OpenRaiser/Intern-Atlas huggingface dataset:
A Layered Reference Model for Penetration Testing with Reinforcement Learning and Attack Graphs
Venue: arXiv cs.CR 2022
Year: 2022

This paper considers key challenges to using reinforcement learning (RL) with attack graphs to automate penetration testing in real-world applications from a systems perspective. RL approaches to automated penetration testing are actively being developed, but there is no consensus view on the representation of computer networks with which RL should be interacting. Moreover, there are significant open challenges to how those representations can be grounded to the real networks where RL solution methods are applied. This paper elaborates on representation and grounding using topic challenges of interacting with real networks in real-time, emulating realistic adversary behavior, and handling unstable, evolving networks. These challenges are both practical and mathematical, and they directly concern the reliability and dependability of penetration testing systems. This paper proposes a layered reference model to help organize related research and engineering efforts. The presented layered reference model contrasts traditional models of attack graph workflows because it is not scoped to a sequential, feed-forward generation and analysis process, but to broader aspects of lifecycle and continuous deployment. Researchers and practitioners can use the presented layered reference model as a first-principles outline to help orient the systems engineering of their penetration testing systems.
Worst-case Design for RIS-aided Over-the-air Computation with Imperfect CSI
Venue: arXiv cs.IT 2022
Year: 2022

Over-the-air computation (AirComp) enables fast wireless data aggregation at the receiver through concurrent transmission by sensors in the application of Internet-of-Things (IoT). To further improve the performance of AirComp under unfavorable propagation channel conditions, we consider the problem of computation distortion minimization in a reconfigurable intelligent surface (RIS)-aided AirComp system. In particular, we take into account an additive bounded uncertainty of the channel state information (CSI) and the total power constraint, and jointly optimize the transceiver (Tx-Rx) and the RIS phase design from the perspective of worst-case robustness by minimizing the mean squared error (MSE) of the computation. To solve this intractable nonconvex problem, we develop an efficient alternating algorithm where both solutions to the robust sub-problem and to the joint design of Tx-Rx and RIS are obtained in closed forms. Simulation results demonstrate the effectiveness of the proposed method.
FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents
Venue: arXiv cs.CL 2022
Year: 2022

Unstructured data, especially text, continues to grow rapidly in various domains. In particular, in the financial sphere, there is a wealth of accumulated unstructured financial data, such as the textual disclosure documents that companies submit on a regular basis to regulatory agencies, such as the Securities and Exchange Commission (SEC). These documents are typically very long and tend to contain valuable soft information about a company's performance. It is therefore of great interest to learn predictive models from these long textual documents, especially for forecasting numerical key performance indicators (KPIs). Whereas there has been a great progress in pre-trained language models (LMs) that learn from tremendously large corpora of textual data, they still struggle in terms of effective representations for long documents. Our work fills this critical need, namely how to develop better models to extract useful information from long textual documents and learn effective features that can leverage the soft financial and risk information for text regression (prediction) tasks. In this paper, we propose and implement a deep learning framework that splits long documents into chunks and utilizes pre-trained LMs to process and aggregate the chunks into vector representations, followed by self-attention to extract valuable document-level features. We evaluate our model on a collection of 10-K public disclosure reports from US banks, and another dataset of reports submitted by US companies. Overall, our framework outperforms strong baseline methods for textual modeling as well as a baseline regression model using only numerical data. Our work provides better insights into how utilizing pre-trained domain-specific and fine-tuned long-input LMs in representing long documents can improve the quality of representation of textual data, and therefore, help in improving predictive analyses.
Survey on the State-of-the-Art in Device-to-Device Communication: A Resource Allocation Perspective
Venue: arXiv cs.IT 2022
Year: 2022

Device to Device (D2D) communication takes advantage of the proximity between the communicating devices in order to achieve efficient resource utilization, improved throughput and energy efficiency, simultaneous serviceability and reduced latency. One of the main characteristics of D2D communication is reuse of the frequency resource in order to improve spectral efficiency of the system. Nevertheless, frequency reuse introduces significantly high interference levels thus necessitating efficient resource allocation algorithms that can enable simultaneous communication sessions through effective channel and/or power allocation. This survey paper presents a comprehensive investigation of the state-of-the-art resource allocation algorithms in D2D communication underlaying cellular networks. The surveyed algorithms are evaluated based on heterogeneous parameters which constitute the elementary features of a resource allocation algorithm in D2D paradigm. Additionally, in order to familiarize the readers with the basic design of the surveyed resource allocation algorithms, brief description of the mode of operation of each algorithm is presented. The surveyed algorithms are divided into four categories based on their technical doctrine i.e., conventional optimization based, Non-Orthogonal-Multiple-Access (NOMA) based, game theory based and machine learning based techniques. Towards the end, several open challenges are remarked as the future research directions in resource allocation for D2D communication.
Learning Behavior Representations Through Multi-Timescale Bootstrapping
Venue: arXiv cs.LG 2022
Year: 2022

