Reviewerly/exHarmony huggingface dataset:
I-RAFT: Optical Flow Estimation Model Based on Multi-scale Initialization Strategy

Optical flow estimation is a fundamental task in the field of computer vision, and recent advancements in deep learning networks have led to significant performance improvements. However, existing models that employ recurrent neural networks to update optical flow from an initial value of 0 suffer from issues of instability and slow training. To address this, we propose a simple yet effective optical flow initialization module as part of the optical flow initialization stage, leading to the development of an optical flow estimation model named I-RAFT. Our approach draws inspiration from other successful algorithms in computer vision to tackle the multi-scale problem. By ting initial optical flow values from the 4D cost volume and employing a voting module, we achieve initialization. Importantly, the initialization module can be seamlessly integrated into other optical flow estimation models. Additionally, we introduce a novel multi-scale extraction module for capturing context features. Extensive experiments demonstrate the simplicity and effectiveness of our proposed model, with I-RAFT achieving state-of-the-art performance on the Sintel dataset and the second-best performance on the KITTI dataset. Remarkably, our model achieves these results with a 24.48% reduction in parameters compared to the previous state-of-the-art MatchFlow model. We have made our code publicly available to facilitate further research and development ( https://github.com/zhangxirui-1997/I-RAFT ).
Communication Lower Bounds of Key-Agreement Protocols via Density Increment Arguments

Constructing key-agreement protocols in the random oracle model (ROM) is a viable method to assess the feasibility of developing public-key cryptography within Minicrypt. Unfortunately, as shown by Impagliazzo and Rudich (STOC 1989) and Barak and Mahmoody (Crypto 2009), such protocols can only guarantee limited security: any $$\ell $$ -query protocol can be attacked by an $$O(\ell ^2)$$ -query adversary. This quadratic gap matches the key-agreement protocol proposed by Merkle (CACM 78), known as Merkle’s Puzzles. Besides query complexity, the communication complexity of key-agreement protocols in the ROM is also an interesting question in the realm of find-grained cryptography, even though only limited security is achievable. Haitner et al. (ITCS 2019) first observed that in Merkle’s Puzzles, to obtain secrecy against an eavesdropper with $$O(\ell ^2)$$ queries, the honest parties must exchange $$\varOmega (\ell )$$ bits. Therefore, they conjectured that high communication complexity is unavoidable, any $$\ell $$ -query protocols with c bits of communication could be attacked by an $$O(c\cdot \ell )$$ -query adversary. This, if true, will suggest that Merkle’s Puzzle is also optimal regarding communication complexity. Building upon techniques from communication complexity, Haitner et al. (ITCS 2019) confirmed this conjecture for two types of key agreement protocols with certain natural properties. This work affirms the above conjecture for all non-adaptive protocols with perfect completeness. Our proof uses a novel idea called density increment argument. This method could be of independent interest as it differs from previous communication lower bounds techniques (and bypasses some technical barriers).
Immunizing Backdoored PRGs

A backdoored Pseudorandom Generator (PRG) is a PRG which looks pseudorandom to the outside world, but a saboteur can break PRG security by planting a backdoor into a seemingly honest choice of public parameters, pk, for the system. Backdoored PRGs became increasingly important due to revelations about NIST’s backdoored Dual EC PRG, and later results about its practical exploitability. Motivated by this, at Eurocrypt’15 Dodis et al. [22] initiated the question of immunizing backdoored PRGs. A k-immunization scheme repeatedly applies a post-processing function to the output of k backdoored PRGs, to render any (unknown) backdoors provably useless. For $$k=1$$ , [22] showed that no deterministic immunization is possible, but then constructed “seeded” 1-immunizer either in the random oracle model, or under strong non-falsifiable assumptions. As our first result, we show that no seeded 1-immunization scheme can be black-box reduced to any efficiently falsifiable assumption. This motivates studying k-immunizers for $$k\ge 2$$ , which have an additional advantage of being deterministic (i.e., “seedless”). Indeed, prior work at CCS’17 [37] and CRYPTO’18 [8] gave supporting evidence that simple k-immunizers might exist, albeit in slightly different settings. Unfortunately, we show that simple standard model proposals of [8, 37] (including the XOR function [8]) provably do not work in our setting. On a positive, we confirm the intuition of [37] that a (seedless) random oracle is a provably secure 2-immunizer. On a negative, no (seedless) 2-immunization scheme can be black-box reduced to any efficiently falsifiable assumption, at least for a large class of natural 2-immunizers which includes all “cryptographic hash functions.” In summary, our results show that k-immunizers occupy a peculiar place in the cryptographic world. While they likely exist, and can be made practical and efficient, it is unlikely one can reduce their security to a “clean” standard-model assumption.
Multilinear Schwartz-Zippel Mod N and Lattice-Based Succinct Arguments

