OpenRaiser/Intern-Atlas huggingface dataset:
Physical Adversarial Examples for Multi-Camera Systems
Venue: arXiv cs.CV 2023
Year: 2023

Neural networks build the foundation of several intelligent systems, which, however, are known to be easily fooled by adversarial examples. Recent advances made these attacks possible even in air-gapped scenarios, where the autonomous system observes its surroundings by, e.g., a camera. We extend these ideas in our research and evaluate the robustness of multi-camera setups against such physical adversarial examples. This scenario becomes ever more important with the rise in popularity of autonomous vehicles, which fuse the information of several cameras for their driving decision. While we find that multi-camera setups provide some robustness towards past attack methods, we see that this advantage reduces when optimizing on multiple perspectives at once. We propose a novel attack method that we call Transcender-MC, where we incorporate online 3D renderings and perspective projections in the training process. Moreover, we motivate that certain data augmentation techniques can facilitate the generation of successful adversarial examples even further. Transcender-MC is 11% more effective in successfully attacking multi-camera setups than state-of-the-art methods. Our findings offer valuable insights regarding the resilience of object detection in a setup with multiple cameras and motivate the need of developing adequate defense mechanisms against them.
UT5: Pretraining Non autoregressive T5 with unrolled denoising
Venue: arXiv cs.CL 2023
Year: 2023

Recent advances in Transformer-based Large Language Models have made great strides in natural language generation. However, to decode K tokens, an autoregressive model needs K sequential forward passes, which may be a performance bottleneck for large language models. Many non-autoregressive (NAR) research are aiming to address this sequentiality bottleneck, albeit many have focused on a dedicated architecture in supervised benchmarks. In this work, we studied unsupervised pretraining for non auto-regressive T5 models via unrolled denoising and shown its SoTA results in downstream generation tasks such as SQuAD question generation and XSum.
Towards Evaluating AI Systems for Moral Status Using Self-Reports
Venue: arXiv cs.LG 2023
Year: 2023

As AI systems become more advanced and widely deployed, there will likely be increasing debate over whether AI systems could have conscious experiences, desires, or other states of potential moral significance. It is important to inform these discussions with empirical evidence to the extent possible. We argue that under the right circumstances, self-reports, or an AI system's statements about its own internal states, could provide an avenue for investigating whether AI systems have states of moral significance. Self-reports are the main way such states are assessed in humans ("Are you in pain?"), but self-reports from current systems like large language models are spurious for many reasons (e.g. often just reflecting what humans would say). To make self-reports more appropriate for this purpose, we propose to train models to answer many kinds of questions about themselves with known answers, while avoiding or limiting training incentives that bias self-reports. The hope of this approach is that models will develop introspection-like capabilities, and that these capabilities will generalize to questions about states of moral significance. We then propose methods for assessing the extent to which these techniques have succeeded: evaluating self-report consistency across contexts and between similar models, measuring the confidence and resilience of models' self-reports, and using interpretability to corroborate self-reports. We also discuss challenges for our approach, from philosophical difficulties in interpreting self-reports to technical reasons why our proposal might fail. We hope our discussion inspires philosophers and AI researchers to criticize and improve our proposed methodology, as well as to run experiments to test whether self-reports can be made reliable enough to provide information about states of moral significance.
Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption
Venue: arXiv cs.LG 2023
Year: 2023

Designing privacy-preserving deep learning models is a major challenge within the deep learning community. Homomorphic Encryption (HE) has emerged as one of the most promising approaches in this realm, enabling the decoupling of knowledge between the model owner and the data owner. Despite extensive research and application of this technology, primarily in convolutional neural networks, incorporating HE into transformer models has been challenging because of the difficulties in converting these models into a polynomial form. We break new ground by introducing the first polynomial transformer, providing the first demonstration of secure inference over HE with transformers. This includes a transformer architecture tailored for HE, alongside a novel method for converting operators to their polynomial equivalent. This innovation enables us to perform secure inference on LMs with WikiText-103. It also allows us to perform image classification with CIFAR-100 and Tiny-ImageNet. Our models yield results comparable to traditional methods, bridging the performance gap with transformers of similar scale and underscoring the viability of HE for state-of-the-art applications. Finally, we assess the stability of our models and conduct a series of ablations to quantify the contribution of each model component.
DEED: Dynamic Early Exit on Decoder for Accelerating Encoder-Decoder Transformer Models
Venue: arXiv cs.CV 2023
Year: 2023

