OpenRaiser/Intern-Atlas huggingface dataset:
Implications of Multi-Word Expressions on English to Bharti Braille Machine Translation
Venue: arXiv cs.CL 2023
Year: 2023

In this paper, we have shown the improvement of English to Bharti Braille machine translation system. We have shown how we can improve a baseline NMT model by adding some linguistic knowledge to it. This was done for five language pairs where English sentences were translated into five Indian languages and then subsequently to corresponding Bharti Braille. This has been demonstrated by adding a sub-module for translating multi-word expressions. The approach shows promising results as across language pairs, we could see improvement in the quality of NMT outputs. The least improvement was observed in English-Nepali language pair with 22.08% and the most improvement was observed in the English-Hindi language pair with 23.30%.
Algebra Error Classification with Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

Automated feedback as students answer open-ended math questions has significant potential in improving learning outcomes at large scale. A key part of automated feedback systems is an error classification component, which identifies student errors and enables appropriate, predefined feedback to be deployed. Most existing approaches to error classification use a rule-based method, which has limited capacity to generalize. Existing data-driven methods avoid these limitations but specifically require mathematical expressions in student responses to be parsed into syntax trees. This requirement is itself a limitation, since student responses are not always syntactically valid and cannot be converted into trees. In this work, we introduce a flexible method for error classification using pre-trained large language models. We demonstrate that our method can outperform existing methods in algebra error classification, and is able to classify a larger set of student responses. Additionally, we analyze common classification errors made by our method and discuss limitations of automated error classification.
The Undecidability of Unification Modulo $σ$ Alone
Venue: arXiv cs.LO 2023
Year: 2023

The rewriting system sigma is the set of rules propagating explicit substitutions in the lambda-calculus with explicit substitutions. In this note, we prove the undecidability of unification modulo sigma.
Explainable Knowledge Distillation for On-device Chest X-Ray Classification
Venue: arXiv cs.CV 2023
Year: 2023

Automated multi-label chest X-rays (CXR) image classification has achieved substantial progress in clinical diagnosis via utilizing sophisticated deep learning approaches. However, most deep models have high computational demands, which makes them less feasible for compact devices with low computational requirements. To overcome this problem, we propose a knowledge distillation (KD) strategy to create the compact deep learning model for the real-time multi-label CXR image classification. We study different alternatives of CNNs and Transforms as the teacher to distill the knowledge to a smaller student. Then, we employed explainable artificial intelligence (XAI) to provide the visual explanation for the model decision improved by the KD. Our results on three benchmark CXR datasets show that our KD strategy provides the improved performance on the compact student model, thus being the feasible choice for many limited hardware platforms. For instance, when using DenseNet161 as the teacher network, EEEA-Net-C2 achieved an AUC of 83.7%, 87.1%, and 88.7% on the ChestX-ray14, CheXpert, and PadChest datasets, respectively, with fewer parameters of 4.7 million and computational cost of 0.3 billion FLOPS.
Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration
Venue: arXiv cs.CV 2023
Year: 2023

We present a novel deep learning-based framework: Embedded Feature Similarity Optimization with Specific Parameter Initialization (SOPI) for 2D/3D medical image registration which is a most challenging problem due to the difficulty such as dimensional mismatch, heavy computation load and lack of golden evaluation standard. The framework we design includes a parameter specification module to efficiently choose initialization pose parameter and a fine-registration module to align images. The proposed framework takes extracting multi-scale features into consideration using a novel composite connection encoder with special training techniques. We compare the method with both learning-based methods and optimization-based methods on a in-house CT/X-ray dataset as well as simulated data to further evaluate performance. Our experiments demonstrate that the method in this paper has improved the registration performance, and thereby outperforms the existing methods in terms of accuracy and running time. We also show the potential of the proposed method as an initial pose estimator. The code is available at https://github.com/m1nhengChen/SOPI
Context-Aware Document Simplification
Venue: arXiv cs.CL 2023
Year: 2023

To date, most work on text simplification has focused on sentence-level inputs. Early attempts at document simplification merely applied these approaches iteratively over the sentences of a document. However, this fails to coherently preserve the discourse structure, leading to suboptimal output quality. Recently, strategies from controllable simplification have been leveraged to achieve state-of-the-art results on document simplification by first generating a document-level plan (a sequence of sentence-level simplification operations) and using this plan to guide sentence-level simplification downstream. However, this is still limited in that the simplification model has no direct access to the local inter-sentence document context, likely having a negative impact on surface realisation. We explore various systems that use document context within the simplification process itself, either by iterating over larger text units or by extending the system architecture to attend over a high-level representation of document context. In doing so, we achieve state-of-the-art performance on the document simplification task, even when not relying on plan-guidance. Further, we investigate the performance and efficiency tradeoffs of system variants and make suggestions of when each should be preferred.
SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection
Venue: arXiv cs.CV 2023
Year: 2023

