Reviewerly/exHarmony huggingface dataset:
Designing Ceramic Tureen Surface Pattern through the Influence of Malaysian Batik

Ceramic Tureen refers to the ‘serving ware' that are widely used in Malaysian community. On the other hand, the surface decoration increases aesthetic value of the ‘serving ware' especially in promoting the local contexts and meaning. However, this local context design in regard to tureen surface design is being over shadowed by European style and identity design pattern. This study is to associate the tureen with local context design of batik character by introducing batik as an idea to pattern design. Batik pattern design will be applied on the ceramic tureen ‘serving ware'.
Analysis of the technical difference between speed skating and speed roller skating

This article compares the technologies of speed skating and speed roller skating to analyze the differences between speed roller skating and speed skating between technology and posture.In terms of comparative analysis, summarize the differences in speed skating and speed roller skating, as well as problems that are easy to occur in teaching, training and competitions in two items, so as to put forward some methods and suggestions to provide a reference for improving the level of speed skating and performance.
Can Graph Neural Networks Learn to Solve the MaxSAT Problem? (Student Abstract)

The paper presents an attempt to bridge the gap between machine learning and symbolic reasoning. We build graph neural networks (GNNs) to predict the solution of the Maximum Satisfiability (MaxSAT) problem, an optimization variant of SAT. Two closely related graph representations are adopted, and we prove their theoretical equivalence. We also show that GNNs can achieve attractive performance to solve hard MaxSAT problems in certain distributions even compared with state-of-the-art solvers through experimental evaluation.
Neural Policy Safety Verification via Predicate Abstraction: CEGAR

Neural networks (NN) are an increasingly important representation of action policies pi. Recent work has extended predicate abstraction to prove safety of such pi, through policy predicate abstraction (PPA) which over-approximates the state space subgraph induced by pi. The advantage of PPA is that reasoning about the NN – calls to SMT solvers – is required only locally, at individual abstract state transitions, in contrast to bounded model checking (BMC) where SMT must reason globally about sequences of NN decisions. Indeed, it has been shown that PPA can outperform a simple BMC implementation. However, the abstractions underlying these results (i.e., the abstraction predicates) were supplied manually. Here we automate this step. We extend counterexample guided abstraction refinement (CEGAR) to PPA. This involves dealing with a new source of spuriousness in abstract unsafe paths, pertaining not to transition behavior but to the decisions of the neural network pi. We introduce two methods tackling this issue based on the states involved, and we show that global SMT calls deciding spuriousness exactly can be avoided. We devise algorithmic enhancements leveraging incremental computation and heuristic search. We show empirically that the resulting verification tool has significant advantages over an encoding into the state-of-the-art model checker nuXmv. In particular, ours is the only approach in our experiments that succeeds in proving policies safe.
GAAMA 2.0: An Integrated System That Answers Boolean and Extractive Questions

Recent machine reading comprehension datasets include extractive and boolean questions but current approaches do not offer integrated support for answering both question types. We present a front-end demo to a multilingual machine reading comprehension system that handles boolean and extractive questions. It provides a yes/no answer and highlights the supporting evidence for boolean questions. It provides an answer for extractive questions and highlights the answer in the passage. Our system, GAAMA 2.0, achieved first place on the TyDi QA leaderboard at the time of submission. We contrast two different implementations of our approach: including multiple transformer models for easy deployment, and a shared transformer model utilizing adapters to reduce GPU memory footprint for a resource-constrained environment.
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost

Lottery tickets (LTs) is able to discover accurate and sparse subnetworks that could be trained in isolation to match the performance of dense networks. Ensemble, in parallel, is one of the oldest time-proven tricks in machine learning to improve performance by combining the output of multiple independent models. However, the benefits of ensemble in the context of LTs will be diluted since ensemble does not directly lead to stronger sparse subnetworks, but leverages their predictions for a better decision. In this work, we first observe that directly averaging the weights of the adjacent learned subnetworks significantly boosts the performance of LTs. Encouraged by this observation, we further propose an alternative way to perform an "ensemble'' over the subnetworks identified by iterative magnitude pruning via a simple interpolating strategy. We call our method Lottery Pools. In contrast to the naive ensemble which brings no performance gains to each single subnetwork, Lottery Pools yields much stronger sparse subnetworks than the original LTs without requiring any extra training or inference cost. Across various modern architectures on CIFAR-10/100 and ImageNet, we show that our method achieves significant performance gains in both, in-distribution and out-of-distribution scenarios. Impressively, evaluated with VGG-16 and ResNet-18, the produced sparse subnetworks outperform the original LTs by up to 1.88% on CIFAR-100 and 2.36% on CIFAR-100-C; the resulting dense network surpasses the pre-trained dense-model up to 2.22% on CIFAR-100 and 2.38% on CIFAR-100-C. Our source code can be found at https://github.com/luuyin/Lottery-pools.
Question Decomposition Tree for Answering Complex Questions over Knowledge Bases

