Reviewerly/exHarmony huggingface dataset:
Targeted Invertible Pseudorandom Functions and Deterministic Format-Transforming Encryption

In this paper, we continue the study of invertible pseudorandom functions (IPFs) initiated by Boneh et al. (TCC 2017). In particular, we focus on constructing IPFs with custom domains and codomains, which we informally refer to as targeted IPFs. Such IPFs are useful for building format preserving and format transforming encryption schemes, but may find applications elsewhere. We first describe a general paradigm for building such targeted IPFs, called Map-then-Permute, and show how it gives immediate constructions in a number of situations. We then focus on how to construct targeted IPFs more generally, and describe a new algorithm called nested n-cycle walking that, when used in tandem with Map-then-Permute, gives us new constructions of deterministic format-transforming encryption schemes for a variety of domains and codomains.
Improved Heuristics for Low-Latency Implementations of Linear Layers

In many applications, low area and low latency are required for the chip-level implementation of cryptographic primitives. The low-cost implementations of linear layers usually play a crucial role for symmetric ciphers. Some heuristic methods, such as the forward search and the backward search, minimize the number of XOR gates of the linear layer under the minimum latency limitation. For the sake of achieving further optimization for such implementation of the linear layer, we put forward a new general search framework attaching the division optimization and extending base techniques in this paper. In terms of the number of XOR gates and the searching time, our new search algorithm is better than the previous heuristics, including the forward search and the backward search when testing matrices provided by them. We obtain an improved implementation of AES MixColumns requiring only 102 XORs under minimum latency, which outdoes the previous best record provided by the forward search.
Vehicle Damage Detection based on MD R-CNN

The traditional vehicle damage assessment process is complicated and time-consuming, asking for intelligent methods for detecting vehicle damage. At present, most damage detection methods for vehicles require two models to detect damage and component where the damage is located separately, which is complex and inefficient. To this end, we propose an end-to-end multi-detection model named MD R-CNN, which simultaneously outputs damage detection and component recognition results by adding an extra classification branch. To improve the positioning precision of detection, the regression of the detection box adopts a self-attention convolution head (SA-Head) composed of a residual module and two SC Attention modules; Moreover, since there are few damage annotation datasets available, D-FPN is proposed to enhance the multi-scale detection performance. The experimental results show that MD R-CNN increases the Average precision (AP) by about 2.4% on vehicle damage datasets, and the precision can reach 80.22%, which has a favorable performance.
Structure Similarity Graph for Cross-Network Node Classification

Cross-network node classification aims to use a labeled source network to classify nodes in an unlabeled target network. Most of the existing cross-network node classification methods learn the network representations by capturing the node neighborhood and train the classifier on these representations. The performance is highly dependent on the high-quality neighborhood in the network. However, in applications, the degree of nodes generally follows a long-tail distribution, i.e., a significant proportion of nodes are tail nodes with sparse neighborhood. It poses a challenge to existing methods. To this end, a structure similarity graph for cross-network node classitication method (SCNC) is proposed in this paper. Firstly, the potential links between nodes are predicted with the structural similarity metric to construct structure similarity graph, which can enrich the neighborhood of tail nodes. Then, the embedding representations of the structural similarity graph are learned to capture more neighborhood information. Finally, the adversarial is used to learn the domain invariant representations to address cross-network divergence. Extensive experimental results show that our SCNC outperforms the state-of-the-art methods.
Zero-Shot Stance Detection via Sentiment-Stance Contrastive Learning

Zero-shot stance detection (ZSSD) is an important research problem that requires algorithms to have good stance detection capability even for unseen targets. In general, stance features can be grouped into two types: target-invariant and target-specific. Target-invariant features express the same stance regardless of the targets they are associated with, and such features are general and transferable. On the contrary, target-specific features will only be directly associated with specific targets. Therefore, it is crucial to effectively mine target-invariant features in texts in ZSSD. In this paper, we develop a method based on contrastive learning to mine certain transferable target-invariant expression features in texts from two dimensions of sentiment and stance and then generalize them to unseen targets. Specifically, we first grouped all texts into several types in terms of two orthogonal dimensions: sentiment polarity and stance polarity. Then we devise a supervised contrastive learning-based strategy to capture each type's common and transferable expressive features. Finally, we fuse the above-mentioned expressive features with the semantic features of the original texts about specific targets to deal with the stance detection for unseen targets. Extensive experiments on three benchmark datasets show that our proposed model achieves the state-of-the-art performance on most datasets. Code and other resources are available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/zoujiaying1995/sscl-project.
RIA: A Reversible Network-based Imperceptible Adversarial Attack