Malicious Deepfakes have led to a sharp conflict over distinguishing between genuine and forged faces. Although many countermeasures have been developed to detect Deepfakes ex-post, undoubtedly, passive forensics has not considered any preventive measures for the pristine face before foreseeable manipulations. To complete this forensics ecosystem, we thus put forward the proactive solution dubbed SepMark, which provides a unified framework for source tracing and Deepfake detection. SepMark originates from encoder-decoder-based deep watermarking but with two separable decoders. For the first time the deep separable watermarking, SepMark brings a new paradigm to the established study of deep watermarking, where a single encoder embeds one watermark elegantly, while two decoders can extract the watermark separately at different levels of robustness. The robust decoder termed Tracer that resists various distortions may have an overly high level of robustness, allowing the watermark to survive both before and after Deepfake. The semi-robust one termed Detector is selectively sensitive to malicious distortions, making the watermark disappear after Deepfake. Only SepMark comprising of Tracer and Detector can reliably trace the trusted source of the marked face and detect whether it has been altered since being marked; neither of the two alone can achieve this. Extensive experiments demonstrate the effectiveness of the proposed SepMark on typical Deepfakes, including face swapping, expression reenactment, and attribute editing.
Suggestion Bot: Analyzing the Impact of Automated Suggested Changes on Code Reviews
Venue: arXiv cs.SE 2023
Year: 2023

Peer code reviews are crucial for maintaining the quality of the code in software repositories. Developers have introduced a number of software bots to help with the code review process. Despite the benefits of automating code review tasks, many developers face challenges interacting with these bots due to non-comprehensive feedback and disruptive notifications. In this paper, we analyze how incorporating a bot in software development cycle will decrease turnaround time of pull request. We created a bot called SUGGESTION BOT to automatically review the code base using GitHub's suggested changes functionality in order to solve this issue. A preliminary comparative empirical investigation between the utilization of this bot and manual review procedures was also conducted in this study. We evaluate SUGGESTION BOT concerning its impact on review time and also analyze whether the comments given by the bot are clear and useful for users. Our results provide implications for the design of future systems and improving human-bot interactions for code review.
Prior Global Search Stability on Finite Graphs with Uncertainty. May Greedy Search Win?
Venue: arXiv cs.SI 2023
Year: 2023

This research paper addresses the stability of search algorithms in complex networks when dealing with incomplete information or uncertainty. We propose a theoretical model to investigate whether a global search algorithm with incomplete prior information can be outperformed by a stochastic greedy search on average. The model incorporates random variables to perturb edge weights in the graph, thus capturing the uncertainty of available information. Our findings indicate that some graphs and uncertainty model parameters exist where the global search algorithm fails under uncertainty conditions, while the random greedy search performs better. We derive a critical curve that separates stable from unstable graphs for global search with incomplete information. Interestingly, the critical curve's behavior changes from monotonic to bell-shaped depending on the uncertainty parameters. We test our proposed model through numerical simulations on various synthetic and real-world graphs with different structures. Our results offer insights into the design and optimization of search algorithms for network-based applications, such as communication networks, social networks, and biological networks. We also discuss the study of memory and associative learning in miniature insects, highlighting the potential of efficient search and walking strategies for small robots or devices that operate in a limited area in space.
SWDPM: A Social Welfare-Optimized Data Pricing Mechanism
Venue: arXiv cs.GT 2023
Year: 2023

Data trading has been hindered by privacy concerns associated with user-owned data and the infinite reproducibility of data, making it challenging for data owners to retain exclusive rights over their data once it has been disclosed. Traditional data pricing models relied on uniform pricing or subscription-based models. However, with the development of Privacy-Preserving Computing techniques, the market can now protect the privacy and complete transactions using progressively disclosed information, which creates a technical foundation for generating greater social welfare through data usage. In this study, we propose a novel approach to modeling multi-round data trading with progressively disclosed information using a matchmaking-based Markov Decision Process (MDP) and introduce a Social Welfare-optimized Data Pricing Mechanism (SWDPM) to find optimal pricing strategies. To the best of our knowledge, this is the first study to model multi-round data trading with progressively disclosed information. Numerical experiments demonstrate that the SWDPM can increase social welfare 3 times by up to 54\% in trading feasibility, 43\% in trading efficiency, and 25\% in trading fairness by encouraging better matching of demand and price negotiation among traders.
Combo of Thinking and Observing for Outside-Knowledge VQA
Venue: arXiv cs.CV 2023
Year: 2023