Encoder-decoder transformer models have achieved great success on various vision-language (VL) tasks, but they suffer from high inference latency. Typically, the decoder takes up most of the latency because of the auto-regressive decoding. To accelerate the inference, we propose an approach of performing Dynamic Early Exit on Decoder (DEED). We build a multi-exit encoder-decoder transformer model which is trained with deep supervision so that each of its decoder layers is capable of generating plausible predictions. In addition, we leverage simple yet practical techniques, including shared generation head and adaptation modules, to keep accuracy when exiting at shallow decoder layers. Based on the multi-exit model, we perform step-level dynamic early exit during inference, where the model may decide to use fewer decoder layers based on its confidence of the current layer at each individual decoding step. Considering different number of decoder layers may be used at different decoding steps, we compute deeper-layer decoder features of previous decoding steps just-in-time, which ensures the features from different decoding steps are semantically aligned. We evaluate our approach with two state-of-the-art encoder-decoder transformer models on various VL tasks. We show our approach can reduce overall inference latency by 30%-60% with comparable or even higher accuracy compared to baselines.
Review of AlexNet for Medical Image Classification
Venue: arXiv cs.CV 2023
Year: 2023

In recent years, the rapid development of deep learning has led to a wide range of applications in the field of medical image classification. The variants of neural network models with ever-increasing performance share some commonalities: to try to mitigate overfitting, improve generalization, avoid gradient vanishing and exploding, etc. AlexNet first utilizes the dropout technique to mitigate overfitting and the ReLU activation function to avoid gradient vanishing. Therefore, we focus our discussion on AlexNet, which has contributed greatly to the development of CNNs in 2012. After reviewing over 40 papers, including journal papers and conference papers, we give a narrative on the technical details, advantages, and application areas of AlexNet.
It Takes Two to Negotiate: Modeling Social Exchange in Online Multiplayer Games
Venue: arXiv cs.CL 2023
Year: 2023

Online games are dynamic environments where players interact with each other, which offers a rich setting for understanding how players negotiate their way through the game to an ultimate victory. This work studies online player interactions during the turn-based strategy game, Diplomacy. We annotated a dataset of over 10,000 chat messages for different negotiation strategies and empirically examined their importance in predicting long- and short-term game outcomes. Although negotiation strategies can be predicted reasonably accurately through the linguistic modeling of the chat messages, more is needed for predicting short-term outcomes such as trustworthiness. On the other hand, they are essential in graph-aware reinforcement learning approaches to predict long-term outcomes, such as a player's success, based on their prior negotiation history. We close with a discussion of the implications and impact of our work. The dataset is available at https://github.com/kj2013/claff-diplomacy.
Thread of Thought Unraveling Chaotic Contexts
Venue: arXiv cs.CL 2023
Year: 2023

Large Language Models (LLMs) have ushered in a transformative era in the field of natural language processing, excelling in tasks related to text comprehension and generation. Nevertheless, they encounter difficulties when confronted with chaotic contexts (e.g., distractors rather than long irrelevant context), leading to the inadvertent omission of certain details within the chaotic context. In response to these challenges, we introduce the "Thread of Thought" (ThoT) strategy, which draws inspiration from human cognitive processes. ThoT systematically segments and analyzes extended contexts while adeptly selecting pertinent information. This strategy serves as a versatile "plug-and-play" module, seamlessly integrating with various LLMs and prompting techniques. In the experiments, we utilize the PopQA and EntityQ datasets, as well as a Multi-Turn Conversation Response dataset (MTCR) we collected, to illustrate that ThoT significantly improves reasoning performance compared to other prompting techniques.
Three Conjectures on Unexpectedeness
Venue: arXiv cs.AI 2023
Year: 2023

Unexpectedness is a central concept in Simplicity Theory, a theory of cognition relating various inferential processes to the computation of Kolmogorov complexities, rather than probabilities. Its predictive power has been confirmed by several experiments with human subjects, yet its theoretical basis remains largely unexplored: why does it work? This paper lays the groundwork for three theoretical conjectures. First, unexpectedness can be seen as a generalization of Bayes' rule. Second, the frequentist core of unexpectedness can be connected to the function of tracking ergodic properties of the world. Third, unexpectedness can be seen as constituent of various measures of divergence between the entropy of the world (environment) and the variety of the observer (system). The resulting framework hints to research directions that go beyond the division between probabilistic and logical approaches, potentially bringing new insights into the extraction of causal relations, and into the role of descriptive mechanisms in learning.
Gitor: Scalable Code Clone Detection by Building Global Sample Graph
Venue: arXiv cs.SE 2023
Year: 2023

