the constituent closest to the main verb are much better predictors of whether a sentence appeared in the corpus than total dependency length. Overall, our findings suggest that cognitive resource constraints play a crucial role in shaping natural languages.
Read-once machines and the thermodynamic complexity of Maxwell's demons
Venue: arXiv cs.IT 2023
Year: 2023

The thermodynamical costs imposed by computational resource limitations like memory and time have been investigated before. We focus on a new computational limitation, namely, the machine being allowed to scan the input only once, and prove that it is associated with unavoidable thermodynamical cost, even in the presence of infinite time and memory resources. We identify this limitation to be the one suffered by Maxwell's demons. This provides a framework for quantifying the complexity associated with an experiment that effectuates a "decrease" in the entropy of a thermodynamic system.