Reviewerly/exHarmony huggingface dataset:
TextHoaxer: Budgeted Hard-Label Adversarial Attacks on Text

This paper focuses on a newly challenging setting in hard-label adversarial attacks on text data by taking the budget information into account. Although existing approaches can successfully generate adversarial examples in the hard-label setting, they follow an ideal assumption that the victim model does not restrict the number of queries. However, in real-world applications the query budget is usually tight or limited. Moreover, existing hard-label adversarial attack techniques use the genetic algorithm to optimize discrete text data by maintaining a number of adversarial candidates during optimization, which can lead to the problem of generating low-quality adversarial examples in the tight-budget setting. To solve this problem, in this paper, we propose a new method named TextHoaxer by formulating the budgeted hard-label adversarial attack task on text data as a gradient-based optimization problem of perturbation matrix in the continuous word embedding space. Compared with the genetic algorithm-based optimization, our solution only uses a single initialized adversarial example as the adversarial candidate for optimization, which significantly reduces the number of queries. The optimization is guided by a new objective function consisting of three terms, i.e., semantic similarity term, pair-wise perturbation constraint, and sparsity constraint. Semantic similarity term and pair-wise perturbation constraint can ensure the high semantic similarity of adversarial examples from both comprehensive text-level and individual word-level, while the sparsity constraint explicitly restricts the number of perturbed words, which is also helpful for enhancing the quality of generated text. We conduct extensive experiments on eight text datasets against three representative natural language models, and experimental results show that TextHoaxer can generate high-quality adversarial examples with higher semantic similarity and lower perturbation rate under the tight-budget setting.
Evidential Neighborhood Contrastive Learning for Universal Domain Adaptation

Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain without any constraints on the label sets. However, domain shift and category shift make UniDA extremely challenging, mainly attributed to the requirement of identifying both shared “known” samples and private “unknown” samples. Previous methods barely exploit the intrinsic manifold structure relationship between two domains for feature alignment, and they rely on the softmax-based scores with class competition nature to detect underlying “unknown” samples. Therefore, in this paper, we propose a novel evidential neighborhood contrastive learning framework called TNT to address these issues. Specifically, TNT first proposes a new domain alignment principle: semantically consistent samples should be geometrically adjacent to each other, whether within or across domains. From this criterion, a cross-domain multi-sample contrastive loss based on mutual nearest neighbors is designed to achieve common category matching and private category separation. Second, toward accurate “unknown” sample detection, TNT introduces a class competition-free uncertainty score from the perspective of evidential deep learning. Instead of setting a single threshold, TNT learns a category-aware heterogeneous threshold vector to reject diverse “unknown” samples. Extensive experiments on three benchmarks demonstrate that TNT significantly outperforms previous state-of-the-art UniDA methods.
Clustering based Behavior Sampling with Long Sequential Data for CTR Prediction

Click-through rate (CTR) prediction is fundamental in many industrial applications, such as online advertising and recommender systems. With the development of the online platforms, the sequential user behaviors grow rapidly, bringing us great opportunity to better understand user preferences.However, it is extremely challenging for existing sequential models to effectively utilize the entire behavior history of each user. First, there is a lot of noise in such long histories, which can seriously hurt the prediction performance. Second, feeding the long behavior sequence directly results in infeasible inference time and storage cost. In order to tackle these challenges, in this paper we propose a novel framework, which we name as User Behavior Clustering Sampling (UBCS). In UBCS, short sub-sequences will be obtained from the whole user history sequence with two cascaded modules: (i) Behavior Sampling module samples short sequences related to candidate items using a novel sampling method which takes relevance and temporal information into consideration; (ii) Item Clustering module clusters items into a small number of cluster centroids, mitigating the impact of noise and improving efficiency. Then, the sampled short sub-sequences will be fed into the CTR prediction module for efficient prediction. Moreover, we conduct a self-supervised consistency pre-training task to extract user persona preference and optimize the sampling module effectively. Experiments on real-world datasets demonstrate the superiority and efficiency of our proposed framework.
A Python Interface to PISA!