We show that for $$\textbf{x}\overset{\$}{\leftarrow }[0,2^\lambda )^\mu $$ and any integer N the probability that $$f(\textbf{x})\equiv 0 \bmod N$$ for any non-zero multilinear polynomial $$f\in \mathbb {Z}[X_1, \dots ,X_\mu ]$$ , co-prime to N is inversely proportional to N. As a corollary we show that if $$\log _2 N\ge \log _2(2\mu )\lambda +8\mu ^2 $$ then the probability is bounded by $$\frac{\mu +1}{2^\lambda }$$ . We also give tighter numerically derived bounds, showing that if $$\log _2 N\ge {418}$$ , and $$\mu \le 20$$ the probability is bounded by $$\frac{\mu }{2^\lambda }+2^{-120}$$ . We then apply this Multilinear Composite Schwartz-Zippel Lemma (LCSZ) to resolve an open problem in the literature on succinct arguments: that the Bulletproofs protocol for linear relations over classical Pedersen commitments in prime-order groups remains knowledge sound when generalized to commitment schemes that are binding only over short integer vectors. In particular, this means that the Bulletproofs protocol can be instantiated with plausibly post-quantum commitments from lattice hardness assumptions (SIS/R-SIS/M-SIS). It can also be instantiated with commitments based on groups of unknown order (GUOs), in which case the verification time becomes logarithmic instead of linear time. $$^{1}$$ Prior work on lattice-based Bulletproofs (Crypto 2020) and its extensions required modifying the protocol to sample challenges from special sets of polynomial size. This results in a non-negligible knowledge error, necessitating parallel repetition to amplify soundness, which impacts efficiency and poses issues for the Fiat-Shamir transform. Our analysis shows knowledge soundness for the original Bulletproofs protocol with the exponential-size integer challenge set $$[0,2^\lambda ]$$ and thus achieves a negligible soundness error without repetition, circumventing a previous impossibility result (Crypto 2021). Our analysis also closes a critical gap in the original security proof of DARK, a GUO-based polynomial commitment scheme (Eurocrypt 2020). Along the way to achieving our result we also define Almost Special Soundness (AMSS), a generalization of Special-Soundness. Our main result is divided into two parts: (1) that the Bulletproofs protocol over generalized commitments is AMSS, and (2) that AMSS implies knowledge soundness. This framework serves to simplify the application of our analytical techniques to protocols beyond Bulletproofs in the future( $$^{1}$$ This paper incorporates content published in the updated EPRINT of DARK [18]. The full version of this paper containing proofs is available online [17].).
Aspect-Based Sentiment Analysis Using Dual Probability Graph Convolutional Networks (DP-GCN) Integrating Multi-scale Information

Aspect-based sentiment analysis (ABSA) is a fine-grained entity-level sentiment analysis task that aims to identify the emotions associated with specific aspects or details within text. ABSA has been widely applied to various areas such as analyzing product reviews and monitoring public opinion on social media. In recent years, methods based on graph neural networks combined with syntactic information have achieved promising results in the task of ABSA. However, existing methods using syntactic dependency trees contain redundant information, and the relationships with identical weights do not reflect the importance of the aspect words and opinion words' dependencies. Moreover, ABSA is limited by issues such as short sentence length and informal expression. Therefore, this paper proposes a Double Probabilistic Graph Convolutional Network (DP-GCN) integrating multi-scale information to address the aforementioned issues. Firstly, the original dependency tree is reshaped through pruning, creating aspect-based syntactic dependency tree corresponding syntactic dependency weights. Next, two probability attention matrixes are constructed based on both semantic and syntactic information. The semantic probability attention matrix represents the weighted directed graph of semantic correlations between words. Compared with the discrete adjacency matrix directly constructed by the syntax dependency tree, the probability matrix representing the dependency relationship between words based on syntax information contains rich syntactic information. Based on this, semantic information and syntactic dependency information are separately extracted via graph convolutional networks. Interactive attention is used to guide mutual learning between semantic information and syntactic dependency information, enabling full interaction and fusion of both types of information before finally carrying out sentiment polarity classification. Our model was tested on four public datasets, Restaurant, Laptop, Twitter and MAMS. The accuracy (ACC) and F1 score improved by 0.14% to 1.26% and 0.4% to 2.19%, respectively, indicating its outstanding performance.
Partial Multi-label Learning via Constraint Clustering

