OpenRaiser/Intern-Atlas huggingface dataset:
Life after BERT: What do Other Muppets Understand about Language?
Venue: arXiv cs.CL 2022
Year: 2022

Existing pre-trained transformer analysis works usually focus only on one or two model families at a time, overlooking the variability of the architecture and pre-training objectives. In our work, we utilize the oLMpics benchmark and psycholinguistic probing datasets for a diverse set of 29 models including T5, BART, and ALBERT. Additionally, we adapt the oLMpics zero-shot setup for autoregressive models and evaluate GPT networks of different sizes. Our findings show that none of these models can resolve compositional questions in a zero-shot fashion, suggesting that this skill is not learnable using existing pre-training objectives. Furthermore, we find that global model decisions such as architecture, directionality, size of the dataset, and pre-training objective are not predictive of a model's linguistic capabilities.
Phrase-level Textual Adversarial Attack with Label Preservation
Venue: arXiv cs.CL 2022
Year: 2022

Generating high-quality textual adversarial examples is critical for investigating the pitfalls of natural language processing (NLP) models and further promoting their robustness. Existing attacks are usually realized through word-level or sentence-level perturbations, which either limit the perturbation space or sacrifice fluency and textual quality, both affecting the attack effectiveness. In this paper, we propose Phrase-Level Textual Adversarial aTtack (PLAT) that generates adversarial samples through phrase-level perturbations. PLAT first extracts the vulnerable phrases as attack targets by a syntactic parser, and then perturbs them by a pre-trained blank-infilling model. Such flexible perturbation design substantially expands the search space for more effective attacks without introducing too many modifications, and meanwhile maintaining the textual fluency and grammaticality via contextualized generation using surrounding texts. Moreover, we develop a label-preservation filter leveraging the likelihoods of language models fine-tuned on each class, rather than textual similarity, to rule out those perturbations that potentially alter the original class label for humans. Extensive experiments and human evaluation demonstrate that PLAT has a superior attack effectiveness as well as a better label consistency than strong baselines.
Real Time Detection Free Tracking of Multiple Objects Via Equilibrium Optimizer
Venue: arXiv cs.CV 2022
Year: 2022

Multiple objects tracking (MOT) is a difficult task, as it usually requires special hardware and higher computation complexity. In this work, we present a new framework of MOT by using of equilibrium optimizer (EO) algorithm and reducing the resolution of the bounding boxes of the objects to solve such problems in the detection free framework. First, in the first frame the target objects are initialized and its size is computed, then its resolution is reduced if it is higher than a threshold, and then modeled by their kernel color histogram to establish a feature model. The Bhattacharya distances between the histogram of object models and other candidates are used as the fitness function to be optimized. Multiple agents are generated by EO, according to the number of the target objects to be tracked. EO algorithm is used because of its efficiency and lower computation cost compared to other algorithms in global optimization. Experimental results confirm that EO multi-object tracker achieves satisfying tracking results then other trackers.
A Deep Conjugate Direction Method for Iteratively Solving Linear Systems
Venue: arXiv cs.LG 2022
Year: 2022

We present a novel deep learning approach to approximate the solution of large, sparse, symmetric, positive-definite linear systems of equations. These systems arise from many problems in applied science, e.g., in numerical methods for partial differential equations. Algorithms for approximating the solution to these systems are often the bottleneck in problems that require their solution, particularly for modern applications that require many millions of unknowns. Indeed, numerical linear algebra techniques have been investigated for many decades to alleviate this computational burden. Recently, data-driven techniques have also shown promise for these problems. Motivated by the conjugate gradients algorithm that iteratively selects search directions for minimizing the matrix norm of the approximation error, we design an approach that utilizes a deep neural network to accelerate convergence via data-driven improvement of the search directions. Our method leverages a carefully chosen convolutional network to approximate the action of the inverse of the linear operator up to an arbitrary constant. We train the network using unsupervised learning with a loss function equal to the $L^2$ difference between an input and the system matrix times the network evaluation, where the unspecified constant in the approximate inverse is accounted for. We demonstrate the efficacy of our approach on spatially discretized Poisson equations with millions of degrees of freedom arising in computational fluid dynamics applications. Unlike state-of-the-art learning approaches, our algorithm is capable of reducing the linear system residual to a given tolerance in a small number of iterations, independent of the problem size. Moreover, our method generalizes effectively to various systems beyond those encountered during training.
A Domain-adaptive Pre-training Approach for Language Bias Detection in News
Venue: arXiv cs.CL 2022
Year: 2022

