OpenRaiser/Intern-Atlas huggingface dataset:
Supporting Human-AI Collaboration in Auditing LLMs with LLMs
Venue: arXiv cs.HC 2023
Year: 2023

Large language models are becoming increasingly pervasive and ubiquitous in society via deployment in sociotechnical systems. Yet these language models, be it for classification or generation, have been shown to be biased and behave irresponsibly, causing harm to people at scale. It is crucial to audit these language models rigorously. Existing auditing tools leverage either or both humans and AI to find failures. In this work, we draw upon literature in human-AI collaboration and sensemaking, and conduct interviews with research experts in safe and fair AI, to build upon the auditing tool: AdaTest (Ribeiro and Lundberg, 2022), which is powered by a generative large language model (LLM). Through the design process we highlight the importance of sensemaking and human-AI communication to leverage complementary strengths of humans and generative models in collaborative auditing. To evaluate the effectiveness of the augmented tool, AdaTest++, we conduct user studies with participants auditing two commercial language models: OpenAI's GPT-3 and Azure's sentiment analysis model. Qualitative analysis shows that AdaTest++ effectively leverages human strengths such as schematization, hypothesis formation and testing. Further, with our tool, participants identified a variety of failures modes, covering 26 different topics over 2 tasks, that have been shown before in formal audits and also those previously under-reported.
Digital Twin Graph: Automated Domain-Agnostic Construction, Fusion, and Simulation of IoT-Enabled World
Venue: arXiv cs.LG 2023
Year: 2023

With the advances of IoT developments, copious sensor data are communicated through wireless networks and create the opportunity of building Digital Twins to mirror and simulate the complex physical world. Digital Twin has long been believed to rely heavily on domain knowledge, but we argue that this leads to a high barrier of entry and slow development due to the scarcity and cost of human experts. In this paper, we propose Digital Twin Graph (DTG), a general data structure associated with a processing framework that constructs digital twins in a fully automated and domain-agnostic manner. This work represents the first effort that takes a completely data-driven and (unconventional) graph learning approach to addresses key digital twin challenges.
Learning Channel Codes from Data: Performance Guarantees in the Finite Blocklength Regime
Venue: arXiv cs.IT 2023
Year: 2023

This paper examines the maximum code rate achievable by a data-driven communication system over some unknown discrete memoryless channel in the finite blocklength regime. A class of channel codes, called learning-based channel codes, is first introduced. Learning-based channel codes include a learning algorithm to transform the training data into a pair of encoding and decoding functions that satisfy some statistical reliability constraint. Data-dependent achievability and converse bounds in the non-asymptotic regime are established for this class of channel codes. It is shown analytically that the asymptotic expansion of the bounds for the maximum achievable code rate of the learning-based channel codes are tight for sufficiently large training data.
Dynablox: Real-time Detection of Diverse Dynamic Objects in Complex Environments
Venue: arXiv cs.RO 2023
Year: 2023

Real-time detection of moving objects is an essential capability for robots acting autonomously in dynamic environments. We thus propose Dynablox, a novel online mapping-based approach for robust moving object detection in complex environments. The central idea of our approach is to incrementally estimate high confidence free-space areas by modeling and accounting for sensing, state estimation, and mapping limitations during online robot operation. The spatio-temporally conservative free space estimate enables robust detection of moving objects without making any assumptions on the appearance of objects or environments. This allows deployment in complex scenes such as multi-storied buildings or staircases, and for diverse moving objects such as people carrying various items, doors swinging or even balls rolling around. We thoroughly evaluate our approach on real-world data sets, achieving 86% IoU at 17 FPS in typical robotic settings. The method outperforms a recent appearance-based classifier and approaches the performance of offline methods. We demonstrate its generality on a novel data set with rare moving objects in complex environments. We make our efficient implementation and the novel data set available as open-source.
Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis
Venue: arXiv cs.CV 2023
Year: 2023

How can we teach a computer to recognize 10,000 different actions? Deep learning has evolved from supervised and unsupervised to self-supervised approaches. In this paper, we present a new contrastive learning-based framework for decision tree-based classification of actions, including human-human interactions (HHI) and human-object interactions (HOI). The key idea is to translate the original multi-class action recognition into a series of binary classification tasks on a pre-constructed decision tree. Under the new framework of contrastive learning, we present the design of an interaction adjacent matrix (IAM) with skeleton graphs as the backbone for modeling various action-related attributes such as periodicity and symmetry. Through the construction of various pretext tasks, we obtain a series of binary classification nodes on the decision tree that can be combined to support higher-level recognition tasks. Experimental justification for the potential of our approach in real-world applications ranges from interaction recognition to symmetry detection. In particular, we have demonstrated the promising performance of video-based autism spectrum disorder (ASD) diagnosis on the CalTech interview video database.
Automatic Procurement Fraud Detection with Machine Learning
Venue: arXiv cs.LG 2023
Year: 2023

