OpenRaiser/Intern-Atlas huggingface dataset:
Improving Real-time Communication for Educational Metaverse by Alternative WebRTC SFU and Delegating Transmission of Avatar Transform
Venue: arXiv cs.HC 2023
Year: 2023

Maintaining real-time communication quality in metaverse has always been a challenge, especially when the number of participants increase. We introduce a proprietary WebRTC SFU service to an open-source web-based VR platform, to realize a more stable and reliable platform suitable for educational communication of audio, video, and avatar transform. We developed the web-based VR platform and conducted a preliminary validation on the implementation for proof of concept, and high performance in both server and client sides are confirmed, which may indicates better user experience in communication and imply a solution to realize educational metaverse.
Online Learning for the Random Feature Model in the Student-Teacher Framework
Venue: arXiv cs.LG 2023
Year: 2023

Deep neural networks are widely used prediction algorithms whose performance often improves as the number of weights increases, leading to over-parametrization. We consider a two-layered neural network whose first layer is frozen while the last layer is trainable, known as the random feature model. We study over-parametrization in the context of a student-teacher framework by deriving a set of differential equations for the learning dynamics. For any finite ratio of hidden layer size and input dimension, the student cannot generalize perfectly, and we compute the non-zero asymptotic generalization error. Only when the student's hidden layer size is exponentially larger than the input dimension, an approach to perfect generalization is possible.
Differentially Private Synthetic Control
Venue: arXiv cs.LG 2023
Year: 2023

Synthetic control is a causal inference tool used to estimate the treatment effects of an intervention by creating synthetic counterfactual data. This approach combines measurements from other similar observations (i.e., donor pool ) to predict a counterfactual time series of interest (i.e., target unit) by analyzing the relationship between the target and the donor pool before the intervention. As synthetic control tools are increasingly applied to sensitive or proprietary data, formal privacy protections are often required. In this work, we provide the first algorithms for differentially private synthetic control with explicit error bounds. Our approach builds upon tools from non-private synthetic control and differentially private empirical risk minimization. We provide upper and lower bounds on the sensitivity of the synthetic control query and provide explicit error bounds on the accuracy of our private synthetic control algorithms. We show that our algorithms produce accurate predictions for the target unit, and that the cost of privacy is small. Finally, we empirically evaluate the performance of our algorithm, and show favorable performance in a variety of parameter regimes, as well as providing guidance to practitioners for hyperparameter tuning.
Experiential Futures In-the-wild to Inform Policy Design
Venue: arXiv cs.HC 2023
Year: 2023

As technological innovation continues to shape our world at an accelerating pace, policy makers struggle to keep up with the unintended consequences of these new technologies. To address this policy-novelty gap, Responsible Research Innovation (RRI) has been proposed as a way to drive science and technology innovation towards socially desirable goals. This work suggests a more active HCI's position in the materialisation of pluralistic future visions and emphasizes the engagement between policy design and HCI for more agile and responsive evaluation environments. It calls for both fields to engage in questioning which and how futures are constructed, who they are benefiting, and how the findings of these interventions are interpreted towards other futures.
Optimal Smoothing Distribution Exploration for Backdoor Neutralization in Deep Learning-based Traffic Systems
Venue: arXiv cs.LG 2023
Year: 2023

Deep Reinforcement Learning (DRL) enhances the efficiency of Autonomous Vehicles (AV), but also makes them susceptible to backdoor attacks that can result in traffic congestion or collisions. Backdoor functionality is typically incorporated by contaminating training datasets with covert malicious data to maintain high precision on genuine inputs while inducing the desired (malicious) outputs for specific inputs chosen by adversaries. Current defenses against backdoors mainly focus on image classification using image-based features, which cannot be readily transferred to the regression task of DRL-based AV controllers since the inputs are continuous sensor data, i.e., the combinations of velocity and distance of AV and its surrounding vehicles. Our proposed method adds well-designed noise to the input to neutralize backdoors. The approach involves learning an optimal smoothing (noise) distribution to preserve the normal functionality of genuine inputs while neutralizing backdoors. By doing so, the resulting model is expected to be more resilient against backdoor attacks while maintaining high accuracy on genuine inputs. The effectiveness of the proposed method is verified on a simulated traffic system based on a microscopic traffic simulator, where experimental results showcase that the smoothed traffic controller can neutralize all trigger samples and maintain the performance of relieving traffic congestion
Feature Space Sketching for Logistic Regression
Venue: arXiv cs.LG 2023
Year: 2023

