Reviewerly/exHarmony huggingface dataset:
Defending against Backdoor Attacks in Natural Language Generation

The frustratingly fragile nature of neural network models make current natural language generation (NLG) systems prone to backdoor attacks and generate malicious sequences that could be sexist or offensive. Unfortunately, little effort has been invested to how backdoor attacks can affect current NLG models and how to defend against these attacks. In this work, by giving a formal definition of backdoor attack and defense, we investigate this problem on two important NLG tasks, machine translation and dialog generation. Tailored to the inherent nature of NLG models (e.g., producing a sequence of coherent words given contexts), we design defending strategies against attacks. We find that testing the backward probability of generating sources given targets yields effective defense performance against all different types of attacks, and is able to handle the one-to-many issue in many NLG tasks such as dialog generation. We hope that this work can raise the awareness of backdoor risks concealed in deep NLG systems and inspire more future work (both attack and defense) towards this direction.
Better Peer Grading through Bayesian Inference

Peer grading systems aggregate noisy reports from multiple students to approximate a "true" grade as closely as possible. Most current systems either take the mean or median of reported grades; others aim to estimate students’ grading accuracy under a probabilistic model. This paper extends the state of the art in the latter approach in three key ways: (1) recognizing that students can behave strategically (e.g., reporting grades close to the class average without doing the work); (2) appropriately handling censored data that arises from discrete-valued grading rubrics; and (3) using mixed integer programming to improve the interpretability of the grades assigned to students. We demonstrate how to make Bayesian inference practical in this model and evaluate our approach on both synthetic and real-world data obtained by using our implemented system in four large classes. These extensive experiments show that grade aggregation using our model accurately estimates true grades, students' likelihood of submitting uninformative grades, and the variation in their inherent grading error; we also characterize our models' robustness.
Deep Graph Structural Infomax

In the scene of self-supervised graph learning, Mutual Information (MI) was recently introduced for graph encoding to generate robust node embeddings. A successful representative is Deep Graph Infomax (DGI), which essentially operates on the space of node features but ignores topological structures, and just considers global graph summary. In this paper, we present an effective model called Deep Graph Structural Infomax (DGSI) to learn node representation. We explore to derive the structural mutual information from the perspective of Information Bottleneck (IB), which defines a trade-off between the sufficiency and minimality of representation on the condition of the topological structure preservation. Intuitively, the derived constraints formally maximize the structural mutual information both edge-wise and local neighborhood-wise. Besides, we develop a general framework that incorporates the global representational mutual information, local representational mutual information, and sufficient structural information into the node representation. Essentially, our DGSI extends DGI and could capture more fine-grained semantic information as well as beneficial structural information in a self-supervised manner, thereby improving node representation and further boosting the learning performance. Extensive experiments on different types of datasets demonstrate the effectiveness and superiority of the proposed method.
TinyNeRF: Towards 100 x Compression of Voxel Radiance Fields

Voxel grid representation of 3D scene properties has been widely used to improve the training or rendering speed of the Neural Radiance Fields (NeRF) while at the same time achieving high synthesis quality. However, these methods accelerate the original NeRF at the expense of extra storage demand, which hinders their applications in many scenarios. To solve this limitation, we present TinyNeRF, a three-stage pipeline: frequency domain transformation, pruning and quantization that work together to reduce the storage demand of the voxel grids with little to no effects on their speed and synthesis quality. Based on the prior knowledge of visual signals sparsity in the frequency domain, we convert the original voxel grids in the frequency domain via block-wise discrete cosine transformation (DCT). Next, we apply pruning and quantization to enforce the DCT coefficients to be sparse and low-bit. Our method can be optimized from scratch in an end-to-end manner, and can typically compress the original models by 2 orders of magnitude with minimal sacrifice on speed and synthesis quality.
RSPT: Reconstruct Surroundings and Predict Trajectory for Generalizable Active Object Tracking