Outside-knowledge visual question answering is a challenging task that requires both the acquisition and the use of open-ended real-world knowledge. Some existing solutions draw external knowledge into the cross-modality space which overlooks the much vaster textual knowledge in natural-language space, while others transform the image into a text that further fuses with the textual knowledge into the natural-language space and completely abandons the use of visual features. In this paper, we are inspired to constrain the cross-modality space into the same space of natural-language space which makes the visual features preserved directly, and the model still benefits from the vast knowledge in natural-language space. To this end, we propose a novel framework consisting of a multimodal encoder, a textual encoder and an answer decoder. Such structure allows us to introduce more types of knowledge including explicit and implicit multimodal and textual knowledge. Extensive experiments validate the superiority of the proposed method which outperforms the state-of-the-art by 6.17% accuracy. We also conduct comprehensive ablations of each component, and systematically study the roles of varying types of knowledge. Codes and knowledge data can be found at https://github.com/PhoebusSi/Thinking-while-Observing.
Accelerating Batch Active Learning Using Continual Learning Techniques
Venue: arXiv cs.LG 2023
Year: 2023

A major problem with Active Learning (AL) is high training costs since models are typically retrained from scratch after every query round. We start by demonstrating that standard AL on neural networks with warm starting fails, both to accelerate training and to avoid catastrophic forgetting when using fine-tuning over AL query rounds. We then develop a new class of techniques, circumventing this problem, by biasing further training towards previously labeled sets. We accomplish this by employing existing, and developing novel, replay-based Continual Learning (CL) algorithms that are effective at quickly learning the new without forgetting the old, especially when data comes from an evolving distribution. We call this paradigm Continual Active Learning (CAL). We show CAL achieves significant speedups using a plethora of replay schemes that use model distillation and that select diverse, uncertain points from the history. We conduct experiments across many data domains, including natural language, vision, medical imaging, and computational biology, each with different neural architectures and dataset sizes. CAL consistently provides a 3x reduction in training time, while retaining performance.
WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?
Venue: arXiv cs.HC 2023
Year: 2023

Studies conducted on Western, Educated, Industrialized, Rich, and Democratic (WEIRD) samples are considered atypical of the world's population and may not accurately represent human behavior. In this study, we aim to quantify the extent to which the ACM FAccT conference, the leading venue in exploring Artificial Intelligence (AI) systems' fairness, accountability, and transparency, relies on WEIRD samples. We collected and analyzed 128 papers published between 2018 and 2022, accounting for 30.8% of the overall proceedings published at FAccT in those years (excluding abstracts, tutorials, and papers without human-subject studies or clear country attribution for the participants). We found that 84% of the analyzed papers were exclusively based on participants from Western countries, particularly exclusively from the U.S. (63%). Only researchers who undertook the effort to collect data about local participants through interviews or surveys added diversity to an otherwise U.S.-centric view of science. Therefore, we suggest that researchers collect data from under-represented populations to obtain an inclusive worldview. To achieve this goal, scientific communities should champion data collection from such populations and enforce transparent reporting of data biases.
Speranza: Usable, privacy-friendly software signing
Venue: arXiv cs.CR 2023
Year: 2023

Software repositories, used for wide-scale open software distribution, are a significant vector for security attacks. Software signing provides authenticity, mitigating many such attacks. Developer-managed signing keys pose usability challenges, but certificate-based systems introduce privacy problems. This work, Speranza, uses certificates to verify software authenticity but still provides anonymity to signers using zero-knowledge identity co-commitments. In Speranza, a signer uses an automated certificate authority (CA) to create a private identity-bound signature and proof of authorization. Verifiers check that a signer was authorized to publish a package without learning the signer's identity. The package repository privately records each package's authorized signers, but publishes only commitments to identities in a public map. Then, when issuing certificates, the CA issues the certificate to a distinct commitment to the same identity. The signer then creates a zero-knowledge proof that these are identity co-commitments. We implemented a proof-of-concept for Speranza. We find that costs to maintainers (signing) and end users (verifying) are small (< 1 ms), even for a repository with millions of packages. Techniques inspired by recent key transparency systems reduce the bandwidth for serving authorization policies to 2 KiB. Server costs in this system are negligible. Our evaluation finds that Speranza is practical on the scale of the largest software repositories. We also emphasize practicality and deployability in this project. By building on existing technology and employing relatively simple and well-established cryptographic techniques, Speranza can be deployed for wide-scale use with only a few hundred lines of code and minimal changes to existing infrastructure. Speranza is a practical way to bring privacy and authenticity together for more trustworthy open-source software.
SENDD: Sparse Efficient Neural Depth and Deformation for Tissue Tracking
Venue: arXiv cs.CV 2023
Year: 2023

