pwc-archive/papers-with-abstracts huggingface dataset:
Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detection
Authors: Sun RuiJin, Guo ShiZe, Guo JinHong, Xing ChangYou, Yang LuMing, Guo Xi, Pan ZhiSong
Date: 2022-09-06 00:00:00
Tasks: Data Augmentation, Malware Detection

Deep neural networks (DNNs) are increasingly being applied in malware detection and their robustness has been widely debated. Traditionally an adversarial example generation scheme relies on either detailed model information (gradient-based methods) or lots of samples to train a surrogate model, neither of which are available in most scenarios. We propose the notion of the instance-based attack. Our scheme is interpretable and can work in a black-box environment. Given a specific binary example and a malware classifier, we use the data augmentation strategies to produce enough data from which we can train a simple interpretable model. We explain the detection model by displaying the weight of different parts of the specific binary. By analyzing the explanations, we found that the data subsections play an important role in Windows PE malware detection. We proposed a new function preserving transformation algorithm that can be applied to data subsections. By employing the binary-diversification techniques that we proposed, we eliminated the influence of the most weighted part to generate adversarial examples. Our algorithm can fool the DNNs in certain cases with a success rate of nearly 100\%. Our method outperforms the state-of-the-art method . The most important aspect is that our method operates in black-box settings and the results can be validated with domain knowledge. Our analysis model can assist people in improving the robustness of malware detectors.
SimCLF: A Simple Contrastive Learning Framework for Function-level Binary Embeddings
Authors: Sun RuiJin, Guo ShiZe, Guo JinHong, Li Wei, Zhan Dazhi, Sun Meng, Pan ZhiSong
Date: 2022-09-06 00:00:00
Tasks: Compiler Optimization, Contrastive Learning

Function-level binary code similarity detection is a crucial aspect of cybersecurity. It enables the detection of bugs and patent infringements in released software and plays a pivotal role in preventing supply chain attacks. A practical embedding learning framework relies on the robustness of the assembly code representation and the accuracy of function-pair annotation, which is traditionally accomplished using supervised learning-based frameworks. However, annotating different function pairs with accurate labels poses considerable challenges. These supervised learning methods can be easily overtrained and suffer from representation robustness problems. To address these challenges, we propose SimCLF: A Simple Contrastive Learning Framework for Function-level Binary Embeddings. We take an unsupervised learning approach and formulate binary code similarity detection as instance discrimination. SimCLF directly operates on disassembled binary functions and could be implemented with any encoder. It does not require manually annotated information but only augmented data. Augmented data is generated using compiler optimization options and code obfuscation techniques. The experimental results demonstrate that SimCLF surpasses the state-of-the-art in accuracy and has a significant advantage in few-shot settings.
Continual Learning, Fast and Slow
Authors: Quang Pham, Chenghao Liu, Steven C. H. Hoi
Date: 2022-09-06 00:00:00
Tasks: Continual Learning, Hippocampus, Representation Learning, Self-Supervised Learning

According to the Complementary Learning Systems (CLS) theory~\cite{mcclelland1995there} in neuroscience, humans do effective \emph{continual learning} through two complementary systems: a fast learning system centered on the hippocampus for rapid learning of the specifics, individual experiences; and a slow learning system located in the neocortex for the gradual acquisition of structured knowledge about the environment. Motivated by this theory, we propose \emph{DualNets} (for Dual Networks), a general continual learning framework comprising a fast learning system for supervised learning of pattern-separated representation from specific tasks and a slow learning system for representation learning of task-agnostic general representation via Self-Supervised Learning (SSL). DualNets can seamlessly incorporate both representation types into a holistic framework to facilitate better continual learning in deep neural networks. Via extensive experiments, we demonstrate the promising results of DualNets on a wide range of continual learning protocols, ranging from the standard offline, task-aware setting to the challenging online, task-free scenario. Notably, on the CTrL~\cite{veniat2020efficient} benchmark that has unrelated tasks with vastly different visual images, DualNets can achieve competitive performance with existing state-of-the-art dynamic architecture strategies~\cite{ostapenko2021continual}. Furthermore, we conduct comprehensive ablation studies to validate DualNets efficacy, robustness, and scalability. Code will be made available at \url{https://github.com/phquang/DualNet}.
A first-order logic characterization of safety and co-safety languages
Authors: Alessandro Cimatti, Luca Geatti, Nicola Gigante, Angelo Montanari, Stefano Tonetta
Date: 2022-09-06 00:00:00