Active Object Tracking (AOT) aims to maintain a specific relation between the tracker and object(s) by autonomously controlling the motion system of a tracker given observations. It is widely used in various applications such as mobile robots and autonomous driving. However, Building a generalizable active tracker that works robustly across various scenarios remains a challenge, particularly in unstructured environments with cluttered obstacles and diverse layouts. To realize this, we argue that the key is to construct a state representation that can model the geometry structure of the surroundings and the dynamics of the target. To this end, we propose a framework called RSPT to form a structure-aware motion representation by Reconstructing Surroundings and Predicting the target Trajectory. Moreover, we further enhance the generalization of the policy network by training in the asymmetric dueling mechanism. Empirical results show that RSPT outperforms existing methods in unseen environments, especially those with cluttered obstacles and diverse layouts. We also demonstrate good sim-to-real transfer when deploying RSPT in real-world scenarios.
LORE: Logical Location Regression Network for Table Structure Recognition

Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats. Recent methods solve this problem by predicting the adjacency relations of detected cell boxes, or learning to generate the corresponding markup sequences from the table images. However, they either count on additional heuristic rules to recover the table structures, or require a huge amount of training data and time-consuming sequential decoders. In this paper, we propose an alternative paradigm. We model TSR as a logical location regression problem and propose a new TSR framework called LORE, standing for LOgical location REgression network, which for the first time combines logical location regression together with spatial location regression of table cells. Our proposed LORE is conceptually simpler, easier to train and more accurate than previous TSR models of other paradigms. Experiments on standard benchmarks demonstrate that LORE consistently outperforms prior arts. Code is available at https:// github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/LORE-TSR.
Multi-Stream Representation Learning for Pedestrian Trajectory Prediction

Forecasting the future trajectory of pedestrians is an important task in computer vision with a range of applications, from security cameras to autonomous driving. It is very challenging because pedestrians not only move individually across time but also interact spatially, and the spatial and temporal information is deeply coupled with one another in a multi-agent scenario. Learning such complex spatio-temporal correlation is a fundamental issue in pedestrian trajectory prediction. Inspired by the procedure that the hippocampus processes and integrates spatio-temporal information to form memories, we propose a novel multi-stream representation learning module to learn complex spatio-temporal features of pedestrian trajectory. Specifically, we learn temporal, spatial and cross spatio-temporal correlation features in three respective pathways and then adaptively integrate these features with learnable weights by a gated network. Besides, we leverage the sparse attention gate to select informative interactions and correlations brought by complex spatio-temporal modeling and reduce complexity of our model. We evaluate our proposed method on two commonly used datasets, i.e. ETH-UCY and SDD, and the experimental results demonstrate our method achieves the state-of-the-art performance. Code: https://github.com/YuxuanIAIR/MSRL-master
Exploring Non-target Knowledge for Improving Ensemble Universal Adversarial Attacks

The ensemble attack with average weights can be leveraged for increasing the transferability of universal adversarial perturbation (UAP) by training with multiple Convolutional Neural Networks (CNNs). However, after analyzing the Pearson Correlation Coefficients (PCCs) between the ensemble logits and individual logits of the crafted UAP trained by the ensemble attack, we find that one CNN plays a dominant role during the optimization. Consequently, this average weighted strategy will weaken the contributions of other CNNs and thus limit the transferability for other black-box CNNs. To deal with this bias issue, the primary attempt is to leverage the Kullback–Leibler (KL) divergence loss to encourage the joint contribution from different CNNs, which is still insufficient. After decoupling the KL loss into a target-class part and a non-target-class part, the main issue lies in that the non-target knowledge will be significantly suppressed due to the increasing logit of the target class. In this study, we simply adopt a KL loss that only considers the non-target classes for addressing the dominant bias issue. Besides, to further boost the transferability, we incorporate the min-max learning framework to self-adjust the ensemble weights for each CNN. Experiments results validate that considering the non-target KL loss can achieve superior transferability than the original KL loss by a large margin, and the min-max training can provide a mutual benefit in adversarial ensemble attacks. The source code is available at: https://github.com/WJJLL/ND-MM.
Learning Continuous Depth Representation via Geometric Spatial Aggregator