Deformable tracking and real-time estimation of 3D tissue motion is essential to enable automation and image guidance applications in robotically assisted surgery. Our model, Sparse Efficient Neural Depth and Deformation (SENDD), extends prior 2D tracking work to estimate flow in 3D space. SENDD introduces novel contributions of learned detection, and sparse per-point depth and 3D flow estimation, all with less than half a million parameters. SENDD does this by using graph neural networks of sparse keypoint matches to estimate both depth and 3D flow anywhere. We quantify and benchmark SENDD on a comprehensively labelled tissue dataset, and compare it to an equivalent 2D flow model. SENDD performs comparably while enabling applications that 2D flow cannot. SENDD can track points and estimate depth at 10fps on an NVIDIA RTX 4000 for 1280 tracked (query) points and its cost scales linearly with an increasing/decreasing number of points. SENDD enables multiple downstream applications that require estimation of 3D motion in stereo endoscopy.
Demand Privacy in Hotplug Caching Systems
Venue: arXiv cs.IT 2023
Year: 2023

Coded caching, introduced by Maddah-Ali and Niesen (MAN), is a model where a server broadcasts multicast packets to users with a local cache that is leveraged so as to reduce the peak network communication load. The original MAN model does not consider missing demands (i.e., some users may not request a file) or privacy issues (i.e., decoding the multicast packets may expose the users' demands). The former issue was captured by the hotplug model with offline users, where the server starts sending multicast packets after having received a certain number of file requests. The latter issue was addressed by devoting part of the cache to store privacy keys to help users decode their requested file while remaining completely ignorant about the demands of the remaining users. This paper investigates the problem of private demands against colluding users in the hotplug model with offline users. Two achievable schemes are proposed based on Maximum Distance Separable (MDS) codes. They achieve lower subpacketization, and lower load in the small memory regime compared to baseline schemes that trivially include demand privacy or offline users in known schemes.
Pyramid Texture Filtering
Venue: arXiv cs.CV 2023
Year: 2023

We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation -- the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. This inspires our central idea for texture filtering, which is to progressively upsample the very low-resolution coarsest Gaussian pyramid level to a full-resolution texture smoothing result with well-preserved structures, under the guidance of each fine-scale Gaussian pyramid level and its associated Laplacian pyramid level. We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts. We also demonstrate the applicability of our method on various applications including detail enhancement, image abstraction, HDR tone mapping, inverse halftoning, and LDR image enhancement.
Probabilistic Group Testing in Distributed Computing with Attacked Workers
Venue: arXiv cs.IT 2023
Year: 2023

The problem of distributed matrix-vector product is considered, where the server distributes the task of the computation among $n$ worker nodes, out of which $L$ are compromised (but non-colluding) and may return incorrect results. Specifically, it is assumed that the compromised workers are unreliable, that is, at any given time, each compromised worker may return an incorrect and correct result with probabilities $α$ and $1-α$, respectively. Thus, the tests are noisy. This work proposes a new probabilistic group testing approach to identify the unreliable/compromised workers with $O\left(\frac{L\log(n)}α\right)$ tests. Moreover, using the proposed group testing method, sparse parity-check codes are constructed and used in the considered distributed computing framework for encoding, decoding and identifying the unreliable workers. This methodology has two distinct features: (i) the cost of identifying the set of $L$ unreliable workers at the server can be shown to be considerably lower than existing distributed computing methods, and (ii) the encoding and decoding functions are easily implementable and computationally efficient.
Semantic uncertainty guides the extension of conventions to new referents
Venue: arXiv cs.CL 2023
Year: 2023

A long tradition of studies in psycholinguistics has examined the formation and generalization of ad hoc conventions in reference games, showing how newly acquired conventions for a given target transfer to new referential contexts. However, another axis of generalization remains understudied: how do conventions formed for one target transfer to completely distinct targets, when specific lexical choices are unlikely to repeat? This paper presents two dyadic studies (N = 240) that address this axis of generalization, focusing on the role of nameability -- the a priori likelihood that two individuals will share the same label. We leverage the recently-released KiloGram dataset, a collection of abstract tangram images that is orders of magnitude larger than previously available, exhibiting high diversity of properties like nameability. Our first study asks how nameability shapes convention formation, while the second asks how new conventions generalize to entirely new targets of reference. Our results raise new questions about how ad hoc conventions extend beyond target-specific re-use of specific lexical choices.