Natural behavior consists of dynamics that are both unpredictable, can switch suddenly, and unfold over many different timescales. While some success has been found in building representations of behavior under constrained or simplified task-based conditions, many of these models cannot be applied to free and naturalistic settings due to the fact that they assume a single scale of temporal dynamics. In this work, we introduce Bootstrap Across Multiple Scales (BAMS), a multi-scale representation learning model for behavior: we combine a pooling module that aggregates features extracted over encoders with different temporal receptive fields, and design a set of latent objectives to bootstrap the representations in each respective space to encourage disentanglement across different timescales. We first apply our method on a dataset of quadrupeds navigating in different terrain types, and show that our model captures the temporal complexity of behavior. We then apply our method to the MABe 2022 Multi-agent behavior challenge, where our model ranks 3rd overall and 1st on two subtasks, and show the importance of incorporating multi-timescales when analyzing behavior.
Synthesizing Mathematical Identities with E-Graphs
Venue: arXiv cs.SC 2022
Year: 2022

Identities compactly describe properties of a mathematical expression and can be leveraged into faster and more accurate function implementations. However, identities must currently be discovered manually, which requires a lot of expertise. We propose a two-phase synthesis and deduplication pipeline that discovers these identities automatically. In the synthesis step, a set of rewrite rules is composed, using an e-graph, to discover candidate identities. However, most of these candidates are duplicates, which a secondary deduplication step discards using integer linear programming and another e-graph. Applied to a set of 61 benchmarks, the synthesis phase generates 7215 candidate identities which the deduplication phase then reduces down to 125 core identities.
Most, And Least, Compact Spanning Trees of a Graph
Venue: arXiv cs.DC 2022
Year: 2022

We introduce the concept of Most, and Least, Compact Spanning Trees - denoted respectively by $T^*(G)$ and $T^\#(G)$ - of a simple, connected, undirected and unweighted graph $G(V, E, W)$. For a spanning tree $T(G) \in \mathcal{T}(G)$ to be considered $T^*(G)$, where $\mathcal{T}(G)$ represents the set of all the spanning trees of the graph $G$, it must have the least average inter-vertex pair (shortest path) distances from amongst the members of the set $\mathcal{T}(G)$. Similarly, for it to be considered $T^\#(G)$, it must have the highest average inter-vertex pair (shortest path) distances. In this work, we present an iteratively greedy rank-and-regress method that produces at least one $T^*(G)$ or $T^\#(G)$ by eliminating one extremal edge per iteration. The rank function for performing the elimination is based on the elements of the matrix of relative forest accessibilities of a graph and the related forest distance. We provide empirical evidence in support of our methodology using some standard graph families: complete graphs, the Erdős-Renyi random graphs and the Barabási-Albert scale-free graphs; and discuss computational complexity of the underlying methods which incur polynomial time costs.
MBGDT:Robust Mini-Batch Gradient Descent
Venue: arXiv cs.LG 2022
Year: 2022

In high dimensions, most machine learning method perform fragile even there are a little outliers. To address this, we hope to introduce a new method with the base learner, such as Bayesian regression or stochastic gradient descent to solve the problem of the vulnerability in the model. Because the mini-batch gradient descent allows for a more robust convergence than the batch gradient descent, we work a method with the mini-batch gradient descent, called Mini-Batch Gradient Descent with Trimming (MBGDT). Our method show state-of-art performance and have greater robustness than several baselines when we apply our method in designed dataset.
Self-Supervision on Images and Text Reduces Reliance on Visual Shortcut Features
Venue: arXiv cs.LG 2022
Year: 2022

Deep learning models trained in a fully supervised manner have been shown to rely on so-called "shortcut" features. Shortcut features are inputs that are associated with the outcome of interest in the training data, but are either no longer associated or not present in testing or deployment settings. Here we provide experiments that show recent self-supervised models trained on images and text provide more robust image representations and reduce the model's reliance on visual shortcut features on a realistic medical imaging example. Additionally, we find that these self-supervised models "forget" shortcut features more quickly than fully supervised ones when fine-tuned on labeled data. Though not a complete solution, our experiments provide compelling evidence that self-supervised models trained on images and text provide some resilience to visual shortcut features.
To Aggregate or Not? Learning with Separate Noisy Labels
Venue: arXiv cs.LG 2022
Year: 2022