Knowledge base question answering (KBQA) has attracted a lot of interest in recent years, especially for complex questions which require multiple facts to answer. Question decomposition is a promising way to answer complex questions. Existing decomposition methods split the question into sub-questions according to a single compositionality type, which is not sufficient for questions involving multiple compositionality types. In this paper, we propose Question Decomposition Tree (QDT) to represent the structure of complex questions. Inspired by recent advances in natural language generation (NLG), we present a two-staged method called Clue-Decipher to generate QDT. It can leverage the strong ability of NLG model and simultaneously preserve the original questions. To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA system called QDTQA. Extensive experiments show that QDTQA outperforms previous state-of-the-art methods on ComplexWebQuestions dataset. Besides, our decomposition method improves an existing KBQA system by 12% and sets a new state-of-the-art on LC-QuAD 1.0.
CP-Rec: Contextual Prompting for Conversational Recommender Systems

The conversational recommender system (CRS) aims to provide high-quality recommendations through interactive dialogues. However, previous CRS models have no effective mechanisms for task planning and topic elaboration, and thus they hardly maintain coherence in multi-task recommendation dialogues. Inspired by recent advances in prompt-based learning, we propose a novel contextual prompting framework for dialogue management, which optimizes prompts based on context, topics, and user profiles. Specifically, we develop a topic controller to sequentially plan the subtasks, and a prompt search module to construct context-aware prompts. We further adopt external knowledge to enrich user profiles and make knowledge-aware recommendations. Incorporating these techniques, we propose a conversational recommender system with contextual prompting, namely CP-Rec. Experimental results demonstrate that it achieves state-of-the-art recommendation accuracy and generates more coherent and informative conversations.
Rethinking Label Refurbishment: Model Robustness under Label Noise

A family of methods that generate soft labels by mixing the hard labels with a certain distribution, namely label refurbishment, are widely used to train deep neural networks. However, some of these methods are still poorly understood in the presence of label noise. In this paper, we revisit four label refurbishment methods and reveal the strong connection between them. We find that they affect the neural network models in different manners. Two of them smooth the estimated posterior for regularization effects, and the other two force the model to produce high-confidence predictions. We conduct extensive experiments to evaluate related methods and observe that both effects improve the model generalization under label noise. Furthermore, we theoretically show that both effects lead to generalization guarantees on the clean distribution despite being trained with noisy labels.
FC-TrackNet: Fast Convergence Net for 6D Pose Tracking in Synthetic Domains

In this work, we propose a fast convergence track net, or FC-TrackNet, based on a synthetic data-driven approach to maintaining long-term 6D pose tracking. Comparison experiments are performed on two different datasets, The results demonstrate that our approach can achieve a consistent tracking frequency of 90.9 Hz as well as higher accuracy than the state-of-the art approaches.
Prototypical Partial Optimal Transport for Universal Domain Adaptation

Universal domain adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain without requiring the same label sets of both domains. The existence of domain and category shift makes the task challenging and requires us to distinguish “known” samples (i.e., samples whose labels exist in both domains) and “unknown” samples (i.e., samples whose labels exist in only one domain) in both domains before reducing the domain gap. In this paper, we consider the problem from the point of view of distribution matching which we only need to align two distributions partially. A novel approach, dubbed mini-batch Prototypical Partial Optimal Transport (m-PPOT), is proposed to conduct partial distribution alignment for UniDA. In training phase, besides minimizing m-PPOT, we also leverage the transport plan of m-PPOT to reweight source prototypes and target samples, and design reweighted entropy loss and reweighted cross-entropy loss to distinguish “known” and “unknown” samples. Experiments on four benchmarks show that our method outperforms the previous state-of-the-art UniDA methods.
Exploiting High-Order Interaction Relations to Explore User Intent (Student Abstract)