Although procurement fraud is always a critical problem in almost every free market, audit departments still have a strong reliance on reporting from informed sources when detecting them. With our generous cooperator, SF Express, sharing the access to the database related with procurements took place from 2015 to 2017 in their company, our team studies how machine learning techniques could help with the audition of one of the most profound crime among current chinese market, namely procurement frauds. By representing each procurement event as 9 specific features, we construct neural network models to identify suspicious procurements and classify their fraud types. Through testing our models over 50000 samples collected from the procurement database, we have proven that such models -- despite having space for improvements -- are useful in detecting procurement frauds.
Securing Semantic Communications with Physical-layer Semantic Encryption and Obfuscation
Venue: arXiv cs.CR 2023
Year: 2023

Deep learning based semantic communication(DLSC) systems have shown great potential of making wireless networks significantly more efficient by only transmitting the semantics of the data. However, the open nature of wireless channel and fragileness of neural models cause DLSC systems extremely vulnerable to various attacks. Traditional wireless physical layer key (PLK), which relies on reciprocal channel and randomness characteristics between two legitimate users, holds the promise of securing DLSC. The main challenge lies in generating secret keys in the static environment with ultra-low/zero rate. Different from prior efforts that use relays or reconfigurable intelligent surfaces (RIS) to manipulate wireless channels, this paper proposes a novel physical layer semantic encryption scheme by exploring the randomness of bilingual evaluation understudy (BLEU) scores in the field of machine translation, and additionally presents a novel semantic obfuscation mechanism to provide further physical layer protections. Specifically, 1) we calculate the BLEU scores and corresponding weights of the DLSC system. Then, we generate semantic keys (SKey) by feeding the weighted sum of the scores into a hash function. 2) Equipped with the SKey, our proposed subcarrier obfuscation is able to further secure semantic communications with a dynamic dummy data insertion mechanism. Experiments show the effectiveness of our method, especially in the static wireless environment.
Proving Non-Termination by Acceleration Driven Clause Learning
Venue: arXiv cs.LO 2023
Year: 2023

We recently proposed Acceleration Driven Clause Learning (ADCL), a novel calculus to analyze satisfiability of Constrained Horn Clauses (CHCs). Here, we adapt ADCL to transition systems and introduce ADCL-NT, a variant for disproving termination. We implemented ADCL-NT in our tool LoAT and evaluate it against the state of the art.
On Quantification for SOTIF Validation of Automated Driving Systems
Venue: arXiv cs.RO 2023
Year: 2023

Automated driving systems are safety-critical cyber-physical systems whose safety of the intended functionality (SOTIF) can not be assumed without proper argumentation based on appropriate evidences. Recent advances in standards and regulations on the safety of driving automation are therefore intensely concerned with demonstrating that the intended functionality of these systems does not introduce unreasonable risks to stakeholders. In this work, we critically analyze the ISO 21448 standard which contains requirements and guidance on how the SOTIF can be provably validated. Emphasis lies on developing a consistent terminology as a basis for the subsequent definition of a validation strategy when using quantitative acceptance criteria. In the broad picture, we aim to achieve a well-defined risk decomposition that enables rigorous, quantitative validation approaches for the SOTIF of automated driving systems.
A Large-scale Examination of "Socioeconomic" Fairness in Mobile Networks
Venue: arXiv cs.CY 2023
Year: 2023

Internet access is a special resource of which needs has become universal across the public whereas the service is operated in the private sector. Mobile Network Operators (MNOs) put efforts for management, planning, and optimization; however, they do not link such activities to socioeconomic fairness. In this paper, we make a first step towards understanding the relation between socioeconomic status of customers and network performance, and investigate potential discrimination in network deployment and management. The scope of our study spans various aspects, including urban geography, network resource deployment, data consumption, and device distribution. A novel methodology that enables a geo-socioeconomic perspective to mobile network is developed for the study. The results are based on an actual infrastructure in multiple cities, covering millions of users densely covering the socioeconomic scale. We report a thorough examination of the fairness status, its relationship with various structural factors, and potential class specific solutions.
A categorical account of composition methods in logic
Venue: arXiv cs.LO 2023
Year: 2023