Code clone detection is about finding out similar code fragments, which has drawn much attention in software engineering since it is important for software maintenance and evolution. Researchers have proposed many techniques and tools for source code clone detection, but current detection methods concentrate on analyzing or processing code samples individually without exploring the underlying connections among code samples. In this paper, we propose Gitor to capture the underlying connections among different code samples. Specifically, given a source code database, we first tokenize all code samples to extract the pre-defined individual information. After obtaining all samples individual information, we leverage them to build a large global sample graph where each node is a code sample or a type of individual information. Then we apply a node embedding technique on the global sample graph to extract all the samples vector representations. After collecting all code samples vectors, we can simply compare the similarity between any two samples to detect possible clone pairs. More importantly, since the obtained vector of a sample is from a global sample graph, we can combine it with its own code features to improve the code clone detection performance. To demonstrate the effectiveness of Gitor, we evaluate it on a widely used dataset namely BigCloneBench. Our experimental results show that Gitor has higher accuracy in terms of code clone detection and excellent execution time for inputs of various sizes compared to existing state-of-the-art tools. Moreover, we also evaluate the combination of Gitor with other traditional vector-based clone detection methods, the results show that the use of Gitor enables them detect more code clones with higher F1.
ICRA Roboethics Challenge 2023: Intelligent Disobedience in an Elderly Care Home
Venue: arXiv cs.RO 2023
Year: 2023

With the projected surge in the elderly population, service robots offer a promising avenue to enhance their well-being in elderly care homes. Such robots will encounter complex scenarios which will require them to perform decisions with ethical consequences. In this report, we propose to leverage the Intelligent Disobedience framework in order to give the robot the ability to perform a deliberation process over decisions with potential ethical implications. We list the issues that this framework can assist with, define it formally in the context of the specific elderly care home scenario, and delineate the requirements for implementing an intelligently disobeying robot. We conclude this report with some critical analysis and suggestions for future work.
SparseSpikformer: A Co-Design Framework for Token and Weight Pruning in Spiking Transformer
Venue: arXiv cs.CV 2023
Year: 2023

As the third-generation neural network, the Spiking Neural Network (SNN) has the advantages of low power consumption and high energy efficiency, making it suitable for implementation on edge devices. More recently, the most advanced SNN, Spikformer, combines the self-attention module from Transformer with SNN to achieve remarkable performance. However, it adopts larger channel dimensions in MLP layers, leading to an increased number of redundant model parameters. To effectively decrease the computational complexity and weight parameters of the model, we explore the Lottery Ticket Hypothesis (LTH) and discover a very sparse ($\ge$90%) subnetwork that achieves comparable performance to the original network. Furthermore, we also design a lightweight token selector module, which can remove unimportant background information from images based on the average spike firing rate of neurons, selecting only essential foreground image tokens to participate in attention calculation. Based on that, we present SparseSpikformer, a co-design framework aimed at achieving sparsity in Spikformer through token and weight pruning techniques. Experimental results demonstrate that our framework can significantly reduce 90% model parameters and cut down Giga Floating-Point Operations (GFLOPs) by 20% while maintaining the accuracy of the original model.
Comments on "Dynamic Consensus Committee-Based for Secure Data Sharing With Authorized Multi-Receiver Searchable Encryption"
Venue: arXiv cs.CR 2023
Year: 2023

Recently, Yang et al. introduced an efficient searchable encryption scheme titled "Dynamic Consensus Committee-Based for Secure Data Sharing With Authorized Multi-Receiver Searchable Encryption (DCC-SE)," published in IEEE Transactions on Information Forensics and Security (DOI: 10.1109/TIFS.2023.3305183). According to the authors, DCC-SE meets various security requirements, especially the keyword trapdoor indistinguishability against chosen keyword attacks (KT-IND-CKA). In this letter, however, we reveal a significant vulnerability of DCC-SE: any users involved in the system can execute attacks against KT-IND-CKA security. This flaw potentially results in the unintended disclosure of sensitive keyword information related to the documents. We present a detailed cryptanalysis on DCC-SE. In addition, to address this vulnerability, we discuss the root cause and identify a flaw in the security proof of DCC-SE. Subsequently, we provide a solution that effectively addresses this concern without significantly increasing computational overhead.
Evaluating Gender Bias in the Translation of Gender-Neutral Languages into English
Venue: arXiv cs.CL 2023
Year: 2023

Machine Translation (MT) continues to improve in quality and adoption, yet the inadvertent perpetuation of gender bias remains a significant concern. Despite numerous studies into gender bias in translations from gender-neutral languages such as Turkish into more strongly gendered languages like English, there are no benchmarks for evaluating this phenomenon or for assessing mitigation strategies. To address this gap, we introduce GATE X-E, an extension to the GATE (Rarrick et al., 2023) corpus, that consists of human translations from Turkish, Hungarian, Finnish, and Persian into English. Each translation is accompanied by feminine, masculine, and neutral variants for each possible gender interpretation. The dataset, which contains between 1250 and 1850 instances for each of the four language pairs, features natural sentences with a wide range of sentence lengths and domains, challenging translation rewriters on various linguistic phenomena. Additionally, we present an English gender rewriting solution built on GPT-3.5 Turbo and use GATE X-E to evaluate it. We open source our contributions to encourage further research on gender debiasing.
Proving Calculational Proofs Correct
Venue: arXiv cs.LO 2023
Year: 2023