The robustness and security of deep neural network (DNN) models have received much attention in recent years. In-depth research on adversarial example generation methods that make DNN models make wrong judgments and decisions will facilitate further research on more comprehensive and practical adversarial defense methods. Most existing adversarial example generation methods focus too much on attack performance and design adversarial noise at the pixel level, resulting in the generated adversarial examples with redundant noise and evident perturbations. In this paper, we try to find the well-designed perturbations at the feature-level and propose a novel deep reversible network-based imperceptible adversarial examples generation method called RIA. Experimental results show that RIA can obtain more natural adversarial examples without losing attack performance and reducing redundant noise based on well-designed feature maps. To the best of our knowledge, in the white-box attack method research, this work is the first attempt to directly add perturbations to feature maps and use an reversible network to generate adversarial examples based on the perturbed feature maps.
Pre-reading Activity over Question for Machine Reading Comprehension

Machine reading comprehension (MRC) is a chal-lenging task, which has the long-standing goal of developing models that can determine the answer based on the given passage and question. Recent works have achieved promising results; however, most of them lose sight of the multiplicate information carried by the question. Inspired by the hot issue of research in the educational field, we propose a novel machine pre-reading activity over questions considering an efficient reading comprehension process of humans. Concretely, in the proposed pre-reading activity, the machine first pre-reads the question text and distinguishes the search term of the question using a question-answer type extraction algorithm (QAE). Second, the MRC model leverages two new search and enhancement methods to point out the most informative contents contained in the question, and highlight the question-based information in the passage accordingly. Third, armed with valid information, we introduce a question-based feature shunt module before the multi-head predictor. Moreover, we propose a latent A/B answer type, which not only makes the feature shunt more accurate but also enlarges the range of answer types. Experiments show that our strategies and their combinations achieve considerable improvements compared with existing methods.
NCCR: Neighbor and Cluster Consistency Regularization for Improving Graph Node Classification

Semi-supervised node classification in graphs is a key problem in machine learning, and graph neural networks (GNNs) currently achieve state-of-the-art performance. However, traditional GNNs fail to make use of a substantial amount of information available in a graph. For example, the training loss is often defined only with respect to labeled training nodes, which usually make up a small proportion of the graph. Also, most graphs exhibit a certain degree of homophily, in which neighboring nodes are likely to belong to the same class, but GNNs typically do not make use of this property in an explicit way. In this work, we introduce a new type of consistency regularization which is able to make use of data from unlabeled nodes and also exploits graph homophily in a novel and more accurate way. Additionally, we observe that nodes in a graph may exhibit different amounts of homophily, so that uniformly enforcing neighbor consistency regularization across all nodes can reduce accuracy. We thus introduce a second clustering based regularization targeting low homophily nodes which lack reliable information from their neighbors. We show that we can flexibly combine the two regularizations with existing GNN backbones, and then demonstrate the effectiveness of the combined method by achieving state-of-the-art accuracy on a number of datasets.
Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets

Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This paper presents a preliminary study with the goal to help researchers and practitioners gain insight into relatively new datasets as well as emotion classification in general. We focus on three datasets that were recently presented in the related literature, and we explore the performance of traditional as well as state-of-the-art deep learning models in the presence of different characteristics in the data. We also explore the use of data augmentation in order to improve performance. Our experi-mental work shows that state-of-the-art models such as RoBERTa perform the best for all cases. We also provide observations and discussion that highlight the complexity of emotion classification in these datasets and test out the applicability of the models to actual social media posts we collected and labeled.
Document-Level Sentiment Knowledge Transfer Network for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) task has been widely studied in Natural Language Processing (NLP). Nevertheless, existing ASTE methods are processed on a small public dataset, leading to frequent appeared sentiment words being focused on and neglecting the processing of niche sentiment words with strong sentiment tendencies. Therefore, the model extracts the wrong triples. To address this problem, we combine two attention weights of the Document-Level Sentiment Classification (DSC) and the ASTE based on the attention mechanism to obtain the ASTE representation with enhanced sentiment knowledge. Meanwhile, an attention masking strategy is introduced in the DSC model to ignore noisy words irrelevant to sentiment classification and ensure the effective extraction of sentiment triples. Experiments on four benchmark datasets from ASTE-Data-V1 show that our proposed method can acquire sentiment knowledge from large-scale document-level corpora, outperforming existing baseline models.
Incorporating the Graph Representation of Video and Text into Video Captioning