We present a categorical theory of the composition methods in finite model theory -- a key technique enabling modular reasoning about complex structures by building them out of simpler components. The crucial results required by the composition methods are Feferman-Vaught-Mostowski (FVM) type theorems, which characterize how logical equivalence behaves under composition and transformation of models. Our results are developed by extending the recently introduced game comonad semantics for model comparison games. This level of abstraction allow us to give conditions yielding FVM type results in a uniform way. Our theorems are parametric in the classes of models, logics and operations involved. Furthermore, they naturally account for the positive existential fragment, and extensions with counting quantifiers of these logics. We also reveal surprising connections between FVM type theorems, and classical concepts in the theory of monads. We illustrate our methods by recovering many classical theorems of practical interest, including a refinement of a previous result by Dawar, Severini, and Zapata concerning the 3-variable counting logic and cospectrality. To highlight the importance of our techniques being parametric in the logic of interest, we prove a family of FVM theorems for products of structures, uniformly in the logic in question, which cannot be done using specific game arguments.
Filter-Aware Model-Predictive Control
Venue: arXiv cs.LG 2023
Year: 2023

Partially-observable problems pose a trade-off between reducing costs and gathering information. They can be solved optimally by planning in belief space, but that is often prohibitively expensive. Model-predictive control (MPC) takes the alternative approach of using a state estimator to form a belief over the state, and then plan in state space. This ignores potential future observations during planning and, as a result, cannot actively increase or preserve the certainty of its own state estimate. We find a middle-ground between planning in belief space and completely ignoring its dynamics by only reasoning about its future accuracy. Our approach, filter-aware MPC, penalises the loss of information by what we call "trackability", the expected error of the state estimator. We show that model-based simulation allows condensing trackability into a neural network, which allows fast planning. In experiments involving visual navigation, realistic every-day environments and a two-link robot arm, we show that filter-aware MPC vastly improves regular MPC.
Social Distance Detection Using Deep Learning And Risk Management System
Venue: arXiv cs.CV 2023
Year: 2023

An outbreak of the coronavirus disease which occurred three years later and it has hit the world again with many evolutions. The effects on the human race have already been profound. We can only safeguard ourselves against this pandemic by mandating a "Face Mask" also maintaining the "Social Distancing." The necessity of protective face masks in all gatherings is required by many civil institutions in India. As a result of the substantial human resource utilization, personally examining the whole country with a huge population like India, to determine whether the execution of mask wearing and social distance maintained is unfeasible. The COVID-19 Social Distancing Detector System is a single-stage detector that employs deep learning to integrate high-end semantic data to a CNN module in order to maintain social distances and simultaneously monitor violations within a specified region. By deploying current Security footages, CCTV cameras, and computer vision (CV), it will also be able to identify those who are experiencing the calamity of social separation. Providing tools for safety and security, this technology disposes the need for a labor-force based surveillance system, yet a manual governing body is still required to monitor, track, and inform on the violations that are committed. Any sort of infrastructure, including universities, hospitals, offices of the government, schools, and building sites, can employ the technology. Therefore, the risk management system created to report and analyze video streams along with the social distance detector system might help to ensure our protection and security as well as the security of our loved ones. Furthermore, we will discuss about deployment and improvement of the project overall.
The Seven Worlds and Experiences of the Wireless Metaverse: Challenges and Opportunities
Venue: arXiv cs.IT 2023
Year: 2023

The wireless metaverse will create diverse user experiences at the intersection of the physical, digital, and virtual worlds. These experiences will enable novel interactions between the constituents (e.g., extended reality (XR) users and avatars) of the three worlds. However, remarkably, to date, there is no holistic vision that identifies the full set of metaverse worlds, constituents, and experiences, and the implications of their associated interactions on next-generation communication and computing systems. In this paper, we present a holistic vision of a limitless, wireless metaverse that distills the metaverse into an intersection of seven worlds and experiences that include the: i) physical, digital, and virtual worlds, along with the ii) cyber, extended, live, and parallel experiences. We then articulate how these experiences bring forth interactions between diverse metaverse constituents, namely, a) humans and avatars and b) connected intelligence systems and their digital twins (DTs). Then, we explore the wireless, computing, and artificial intelligence (AI) challenges that must be addressed to establish metaverse-ready networks that support these experiences and interactions. We particularly highlight the need for end-to-end synchronization of DTs, and the role of human-level AI and reasoning abilities for cognitive avatars. Moreover, we articulate a sequel of open questions that should ignite the quest for the future metaverse. We conclude with a set of recommendations to deploy the limitless metaverse over future wireless systems.
Ensembling Instance and Semantic Segmentation for Panoptic Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

We demonstrate our solution for the 2019 COCO panoptic segmentation task. Our method first performs instance segmentation and semantic segmentation separately, then combines the two to generate panoptic segmentation results. To enhance the performance, we add several expert models of Mask R-CNN in instance segmentation to tackle the data imbalance problem in the training data; also HTC model is adopted yielding our best instance segmentation results. In semantic segmentation, we trained several models with various backbones and use an ensemble strategy which further boosts the segmentation results. In the end, we analyze various combinations of instance and semantic segmentation, and report on their performance for the final panoptic segmentation results. Our best model achieves $PQ$ 47.1 on 2019 COCO panoptic test-dev data.
Prediction of the evolution of the nuclear reactor core parameters using artificial neural network
Venue: arXiv cs.LG 2023
Year: 2023