PISA (Performant Indexes and Search for Academia) provides very efficient implementations of various retrieval algorithms over sparse inverted indices. The highly-optimized C++ implementation, however, has previously only been accessible via command line tools. From indexing to retrieval, 5--6 commands need to be executed in sequence, making the process relatively involved. Further complications when using PISA include a lengthy build process and minimal interoperability with other tools. In this work, we demonstrate a new tool that provides a native Python wrapper around PISA. The wrapper features a simplified interface that adheres to the PyTerrier API, making it easy to use (e.g., via Pandas DataFrames), apply to a multitude of datasets (e.g., those from the ir_datasets package) and combine with other methods (e.g., neural re-ranking and dense retrieval methods).
Compute Cost Amortized Transformer for Streaming ASR

We present a streaming, Transformer-based end-to-end automatic speech recognition (ASR) architecture which achieves efficient neural inference through compute cost amortization. Our architecture creates sparse computation pathways dynamically at inference time, resulting in selective use of compute resources throughout decoding, enabling significant reductions in compute with minimal impact on accuracy. The fully differentiable architecture is trained end-to-end with an accompanying lightweight arbitrator mechanism operating at the frame-level to make dynamic decisions on each input while a tunable loss function is used to regularize the overall level of compute against predictive performance. We report empirical results from experiments using the compute amortized Transformer-Transducer (T-T) model conducted on LibriSpeech data. Our best model can achieve a 60% compute cost reduction with only a 3% relative word error rate (WER) increase.
Hierarchical Superpixel Segmentation by Parallel CRTrees Labeling

This paper proposes a hierarchical superpixel segmentation by representing an image as a hierarchy of 1-nearest neighbor (1-NN) graphs with pixels/superpixels denoting the graph vertices. The 1-NN graphs are built from the pixel/superpixel adjacent matrices to ensure connectivity. To determine the next-level superpixel hierarchy, inspired by FINCH clustering, the weakly connected components (WCCs) of the 1-NN graph are labeled as superpixels. We reveal that the WCCs of a 1-NN graph consist of a forest of cycle-root-trees (CRTrees). The forest-like structure inspires us to propose a two-stage parallel CRTrees labeling which first links the child vertices to the cycle-roots and then labels all the vertices by the cycle-roots. We also propose an inter-inner superpixel distance penalization and a Lab color lightness penalization base on the property that the distance of a CRTree decreases monotonically from the child to root vertices. Experiments show the parallel CRTrees labeling is several times faster than recent advanced sequential and parallel connected components labeling algorithms. The proposed hierarchical superpixel segmentation has comparable performance to the best performer ETPS (state-of-the-arts) on the BSDS500, NYUV2, and Fash datasets. At the same time, it can achieve 200FPS for 480P video streams.
A Study of the Elderly"s Online Home-Based Exercise Experience in COVID-19 Pandemic

This study explores the online home-based exercise experience of the elderly in the COVID-19 situation, to seek improvements by analyzing the effects and limiting factors of the existing online home-based exercise, and then to provide implications for the development of a new online home-based exercise program. With the online home-based exercise experience of the elderly as the research subject, this study examines the actual experiences of the study participants through individual in-depth interviews as a phenomenological method among qualitative research methods. The study participants
Adaptive-Resolution Field Mapping Using Gaussian Process Fusion With Integral Kernels

Unmanned aerial vehicles are rapidly gaining popularity in many environmental monitoring tasks. A prerequisite for their autonomous operation is the ability to perform efficient and accurate mapping online, given limited on-board resources constraining operation time and computational capacity. To address this, we present an online adaptive-resolution approach for field mapping based on Gaussian Process fusion, a strategy in which Bayesian fusion is applied to update a Gaussian Process prior map. A key aspect of our approach is an integral kernel encoding spatial correlation over the areas of grid cells. This enables efficient information compression in uninteresting areas to achieve a compact map representation while maintaining spatial correlations in a theoretically sound fashion. We evaluate the performance of our approach on both synthetic and real-world data. Results show that our method is more efficient in terms of mapping time and memory consumption without compromising on map quality. Further, we integrate our mapping strategy into an adaptive path planning framework to show that it facilitates information gathering efficiency in online settings.
Gray-Scale Image Encryption Using DNA Operations

Unsecured networks have recently become widely used for the transmission of confidential images.Consequently, cryptography is crucial for ensuring data confidentiality.Developing a key that is resistant to statistical and differential attacks has always been a challenging objective.In this paper, a novel model is proposed to boost image encryption while maintaining key strength.The proposed model adapts MD5 and SHA-256 hash functions to produce a key.It generates four matrices, X, Y, Z, and W, by using a memristor hyperchaotic system.Arnold's transform was applied to the original image once the key was created.The images were then scrambled using five chaotic maps.The image is then DNA-encoded, diffused using three matrices, and finally DNA-decoded.The proposed model was assessed using twelve performance measures on nine popular images.Compared to previous studies, the results of the proposed model indicate a promising improvement in performance.It achieves a better performance by expanding the key space and increasing its sensitivity.
Prediction Difference Regularization against Perturbation for Neural Machine Translation