This paper studies the problem of exploring the user intent for session-based recommendations. Its challenges come from the uncertainty of user behavior and limited information. However, current endeavors cannot fully explore the mutual interactions among sessions and do not explicitly model the complex high-order relations among items. To circumvent these critical issues, we innovatively propose a HyperGraph Convolutional Contrastive framework (termed HGCC) that consists of two crucial tasks: 1) The session-based recommendation (SBR task) that aims to capture the beyond pair-wise relationships between items and sessions. 2) The self-supervised learning (SSL task) acted as the auxiliary task to boost the former task. By jointly optimizing the two tasks, the performance of the recommendation task achieves decent gains. Experiments on multiple real-world datasets demonstrate the superiority of the proposed approach over the state-of-the-art methods.
Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training

Understanding mathematical questions effectively is a crucial task, which can benefit many applications, such as difficulty estimation. Researchers have drawn much attention to designing pre-training models for question representations due to the scarcity of human annotations (e.g., labeling difficulty). However, unlike general free-format texts (e.g., user comments), mathematical questions are generally designed with explicit purposes and mathematical logic, and usually consist of more complex content, such as formulas, and related mathematical knowledge (e.g., Function). Therefore, the problem of holistically representing mathematical questions remains underexplored. To this end, in this paper, we propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo, which attempts to bring questions with more similar purposes closer. Specifically, we first design two-level question augmentations, including content-level and structure-level, which generate literally diverse question pairs with similar purposes. Then, to fully exploit hierarchical information of knowledge concepts, we propose a knowledge hierarchy-aware rank strategy (KHAR), which ranks the similarities between questions in a fine-grained manner. Next, we adopt a ranking contrastive learning task to optimize our model based on the augmented and ranked questions. We conduct extensive experiments on two real-world mathematical datasets. The experimental results demonstrate the effectiveness of our model.
Does It Pay to Optimize AUC?

The Area Under the ROC Curve (AUC) is an important model metric for evaluating binary classifiers, and many algorithms have been proposed to optimize AUC approximately. It raises the question of whether the generally insignificant gains observed by previous studies are due to inherent limitations of the metric or the inadequate quality of optimization. To better understand the value of optimizing for AUC, we present an efficient algorithm, namely AUC-opt, to find the provably optimal AUC linear classifier in R2, which runs in O(n+n- log n+n-) where n+ and n- are the number of positive and negative samples respectively. Furthermore, it can be naturally extended to Rd in O(n+n-d-1 log (n+n-)) by recursively calling AUC-opt in lower-dimensional spaces. We prove the problem is NP-complete when d is not fixed, reducing from the open hemisphere problem. Compared with other methods, experiments show that AUC-opt achieves statistically significant improvements between 17 to 40 in R2 and 4 to 42 in R3 of 50 t-SNE training datasets. However, generally, the gain proves insignificant on most testing datasets compared to the best standard classifiers. Similar observations are found for nonlinear AUC methods under real-world datasets.
Backpropagation-Free Deep Learning with Recursive Local Representation Alignment

Training deep neural networks on large-scale datasets requires significant hardware resources whose costs (even on cloud platforms) put them out of reach of smaller organizations, groups, and individuals. Backpropagation (backprop), the workhorse for training these networks, is an inherently sequential process that is difficult to parallelize. Furthermore, researchers must continually develop various specialized techniques, such as particular weight initializations and enhanced activation functions, to ensure stable parameter optimization. Our goal is to seek an effective, neuro-biologically plausible alternative to backprop that can be used to train deep networks. In this paper, we propose a backprop-free procedure, recursive local representation alignment, for training large-scale architectures. Experiments with residual networks on CIFAR-10 and the large benchmark, ImageNet, show that our algorithm generalizes as well as backprop while converging sooner due to weight updates that are parallelizable and computationally less demanding. This is empirical evidence that a backprop-free algorithm can scale up to larger datasets.
Imbalanced Label Distribution Learning