Teaching proofs is a crucial component of any undergraduate-level program that covers formal reasoning. We have developed a calculational reasoning format and refined it over several years of teaching a freshman-level course, "Logic and Computation", to thousands of undergraduate students. In our companion paper, we presented our calculational proof format, gave an overview of the calculational proof checker (CPC) tool that we developed to help users write and validate proofs, described some of the technical and implementation details of CPC and provided several publicly available proofs written using our format. In this paper, we dive deeper into the implementation details of CPC, highlighting how proof validation works, which helps us argue that our proof checking process is sound.
ACL2 Proofs of Nonlinear Inequalities with Imandra
Venue: arXiv cs.LO 2023
Year: 2023

We present a proof-producing integration of ACL2 and Imandra for proving nonlinear inequalities. This leverages a new Imandra interface exposing its nonlinear decision procedures. The reasoning takes place over the reals, but the proofs produced are valid over the rationals and may be run in both ACL2 and ACL2(r). The ACL2 proofs Imandra constructs are extracted from Positivstellensatz refutations, a real algebraic analogue of the Nullstellensatz, and are found using convex optimization.
Voice App Developer Experiences with Alexa and Google Assistant: Juggling Risks, Liability, and Security
Venue: arXiv cs.HC 2023
Year: 2023

Voice applications (voice apps) are a key element in Voice Assistant ecosystems such as Amazon Alexa and Google Assistant, as they provide assistants with a wide range of capabilities that users can invoke with a voice command. Most voice apps, however, are developed by third parties - i.e., not by Amazon/Google - and they are included in the ecosystem through marketplaces akin to smartphone app stores but with crucial differences, e.g., the voice app code is not hosted by the marketplace and is not run on the local device. Previous research has studied the security and privacy issues of voice apps in the wild, finding evidence of bad practices by voice app developers. However, developers' perspectives are yet to be explored. In this paper, we report a qualitative study of the experiences of voice app developers and the challenges they face. Our findings suggest that: 1) developers face several risks due to liability pushed on to them by the more powerful voice assistant platforms, which are linked to negative privacy and security outcomes on voice assistant platforms; and 2) there are key issues around monetization, privacy, design, and testing rooted in problems with the voice app certification process. We discuss the implications of our results for voice app developers, platforms, regulators, and research on voice app development and certification.
Energy-Efficient Design of Satellite-Terrestrial Computing in 6G Wireless Networks
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we investigate the issue of satellite-terrestrial computing in the sixth generation (6G) wireless networks, where multiple terrestrial base stations (BSs) and low earth orbit (LEO) satellites collaboratively provide edge computing services to ground user equipments (GUEs) and space user equipments (SUEs) over the world. In particular, we design a complete process of satellite-terrestrial computing in terms of communication and computing according to the characteristics of 6G wireless networks. In order to minimize the weighted total energy consumption while ensuring delay requirements of computing tasks, an energy-efficient satellite-terrestrial computing algorithm is put forward by jointly optimizing offloading selection, beamforming design and resource allocation. Finally, both theoretical analysis and simulation results confirm fast convergence and superior performance of the proposed algorithm for satellite-terrestrial computing in 6G wireless networks.
DLAS: An Exploration and Assessment of the Deep Learning Acceleration Stack
Venue: arXiv cs.LG 2023
Year: 2023

Deep Neural Networks (DNNs) are extremely computationally demanding, which presents a large barrier to their deployment on resource-constrained devices. Since such devices are where many emerging deep learning applications lie (e.g., drones, vision-based medical technology), significant bodies of work from both the machine learning and systems communities have attempted to provide optimizations to accelerate DNNs. To help unify these two perspectives, in this paper we combine machine learning and systems techniques within the Deep Learning Acceleration Stack (DLAS), and demonstrate how these layers can be tightly dependent on each other with an across-stack perturbation study. We evaluate the impact on accuracy and inference time when varying different parameters of DLAS across two datasets, seven popular DNN architectures, four DNN compression techniques, three algorithmic primitives with sparse and dense variants, untuned and auto-scheduled code generation, and four hardware platforms. Our evaluation highlights how perturbations across DLAS parameters can cause significant variation and across-stack interactions. The highest level observation from our evaluation is that the model size, accuracy, and inference time are not guaranteed to be correlated. Overall we make 13 key observations, including that speedups provided by compression techniques are very hardware dependent, and that compiler auto-tuning can significantly alter what the best algorithm to use for a given configuration is. With DLAS, we aim to provide a reference framework to aid machine learning and systems practitioners in reasoning about the context in which their respective DNN acceleration solutions exist in. With our evaluation strongly motivating the need for co-design, we believe that DLAS can be a valuable concept for exploring the next generation of co-designed accelerated deep learning solutions.