Reviewerly/exHarmony huggingface dataset:
A comprehensive review of visualization methods for association rule mining: Taxonomy, challenges, open problems and future ideas

Association rule mining is intended for searching for the relationships between attributes in transaction databases. The whole process of rule discovery is very complex, and involves pre-processing techniques, a rule mining step, and post-processing, in which visualization is carried out. Visualization of discovered association rules is an essential step within the whole association rule mining pipeline, to enhance the understanding of users on the results of rule mining. Several association rule mining and visualization methods have been developed during the past decades. This review paper aims to create a literature review, identify the main techniques published in peer-reviewed literature, examine each method's main features, and present the main applications in the field. Defining the future steps of this research area is another goal of this review paper.
Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction

The typical way for relation extraction is fine-tuning large pre-trained language models on task-specific datasets, then selecting the label with the highest probability of the output distribution as the final prediction. However, the usage of the Top-k prediction set for a given sample is commonly overlooked. In this paper, we first reveal that the Top-k prediction set of a given sample contains useful information for predicting the correct label. To effectively utilizes the Top-k prediction set, we propose Label Graph Network with Top-k Prediction Set, termed as KLG. Specifically, for a given sample, we build a label graph to review candidate labels in the Top-k prediction set and learn the connections between them. We also design a dynamic k selection mechanism to learn more powerful and discriminative relation representation. Our experiments show that KLG achieves the best performances on three relation extraction datasets. Moreover, we observe thatKLG is more effective in dealing with long-tailed classes.
Rethinking Data-Free Quantization as a Zero-Sum Game

Data-free quantization (DFQ) recovers the performance of quantized network (Q) without accessing the real data, but generates the fake sample via a generator (G) by learning from full-precision network (P) instead. However, such sample generation process is totally independence of Q, specialized as failing to consider the adaptability of the generated samples, i.e., beneficial or adversarial, over the learning process of Q, resulting into non-ignorable performance loss. Building on this, several crucial questions --- how to measure and exploit the sample adaptability to Q under varied bit-width scenarios? how to generate the samples with desirable adaptability to benefit the quantized network? --- impel us to revisit DFQ. In this paper, we answer the above questions from a game-theory perspective to specialize DFQ as a zero-sum game between two players --- a generator and a quantized network, and further propose an Adaptability-aware Sample Generation (AdaSG) method. Technically, AdaSG reformulates DFQ as a dynamic maximization-vs-minimization game process anchored on the sample adaptability. The maximization process aims to generate the sample with desirable adaptability, such sample adaptability is further reduced by the minimization process after calibrating Q for performance recovery. The Balance Gap is defined to guide the stationarity of the game process to maximally benefit Q. The theoretical analysis and empirical studies verify the superiority of AdaSG over the state-of-the-arts. Our code is available at https://github.com/hfutqian/AdaSG.
Riemannian Local Mechanism for SPD Neural Networks

The Symmetric Positive Definite (SPD) matrices have received wide attention for data representation in many scientific areas. Although there are many different attempts to develop effective deep architectures for data processing on the Riemannian manifold of SPD matrices, very few solutions explicitly mine the local geometrical information in deep SPD feature representations. Given the great success of local mechanisms in Euclidean methods, we argue that it is of utmost importance to ensure the preservation of local geometric information in the SPD networks. We first analyse the convolution operator commonly used for capturing local information in Euclidean deep networks from the perspective of a higher level of abstraction afforded by category theory. Based on this analysis, we define the local information in the SPD manifold and design a multi-scale submanifold block for mining local geometry. Experiments involving multiple visual tasks validate the effectiveness of our approach.
SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud

LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving systems. Though impressive detection results have been achieved by superior 3D detectors, they suffer from significant performance degeneration when facing unseen domains, such as different LiDAR configurations, different cities, and weather conditions. The mainstream approaches tend to solve these challenges by leveraging unsupervised domain adaptation (UDA) techniques. However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e.g., from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D), where only a few labeled target data is available, yet can significantly improve the adaptation performance. In particular, our SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage. In the first stage, an Inter-domain Point-CutMix module is presented to efficiently align the point cloud distribution across domains. The Point-CutMix generates mixed samples of an intermediate domain, thus encouraging to learn domain-invariant knowledge. Then, in the second stage, we further enhance the model for better generalization on the unlabeled target set. This is achieved by exploring Intra-domain Point-MixUp in semi-supervised learning, which essentially regularizes the pseudo label distribution. Experiments from Waymo to nuScenes show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label. Our code is available at https://github.com/yinjunbo/SSDA3D.
One-for-All: Proposal Masked Cross-Class Anomaly Detection