Label distribution covers a certain number of labels, representing the degree to which each label describes an instance. The learning process on the instances labeled by label distributions is called Label Distribution Learning (LDL). Although LDL has been applied successfully to many practical applications, one problem with existing LDL methods is that they are limited to data with balanced label information. However, annotation information in real-world data often exhibits imbalanced distributions, which significantly degrades the performance of existing methods. In this paper, we investigate the Imbalanced Label Distribution Learning (ILDL) problem. To handle this challenging problem, we delve into the characteristics of ILDL and empirically find that the representation distribution shift is the underlying reason for the performance degradation of existing methods. Inspired by this finding, we present a novel method named Representation Distribution Alignment (RDA). RDA aligns the distributions of feature representations and label representations to alleviate the impact of the distribution gap between the training set and the test set caused by the imbalance issue. Extensive experiments verify the superior performance of RDA. Our work fills the gap in benchmarks and techniques for practical ILDL problems.
Compressing Transformers: Features Are Low-Rank, but Weights Are Not!

Transformer and its variants achieve excellent results in various computer vision and natural language processing tasks, but high computational costs and reliance on large training datasets restrict their deployment in resource-constrained settings. Low-rank approximation of model weights has been effective in compressing CNN models, but its application to transformers has been less explored and is less effective. Existing methods require the complete dataset to fine-tune compressed models, which are both time-consuming and data-hungry. This paper reveals that the features (i.e., activations) are low-rank, but model weights are surprisingly not low-rank. Hence, AAFM is proposed, which adaptively determines the compressed model structure and locally compresses each linear layer's output features rather than the model weights. A second stage, GFM, optimizes the entire compressed network holistically. Both AAFM and GFM only use few training samples without labels, that is, they are few-shot, unsupervised, fast and effective. For example, with only 2K images without labels, 33% of the parameters are removed in DeiT-B with 18.8% relative throughput increase, but only a 0.23% accuracy loss for ImageNet recognition. The proposed methods are successfully applied to the language modeling task in NLP, too. Besides, the few-shot compressed models generalize well in downstream tasks.
Generalized Semantic Segmentation by Self-Supervised Source Domain Projection and Multi-Level Contrastive Learning

Deep networks trained on the source domain show degraded performance when tested on unseen target domain data. To enhance the model's generalization ability, most existing domain generalization methods learn domain invariant features by suppressing domain sensitive features. Different from them, we propose a Domain Projection and Contrastive Learning (DPCL) approach for generalized semantic segmentation, which includes two modules: Self-supervised Source Domain Projection (SSDP) and Multi-Level Contrastive Learning (MLCL). SSDP aims to reduce domain gap by projecting data to the source domain, while MLCL is a learning scheme to learn discriminative and generalizable features on the projected data. During test time, we first project the target data by SSDP to mitigate domain shift, then generate the segmentation results by the learned segmentation network based on MLCL. At test time, we can update the projected data by minimizing our proposed pixel-to-pixel contrastive loss to obtain better results. Extensive experiments for semantic segmentation demonstrate the favorable generalization capability of our method on benchmark datasets.
People Taking Photos That Faces Never Share: Privacy Protection and Fairness Enhancement from Camera to User

The soaring number of personal mobile devices and public cameras poses a threat to fundamental human rights and ethical principles. For example, the stolen of private information such as face image by malicious third parties will lead to catastrophic consequences. By manipulating appearance of face in the image, most of existing protection algorithms are effective but irreversible. Here, we propose a practical and systematic solution to invertiblely protect face information in the full-process pipeline from camera to final users. Specifically, We design a novel lightweight Flow-based Face Encryption Method (FFEM) on the local embedded system privately connected to the camera, minimizing the risk of eavesdropping during data transmission. FFEM uses a flow-based face encoder to encode each face to a Gaussian distribution and encrypts the encoded face feature by random rotating the Gaussian distribution with the rotation matrix is as the password. While encrypted latent-variable face images are sent to users through public but less reliable channels, password will be protected through more secure channels through technologies such as asymmetric encryption, blockchain, or other sophisticated security schemes. User could select to decode an image with fake faces from the encrypted image on the public channel. Only trusted users are able to recover the original face using the encrypted matrix transmitted in secure channel. More interestingly, by tuning Gaussian ball in latent space, we could control the fairness of the replaced face on attributes such as gender and race. Extensive experiments demonstrate that our solution could protect privacy and enhance fairness with minimal effect on high-level downstream task.