Reviewerly/exHarmony huggingface dataset:
MEFaceNets: Muti-scale Efficient CNNs for Real-Time Face Recognition on Embedded Devices

The growing trend of applying face recognition technology on terminals and embedded devices highlights the critical need to strike a balance between recognition accuracy and real-time inference latency. In response to this challenge, we propose an efficient bottleneck named MEBottleneck, which utilizes convolution kernels of different sizes on two parallel branches to capture multi-scale features in the bottleneck, followed by a $$1 \times 1$$ expansion layer to fuse multi-scale features, thereby improving the representation ability. Then, to balance the trade-off between accuracy and latency, we design a family of lightweight models with MEBottleneck, specifically tailored for face recognition and named MEFaceNets. Large kernels are used for depthwise convolutions in shallow layers, leading to notable improvements in accuracy. We evaluate the proposed models on several popular face recognition benchmarks. Our primary model achieves 99.80% face verification accuracy on LFW and exhibits excellent performance on the larger and more challenging benchmarks, including MegaFace Challenge 1, IJB-B and IJB-C. Furthermore, our proposed models have demonstrated impressive real-time performance on both the CPU and GPU of embedded devices.
Enhancing Spatial Consistency and Class-Level Diversity for Segmenting Fine-Grained Objects

Semantic segmentation is a fundamental computer vision task attracting a lot of attention. However, limited works focus on semantic segmentation on fine-grained class scenario, which has more classes and greater inter-class similarity. Due to the lack of data available for this task, we establish two segmentation benchmarks, CUB-seg and FGSCR42-seg, based on CUB and FGSCR42 datasets. To solve the two major problems in this task, spatial inconsistency and extremely similar classes confusion, we propose the Spatial Consistency and Class-level Diversity enhancement Network. First, we build the Spatial Consistency Enhancement Module to take advantage of the low-frequency information in the feature, enhancing the spatial consistency. Second, Fine-grained Regions Contrastive Loss is designed to make the features of different classes more discriminative, promoting the class-level diversity. Extensive experiments show that our method can significantly improve the performance compared to baseline models. Visualization study also prove the effectiveness of our method for enhancing spatial consistency and class-level diversity.
Graph-Based Feature Learning from Image Markers

Deep learning methods have achieved impressive results for object detection, but they usually require powerful GPUs and large annotated datasets. In contrast, there is a lack of explainable networks in the literature. For instance, Feature Learning from Image Markers (FLIM) is a feature extraction strategy for lightweight CNNs without backpropagation that requires only a few training images. In this work, we extend FLIM for general image graph modeling, allowing it for a non-strict kernel shape and taking advantage of the adjacency relation between nodes to extract feature vectors based on neighbors’ features. To produce saliency maps by combining learned features, we proposed a User-Guided Decoder (UGD) that does not require training and is suitable for any FLIM-based strategy. Our results indicate that the proposed Graph-based FLIM, named GFLIM, not only outperforms FLIM but also produces competitive detections with deep models, even having an architecture thousands of times smaller in the number of parameters. Our code is publicly available at https://github.com/IMScience-PPGINF-PucMinas/GFLIM .
Hybrid U-Net: Instrument Semantic Segmentation in RMIS

Accurate semantic segmentation of surgical instruments from images captured by the laparoscopic system plays a crucial role in ensuring the reliability of vision-based Robot-Assisted Minimally Invasive Surgery. Despite numerous notable advancements in semantic segmentation, the achieved segmentation accuracy still falls short of meeting the requirements for surgical safety. To enhance the accuracy further, we propose several modifications to a conventional medical image segmentation network, including a modified Feature Pyramid Module. Within this modified module, Patch-Embedding with varying rates and Self-Attention Blocks are employed to mitigate the loss of feature information while simultaneously expanding the receptive field. As for the network architecture, all feature maps extracted by the encoder are seamlessly integrated into the proposed modified Feature Pyramid Module via element-wise connections. The resulting output from this module is then transmitted to the decoder blocks at each stage. Considering these hybrid properties, the proposed method is called Hybrid U-Net. Subsequently, multiple experiments were conducted on two available medical datasets and the experimental results reveal that our proposed method outperforms the recent methods in terms of accuracy on both medical datasets.
Exploring the Capability of ChatGPT for Cross-Linguistic Agricultural Document Classification: Investigation and Evaluation