Video captioning is to translate the video content into the textual descriptions. In the encoding phase, the existing approaches encode the irrelevant background and uncorrelated visual objects into visual features. That leads to semantic aberration between the visual features and the expected textual caption. In the decoding phase, the word-by-word prediction infers the next word only from the previously generated caption. That local text context is insufficient for word prediction. To tackle the above two issues, the representations of video and text stem from the convolution on two graphs. The convolution on the video graph distills the visual features by filtering the irrelevant background and uncorrelated salient objects. The key issue is to figure out the similar videos according to the video semantic feature. The word graph is constructed to help incorporate global neighborhood among words into word representation. That word global neigh-borhood serves as the global text context and compensates the local text context. Results on two benchmark datasets show the advantage of the proposed method. Experimental analysis is also conducted to verify the effectiveness of the proposed modules.
GANs for Class-Imbalanced Data: A Meta-Analysis of GitHub Projects

Generative Adversarial Networks (GANs) have increasingly been the subject of intense research interest for their ability to augment datasets to correct for class imbalance. The collaborative and complex code bases for these GANs are often written in a high-level, general-purpose programming language such as Python and housed on the GitHub platform. The goal of this work is to summarize the research aims of 18 GitHub repositories of projects that implement GANs in regimes of class imbalance, in both tabular and non-tabular settings, as well to summarize and analyze the patterns and characteristics of these code bases. With respect to the latter task, we conduct our analysis from a perspective of library reliance and from the structural properties of the code base. The insights discovered herein are meant to serve as a gentle introduction to the various tools available and recommended best practices for the enterprising researcher seeking to apply GANs as data augmenters in imbalanced settings.
Reshaping the Semantic Logits for Proposal-free Panoptic Segmentation

We propose to enable the general semantic segmentation frameworks to separate instances so that such frameworks can be used for the panoptic segmentation task. In the semantic segmentation frameworks, the logits which are output from the neural network and normalized by the following softmax function can only distinguish classes but not instances. In this work, we find simple regularization on the logits can help to single out the instances, which is modeled by an energy-based representation, energy surface. Several regularization approaches are discussed and a novel persistent homology-based instance extraction method is proposed to obtain the instances. Finally, we demonstrate the generality of the logit regularization on different base semantic segmentation frameworks and evaluating them on Cityscapes, Mapillary Vistas, and COCO. High-quality semantic segmentation frameworks such as DeepLabV3+ and HRNet-OCR can achieve competitive performance to the state-of-the-art proposal-free panoptic segmentation solver. Codes and trained models will be made public.
UCL-AST: Active Self-Training with Uncertainty-Aware Clouded Logits for Few-Shot Text Classification

Although the recent advances in pre-training language models have achieved great success and migrated annotation bottleneck for many tasks, the task-specific fine-tuning for text classification still requires thousands of labeled samples or even more. When few human annotations are available, active self-training is one of the efficient approaches for reducing human annotation costs which integrates pseudo labels of self-training into the procedure of active learning. The traditional active self-training approaches mix manual labels and pseudo labels indistinguishably without considering the impact of active learning and self-training on each other. There are two problems for active self-training in few-shot text classification: (1) The noise in a large number of pseudo-labels may mislead the model training. (2) The confusing samples in overlap boundaries between classes are especially difficult to identify correctly with a few labels. To solve these problems, we propose UCL-AST, an active self-training framework that boosts the performance of the active teacher model to provide superior pseudo labels for the self-training student model and facilitates the active teacher model to learn more clear class boundaries with uncertainty-aware clouded logits in few-shot. The extensive experiments and visualization analysis have demonstrated the effectiveness of the proposed framework on four datasets compared with state-of-the-art methods.
An Efficient Strategy to Construct a Better Differential on Multiple-Branch-Based Designs: Application to Orthros