A nuclear reactor based on MIT BEAVRS benchmark was used as a typical power generating Pressurized Water Reactor (PWR). The PARCS v3.2 nodal-diffusion core simulator was used as a full-core reactor physics solver to emulate the operation of a reactor and to generate training, and validation data for the ANN. The ANN was implemented with dedicated Python 3.8 code with Google's TensorFlow 2.0 library. The effort was based to a large extent on the process of appropriate automatic transformation of data generated by PARCS simulator, which was later used in the process of the ANN development. Various methods that allow obtaining better accuracy of the ANN predicted results were studied, such as trying different ANN architectures to find the optimal number of neurons in the hidden layers of the network. Results were later compared with the architectures proposed in the literature. For the selected best architecture predictions were made for different core parameters and their dependence on core loading patterns. In this study, a special focus was put on the prediction of the fuel cycle length for a given core loading pattern, as it can be considered one of the targets for plant economic operation. For instance, the length of a single fuel cycle depending on the initial core loading pattern was predicted with very good accuracy (>99%). This work contributes to the exploration of the usefulness of neural networks in solving nuclear reactor design problems. Thanks to the application of ANN, designers can avoid using an excessive amount of core simulator runs and more rapidly explore the space of possible solutions before performing more detailed design considerations.
Adaptive Greedy Rejection Sampling
Venue: arXiv cs.IT 2023
Year: 2023

We consider channel simulation protocols between two communicating parties, Alice and Bob. First, Alice receives a target distribution $Q$, unknown to Bob. Then, she employs a shared coding distribution $P$ to send the minimum amount of information to Bob so that he can simulate a single sample $X \sim Q$. For discrete distributions, Harsha et al. (2009) developed a well-known channel simulation protocol -- greedy rejection sampling (GRS) -- with a bound of ${D_{KL}[Q \,\Vert\, P] + 2\ln(D_{KL}[Q \,\Vert\, P] + 1) + \mathcal{O}(1)}$ on the expected codelength of the protocol. In this paper, we extend the definition of GRS to general probability spaces and allow it to adapt its proposal distribution after each step. We call this new procedure Adaptive GRS (AGRS) and prove its correctness. Furthermore, we prove the surprising result that the expected runtime of GRS is exactly $\exp(D_\infty[Q \,\Vert\, P])$, where $D_\infty[Q \,\Vert\, P]$ denotes the Rényi $\infty$-divergence. We then apply AGRS to Gaussian channel simulation problems. We show that the expected runtime of GRS is infinite when averaged over target distributions and propose a solution that trades off a slight increase in the coding cost for a finite runtime. Finally, we describe a specific instance of AGRS for 1D Gaussian channels inspired by hybrid coding. We conjecture and demonstrate empirically that the runtime of AGRS is $\mathcal{O}(D_{KL}[Q \,\Vert\, P])$ in this case.
Wasserstein Loss for Semantic Editing in the Latent Space of GANs
Venue: arXiv cs.CV 2023
Year: 2023

The latent space of GANs contains rich semantics reflecting the training data. Different methods propose to learn edits in latent space corresponding to semantic attributes, thus allowing to modify generated images. Most supervised methods rely on the guidance of classifiers to produce such edits. However, classifiers can lead to out-of-distribution regions and be fooled by adversarial samples. We propose an alternative formulation based on the Wasserstein loss that avoids such problems, while maintaining performance on-par with classifier-based approaches. We demonstrate the effectiveness of our method on two datasets (digits and faces) using StyleGAN2.
Why Does ChatGPT Fall Short in Providing Truthful Answers?
Venue: arXiv cs.CL 2023
Year: 2023

Recent advancements in large language models, such as ChatGPT, have demonstrated significant potential to impact various aspects of human life. However, ChatGPT still faces challenges in providing reliable and accurate answers to user questions. To better understand the model's particular weaknesses in providing truthful answers, we embark an in-depth exploration of open-domain question answering. Specifically, we undertake a detailed examination of ChatGPT's failures, categorized into: comprehension, factuality, specificity, and inference. We further pinpoint factuality as the most contributing failure and identify two critical abilities associated with factuality: knowledge memorization and knowledge recall. Through experiments focusing on factuality, we propose several potential enhancement strategies. Our findings suggest that augmenting the model with granular external knowledge and cues for knowledge recall can enhance the model's factuality in answering questions.