We present novel bounds for coreset construction, feature selection, and dimensionality reduction for logistic regression. All three approaches can be thought of as sketching the logistic regression inputs. On the coreset construction front, we resolve open problems from prior work and present novel bounds for the complexity of coreset construction methods. On the feature selection and dimensionality reduction front, we initiate the study of forward error bounds for logistic regression. Our bounds are tight up to constant factors and our forward error bounds can be extended to Generalized Linear Models.
Fast, Accurate, but Sometimes Too-Compelling Support: The Impact of Imperfectly Automated Cues in an Augmented Reality Head-Mounted Display on Visual Search Performance
Venue: arXiv cs.HC 2023
Year: 2023

While visual search for targets within a complex scene might benefit from using augmented-reality (AR) head-mounted display (HMD) technologies helping to efficiently direct human attention, imperfectly reliable automation support could manifest in occasional errors. The current study examined the effectiveness of different HMD cues that might support visual search performance and their respective consequences following automation errors. Fifty-six participants searched a 3D environment containing 48 objects in a room, in order to locate a target object that was viewed prior to each trial. They searched either unaided or assisted by one of three HMD types of cues: an arrow pointing to the target, a plan-view minimap highlighting the target, and a constantly visible icon depicting the appearance of the target object. The cue was incorrect on 17% of the trials for one group of participants and 100% correct for the second group. Through both analysis and modeling of both search speed and accuracy, the results indicated that the arrow and minimap cues depicting location information were more effective than the icon cue depicting visual appearance, both overall, and when the cue was correct. However, there was a tradeoff on the infrequent occasions when the cue erred. The most effective AR-based cue led to a greater automation bias, in which the cue was more often blindly followed without careful examination of the raw images. The results speak to the benefits of augmented reality and the need to examine potential costs when AR-conveyed information may be incorrect because of imperfectly reliable systems.
The WhyRel Prototype for Relational Verification
Venue: arXiv cs.PL 2023
Year: 2023

Verifying relations between programs arises as a task in various verification contexts such as optimizing transformations, relating new versions of programs with older versions (regression verification), and noninterference. However, relational verification for programs acting on dynamically allocated mutable state is not well supported by existing tools, which provide a high level of automation at the cost of restricting the programs considered. Auto-active tools, on the other hand, require more user interaction but enable verification of a broader class of programs. This article presents WhyRel, a tool for the auto-active verification of relational properties of pointer programs based on relational region logic. WhyRel is evaluated through verification case studies, relying on SMT solvers orchestrated by the Why3 platform on which it builds. Case studies include establishing representation independence of ADTs, showing noninterference, and challenge problems from recent literature.
Exact Short Products From Truncated Multipliers
Venue: arXiv cs.DS 2023
Year: 2023

We sometimes need to compute the most significant digits of the product of small integers with a multiplier requiring much storage: e.g., a large integer (e.g., $5^{100}$) or an irrational number ($π$). We only need to access the most significant digits of the multiplier-as long as the integers are sufficiently small. We provide an efficient algorithm to compute the range of integers given a truncated multiplier and a desired number of digits.
Backdoor Attacks with Input-unique Triggers in NLP
Venue: arXiv cs.CL 2023
Year: 2023

Backdoor attack aims at inducing neural models to make incorrect predictions for poison data while keeping predictions on the clean dataset unchanged, which creates a considerable threat to current natural language processing (NLP) systems. Existing backdoor attacking systems face two severe issues:firstly, most backdoor triggers follow a uniform and usually input-independent pattern, e.g., insertion of specific trigger words, synonym replacement. This significantly hinders the stealthiness of the attacking model, leading the trained backdoor model being easily identified as malicious by model probes. Secondly, trigger-inserted poisoned sentences are usually disfluent, ungrammatical, or even change the semantic meaning from the original sentence, making them being easily filtered in the pre-processing stage. To resolve these two issues, in this paper, we propose an input-unique backdoor attack(NURA), where we generate backdoor triggers unique to inputs. IDBA generates context-related triggers by continuing writing the input with a language model like GPT2. The generated sentence is used as the backdoor trigger. This strategy not only creates input-unique backdoor triggers, but also preserves the semantics of the original input, simultaneously resolving the two issues above. Experimental results show that the IDBA attack is effective for attack and difficult to defend: it achieves high attack success rate across all the widely applied benchmarks, while is immune to existing defending methods. In addition, it is able to generate fluent, grammatical, and diverse backdoor inputs, which can hardly be recognized through human inspection.
Optimizing Sepsis Care through Heuristics Methods in Process Mining: A Trajectory Analysis
Venue: arXiv cs.CY 2023
Year: 2023