Regularization methods applying input perturbation have drawn considerable attention and have been frequently explored for NMT tasks in recent years.Despite their simplicity and effectiveness, we argue that these methods are limited by the under-fitting of training data.In this paper, we utilize prediction difference for ground-truth tokens to analyze the fitting of token-level samples and find that underfitting is almost as common as over-fitting.We introduce prediction difference regularization (PD-R), a simple and effective method that can reduce over-fitting and under-fitting at the same time.For all token-level samples, PD-R minimizes the prediction difference between the original pass and the input-perturbed pass, making the model less sensitive to small input changes, thus more robust to both perturbations and under-fitted training data.Experiments on three widely used WMT translation tasks show that our approach can significantly improve over existing perturbation regularization methods.On WMT16 En-De task, our model achieves 1.80 SacreBLEU improvement over vanilla transformer.
Toward Secure Federated Learning for IoT Using DRL-Enabled Reputation Mechanism

Federated learning (FL) has emerged to leverage datasets from multiple devices to improve the performance of a machine learning (ML) model while providing privacy preservation for devices. The training data is collected at the devices, also known as FL workers, which collaboratively train a global learning model and share their local model updates with a central entity or server without sharing their data. However, FL can be susceptible to various adversarial attacks that target its security and privacy. In particular, the workers can upload unreliable local model updates, leading to corruption of the main FL task. Workers may intentionally contribute unreliable local updates by launching poisoning attacks or unintentionally by updating low-quality models caused by high device mobility, limited device resources, or unstable network connection. Consequently, identifying reliable and trustworthy workers becomes critical for FL security. In this article, the concept of reputation is adopted as a metric to evaluate workers’ reliability and trustworthiness. In addition, deep reinforcement learning (DRL)-based reputation mechanism is proposed for optimal selection and evaluation of reliable FL workers. Due to the dynamic nature of worker behavior in the FL environment, the DRL-based algorithm deep deterministic policy gradient (DDPG) is employed to improve the FL model accuracy and stability. We compare the performance of our proposed method with a conventional reputation method and deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -networks (DQNs)-based reputation method. Our simulation results demonstrate that our proposed method can improve FL accuracy by more than 30% under various scenarios and achieves better convergence than the other methods.
MoEfication: Transformer Feed-forward Layers are Mixtures of Experts

Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge.However, the computational patterns of FFNs are still unclear.In this work, we study the computational patterns of FFNs and observe that most inputs only activate a tiny ratio of neurons of FFNs.This phenomenon is similar to the sparsity of the human brain, which drives research on functional partitions of the human brain.To verify whether functional partitions also emerge in FFNs, we propose to convert a model into its MoE version with the same parameters, namely MoEfication.Specifically, MoEfication consists of two phases: (1) splitting the parameters of FFNs into multiple functional partitions as experts, and (2) building expert routers to decide which experts will be used for each input.Experimental results show that MoEfication can conditionally use 10% to 30% of FFN parameters while maintaining over 95% original performance for different models on various downstream tasks.Besides, MoEfication brings two advantages: (1) it significantly reduces the FLOPS of inference, i.e., 2x speedup with 25% of FFN parameters, and (2) it provides a fine-grained perspective to study the inner mechanism of FFNs.The source code of this paper can be obtained from https://github.com/thunlp/MoEfication.
IIITDWD@TamilNLP-ACL2022: Transformer-based approach to classify abusive content in Dravidian Code-mixed text

Identifying abusive content or hate speech in social media text has raised the research community's interest in recent times.The major driving force behind this is the widespread use of social media websites.Further, it also leads to identifying abusive content in lowresource regional languages, which is an important research problem in computational linguistics.As part of ACL-2022, organizers of DravidianLangTech@ACL 2022 have released a shared task on abusive category identification in Tamil and Tamil-English code-mixed text to encourage further research on offensive content identification in low-resource Indic languages.This paper presents the working notes for the model submitted by IIITDWD at Dra-vidianLangTech@ACL 2022.Our team competed in Sub-Task B and finished in 9 th place among the participating teams.In our proposed approach, we used a pre-trained transformer model such as Indic-bert for feature extraction, and on top of that, SVM classifier is used for stance detection.Further, our model achieved 62 % accuracy on code-mixed Tamil-English text.
Modeling Dual Read/Write Paths for Simultaneous Machine Translation