In the sustainable smart agriculture era, a vast amount of agricultural knowledge is available on the internet, making it necessary to explore effective document classification techniques for enhanced accessibility and efficiency. Over the past few years, fine-tuning strategies based on pre-trained language models (PLMs) have gained popularity as mainstream deep learning approaches, showcasing impressive performance. However, these approaches face several challenges, including a limited availability of training data, poor domain transferability, lack of model interpretability, and the challenges in deploying large models. Inspired by ChatGPT's significant success, we investigate its capability and utilization in the field of agricultural information processing. We explore various attempts to maximize ChatGPT's potential, including various prompting construction strategies, ChatGPT question-answering (Q &A) inference, and intermediate answer alignment technique. Our preliminary comparative study demonstrates that ChatGPT effectively addresses research challenges and bottlenecks, positioning it as an ideal solution for agricultural document classification. This findings encourage the development of a general-purpose agricultural document processing paradigm. Our preliminary study also indicates the trend towards achieving Artificial General Intelligence (AGI) for sustainable smart agriculture in the future. Code is available on Github ( https://github.com/albert-jin/agricultural_textual_classification_ChatGPT ).
CACL:Commonsense-Aware Contrastive Learning for Knowledge Graph Completion

Most knowledge graphs (KGs) are incomplete in the real world, so knowledge graph completion (KGC) is widely investigated to predict the most credible missing facts from given knowledge. However, existing KGC methods heavily rely on the given facts to predict missing relations between entities, ignoring the value of external knowledge. In addition, previous knowledge representation methods ignore the multi-perspective characteristics of cognate knowledge, which leds to the inability to obtain high-level semantic representation of knowledge. To alleviate the above issues, this paper proposes a Commonsense Aware Contrastive Learning (CACL) framework, which extracts relevant knowledge triples from existing commonsense knowledge base to assist in the KGC. Moreover, our method employs knowledge contrast representation learning method to acquire the higher-order representation from multiple perspectives. Experiments show that our method improves the performance of basic knowledge graph embedding (KGE) models. Our method also could be easily adapted to various KGE models.
Security Proofs for Key-Alternating Ciphers with Non-Independent Round Permutations

This work studies the key-alternating ciphers (KACs) whose round permutations are not necessarily independent. We revisit existing security proofs for key-alternating ciphers with a single permutation (KACSPs), and extend their method to an arbitrary number of rounds. In particular, we propose new techniques that can significantly simplify the proofs, and also remove two unnatural restrictions in the known security bound of 3-round KACSP (Wu et al., Asiacrypt 2020). With these techniques, we prove the first tight security bound for t-round KACSP, which was an open problem. We stress that our techniques apply to all variants of KACs with non-independent round permutations, as well as to the standard KACs.
BFTracker: A One-Shot Baseline Model with Fusion Similarity Algorithm Towards Real-Time Multi-object Tracking

The key problem of real-time tracking is to achieve the balance between the tracking accuracy and real-time inference. Currently, the one-shot tracker, which joins multiple tasks into a single network, achieves trade-offs between tracking speed and accuracy. Different from previous trackers' practice of exchanging more extra calculation cost for tracking accuracy, a new one-shot baseline that is simple and efficient is proposed by us. We continue to discuss the conflict under the tracking paradigm of joint detection and re-identification tasks and try to explore the source of this conflict and also devote to alleviate the feature conflict in the one-shot model. On the other hand, we propose a fusion similarity algorithm to focus on handling tracking challenges of sudden appearance changes and potential Kalman Filter prediction errors. On the MOT17 test set, the algorithm proposed can reduce the ID switches by 42.2% compared with the initial algorithm. On the MOT16 test set, the proposed BFTracker improves the ID F1 Score (i.e. IDF1) by 2.8% compared with the most popular one-shot tracker. In particular, Proposed baseline is quite simple and runs at 31 FPS on a single GPU.
Concurrent Asynchronous Byzantine Agreement in Expected-Constant Rounds, Revisited