As low-latency designs tend to have a small number of rounds to decrease latency, the differential-type cryptanalysis can become a significant threat to them. In particular, since a multiple-branch-based design, such as Orthros can have the strong clustering effect on differential attacks due to its large internal state, it is crucial to investigate the impact of the clustering effect in such a design. In this paper, we present a new SAT-based automatic search method for evaluating the clustering effect in the multiple-branch-based design. By exploiting an inherent trait of multiple-branch-based designs, our method enables highly efficient evaluations of clustering effects on this-type designs. We apply our method to the low-latency PRF Orthros, and show a best differential distinguisher reaching up to 7 rounds of Orthros with $$2^{116.806}$$ time/data complexity and 9-round distinguisher for each underlying permutation which is 2 more rounds than known longest distinguishers. Besides, we update the designer’s security bound for differential attacks based on the lower bounds for the number of active S-boxes, and obtain the optimal differential characteristic of Orthros, Branch 1, and Branch 2 for the first time. Consequently, we improve the designer’s security bound from 9/12/12 to 7/10/10 rounds for Orthros/Branch 1/Branch 2 based on a single differential characteristic.
Generating Adversarial Examples for Low-Resource NMT via Multi-Reward Reinforcement Learning

Weak robustness and noise adaptability are major issues for Low-Resource Neural Machine Translation (NMT) models. Adversarial example is currently a major tool to improve model robustness and how to generate an adversarial examples that can degrade the performance of the model and ensure semantic consistency is a challenging task. In this paper, we adopt multi-reward reinforcement learning to generate adversarial examples for low-resource NMT. Specifically, utilizing gradient ascent to modify the source sentence, the discriminator and changes estimate are used to determine whether the generated adversarial examples maintain semantic consistency and the overall modifications of adversarial examples. Furthermore, we also install a language model reward to measure the fluency of adversarial examples. Experimental results on low-resource translation tasks show that our method highly aggressive to the model while maintaining semantic constraints greatly. Moreover, the model performance is significantly improved after fine-tuning with adversarial examples.
Enhancing Implicit Discourse Relation Classification by Perceiving External Semantics and Convolving Internal Semantics

Implicit discourse relation classification refers to a task of automatically determining relationships between arguments. It has been widely proven that, in a neural classification architecture, decoding discourse relations heavily relies on the reliable semantic representations of arguments. In addition, our previous survey shows that, for a target argument, the external semantic information hidden in the accompanying argument benefits the encoding of the target, either wholly or partially. Moreover, dependency structure appears as the crucial feature for synthesizing word senses of the entire words in arguments. Accordingly, we propose a novel method to enhance the current representation learning of pairwise arguments, which takes into consideration both external semantic information and internal dependency structure. In particular, we inject external semantic information into the Long-Short Term Memory (LSTM) unit of Recurrent Neural Network (RNN) through the input and forget gates. Different from the existing one-off interactive learning models, our method allows the neuronal memory of internal argument semantics to be affected by external information at each encoding step. On the basis, we apply the parser-based Graph Convolutional Networks (GCN) over the semantic presentations of words, so as to accumulate the closely-related semantic information in terms of dependency structures. We conduct experiments on Penn Discourse TreeBank Corpus of version 2.0 (PDTB 2.0). The test results illustrate that the proposed method enhances the baseline significantly, and it obtains comparable performance compared to the state of the art.
Arg-XAI: a Tool for Explaining Machine Learning Results

The requirement of explainability is gaining more and more importance in Artificial Intelligence applications based on Machine Learning techniques, especially in those contexts where critical decisions are entrusted to software systems (think, for example, of financial and medical consultancy). In this paper, we propose an Argumentation-based methodology for explaining the results predicted by Machine Learning models. Argumentation provides frameworks that can be used to represent and analyse logical relations between pieces of information, serving as a basis for constructing human tailored rational explanations to a given problem. In particular, we use extension-based semantics to find the rationale behind a class prediction.
HIAE: Hyper-Relational Interaction Aware Embedding for Link Prediction

Hyper-relational knowledge graph contains the main triple and additional information(qualifiers). The additional information can assist the main triple to predict missing entities(relations). In the previous methods, people usually combine the main triple and additional information through special methods to predict the missing entities(relations). However, not all additional information is equally essential for predicting missing entities(relations). Some additional information plays a more critical role in predicting missing entities (relations), so how to extract and use important additional information effectively in the link prediction (LP) task is very important. This paper proposes a novel Hyper-relational Interaction Aware Embedding (HIAE) model. Specifically, HIAE learns semantic features between the additional information and fuses the important additional information into the main triple through the attention mechanism to complete the prediction task. We have conducted many experiments to show that HIAE significantly outperforms the state-of-the-art models.