Simultaneous machine translation (SiMT) outputs translation while reading source sentence and hence requires a policy to decide whether to wait for the next source word (READ) or generate a target word (WRITE), the actions of which form a read/write path.Although the read/write path is essential to SiMT performance, no direct supervision is given to the path in the existing methods.In this paper, we propose a method of dual-path SiMT which introduces duality constraints to direct the read/write path.According to duality constraints, the read/write path in source-totarget and target-to-source SiMT models can be mapped to each other.As a result, the two SiMT models can be optimized jointly by forcing their read/write paths to satisfy the mapping.Experiments on En↔Vi and De↔En tasks show that our method can outperform strong baselines under all latency.
Cryptanalysis of a Transaction Scheme With Certificateless Cryptographic Primitives for IoT-Based Mobile Payments

Very recently, Qiao <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2021) constructed an efficient certificateless signature (CLS) scheme, and further used it as the underlying scheme to design a transaction scheme for IoT-based mobile payments. Their CLS scheme is claimed to be secure against Type I and II attackers. Unfortunately, in this article, we provide an attack method to demonstrate that their CLS scheme is insecure against Type II attackers. Furthermore, we present an improved CLS scheme to enhance the security of their scheme.
Are you a hero or a villain? A semantic role labelling approach for detecting harmful memes.

Identifying good and evil through representations of victimhood, heroism, and villainy (i.e., role labeling of entities) has recently caught the research community's interest.Because of the growing popularity of memes, the amount of offensive information published on the internet is expanding at an alarming rate.It generated a larger need to address this issue and analyze the memes for content moderation.Framing is used to show the entities engaged as heroes, villains, victims, or others so that readers may better anticipate and understand their attitudes and behaviors as characters.Positive phrases are used to characterize heroes, whereas negative terms depict victims and villains, and terms that tend to be neutral are mapped to others.In this paper, we propose two approaches to role label the entities of the meme as hero, villain, victim, or other through Named-Entity Recognition(NER), Sentiment Analysis, etc.With an F1-score of 23.855, our team secured eighth position in the Shared Task @ Constraint 2022.
High-Capacity Image Steganography Based on Discrete Hadamard Transform

High capacity and high imperceptibility are the primary targets for ideal image steganography. For the traditional transform-based schemes, the main challenge is to balance the imperceptibility, hiding capacity, and running efficiency. To obtain a high-quality image in dense embedding, existing high-capacity schemes usually sacrifice running efficiency and security. These disadvantages make the schemes less appealing. In this paper, based on a lightweight transform Discrete Hadamard Transform (DHT), we introduce a simple but high-performance image steganographic model. In the case of only stego-image passed, the experiment results demonstrate that the proposed scheme achieves high imperceptibility and security even in the dense embedding of 8 BPP (Bits Per Pixel). Furthermore, the proposed scheme withstands various tests and shows desirable robustness. The comparative analyses are demonstrated that our scheme is efficient and feasible image steganography.
A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images

Incremental learning represents a crucial task in aerial image processing, especially given the limited availability of large-scale annotated datasets. A major issue concerning current deep neural architectures is known as catastrophic forgetting, namely the inability to faithfully maintain past knowledge once a new set of data is provided for retraining. Over the years, several techniques have been proposed to mitigate this problem for image classification and object detection. However, only recently the focus has shifted towards more complex downstream tasks such as instance or semantic segmentation. Starting from incremental-class learning for semantic segmentation tasks, our goal is to adapt this strategy to the aerial domain, exploiting a peculiar feature that differentiates it from natural images, namely the orientation. In addition to the standard knowledge distillation approach, we propose a contrastive regularization, where any given input is compared with its augmented version (i.e. flipping and rotations) in order to minimize the difference between the segmentation features produced by both inputs. We show the effectiveness of our solution on the Potsdam dataset, outperforming the incremental baseline in every test (Code available at: https://github.com/edornd/contrastive-distillation ).
Real-Time Fast Channel Clustering for LiDAR Point Cloud

LiDAR sensors can produce point clouds with precise 3D depth information that is essential for autonomous vehicles and robotic systems. As a perception task, point cloud clustering algorithms can be applied to segment the points into object instances. In this brief, we propose a novel, hardware-friendly fast channel clustering (FCC) algorithm that achieves state-of-the-art performance when evaluated using KITTI panoptic segmentation benchmark. Furthermore, an efficient, pipeline hardware architecture is proposed to implement the FCC algorithm on an FPGA. Experiments show that the hardware design can process each LiDAR frame with 64 channels, 2048 horizontal resolution at various point sparsity in 1.93 ms, which is more than 471.5 times faster than running on the CPU. The code will be released to the public via GitHub.