The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e.g., via crowdsourcing). A typical way of using these separate labels is to first aggregate them into one and apply standard training methods. The literature has also studied extensively on effective aggregation approaches. This paper revisits this choice and aims to provide an answer to the question of whether one should aggregate separate noisy labels into single ones or use them separately as given. We theoretically analyze the performance of both approaches under the empirical risk minimization framework for a number of popular loss functions, including the ones designed specifically for the problem of learning with noisy labels. Our theorems conclude that label separation is preferred over label aggregation when the noise rates are high, or the number of labelers/annotations is insufficient. Extensive empirical results validate our conclusions.
Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising
Venue: arXiv cs.LG 2022
Year: 2022

Neural network policies trained using Deep Reinforcement Learning (DRL) are well-known to be susceptible to adversarial attacks. In this paper, we consider attacks manifesting as perturbations in the observation space managed by the external environment. These attacks have been shown to downgrade policy performance significantly. We focus our attention on well-trained deterministic and stochastic neural network policies in the context of continuous control benchmarks subject to four well-studied observation space adversarial attacks. To defend against these attacks, we propose a novel defense strategy using a detect-and-denoise schema. Unlike previous adversarial training approaches that sample data in adversarial scenarios, our solution does not require sampling data in an environment under attack, thereby greatly reducing risk during training. Detailed experimental results show that our technique is comparable with state-of-the-art adversarial training approaches.
Towards a Solution to Bongard Problems: A Causal Approach
Venue: arXiv cs.LG 2022
Year: 2022

Even though AI has advanced rapidly in recent years displaying success in solving highly complex problems, the class of Bongard Problems (BPs) yet remain largely unsolved by modern ML techniques. In this paper, we propose a new approach in an attempt to not only solve BPs but also extract meaning out of learned representations. This includes the reformulation of the classical BP into a reinforcement learning (RL) setting which will allow the model to gain access to counterfactuals to guide its decisions but also explain its decisions. Since learning meaningful representations in BPs is an essential sub-problem, we further make use of contrastive learning for the extraction of low level features from pixel data. Several experiments have been conducted for analyzing the general BP-RL setup, feature extraction methods and using the best combination for the feature space analysis and its interpretation.
SPENDER: A Platform for Secure and Privacy-Preserving Decentralized P2P E-Commerce
Venue: arXiv cs.CR 2022
Year: 2022

The blockchain technology empowers secure, trustless, and privacy-preserving trading with cryptocurrencies. However, existing blockchain-based trading platforms only support trading cryptocurrencies with digital assets (e.g., NFTs). Although several payment service providers have started to accept cryptocurrency as a payment method for tangible goods (e.g., Visa, PayPal), customers still need to trust and hand over their private information to centralized E-commerce platforms (e.g., Amazon, eBay). To enable trustless and privacy-preserving trading between cryptocurrencies and real goods, we propose SPENDER, a smart-contract-based platform for Secure and Privacy-PresErviNg Decentralized P2P E-commeRce. The design of our platform enables various advantageous features and brings unlimited future potential. Moreover, our platform provides a complete paradigm for designing real-world Web3 infrastructures on the blockchain, which broadens the application scope and exploits the intrinsic values of cryptocurrencies. The platform has been built and tested on the Terra ecosystem, and we plan to open-source the code later.
OpSparse: a Highly Optimized Framework for Sparse General Matrix Multiplication on GPUs
Venue: arXiv cs.DC 2022
Year: 2022

Sparse general matrix multiplication (SpGEMM) is an important and expensive computation primitive in many real-world applications. Due to SpGEMM's inherent irregularity and the vast diversity of its input matrices, developing high-performance SpGEMM implementation on modern processors such as GPUs is challenging. The state-of-the-art SpGEMM libraries (i.e., $nsparse$ and $spECK$) adopt several algorithms to tackle the challenges of global load balance, local load balance, and allocation of the result matrix. While these libraries focus on the high-level algorithm design for SpGEMM, they neglect several low-level architecture-specific optimizations, which causes inefficient implementations in their libraries. In this paper, we classify their inefficient implementations into seven categories. Based on our observations, we propose a highly optimized SpGEMM library called $OpSparse$. The optimizations in $OpSparse$ include 1) optimizing the binning method by improving the utilization of the shared memory, 2) optimizing the hashing method by reducing the access to the hash table, 3) improving the trade-off between hash collision rate and hardware utilization in the hashing method by setting appropriate binning ranges, 4) reducing the overheads of global memory utilization by minimizing the global memory usage of the metadata, and 5) improving the execution parallelism by overlapping global memory allocation with kernel execution. Performance evaluations with 26 commonly used matrices on an Nvidia Tesla V100 GPU show that $OpSparse$ achieves up to $27.8\times$, $1.81\times$, and $2.04\times$ performance speedup over three state-of-the-art libraries: $cuSPARSE$, $nsparse$, and $spECK$, respectively.
Streaming Algorithms for Ellipsoidal Approximation of Convex Polytopes
Venue: arXiv cs.DS 2022
Year: 2022