Media bias is a multi-faceted construct influencing individual behavior and collective decision-making. Slanted news reporting is the result of one-sided and polarized writing which can occur in various forms. In this work, we focus on an important form of media bias, i.e. bias by word choice. Detecting biased word choices is a challenging task due to its linguistic complexity and the lack of representative gold-standard corpora. We present DA-RoBERTa, a new state-of-the-art transformer-based model adapted to the media bias domain which identifies sentence-level bias with an F1 score of 0.814. In addition, we also train, DA-BERT and DA-BART, two more transformer models adapted to the bias domain. Our proposed domain-adapted models outperform prior bias detection approaches on the same data.
Instruction Induction: From Few Examples to Natural Language Task Descriptions
Venue: arXiv cs.CL 2022
Year: 2022

Large language models are able to perform a task by conditioning on a few input-output demonstrations - a paradigm known as in-context learning. We show that language models can explicitly infer an underlying task from a few demonstrations by prompting them to generate a natural language instruction that fits the examples. To explore this ability, we introduce the instruction induction challenge, compile a dataset consisting of 24 tasks, and define a novel evaluation metric based on executing the generated instruction. We discover that, to a large extent, the ability to generate instructions does indeed emerge when using a model that is both large enough and aligned to follow instructions; InstructGPT achieves 65.7% of human performance in our execution-based metric, while the original GPT-3 model reaches only 9.8% of human performance. This surprising result suggests that instruction induction might be a viable learning paradigm in and of itself, where instead of fitting a set of latent continuous parameters to the data, one searches for the best description in the natural language hypothesis space.
Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner
Venue: arXiv cs.LG 2022
Year: 2022

Inverse reinforcement learning (IRL) deals with estimating an agent's utility function from its actions. In this paper, we consider how an agent can hide its strategy and mitigate an adversarial IRL attack; we call this inverse IRL (I-IRL). How should the decision maker choose its response to ensure a poor reconstruction of its strategy by an adversary performing IRL to estimate the agent's strategy? This paper comprises four results: First, we present an adversarial IRL algorithm that estimates the agent's strategy while controlling the agent's utility function. Our second result for I-IRL result spoofs the IRL algorithm used by the adversary. Our I-IRL results are based on revealed preference theory in micro-economics. The key idea is for the agent to deliberately choose sub-optimal responses that sufficiently masks its true strategy. Third, we give a sample complexity result for our main I-IRL result when the agent has noisy estimates of the adversary specified utility function. Finally, we illustrate our I-IRL scheme in a radar problem where a meta-cognitive radar is trying to mitigate an adversarial target.
A Graph Enhanced BERT Model for Event Prediction
Venue: arXiv cs.CL 2022
Year: 2022

Predicting the subsequent event for an existing event context is an important but challenging task, as it requires understanding the underlying relationship between events. Previous methods propose to retrieve relational features from event graph to enhance the modeling of event correlation. However, the sparsity of event graph may restrict the acquisition of relevant graph information, and hence influence the model performance. To address this issue, we consider automatically building of event graph using a BERT model. To this end, we incorporate an additional structured variable into BERT to learn to predict the event connections in the training process. Hence, in the test process, the connection relationship for unseen events can be predicted by the structured variable. Results on two event prediction tasks: script event prediction and story ending prediction, show that our approach can outperform state-of-the-art baseline methods.
On the Parameterized Complexity of the $s$-Club Cluster Edge Deletion Problem
Venue: arXiv cs.DS 2022
Year: 2022

We study the parameterized complexity of the $s$-Club Cluster Edge Deletion problem: Given a graph $G$ and two integers $s \ge 2$ and $k \ge 1$, is it possible to remove at most $k$ edges from $G$ such that each connected component of the resulting graph has diameter at most $s$? This problem is known to be NP-hard already when $s = 2$. We prove that it admits a fixed-parameter tractable algorithm when parameterized by $s$ and the treewidth of the input graph.
On the Price of Fairness of Allocating Contiguous Blocks
Venue: arXiv cs.GT 2022
Year: 2022

In this work, we revisit the problem of fairly allocating a number of indivisible items that are located on a line to multiple agents. A feasible allocation requires that the allocated items to each agent are connected on the line. The items can be goods on which agents have non-negative utilities, or chores on which the utilities are non-positive. Our objective is to understand the extent to which welfare is inevitably sacrificed by enforcing the allocations to be fair, i.e., price of fairness (PoF). We study both egalitarian and utilitarian welfare. Previous works by Suksompong [Discret. Appl. Math., 2019] and Höhne and van Stee [Inf. Comput., 2021] have studied PoF regarding the notions of envy-freeness and proportionality. However, these fair allocations barely exist for indivisible items, and thus in this work, we focus on the relaxations of maximin share fairness and proportionality up to one item, which are guaranteed to be satisfiable. For most settings, we give (almost) tight ratios of PoF and all the upper bounds are proved by designing polynomial time algorithms.
Grad-CAM++ is Equivalent to Grad-CAM With Positive Gradients
Venue: arXiv cs.CV 2022
Year: 2022