Multi-label learning (MLL) refers to a learning task where each instance is associated with a set of labels. However, in most real-world applications, the labeling process is very expensive and time consuming. Partially multi-label learning (PML) refers to MLL where only a part of the labels are correctly annotated and the rest are false positive labels. The main purpose of PML is to learn and predict unseen multi-label data with less annotation cost. To address the ambiguities in the label set, existing popular PML research attempts to extract the label confidence for each candidate label. These methods mainly perform disambiguation by considering the correlation among labels or/and features. However, in PML because of noisy labels, the true correlation among labels is corrupted. These methods can be easily misled by noisy false-positive labels. In this paper, we propose Partial Multi-Label learning method via Constraint Clustering (PML-CC) to address PML based on the underlying structure of data. PML-CC gradually extracts high-confidence labels and then uses them to extract the rest labels. To find the high-confidence labels, it solves PML as a clustering task while considering extracted information from previous steps as constraints. In each step, PML-CC updates the extracted labels and uses them to extract the other labels. Experimental results show that our method successfully tackles PML tasks and outperforms the state-of-the-art methods on artificial and real-world datasets.
Semi-quantum Copy-Protection and More

Properties of quantum mechanics have enabled the emergence of quantum cryptographic protocols achieving important goals which are proven to be impossible classically. Unfortunately, this usually comes at the cost of needing quantum power from every party in the protocol, while arguably a more realistic scenario would be a network of classical clients, classically interacting with a quantum server. In this paper, we focus on copy-protection, which is a quantum primitive that allows a program to be evaluated, but not copied, and has shown interest especially due to its links to other unclonable cryptographic primitives. Our main contribution is to show how to dequantize quantum copy-protection schemes constructed from hidden coset states, by giving a construction for classically-instructed remote state preparation for coset states, which preserves hardness properties of hidden coset states. We then apply this dequantizer to obtain semi-quantum cryptographic protocols for copy-protection and tokenized signatures with strong unforgeability. In the process, we present the first secure copy-protection scheme for point functions in the plain model and a new direct product hardness property of coset states which immediately implies a strongly unforgeable tokenized signature scheme.
LenANet: A Length-Controllable Attention Network for Source Code Summarization

Source code summarization aims at generating brief description of a source code. Existing approaches have made great breakthroughs through encoder-decoder models. They focus on learning common features contained in translation from source code to natural language summaries. As a result, they tend to generate generic summaries independent of the context and lack of details. However, specific summaries which characterize specific features of code snippets are widely present in real-world scenarios. Such summaries are rarely studied as capturing specific features of source code would be difficult. What's more, only the common features learned would result in only the generic short summaries generated. In this paper, we present LenANet to generate specific summaries by considering the desired length information and extracting the specific code sentence. Firstly, we introduce length offset vector to force the generation of summaries which could contain specific amount of information, laying the groundwork for generating specific summaries. Further, forcing the model to generate summaries with a certain length would bring in invalid or generic descriptions, a context-aware code sentence extractor is proposed to extract specific features corresponding to specific information. Besides, we present a innovative sentence-level code tree to capture the structural semantics and learn the representation of code sentence by graph attention network, which is crucial for specific features extraction. The experiments on CodeXGLUE datasets with six programming language demonstrate that LenANet significantly outperforms the baselines and has the potential to generate specific summaries. In particular, the overall BLEU-4 is improved by 0.53 on the basis of CodeT5 with length control.
Abstractive Multi-document Summarization with Cross-Documents Discourse Relations

Generating a summary from a set of documents remains a challenging task. Abstractive multi-document summarization (MDS) methods have shown remarkable advantages when compared with extractive MDS. They can express the original document information in new sentences with higher continuity and readability. However, mainstream abstractive models, which are pre-trained on sentence pairs rather than entire documents, often fail to effectively capture long-range dependencies throughout the document. To address these issues, we propose a novel abstractive MDS model that aims to succinctly inject semantic and structural information of elementary discourse units into the model to improve its generative ability. In particular, we first extract semantic features by splitting the single document into discourses and building the discourse tree. Then, we design discourse Patterns to convert the raw document text and trees into a linearized format while guaranteeing corresponding relationships. Finally, we employ an abstractive model to generate target summaries with the processed input sequence and to learn the discourse semantic information. Extensive experiments show that our model outperforms current mainstream MDS methods in the ROUGE evaluation. This indicates the superiority of our proposed model and the capacity of the abstractive model with the hybrid pattern.
Self-supervised Multimodal Representation Learning for Product Identification and Retrieval