Depth map super-resolution (DSR) has been a fundamental task for 3D computer vision. While arbitrary scale DSR is a more realistic setting in this scenario, previous approaches predominantly suffer from the issue of inefficient real-numbered scale upsampling. To explicitly address this issue, we propose a novel continuous depth representation for DSR. The heart of this representation is our proposed Geometric Spatial Aggregator (GSA), which exploits a distance field modulated by arbitrarily upsampled target gridding, through which the geometric information is explicitly introduced into feature aggregation and target generation. Furthermore, bricking with GSA, we present a transformer-style backbone named GeoDSR, which possesses a principled way to construct the functional mapping between local coordinates and the high-resolution output results, empowering our model with the advantage of arbitrary shape transformation ready to help diverse zooming demand. Extensive experimental results on standard depth map benchmarks, e.g., NYU v2, have demonstrated that the proposed framework achieves significant restoration gain in arbitrary scale depth map super-resolution compared with the prior art. Our codes are available at https://github.com/nana01219/GeoDSR.
An Improved End-to-End Data Security Approach for Cloud Computing

&lt;p&gt;Cloud computing is one of the major cutting-edge technology which is in a gigantic growth to redefine the aspect of computation with its service-oriented computing. It has addressed the issue of owning and managing the computational infrastructure by provid- ing the service through a pay-and-use model as an ultimate consequence of saving money and time. However, the major hindrance can be security breaches, particularly when the sender uploads or receiver downloads the data from a remotely accessed server. A very generic approach to ensuring data security through different encryption techniques, but it might not be able to maintain the security standard. This paper proposed an end-to-end data security approach from sender side to receiver side by adding extra padding sequences, randomized salting followed by hashing and the encryption technique. The efficiency of the proposed approach is analyzed through both the simulated system and mathematical formulations with several performance metrics, like cipher text size, encryption and decryption time, encryption and decryption throughput. Further, the performances are compared with contem- porary algorithms and results have shown that the proposed algorithm creates a larger cipher text which is almost impossible to crack due to randomization modules. Though it has a significantly larger encryption and decryption time, as a consequence, lower throughput also. However, here our prime concern is ensuring security, not the time required to encrypt the text. &lt;/p&gt;
Cross-language Clone Detection for Mobile Apps

Clone detection provides insight about replicated fragments in a code base. With the rise of multi-language code bases, new techniques addressing cross-language code clone detection enable the analysis of polyglot systems. Such techniques have not yet been applied to the mobile apps’ domain, which are naturally polyglot. Native mobile app developers must synchronize their code base in at least two different programming languages. App synchronization is a difficult and time-consuming maintenance task, as features can rapidly diverge between platforms, and feature identification must be performed manually. Our goal is to provide an analysis framework to reduce the impact of app synchronization. A first step in this direction consists on a structural algorithm for cross-language clone detection exploiting the idea behind enriched concrete syntax trees. Such trees are used as a common intermediate representation built from programming languages’ grammars, to detect similarities between app code bases. Our technique finds code similarities with 79% precision for controlled tests where Type 1-3 clones are manually injected for the analysis of both single- and cross-language cases for Kotlin and Dart. We evaluate our tool on a corpus of 52 mobile apps identifying code similarities with a precision of 65% to 84% for the full application logic.
Strong-weak Dual-branch Network with Hard-aware Loss for Long-tailed Classification