We give efficient deterministic one-pass streaming algorithms for finding an ellipsoidal approximation of a symmetric convex polytope. The algorithms are near-optimal in that their approximation factors differ from that of the optimal offline solution only by a factor sub-logarithmic in the aspect ratio of the polytope.
Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs
Venue: arXiv cs.CL 2022
Year: 2022

Providing conversation models with background knowledge has been shown to make open-domain dialogues more informative and engaging. Existing models treat knowledge selection as a sentence ranking or classification problem where each sentence is handled individually, ignoring the internal semantic connection among sentences in the background document. In this work, we propose to automatically convert the background knowledge documents into document semantic graphs and then perform knowledge selection over such graphs. Our document semantic graphs preserve sentence-level information through the use of sentence nodes and provide concept connections between sentences. We jointly apply multi-task learning for sentence-level and concept-level knowledge selection and show that it improves sentence-level selection. Our experiments show that our semantic graph-based knowledge selection improves over sentence selection baselines for both the knowledge selection task and the end-to-end response generation task on HollE and improves generalization on unseen topics in WoW.
From Outcome-Based to Language-Based Preferences
Venue: arXiv cs.GT 2022
Year: 2022

We review the literature on models that try to explain human behavior in social interactions described by normal-form games with monetary payoffs. We start by covering social and moral preferences. We then focus on the growing body of research showing that people react to the language in which actions are described, especially when it activates moral concerns. We conclude by arguing that behavioral economics is in the midst of a paradigm shift towards language-based preferences, which will require an exploration of new models and experimental setups.
Cost-Aware Exploration for Chiplet-Based Architecture with Advanced Packaging Technologies
Venue: arXiv cs.AR 2022
Year: 2022

The chiplet-based System-in-Package~(SiP) technology enables more design flexibility via various inter-chiplet connection and heterogeneous integration. However, it is not known how to convert such flexibility into cost efficiency, which is critical when making a design decision. In this paper, we develop an analytical cost model that can estimate the cost of the 2.5D chiplet-based SiP systems under various interconnection options and technology nodes. We conducted two case studies using our cost model to explore the cost characteristics of the 2.5D chiplet-based SiP system. Based on the case studies, we made several observations on the interposer selection, design partition granularity, and technology node adoption for cost-efficient chiplet-based SiP design.
Online Contextual Decision-Making with a Smart Predict-then-Optimize Method
Venue: arXiv cs.LG 2022
Year: 2022

We study an online contextual decision-making problem with resource constraints. At each time period, the decision-maker first predicts a reward vector and resource consumption matrix based on a given context vector and then solves a downstream optimization problem to make a decision. The final goal of the decision-maker is to maximize the summation of the reward and the utility from resource consumption, while satisfying the resource constraints. We propose an algorithm that mixes a prediction step based on the "Smart Predict-then-Optimize (SPO)" method with a dual update step based on mirror descent. We prove regret bounds and demonstrate that the overall convergence rate of our method depends on the $\mathcal{O}(T^{-1/2})$ convergence of online mirror descent as well as risk bounds of the surrogate loss function used to learn the prediction model. Our algorithm and regret bounds apply to a general convex feasible region for the resource constraints, including both hard and soft resource constraint cases, and they apply to a wide class of prediction models in contrast to the traditional settings of linear contextual models or finite policy spaces. We also conduct numerical experiments to empirically demonstrate the strength of our proposed SPO-type methods, as compared to traditional prediction-error-only methods, on multi-dimensional knapsack and longest path instances.
Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection
Venue: arXiv cs.CR 2022
Year: 2022

Evasion attacks against machine learning models often succeed via iterative probing of a fixed target model, whereby an attack that succeeds once will succeed repeatedly. One promising approach to counter this threat is making a model a moving target against adversarial inputs. To this end, we introduce Morphence-2.0, a scalable moving target defense (MTD) powered by out-of-distribution (OOD) detection to defend against adversarial examples. By regularly moving the decision function of a model, Morphence-2.0 makes it significantly challenging for repeated or correlated attacks to succeed. Morphence-2.0 deploys a pool of models generated from a base model in a manner that introduces sufficient randomness when it responds to prediction queries. Via OOD detection, Morphence-2.0 is equipped with a scheduling approach that assigns adversarial examples to robust decision functions and benign samples to an undefended accurate models. To ensure repeated or correlated attacks fail, the deployed pool of models automatically expires after a query budget is reached and the model pool is seamlessly replaced by a new model pool generated in advance. We evaluate Morphence-2.0 on two benchmark image classification datasets (MNIST and CIFAR10) against 4 reference attacks (3 white-box and 1 black-box). Morphence-2.0 consistently outperforms prior defenses while preserving accuracy on clean data and reducing attack transferability. We also show that, when powered by OOD detection, Morphence-2.0 is able to precisely make an input-based movement of the model's decision function that leads to higher prediction accuracy on both adversarial and benign queries.