The Grad-CAM algorithm provides a way to identify what parts of an image contribute most to the output of a classifier deep network. The algorithm is simple and widely used for localization of objects in an image, although some researchers have point out its limitations, and proposed various alternatives. One of them is Grad-CAM++, that according to its authors can provide better visual explanations for network predictions, and does a better job at locating objects even for occurrences of multiple object instances in a single image. Here we show that Grad-CAM++ is practically equivalent to a very simple variation of Grad-CAM in which gradients are replaced with positive gradients.
Vision-based Anti-UAV Detection and Tracking
Venue: arXiv cs.CV 2022
Year: 2022

Unmanned aerial vehicles (UAV) have been widely used in various fields, and their invasion of security and privacy has aroused social concern. Several detection and tracking systems for UAVs have been introduced in recent years, but most of them are based on radio frequency, radar, and other media. We assume that the field of computer vision is mature enough to detect and track invading UAVs. Thus we propose a visible light mode dataset called Dalian University of Technology Anti-UAV dataset, DUT Anti-UAV for short. It contains a detection dataset with a total of 10,000 images and a tracking dataset with 20 videos that include short-term and long-term sequences. All frames and images are manually annotated precisely. We use this dataset to train several existing detection algorithms and evaluate the algorithms' performance. Several tracking methods are also tested on our tracking dataset. Furthermore, we propose a clear and simple tracking algorithm combined with detection that inherits the detector's high precision. Extensive experiments show that the tracking performance is improved considerably after fusing detection, thus providing a new attempt at UAV tracking using our dataset.The datasets and results are publicly available at: https://github.com/wangdongdut/DUT-Anti-UAV
Fusion Subspace Clustering for Incomplete Data
Venue: arXiv cs.LG 2022
Year: 2022

This paper introduces {\em fusion subspace clustering}, a novel method to learn low-dimensional structures that approximate large scale yet highly incomplete data. The main idea is to assign each datum to a subspace of its own, and minimize the distance between the subspaces of all data, so that subspaces of the same cluster get {\em fused} together. Our method allows low, high, and even full-rank data; it directly accounts for noise, and its sample complexity approaches the information-theoretic limit. In addition, our approach provides a natural model selection {\em clusterpath}, and a direct completion method. We give convergence guarantees, analyze computational complexity, and show through extensive experiments on real and synthetic data that our approach performs comparably to the state-of-the-art with complete data, and dramatically better if data is missing.
Human and technological infrastructures of fact-checking
Venue: arXiv cs.HC 2022
Year: 2022

Increasing demands for fact-checking has led to a growing interest in developing systems and tools to automate the fact-checking process. However, such systems are limited in practice because their system design often does not take into account how fact-checking is done in the real world and ignores the insights and needs of various stakeholder groups core to the fact-checking process. This paper unpacks the fact-checking process by revealing the infrastructures -- both human and technological -- that support and shape fact-checking work. We interviewed 26 participants belonging to 16 fact-checking teams and organizations with representation from 4 continents. Through these interviews, we describe the human infrastructure of fact-checking by identifying and presenting, in-depth, the roles of six primary stakeholder groups, 1) Editors, 2) External fact-checkers, 3) In-house fact-checkers, 4) Investigators and researchers, 5) Social media managers, and 6) Advocators. Our findings highlight that the fact-checking process is a collaborative effort among various stakeholder groups and associated technological and informational infrastructures. By rendering visibility to the infrastructures, we reveal how fact-checking has evolved to include both short-term claims centric and long-term advocacy centric fact-checking. Our work also identifies key social and technical needs and challenges faced by each stakeholder group. Based on our findings, we suggest that improving the quality of fact-checking requires systematic changes in the civic, informational, and technological contexts.
The Case for Perspective in Multimodal Datasets
Venue: arXiv cs.CL 2022
Year: 2022