Natural data usually exhibit a long-tailed distribution, with the minority classes occupying the majority of the data, while the majority classes have few samples.Although deep learning has made remarkable progress in visual recognition on largescale balanced datasets, it remains challenging to model long-tailed distributions.Recent multi-branch methods have shown great potential to address long-tailed problems.We find that these methods work due to the difference between branches, so we also propose a new structure called Strong-weak Dual-branch Network (SDN) to enlarge the difference between branches.In particular, our SDN is equipped with a new Difference to Classification (D2C) learning strategy, designed to amplify the differences between the branches first, and then pay attention to classification.In addition, we propose a new Hard-aware Loss (HL) for the sake of handling hard examples.Our SDNHL method achieves SOTA on four long-tailed datasets: CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT and iNaturalist 2018.
A Survey on Aspect-Based Sentiment Quadruple Extraction with Implicit Aspects and Opinions

Abstract Quadruple sentiment analysis is an essential task in aspect-based sentiment analysis that aims to understand people's viewpoints by analyzing four elements in text: category, aspect, opinion, and sentiment polarity. In recent years, quadruple sentiment analysis tasks have received extensive research attention for capturing comprehensive sentiment information in sentences. This paper focuses on the latest progress in quadruple sentiment analysis tasks. Additionally, the paper provides a summary of the application of methods based on pre-trained language models and datasets in quadruple sentiment analysis with implicit aspects and opinions. It discusses techniques for constructing more practical quadruple sentiment analysis systems in generative large model scenarios. The paper's final section outlines quadruple sentiment analysis research's current challenges and future directions. Moreover, the paper suggests that using advanced language models, such as ChatGPT, holds promise for improving the accuracy and efficiency of quadruple sentiment analysis. Overall, this paper sheds light on the importance of quadruple sentiment analysis and highlights opportunities for further research in this field.
The Lexical Content of High-Stakes National Exams in French, German, and Spanish in England

Surprisingly little is known about the number and frequency of words that beginner-to-low intermediate 16-year-old learners of French, German, and Spanish are expected to know when taking high-stakes national exams in England. This study presents exploratory analyses of the lexical content of the listening and reading tests of these exams, a corpus totalling 116,647 running words. Specifically, it seeks to understand the number and frequency of words that (a) this demographic seems to be expected to know and (b) could be needed for awarding organisations to create exams year on year. Key findings include that the proportion of low(er) frequency words in the corpus of exam papers seemed large, given the stage of the learners and the purpose of the assessments. Critically, these low(er)-frequency words changed at a high rate between papers, likely incurring a heavy reliance on the lexical inferencing abilities of these relatively inexperienced language learners.
Suggestions on the Use of the CAFA System to Promote Teachers’ Digital Competencies

본 연구의 목적은 평가공학적 관점에서 지식의 디지털 전환을 통해 교사의 디지털 역량을 함양시킬 수 있는 방안에 대한 시사점을 제공하는 데 있다. 이를 위해 평가공학 기반의 CAFA(Computer Adaptive Formative Assessment) 시스템을 통해 문항 모형과 평가 모형을 제작하는 절차를 명세화하고, 생성된 문항과 학습지를 제시하였다. 구체적으로 중3 통계 영역 전체를 일례로 들어 하나의 문항 모형으로부터 선다형(정답형, 최선답형, 미완성문장형, 합답형, 부정형), 진위형, 배합형의 7개 문항 유형 및 다양한 매체(그림, 구글차트, 구글번역, 동영상, 문자음성전환, 오디오, HTML, LaTex)들이 포함된 문항 모형들을 제작하였으며, 이들을 통합하여 평가 모형을 만들고, 학습지를 생성하였다. 이를 바탕으로 교사의 디지털 역량을 함양할 수 있는 방안에 대해 지식 측면에서는 교수 및 평가 역량, 수행 측면에서는 협력 역량, 그리고 태도 측면에서는 의사소통 및 공감역량에 초점을 맞추어 논의하였다. The purpose of this study is to provide implications for ways to promote teachers’ digital competencies based on the digital transformation of knowledge in the aspect of assessment engineering. To perform this purpose, based on the CAFA (Computer Adaptive Formative Assessment) system, the procedures for producing item models and worksheet models were specified, and then the generated items and worksheets were explored. Specifically, taking the whole contents of statistics for 9th-grade students as an example, from a single item model, 7 item types were generated as multiple choice type (best-answer variety, correct-answer variety, incomplete-statement variety, combined-response variety, negative variety), true-false type, and matching type. In addition, items were generated using various media (audio, Google charts, Google translation, HTML, LaTex, pictures, TTS, video), and worksheets were generated by integrating them. The implications for promoting the digital competencies of teachers using the CAFA system were presented focusing on teaching and assessment competencies in terms of knowledge, cooperation competency regarding performance, and communication and empathy competencies in the aspect of attitude.
Improved Face Morphing Attack Detection Method Using PCA and Convolutional Neural Network