Solving object similarity remains a persistent challenge in the field of data science. In the context of e-commerce retail, the identification of substitutable and similar products involves similarity measures. Leveraging the multimodal learning derived from real-world experiences, humans can recognize similar products based solely on their titles, even in cases where significant literal differences exist. Motivated by this intuition, we propose a self-supervised mechanism that extracts strong prior knowledge from product image-title pairs. This mechanism serves to enhance the encoder's capacity for learning product representations in a multimodal framework. The similarity between products can be reflected by the distance between their respective representations. Additionally, we introduce a novel attention regularization to effectively direct attention toward product category-related signals. The proposed model exhibits wide applicability as it can be effectively employed in unimodal tasks where only free-text inputs are available. To validate our approach, we evaluate our model on two key tasks: product similarity matching and retrieval. These evaluations are conducted on a real-world dataset consisting of thousands of diverse products. Experimental results demonstrate that multimodal learning significantly enhances the language understanding capabilities within the e-commerce domain. Moreover, our approach outperforms strong unimodal baselines and recently proposed multimodal methods, further validating its superiority.
Computer Vision for Medical Data Analysis

The images are widely used in the medical diagnostics. Most people who visited any kind of physician encountered one of the devices that are used in the medical diagnostics on a daily basis. The most typical are X-ray and ultrasound scans. Some General Practitioners have a dermatoscope to take pictures of the skin. Similarly, thermography can be used to take kind of heatmap images of our body. If the diagnosis of a disease is more complex, magnetic resonance imaging (MRI), computer tomography (CT), or even positron emission tomography is used before surgery. A specific type of X-ray is mammography (MMG) that is used to investigate the breast diseases. A bit more invasive device are endoscopes that can be used to diagnose an illness while introduced into the body through the i.e. mouth. In some cases, when surgery is done, cells must be invested to cure the disease. This procedure is performed on a histopathological image, where the physician checks the image at high magnification.
Mortality Prediction via Logistic Regression in Intensive Care Unit Patients with Pneumonia

This work focuses on the problem of mortality prediction in patients with pneumonia after admission into an intensive care unit, by addressing it via logistic regression. This approach can model the relationship between clinical correlates and the probability of the binary outcome, with obvious advantages such as simplicity and interpretability of the predictive models. This work further inspects the potential of localized models, an approach based on different (parallel) predictive models each one constructed in clusters automatically identified in the training set. The predicted outcome is then obtained via membership separation (M, which corresponds to the outcome of the closest localized model) or weights (W, outcome as the weight average of localized outcomes via inverse distance). The results point out a similar balanced accuracy of 0.73 for the global model M24-48PS (without oversampling) and the W M24-48PSC model (weighted average of localized models without oversampling), which is partially explained by the small separability between the identified clusters. Therefore, a proof of concept was performed to support the usefulness of localized models in more separable data. This study considered a small amount of data for training and testing (chosen as that closest to the centroids of the identified clusters) and the results suggest that the localized approach can outperform the global one in more separable data.
Breast MRI Multi-tumor Segmentation Using 3D Region Growing

Breast tumor is one of the most prominent indicators for diagnosis of breast cancer. Magnetic Resonance Imaging (MRI) is a relevant imaging modality tool for breast cancer screening. Moreover, an accurate 3D segmentation of breast tumors from MRI scans plays a key role in the analysis of the disease. This paper presents a pipeline to automatically segment multiple tumors in breast MRI scans, following the methodology proposed by one previous study, addressing its limitations in detecting multiple tumors and automatically selecting seed points using a 3D region growing algorithm. The pre-processing includes bias field correction, data normalization, and image filtering. The segmentation process involved several steps, including identifying high-intensity points, followed by identifying high-intensity regions using k-means clustering. Then, the centers of the regions were used as seeds for the 3D region growing algorithm, resulting in a mask with 3D structures. These masks were then analyzed in terms of their volume, compactness, and circularity. Despite the need for further adjustments in the model parameters, the successful segmentation of four tumors proved that our solution is a promising approach for automatic multi-tumor segmentation with the potential to be combined with a classification model relying on the characteristics of the segmented structures.
A Proposal for a Lean and Functional Delivery versus Payment across two Blockchains

We propose a lean and functional transaction scheme to establish a secure delivery-versus-payment across two blockchains, where a) no intermediary is required and b) the operator of the payment chain/payment system has a small overhead and does not need to store state.The main idea comes with two requirements: First, the payment chain operator hosts a stateless decryption service that allows the decrypt of messages with his secret key. Second a "Payment Contract" is deployed on the payment chain that implements a function that processes the (trigger-based) payment and emits the decrypted key depending on success or failure of the transaction. The respective key can then trigger an associated transaction, e.g. claiming delivery by the buyer or re-claiming the locked asset by the seller.