This paper argues in favor of the adoption of annotation practices for multimodal datasets that recognize and represent the inherently perspectivized nature of multimodal communication. To support our claim, we present a set of annotation experiments in which FrameNet annotation is applied to the Multi30k and the Flickr 30k Entities datasets. We assess the cosine similarity between the semantic representations derived from the annotation of both pictures and captions for frames. Our findings indicate that: (i) frame semantic similarity between captions of the same picture produced in different languages is sensitive to whether the caption is a translation of another caption or not, and (ii) picture annotation for semantic frames is sensitive to whether the image is annotated in presence of a caption or not.
Power and accountability in reinforcement learning applications to environmental policy
Venue: arXiv cs.CY 2022
Year: 2022

Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental regulations. Of the ML techniques available to address pressing environmental problems (e.g., climate change, biodiversity loss), Reinforcement Learning (RL) may both hold the greatest promise and present the most pressing perils. This paper explores how RL-driven policy refracts existing power relations in the environmental domain while also creating unique challenges to ensuring equitable and accountable environmental decision processes. We leverage examples from RL applications to climate change mitigation and fisheries management to explore how RL technologies shift the distribution of power between resource users, governing bodies, and private industry.
Scalable and Privacy-Focused Company-Centric Supply Chain Management
Venue: arXiv cs.CR 2022
Year: 2022

Blockchain technology promises to overcome trust and privacy concerns inherent to centralized information sharing. However, current decentralized supply chain management systems do either not meet privacy and scalability requirements or require a trustworthy consortium, which is challenging for increasingly dynamic supply chains with constantly changing participants. In this paper, we propose CCChain, a scalable and privacy-aware supply chain management system that stores all information locally to give companies complete sovereignty over who accesses their data. Still, tamper protection of all data through a permissionless blockchain enables on-demand tracking and tracing of products as well as reliable information sharing while affording the detection of data inconsistencies. Our evaluation confirms that CCChain offers superior scalability in comparison to alternatives while also enabling near real-time tracking and tracing for many, less complex products.
Digital Twin for Secure Semiconductor Lifecycle Management: Prospects and Applications
Venue: arXiv cs.CR 2022
Year: 2022

The expansive globalization of the semiconductor supply chain has introduced numerous untrusted entities into different stages of a device's lifecycle. To make matters worse, the increase complexity in the design as well as aggressive time to market requirements of the newer generation of integrated circuits can lead either designers to unintentionally introduce security vulnerabilities or verification engineers to fail in detecting them earlier in the design lifecycle. These overlooked or undetected vulnerabilities can be exploited by malicious entities in subsequent stages of the lifecycle through an ever widening variety of hardware attacks. The ability to ascertain the provenance of these vulnerabilities, therefore, becomes a pressing issue when the security assurance across the whole lifecycle is required to be ensured. We posit that if there is a malicious or unintentional breach of security policies of a device, it will be reflected in the form of anomalies in the traditional design, verification and testing activities throughout the lifecycle. With that, a digital simulacrum of a device's lifecycle, called a digital twin (DT), can be formed by the data gathered from different stages to secure the lifecycle of the device. In this paper, we put forward a realization of intertwined relationships of security vulnerabilities with data available from the silicon lifecycle and formulate different components of an AI driven DT framework. The proposed DT framework leverages these relationships and relational learning to achieve Forward and Backward Trust Analysis functionalities enabling security aware management of the entire lifecycle. Finally, we provide potential future research avenues and challenges for realization of the digital twin framework to enable secure semiconductor lifecycle management.
MolMiner: You only look once for chemical structure recognition
Venue: arXiv cs.CV 2022
Year: 2022

Molecular structures are always depicted as 2D printed form in scientific documents like journal papers and patents. However, these 2D depictions are not machine-readable. Due to a backlog of decades and an increasing amount of these printed literature, there is a high demand for the translation of printed depictions into machine-readable formats, which is known as Optical Chemical Structure Recognition (OCSR). Most OCSR systems developed over the last three decades follow a rule-based approach where the key step of vectorization of the depiction is based on the interpretation of vectors and nodes as bonds and atoms. Here, we present a practical software MolMiner, which is primarily built up using deep neural networks originally developed for semantic segmentation and object detection to recognize atom and bond elements from documents. These recognized elements can be easily connected as a molecular graph with distance-based construction algorithm. We carefully evaluate our software on four benchmark datasets with the state-of-the-art performance. Various real application scenarios are also tested, yielding satisfactory outcomes. The free download links of Mac and Windows versions are available: Mac: https://molminer-cdn.iipharma.cn/pharma-mind/artifact/latest/mac/PharmaMind-mac-latest-setup.dmg and Windows: https://molminer-cdn.iipharma.cn/pharma-mind/artifact/latest/win/PharmaMind-win-latest-setup.exe