Linear Temporal Logic (LTL) is one of the most popular temporal logics, that comes into play in a variety of branches of computer science. Among the various reasons of its widespread use there are its strong foundational properties: LTL is equivalent to counter-free omega-automata, to star-free omega-regular expressions, and (by Kamp's theorem) to the First-Order Theory of Linear Orders (FO-TLO). Safety and co-safety languages, where a finite prefix suffices to establish whether a word does not belong or belongs to the language, respectively, play a crucial role in lowering the complexity of problems like model checking and reactive synthesis for LTL. SafetyLTL (resp., coSafetyLTL) is a fragment of LTL where only universal (resp., existential) temporal modalities are allowed, that recognises safety (resp., co-safety) languages only. The main contribution of this paper is the introduction of a fragment of FO-TLO, called SafetyFO, and of its dual coSafetyFO, which are expressively complete with respect to the LTL-definable safety and co-safety languages. We prove that they exactly characterize SafetyLTL and coSafetyLTL, respectively, a result that joins Kamp's theorem, and provides a clearer view of the characterization of (fragments of) LTL in terms of first-order languages. In addition, it gives a direct, compact, and self-contained proof that any safety language definable in LTL is definable in SafetyLTL as well. As a by-product, we obtain some interesting results on the expressive power of the weak tomorrow operator of SafetyLTL, interpreted over finite and infinite words. Moreover, we prove that, when interpreted over finite words, SafetyLTL (resp. coSafetyLTL) devoid of the tomorrow (resp., weak tomorrow) operator captures the safety (resp., co-safety) fragment of LTL over finite words.
A Survey of Machine Unlearning
Authors: Thanh Tam Nguyen, Thanh Trung Huynh, Zhao Ren, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet Hung Nguyen
Date: 2022-09-06 00:00:00
Tasks: Attribute, Machine Unlearning, Survey

Today, computer systems hold large amounts of personal data. Yet while such an abundance of data allows breakthroughs in artificial intelligence, and especially machine learning (ML), its existence can be a threat to user privacy, and it can weaken the bonds of trust between humans and AI. Recent regulations now require that, on request, private information about a user must be removed from both computer systems and from ML models, i.e. ``the right to be forgotten''). While removing data from back-end databases should be straightforward, it is not sufficient in the AI context as ML models often `remember' the old data. Contemporary adversarial attacks on trained models have proven that we can learn whether an instance or an attribute belonged to the training data. This phenomenon calls for a new paradigm, namely machine unlearning, to make ML models forget about particular data. It turns out that recent works on machine unlearning have not been able to completely solve the problem due to the lack of common frameworks and resources. Therefore, this paper aspires to present a comprehensive examination of machine unlearning's concepts, scenarios, methods, and applications. Specifically, as a category collection of cutting-edge studies, the intention behind this article is to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine unlearning and its formulations, design criteria, removal requests, algorithms, and applications. In addition, we aim to highlight the key findings, current trends, and new research areas that have not yet featured the use of machine unlearning but could benefit greatly from it. We hope this survey serves as a valuable resource for ML researchers and those seeking to innovate privacy technologies. Our resources are publicly available at https://github.com/tamlhp/awesome-machine-unlearning.
Zero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Composition
Authors: Pengfei Deng, Jianhua Yuan, Yanyan Zhao, Bing Qin
Date: 2022-09-06 00:00:00
Tasks: Classification, Sentiment Analysis, Sentiment Classification

As aspect-level sentiment labels are expensive and labor-intensive to acquire, zero-shot aspect-level sentiment classification is proposed to learn classifiers applicable to new domains without using any annotated aspect-level data. In contrast, document-level sentiment data with ratings are more easily accessible. In this work, we achieve zero-shot aspect-level sentiment classification by only using document-level reviews. Our key intuition is that the sentiment representation of a document is composed of the sentiment representations of all the aspects of that document. Based on this, we propose the AF-DSC method to explicitly model such sentiment composition in reviews. AF-DSC first learns sentiment representations for all potential aspects and then aggregates aspect-level sentiments into a document-level one to perform document-level sentiment classification. In this way, we obtain the aspect-level sentiment classifier as the by-product of the document-level sentiment classifier. Experimental results on aspect-level sentiment classification benchmarks demonstrate the effectiveness of explicit utilization of sentiment composition in document-level sentiment classification. Our model with only 30k training data outperforms previous work utilizing millions of data.
Multi-class Classifier based Failure Prediction with Artificial and Anonymous Training for Data Privacy
Authors: Dibakar Das, Vikram Seshasai, Vineet Sudhir Bhat, Pushkal Juneja, Jyotsna Bapat, Debabrata Das
Date: 2022-09-06 00:00:00
Tasks: Decision Making, Prediction