Process mining can help acquire insightful knowledge and heighten the system's performance. In this study, we surveyed the trajectories of 1050 sepsis patients in a regional hospital in the Netherlands from the registration to the discharge phase. Based on this real-world case study, the event log comprises events and activities related to the emergency ward, admission to hospital wards, and discharge enriched with data from lab experiments and triage checklists. At first, we aim to discover this process through Heuristics Miner (HM) and Inductive Miner (IM) methods. Then, we analyze a systematic process model based on organizational information and knowledge. Besides, we address conformance checking given medical guidelines for these patients and monitor the related flows on the systematic process model. The results show that HM and IM are inadequate in identifying the relevant process. However, using a systematic process model based on expert knowledge and organizational information resulted in an average fitness of 97.8%, a simplicity of 77.7%, and a generalization of 80.2%. The analyses demonstrate that process mining can shed light on the patient flow in the hospital and inspect the day-to-day clinical performance versus medical guidelines. Also, the process models obtained by the HM and IM methods cannot provide a concrete comprehension of the process structure for stakeholders compared to the systematic process model. The implications of our findings include the potential for process mining to improve the quality of healthcare services, optimize resource allocation, and reduce costs. Our study also highlights the importance of considering expert knowledge and organizational information in developing effective process models.
Hierarchical Multi-Agent Multi-Armed Bandit for Resource Allocation in Multi-LEO Satellite Constellation Networks
Venue: arXiv cs.LG 2023
Year: 2023

Low Earth orbit (LEO) satellite constellation is capable of providing global coverage area with high-rate services in the next sixth-generation (6G) non-terrestrial network (NTN). Due to limited onboard resources of operating power, beams, and channels, resilient and efficient resource management has become compellingly imperative under complex interference cases. However, different from conventional terrestrial base stations, LEO is deployed at considerable height and under high mobility, inducing substantially long delay and interference during transmission. As a result, acquiring the accurate channel state information between LEOs and ground users is challenging. Therefore, we construct a framework with a two-way transmission under unknown channel information and no data collected at long-delay ground gateway. In this paper, we propose hierarchical multi-agent multi-armed bandit resource allocation for LEO constellation (mmRAL) by appropriately assigning available radio resources. LEOs are considered as collaborative multiple macro-agents attempting unknown trials of various actions of micro-agents of respective resources, asymptotically achieving suitable allocation with only throughput information. In simulations, we evaluate mmRAL in various cases of LEO deployment, serving numbers of users and LEOs, hardware cost and outage probability. Benefited by efficient and resilient allocation, the proposed mmRAL system is capable of operating in homogeneous or heterogeneous orbital planes or constellations, achieving the highest throughput performance compared to the existing benchmarks in open literature.
Intelligent Load Balancing and Resource Allocation in O-RAN: A Multi-Agent Multi-Armed Bandit Approach
Venue: arXiv cs.LG 2023
Year: 2023

The open radio access network (O-RAN) architecture offers a cost-effective and scalable solution for internet service providers to optimize their networks using machine learning algorithms. The architecture's open interfaces enable network function virtualization, with the O-RAN serving as the primary communication device for users. However, the limited frequency resources and information explosion make it difficult to achieve an optimal network experience without effective traffic control or resource allocation. To address this, we consider mobility-aware load balancing to evenly distribute loads across the network, preventing network congestion and user outages caused by excessive load concentration on open radio unit (O-RU) governed by a single open distributed unit (O-DU). We have proposed a multi-agent multi-armed bandit for load balancing and resource allocation (mmLBRA) scheme, designed to both achieve load balancing and improve the effective sum-rate performance of the O-RAN network. We also present the mmLBRA-LB and mmLBRA-RA sub-schemes that can operate independently in non-realtime RAN intelligent controller (Non-RT RIC) and near-RT RIC, respectively, providing a solution with moderate loads and high-rate in O-RUs. Simulation results show that the proposed mmLBRA scheme significantly increases the effective network sum-rate while achieving better load balancing across O-RUs compared to rule-based and other existing heuristic methods in open literature.
Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression
Venue: arXiv cs.CV 2023
Year: 2023