It is well known that without randomization, Byzantine agreement (BA) requires a linear number of rounds in the synchronous setting, while it is flat out impossible in the asynchronous setting. The primitive which allows to bypass the above limitation is known as oblivious common coin (OCC). It allows parties to agree with constant probability on a random coin, where agreement is oblivious, i.e., players are not aware whether or not agreement has been achieved. The starting point of our work is the observation that no known protocol exists for information-theoretic multi-valued OCC with optimal resiliency in the asynchronous setting (with eventual message delivery). This apparent hole in the literature is particularly problematic, as multi-valued OCC is implicitly or explicitly used in several constructions. In this paper, we present the first information-theoretic multi-valued OCC protocol in the asynchronous setting with optimal resiliency, i.e., tolerating $$t<n/3$$ corruptions, thereby filling this important gap. Further, our protocol efficiently implements OCC with an exponential-size domain, a property which is not even achieved by known constructions in the simpler, synchronous setting. We then turn to the problem of round-preserving parallel composition of asynchronous BA. A protocol for this task was proposed by Ben-Or and El-Yaniv [Distributed Computing ’03]. Their construction, however, is flawed in several ways. Thus, as a second contribution, we provide a simpler, more modular protocol for the above task. Finally, and as a contribution of independent interest, we provide proofs in Canetti’s Universal Composability framework; this makes our work the first one offering composability guarantees, which are important as BA is a core building block of secure multi-party computation protocols.
(Verifiable) Delay Functions from Lucas Sequences

Lucas sequences are constant-recursive integer sequences with a long history of applications in cryptography, both in the design of cryptographic schemes and cryptanalysis. In this work, we study the sequential hardness of computing Lucas sequences over an RSA modulus. First, we show that modular Lucas sequences are at least as sequentially hard as the classical delay function given by iterated modular squaring proposed by Rivest, Shamir, and Wagner (MIT Tech. Rep. 1996) in the context of time-lock puzzles. Moreover, there is no obvious reduction in the other direction, which suggests that the assumption of sequential hardness of modular Lucas sequences is strictly weaker than that of iterated modular squaring. In other words, the sequential hardness of modular Lucas sequences might hold even in the case of an algorithmic improvement violating the sequential hardness of iterated modular squaring. Second, we demonstrate the feasibility of constructing practically-efficient verifiable delay functions based on the sequential hardness of modular Lucas sequences. Our construction builds on the work of Pietrzak (ITCS 2019) by leveraging the intrinsic connection between the problem of computing modular Lucas sequences and exponentiation in an appropriate extension field.
Jointly Extractive and Abstractive Training Paradigm for Text Summarization

Text summarization is a classical task in natural language generation, which aims to generate concise summary of the original article. Neural networks based on the Encoder-Decoder architecture have made great progress in recent years in generating abstractive summaries with high fluency. However, due to the randomness of the abstractive model during generation, the summaries risk missing important information in the articles. To address this challenge, this paper proposes a jointly trained text summarization model that combines abstractive and extractive summarization. On the one hand, extractive models have higher ROUGE scores but poorer readability on the other hand, abstractive models can produce a more fluent summary but suffer from the problem of omitting important information in the original text. Therefore, We share the encoder of both models and jointly train both models to obtain a text representation that benefits from regularisation. We also add document level information obtained from an extractive model to the decoder of the abstractive model to improve abstractive summary. Experiments on CNN/Daily Mail dataset, Pubmed dataset and Arxiv dataset demonstrate the effectiveness of the proposed model.
Beyond MPC-in-the-Head: Black-Box Constructions of Short Zero-Knowledge Proofs