This paper proposes a novel non-intrusive system failure prediction technique using available information from developers and minimal information from raw logs (rather than mining entire logs) but keeping the data entirely private with the data owners. A neural network based multi-class classifier is developed for failure prediction, using artificially generated anonymous data set, applying a combination of techniques, viz., genetic algorithm (steps), pattern repetition, etc., to train and test the network. The proposed mechanism completely decouples the data set used for training process from the actual data which is kept private. Moreover, multi-criteria decision making (MCDM) schemes are used to prioritize failures meeting business requirements. Results show high accuracy in failure prediction under different parameter configurations. On a broader context, any classification problem, beyond failure prediction, can be performed using the proposed mechanism with artificially generated data set without looking into the actual data as long as the input features can be translated to binary values (e.g. output from private binary classifiers) and can provide classification-as-a-service.
Entity Aware Syntax Tree Based Data Augmentation for Natural Language Understanding
Authors: Jiaxing Xu, Jianbin Cui, Jiangneng Li, Wenge Rong, Noboru Matsuda
Date: 2022-09-06 00:00:00
Tasks: Data Augmentation, Intent Detection, Natural Language Understanding, slot-filling, Slot Filling, Text Augmentation

Understanding the intention of the users and recognizing the semantic entities from their sentences, aka natural language understanding (NLU), is the upstream task of many natural language processing tasks. One of the main challenges is to collect a sufficient amount of annotated data to train a model. Existing research about text augmentation does not abundantly consider entity and thus performs badly for NLU tasks. To solve this problem, we propose a novel NLP data augmentation technique, Entity Aware Data Augmentation (EADA), which applies a tree structure, Entity Aware Syntax Tree (EAST), to represent sentences combined with attention on the entity. Our EADA technique automatically constructs an EAST from a small amount of annotated data, and then generates a large number of training instances for intent detection and slot filling. Experimental results on four datasets showed that the proposed technique significantly outperforms the existing data augmentation methods in terms of both accuracy and generalization ability.
PTSEFormer: Progressive Temporal-Spatial Enhanced TransFormer Towards Video Object Detection
Authors: Han Wang, Jun Tang, Xiaodong Liu, Shanyan Guan, Rong Xie, Li Song
Date: 2022-09-06 00:00:00
Tasks: object-detection, Object Detection, Video Object Detection

Recent years have witnessed a trend of applying context frames to boost the performance of object detection as video object detection. Existing methods usually aggregate features at one stroke to enhance the feature. These methods, however, usually lack spatial information from neighboring frames and suffer from insufficient feature aggregation. To address the issues, we perform a progressive way to introduce both temporal information and spatial information for an integrated enhancement. The temporal information is introduced by the temporal feature aggregation model (TFAM), by conducting an attention mechanism between the context frames and the target frame (i.e., the frame to be detected). Meanwhile, we employ a Spatial Transition Awareness Model (STAM) to convey the location transition information between each context frame and target frame. Built upon a transformer-based detector DETR, our PTSEFormer also follows an end-to-end fashion to avoid heavy post-processing procedures while achieving 88.1% mAP on the ImageNet VID dataset. Codes are available at https://github.com/Hon-Wong/PTSEFormer.
Real-Time Cattle Interaction Recognition via Triple-stream Network
Authors: Yang Yang, Mizuka Komatsu, Kenji Oyama, Takenao Ohkawa
Date: 2022-09-06 00:00:00
Tasks: Self-Supervised Learning

In stockbreeding of beef cattle, computer vision-based approaches have been widely employed to monitor cattle conditions (e.g. the physical, physiology, and health). To this end, the accurate and effective recognition of cattle action is a prerequisite. Generally, most existing models are confined to individual behavior that uses video-based methods to extract spatial-temporal features for recognizing the individual actions of each cattle. However, there is sociality among cattle and their interaction usually reflects important conditions, e.g. estrus, and also video-based method neglects the real-time capability of the model. Based on this, we tackle the challenging task of real-time recognizing interactions between cattle in a single frame in this paper. The pipeline of our method includes two main modules: Cattle Localization Network and Interaction Recognition Network. At every moment, cattle localization network outputs high-quality interaction proposals from every detected cattle and feeds them into the interaction recognition network with a triple-stream architecture. Such a triple-stream network allows us to fuse different features relevant to recognizing interactions. Specifically, the three kinds of features are a visual feature that extracts the appearance representation of interaction proposals, a geometric feature that reflects the spatial relationship between cattle, and a semantic feature that captures our prior knowledge of the relationship between the individual action and interaction of cattle. In addition, to solve the problem of insufficient quantity of labeled data, we pre-train the model based on self-supervised learning. Qualitative and quantitative evaluation evidences the performance of our framework as an effective method to recognize cattle interaction in real time.
Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents
Authors: Stephen Casper, Taylor Killian, Gabriel Kreiman, Dylan Hadfield-Menell
Date: 2022-09-05 00:00:00
Tasks: Red Teaming, reinforcement-learning, Reinforcement Learning (RL)