Recent vision architectures and self-supervised training methods enable vision models that are extremely accurate and general, but come with massive parameter and computational costs. In practical settings, such as camera traps, users have limited resources, and may fine-tune a pretrained model on (often limited) data from a small set of specific categories of interest. These users may wish to make use of modern, highly-accurate models, but are often computationally constrained. To address this, we ask: can we quickly compress large generalist models into accurate and efficient specialists? For this, we propose a simple and versatile technique called Few-Shot Task-Aware Compression (TACO). Given a large vision model that is pretrained to be accurate on a broad task, such as classification over ImageNet-22K, TACO produces a smaller model that is accurate on specialized tasks, such as classification across vehicle types or animal species. Crucially, TACO works in few-shot fashion, i.e. only a few task-specific samples are used, and the procedure has low computational overheads. We validate TACO on highly-accurate ResNet, ViT/DeiT, and ConvNeXt models, originally trained on ImageNet, LAION, or iNaturalist, which we specialize and compress to a diverse set of "downstream" subtasks. TACO can reduce the number of non-zero parameters in existing models by up to 20x relative to the original models, leading to inference speedups of up to 3$\times$, while remaining accuracy-competitive with the uncompressed models on the specialized tasks.
Orbits, schemes and dynamic programming procedures for the TSP 4-OPT neighborhood
Venue: arXiv cs.DS 2023
Year: 2023

We discuss the way to group all 25 possible 4-OPT moves into 7 orbits of equivalent moves. We then describe two implementations, one for a $Θ(n^3)$ algorithm by de Berg's et al. and one of a $Θ(n^2)$ algorithm by Glover, for finding the best 4-OPT move via dynamic programming.
GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation
Venue: arXiv cs.AI 2023
Year: 2023

Taxonomy is formulated as directed acyclic concepts graphs or trees that support many downstream tasks. Many new coming concepts need to be added to an existing taxonomy. The traditional taxonomy expansion task aims only at finding the best position for new coming concepts in the existing taxonomy. However, they have two drawbacks when being applied to the real-scenarios. The previous methods suffer from low-efficiency since they waste much time when most of the new coming concepts are indeed noisy concepts. They also suffer from low-effectiveness since they collect training samples only from the existing taxonomy, which limits the ability of the model to mine more hypernym-hyponym relationships among real concepts. This paper proposes a pluggable framework called Generative Adversarial Network for Taxonomy Entering Evaluation (GANTEE) to alleviate these drawbacks. A generative adversarial network is designed in this framework by discriminative models to alleviate the first drawback and the generative model to alleviate the second drawback. Two discriminators are used in GANTEE to provide long-term and short-term rewards, respectively. Moreover, to further improve the efficiency, pre-trained language models are used to retrieve the representation of the concepts quickly. The experiments on three real-world large-scale datasets with two different languages show that GANTEE improves the performance of the existing taxonomy expansion methods in both effectiveness and efficiency.
An Open-Source Modular Treadmill for Dynamic Force Measurement with Load Dependant Range Adjustment
Venue: arXiv cs.RO 2023
Year: 2023

Ground reaction force sensing is one of the key components of gait analysis in legged locomotion research. To measure continuous force data during locomotion, we present a novel compound instrumented treadmill design. The treadmill is 1.7m long, with a natural frequency of 170Hz and an adjustable range that can be used for humans and small robots alike. Here, we present the treadmill's design methodology and characterize it in its natural frequency, noise behavior and real-life performance. Additionally, we apply an ISO 376 norm conform calibration procedure for all spatial force directions and center of pressure position. We achieve a force accuracy of $\leq$5.6N for the ground reaction forces and $\leq$13mm in center of pressure position.
Is It the End? Guidelines for Cinematic Endings in Data Videos
Venue: arXiv cs.HC 2023
Year: 2023

Data videos are becoming increasingly popular in society and academia. Yet little is known about how to create endings that strengthen a lasting impression and persuasion. To fulfill the gap, this work aims to develop guidelines for data video endings by drawing inspiration from cinematic arts. To contextualize cinematic endings in data videos, 111 film endings and 105 data video endings are first analyzed to identify four common styles using the framework of ending punctuation marks. We conducted expert interviews (N=11) and formulated 20 guidelines for creating cinematic endings in data videos. To validate our guidelines, we conducted a user study where 24 participants were invited to design endings with and without our guidelines, which are evaluated by experts and the general public. The participants praise the clarity and usability of the guidelines, and results show that the endings with guidelines are perceived to be more understandable, impressive, and reflective.
Context Matters: Adaptive Mutation for Grammars
Venue: arXiv cs.NE 2023
Year: 2023

This work proposes Adaptive Facilitated Mutation, a self-adaptive mutation method for Structured Grammatical Evolution (SGE), biologically inspired by the theory of facilitated variation. In SGE, the genotype of individuals contains a list for each non-terminal of the grammar that defines the search space. In our proposed mutation, each individual contains an array with a different, self-adaptive mutation rate for each non-terminal. We also propose Function Grouped Grammars, a grammar design procedure, to enhance the benefits of the proposed mutation. Experiments were conducted on three symbolic regression benchmarks using Probabilistic Structured Grammatical Evolution (PSGE), a variant of SGE. Results show our approach is similar or better when compared with the standard grammar and mutation.