In their seminal work, Ishai, Kushilevitz, Ostrovsky, and Sahai (STOC‘07) presented the MPC-in-the-Head paradigm, which shows how to design Zero-Knowledge Proofs (ZKPs) from secure Multi-Party Computation (MPC) protocols. This paradigm has since then revolutionized and modularized the design of efficient ZKP systems, with far-reaching applications beyond ZKPs. However, to the best of our knowledge, all previous instantiations relied on fully-secure MPC protocols and have not been able to leverage the fact that the paradigm only imposes relatively weak privacy and correctness requirements on the underlying MPC. In this work, we extend the MPC-in-the-Head paradigm to game-based cryptographic primitives supporting homomorphic computations (e.g., fully-homomorphic encryption, functional encryption, randomized encodings, homomorphic secret sharing, and more). Specifically, we present a simple yet generic compiler from these primitives to ZKPs which use the underlying primitive as a black box. We also generalize our paradigm to capture commit-and-prove protocols, and use it to devise tight black-box compilers from Interactive (Oracle) Proofs to ZKPs, assuming One-Way Functions (OWFs). We use our paradigm to obtain several new ZKP constructions: 1. The first ZKPs for $$\textsf {NP}$$ relations $$\mathcal{R}$$ computable in (polynomial-time uniform) $$\textsf{NC}^1$$ , whose round complexity is bounded by a fixed constant (independent of the depth of $$\mathcal{R}$$ ’s verification circuit), with communication approaching witness length (specifically, $$n\cdot {\textsf{poly}}\left( \kappa \right) $$ , where n is the witness length, and $$\kappa $$ is a security parameter), assuming DCR. Alternatively, if we allow the round complexity to scale with the depth of the verification circuit, our ZKPs can make black-box use of OWFs. 2. Constant-round ZKPs for NP relations computable in bounded polynomial space, with $$O\left( n\right) +o\left( m\right) \cdot {\textsf{poly}}\left( \kappa \right) $$ communication assuming OWFs, where m is the instance length. This gives a black-box alternative to a recent non-black-box construction of Nassar and Ron (CRYPTO‘22). 3. ZKPs for NP relations computable by a logspace-uniform family of depth- $$d\left( m\right) $$ circuits, with $$n\cdot {\textsf{poly}}\left( \kappa ,d\left( m\right) \right) $$ communication assuming OWFs. This gives a black-box alternative to a result of Goldwasser, Kalai and Rothblum (JACM).
Multi-scale Context Aggregation for Video-Based Person Re-Identification

For video-based person re-identification (Re-ID), effectively aggregating video features is the key to dealing with various complicated situations. Different from previous methods that first extracted spatial features and later aggregated temporal features, we propose a Multi-scale Context Aggregation (MSCA) method in this paper to simultaneously learn spatial-temporal features from videos. Specifically, we design an Attention-aided Feature Pyramid Network (AFPN), which can recurrently aggregate detail and semantic information of multi-scale feature maps from the CNN backbone. To enable the aggregation to focus on more salient regions in the video, we embed a particular Spatial-Channel Attention module (SCA) into each layer of the pyramid. To further enhance the feature representations with temporal information while extracting the spatial features, we design a Temporal Enhancement module (TEM), which can plug into each layer of the backbone network in a plug-and-play manner. Comprehensive experiments on three standard video-based person Re-ID benchmarks demonstrate that our method is competitive with most state-of-the-art methods.
Ideal-SVP is Hard for Small-Norm Uniform Prime Ideals