Adversarial examples can be useful for identifying vulnerabilities in AI systems before they are deployed. In reinforcement learning (RL), adversarial policies can be developed by training an adversarial agent to minimize a target agent's rewards. Prior work has studied black-box versions of these attacks where the adversary only observes the world state and treats the target agent as any other part of the environment. However, this does not take into account additional structure in the problem. In this work, we study white-box adversarial policies and show that having access to a target agent's internal state can be useful for identifying its vulnerabilities. We make two contributions. (1) We introduce white-box adversarial policies where an attacker observes both a target's internal state and the world state at each timestep. We formulate ways of using these policies to attack agents in 2-player games and text-generating language models. (2) We demonstrate that these policies can achieve higher initial and asymptotic performance against a target agent than black-box controls. Code is available at https://github.com/thestephencasper/lm_white_box_attacks
To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing
Authors: Luca Ballotta, Giovanni Peserico, Francesco Zanini, Paolo Dini
Date: 2022-09-05 00:00:00
Tasks: Data Compression, Edge-computing

We consider a network of smart sensors for an edge computing application that sample a time-varying signal and send updates to a base station for remote global monitoring. Sensors are equipped with sensing and compute, and can either send raw data or process them on-board before transmission. Limited hardware resources at the edge generate a fundamental latency-accuracy trade-off: raw measurements are inaccurate but timely, whereas accurate processed updates are available after processing delay. Hence, one needs to decide when sensors should transmit raw measurements or rely on local processing to maximize network monitoring performance. To tackle this sensing design problem, we model an estimation-theoretic optimization framework that embeds both computation and communication latency, and propose a Reinforcement Learning-based approach that dynamically allocates computational resources at each sensor. Effectiveness of our proposed approach is validated through numerical experiments motivated by smart sensing for the Internet of Drones and self-driving vehicles. In particular, we show that, under constrained computation at the base station, monitoring performance can be further improved by an online sensor selection.
Improving Assistive Robotics with Deep Reinforcement Learning
Authors: Yash Jakhotiya, Iman Haque
Date: 2022-09-05 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Assistive Robotics is a class of robotics concerned with aiding humans in daily care tasks that they may be inhibited from doing due to disabilities or age. While research has demonstrated that classical control methods can be used to design policies to complete these tasks, these methods can be difficult to generalize to a variety of instantiations of a task. Reinforcement learning can provide a solution to this issue, wherein robots are trained in simulation and their policies are transferred to real-world machines. In this work, we replicate a published baseline for training robots on three tasks in the Assistive Gym environment, and we explore the usage of a Recurrent Neural Network and Phasic Policy Gradient learning to augment the original work. Our baseline implementation meets or exceeds the baseline of the original work, however, we found that our explorations into the new methods was not as effective as we anticipated. We discuss the results of our baseline and some thoughts on why our new methods were not as successful.
A New Approach to Training Multiple Cooperative Agents for Autonomous Driving
Authors: Ruiyang Yang, Siheng Li, Beihong Jin
Date: 2022-09-05 00:00:00
Tasks: Autonomous Driving, Decision Making

Training multiple agents to perform safe and cooperative control in the complex scenarios of autonomous driving has been a challenge. For a small fleet of cars moving together, this paper proposes Lepus, a new approach to training multiple agents. Lepus adopts a pure cooperative manner for training multiple agents, featured with the shared parameters of policy networks and the shared reward function of multiple agents. In particular, Lepus pre-trains the policy networks via an adversarial process, improving its collaborative decision-making capability and further the stability of car driving. Moreover, for alleviating the problem of sparse rewards, Lepus learns an approximate reward function from expert trajectories by combining a random network and a distillation network. We conduct extensive experiments on the MADRaS simulation platform. The experimental results show that multiple agents trained by Lepus can avoid collisions as many as possible while driving simultaneously and outperform the other four methods, that is, DDPG-FDE, PSDDPG, MADDPG, and MAGAIL(DDPG) in terms of stability.
Rare but Severe Neural Machine Translation Errors Induced by Minimal Deletion: An Empirical Study on Chinese and English
Authors: Ruikang Shi, Alvin Grissom II, Duc Minh Trinh
Date: 2022-09-05 00:00:00
Proceeding: COLING 2022 10
Tasks: Machine Translation, Translation

We examine the inducement of rare but severe errors in English-Chinese and Chinese-English in-domain neural machine translation by minimal deletion of the source text with character-based models. By deleting a single character, we can induce severe translation errors. We categorize these errors and compare the results of deleting single characters and single words. We also examine the effect of training data size on the number and types of pathological cases induced by these minimal perturbations, finding significant variation. We find that deleting a word hurts overall translation score more than deleting a character, but certain errors are more likely to occur when deleting characters, with language direction also influencing the effect.