Face recognition is the most extensively utilized security and public safety verification method.In many nations, the Automatic Border Control system uses face recognition to confirm the identification of travelers The ABC system is vulnerable to face morphing attacks; the face recognition systems give acceptance for the traveller, even though the passport photo does not represent the actual image of the person but is a result of the merger of two images.Therefore, it is vital to determine whether the passport image is altering (morph) or actual.This research proposes an improved method to extract features from facial images.The proposed method consists of four phases: In the first stage, morph images were generated using a set of databases of images of real people, used every two images that were similar in general shape or landmarks in producing the morphed image using three types of techniques used in this field (Automatic selection landmark, StyleGAN, and Manual selection landmark).StyleGAN has been relied upon to achieve the best results in producing artefact-free images.In the second phase, a Faster Region Convolution neural network is utilizing for determining and cutting important landmarks area (eyes, nose, mouth, and skin) in the face, where we leave the hair, ears, and image background for every image in the database.In the third phase, the features are extracted using three techniques Principal component analysis, eigenvalue, and eigenvector; a matrix of two-dimensional features is generated with one layer for each technique.Then merge the extracted features (with out s) from each image into one image with three layers.The first layer represents the principal component analysis features, the second the eigenvalue features, and the third the eigenvector features.Finally, the features are introduced into the convolutional neural networks to obtain optimal features.The fourth phase represents the classification process using the Deep Neural Network (DNN) classifier and Support Vector Machine (SVM) second classifier.The DNN classifier achieved an average accuracy of 99.02% compared with SVM, with an accuracy of 98.64%.The power of the proposed work is evident through the FRA and RFF evaluation.Which achieved values as low as possible for DNN FAR 0.018, indicating the error rate in calculating morphed images is actual, and FRR 0.003, meaning the error rate in calculating the actual images is morphed, FAR 0.023, FRR 0.06 for SVM whenever these ratios are less than one, the higher system's accuracy in detection.The AMSL dataset (Accuracy 95.8%, FAR 0.039, FRR 0%) (Accuracy 95.2%, FAR 0.047, FRR 0.98) for DNN and SVM, respectively.It turned out that the training of the proposed network optimized for the features extracted for the landmarks area significantly affects finding the difference and discovering the modified images, even in the case of minor modifications as in the AMSL dataset.
Students' willingness to Communicate in Extramural English Activities in Digital EFL Context: A Narrative Inquiry

The changing of offline language learning into online language learning as a new way to learn English resulted in students’ engagement in social media activities. This research tries to investigate the effects of social media activities towards students’ willingness to communicate. The researcher interviewed 4 EFL students form one state university in Indonesia. All participants are active social media users who have learnt English for about 10 years, and they have experienced international internship for their teaching practicum. The result of this research yielded that engaging in social media helped the students improve their English ability, learn new culture form other countries, and accept the diversity of English. This result validated the existing views regarding the topic of IDLE and WTC. This research may help future researchers that the result of this research presents a narrative avenue for future L2 WTC studies, and enables future researchers to consider digital EFL contexts when conducting L2 WTC research.