One of the most challenges for anomaly detection (AD) is how to learn one unified and generalizable model to adapt to multi-class especially cross-class settings: the model is trained with normal samples from seen classes with the objective to detect anomalies from both seen and unseen classes. In this work, we propose a novel Proposal Masked Anomaly Detection (PMAD) approach for such challenging multi- and cross-class anomaly detection. The proposed PMAD can be adapted to seen and unseen classes by two key designs: MAE-based patch-level reconstruction and prototype-guided proposal masking. First, motivated by MAE (Masked AutoEncoder), we develop a patch-level reconstruction model rather than the image-level reconstruction adopted in most AD methods for this reason: the masked patches in unseen classes can be reconstructed well by using the visible patches and the adaptive reconstruction capability of MAE. Moreover, we improve MAE by ViT encoder-decoder architecture, combinational masking, and visual tokens as reconstruction objectives to make it more suitable for anomaly detection. Second, we develop a two-stage anomaly detection manner during inference. In the proposal masking stage, the prototype-guided proposal masking module is utilized to generate proposals for suspicious anomalies as much as possible, then masked patches can be generated from the proposal regions. By masking most likely anomalous patches, the “shortcut reconstruction” issue (i.e., anomalous regions can be well reconstructed) can be mostly avoided. In the reconstruction stage, these masked patches are then reconstructed by the trained patch-level reconstruction model to determine if they are anomalies. Extensive experiments show that the proposed PMAD can outperform current state-of-the-art models significantly under the multi- and especially cross-class settings. Code will be publicly available at https://github.com/xcyao00/PMAD.
Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees

We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold p in [0,1] over the infinite time horizon. Our method leverages advances in machine learning literature and it represents formal certificates as neural networks. In particular, we learn a certificate in the form of a reach-avoid supermartingale (RASM), a novel notion that we introduce in this work. Our RASMs provide reachability and avoidance guarantees by imposing constraints on what can be viewed as a stochastic extension of level sets of Lyapunov functions for deterministic systems. Our approach solves several important problems -- it can be used to learn a control policy from scratch, to verify a reach-avoid specification for a fixed control policy, or to fine-tune a pre-trained policy if it does not satisfy the reach-avoid specification. We validate our approach on 3 stochastic non-linear reinforcement learning tasks.
MetaZSCIL: A Meta-Learning Approach for Generalized Zero-Shot Class Incremental Learning

Generalized zero-shot learning (GZSL) aims to recognize samples whose categories may not have been seen at training. Standard GZSL cannot handle dynamic addition of new seen and unseen classes. In order to address this limitation, some recent attempts have been made to develop continual GZSL methods. However, these methods require end-users to continuously collect and annotate numerous seen class samples, which is unrealistic and hampers the applicability in the real-world. Accordingly, in this paper, we propose a more practical and challenging setting named Generalized Zero-Shot Class Incremental Learning (CI-GZSL). Our setting aims to incrementally learn unseen classes without any training samples, while recognizing all classes previously encountered. We further propose a bi-level meta-learning based method called MetaZSCIL to directly optimize the network to learn how to incrementally learn. Specifically, we sample sequential tasks from seen classes during the offline training to simulate the incremental learning process. For each task, the model is learned using a meta-objective such that it is capable to perform fast adaptation without forgetting. Note that our optimization can be flexibly equipped with most existing generative methods to tackle CI-GZSL. This work introduces a feature generative framework that leverages visual feature distribution alignment to produce replayed samples of previously seen classes to reduce catastrophic forgetting. Extensive experiments conducted on five widely used benchmarks demonstrate the superiority of our proposed method.
Curriculum Multi-Negative Augmentation for Debiased Video Grounding