The presumed hardness of the Shortest Vector Problem for ideal lattices (Ideal-SVP) has been a fruitful assumption to understand other assumptions on algebraic lattices and as a security foundation of cryptosystems. Gentry [CRYPTO’10] proved that Ideal-SVP enjoys a worst-case to average-case reduction, where the average-case distribution is the uniform distribution over the set of inverses of prime ideals of small algebraic norm (below $$d^{O(d)}$$ for cyclotomic fields, where d refers to the field degree). De Boer et al. [CRYPTO’20] obtained another random self-reducibility result for an average-case distribution involving integral ideals of norm $$2^{O(d^2)}$$ . In this work, we show that Ideal-SVP for the uniform distribution over inverses of small-norm prime ideals reduces to Ideal-SVP for the uniform distribution over small-norm prime ideals. Combined with Gentry’s reduction, this leads to a worst-case to average-case reduction for the uniform distribution over the set of small-norm prime ideals. Using the reduction from Pellet-Mary and Stehlé [ASIACRYPT’21], this notably leads to the first distribution over NTRU instances with a polynomial modulus whose hardness is supported by a worst-case lattice problem.
A Bi-directional Optimization Network for De-obscured 3D High-Fidelity Face Reconstruction

3D detailed face reconstruction based on monocular images aims to reconstruct a 3D face from a single image with rich face detail. The existing methods have achieved significant results, but still suffer from inaccurate face geometry reconstruction and artifacts caused by mistaking hair for wrinkle information. To address these problems, we propose a bi-directional optimization network for de-obscured 3D high-fidelity surface reconstruction. Specifically, our network is divided into two stages: face geometry fitting and face detail optimization. In the first stage, we design a global and local bi-directional optimized feature extraction network that uses both local and global information to jointly constrain the face geometry and ultimately achieve an accurate 3D face geometry reconstruction. In the second stage, we decouple the hair and the face using a segmentation network and use the distribution of depth values in the facial region as a prior for the hair part, after which the FPU-net detail extraction network we designed is able to reconstruct finer 3D face details while removing the hair occlusion problem. With only a small number of training samples, extensive experimental results on multiple evaluation datasets show that our method achieves competitive performance and significant improvements over state-of-the-art methods.
Violence-MFAS: Audio-Visual Violence Detection Using Multimodal Fusion Architecture Search

Audio-visual fusion methods are widely employed to tackle violence detection tasks, since they can effectively integrate the complementary information from both modalities to significantly improve accuracy. However, the design of high-quality multimodal fusion networks is highly dependent on expert experience and substantial efforts. To alleviate this formidable challenge, we propose a novel method named Violence-MFAS, which can automatically design promising multimodal fusion architectures for violence detection tasks using multimodal fusion architecture search (MFAS). To further enable the model to focus on important information, we elaborately design a new search space. Specifically, multilayer neural networks based on attention mechanisms are meticulously constructed to grasp intricate spatio-temporal relationships and extract comprehensive multimodal representation. Finally, extensive experiments are conducted on the commonly used large-scale and multi-scene audio-visual XD-Violence dataset. The promising results demonstrate that our method outperforms the state-of-the-art methods with less parameters.
Research on Relation Extraction Based on BERT with Multifaceted Semantics

Relation extraction is one of the important tasks in natural language processing, aiming to determine the class of relations to which the entities in a sentence belong. In nowadays, researchers tend to use large-scale corpus to retrain language models for relation extraction, which requires too much relevant resources, besides, when analyzing the corpus, we found that in sentences where entities exist, different entities have different correlations with different parts of the sentence, and this may affect the performance of relation extraction. Based on this, this paper proposes a model based on BERT [1] with multifaceted semantics (BERT-LR) for relation extraction, which learns semantics from multiple aspects centered on entities, and is able to better understand the relationship between entities at different locations and the context and perform relation extraction. First, we make full use of the BERT [1] model that has been pre-trained to provide rich initialization parameters for our model. Second, in order to achieve entity-centered relationship extraction, we propose a BERT [1] and multifaceted semantic relationship extraction model based on BERT [1] with multifaceted semantics consisting of left semantics, right semantics, and global semantics, and use a suitable method to fuse the multifaceted semantics. Third, we found that fixing the embedding layer of the model during the fine-tuning process can achieve better results. Our approach achieves excellent results on the SemEval-2010 Task 8 [2] dataset.