Video Grounding (VG) aims to locate the desired segment from a video given a sentence query. Recent studies have found that current VG models are prone to over-rely the groundtruth moment annotation distribution biases in the training set. To discourage the standard VG model's behavior of exploiting such temporal annotation biases and improve the model generalization ability, we propose multiple negative augmentations in a hierarchical way, including cross-video augmentations from clip-/video-level, and self-shuffled augmentations with masks. These augmentations can effectively diversify the data distribution so that the model can make more reasonable predictions instead of merely fitting the temporal biases. However, directly adopting such data augmentation strategy may inevitably carry some noise shown in our cases, since not all of the handcrafted augmentations are semantically irrelevant to the groundtruth video. To further denoise and improve the grounding accuracy, we design a multi-stage curriculum strategy to adaptively train the standard VG model from easy to hard negative augmentations. Experiments on newly collected Charades-CD and ActivityNet-CD datasets demonstrate our proposed strategy can improve the performance of the base model on both i.i.d and o.o.d scenarios.
DAGL-Faster: Domain adaptive faster r-cnn for vehicle object detection in rainy and foggy weather conditions

Convolutional neural networks (CNNs) have made remarkable progress in detecting vehicle objects in normal weather conditions. However, the performance of these networks deteriorates when faced with rain and fog, as these conditions degrade image quality and cause blurring. The network models trained on clear images perform poorly on rainy and foggy images due to the differences in distribution between normal weather and adverse weather conditions, leading to domain bias. To address this challenge, we present a novel algorithm called DAGL-Faster (Domain Adaptive Global-Local Alignment Faster RCNN) , which enables domain-adaptive vehicle object detection specifically for rainy and foggy weather. DAGL-Faster extends the Faster RCNN framework by incorporating three domain classifiers. These classifiers aid the network in extracting features that are invariant to the domain differences between the source domain (normal weather) and the target domains (rain or fog). The algorithm tackles the domain dissimilarities from three perspectives: local image-level, global image-level, and instance-level. Additionally, it introduces consistency regularization to facilitate simultaneous alignment at the image-level and instance-level, optimizing the overall alignment effect. Through extensive experiments, we demonstrate the efficacy of DAGL-Faster on two benchmark datasets: Foggy Cityscapes and Rain Vehicle Color-24. The algorithm achieves an impressive mean average precision (mAP) of up to 36.7% on the Foggy Cityscapes dataset and 49.79% on the Rain Vehicle Color-24 dataset. Moreover, DAGL-Faster demonstrates superior computational efficiency, with a processing time of 1.9 seconds per image using a single GTX 1080 Ti GPU. These results surpass state-of-the-art algorithms for popular domain adaptive object detection methods.
Experimental Evaluation of Extended Reality Technologies in the Development of Individualized Three-Dimensionally Printed Upper Limb Prostheses

This paper presents results from experimental studies that assess the utilization of virtual, augmented, and mixed reality (VR, AR, MR) at different stages of developing personalized 3D printed upper limb prostheses for adult patients. The prostheses are designed automatically using the AutoMedPrint system, leveraging 3D scans as described in various prior publications. Various stages of development of the prosthesis are made as applications of different extended reality technologies. An assembly instruction is implemented as an immersive VR application, a configurator is designed as AR application and a configurator and try-on application is prepared and deployed in MR. The applications are tested by an international group of experts during a scheduled experiment. The experts then participate to surveys and comparatively evaluate the potential of all the XR technologies. The paper presents the development of these applications, provides a detailed account of the experimental process, including the rankings of XR technologies for different applications throughout the lifecycle of a prosthetic device.
Differentially Private Deep Learning With Dynamic Privacy Budget Allocation and Adaptive Optimization

Deep learning (DL) has been adopted in a broad range of Internet-of-Things (IoT) applications such as auto-driving, intelligent healthcare and smart grids, but limitations such as those relating to data and user privacy can complicate its broader implementation. Seeking to jointly address both privacy and utility, in this paper we connect the layer-wise relevance propagation with gradient descent for injecting proper noise into gradients. We also improve the conventional gradient clipping method by dividing the gradients into several groups; thus, minimizing the gradient distortion. Since the noisy gradient causes the undetermined descent direction and might adversely affect the loss minimization, we use the NoisyMin algorithm to select the best step size for each gradient perturbation. Finally, we integrate the adaptive optimizer into the gradient descent. In addition to improving the model utility, we also leverage the leading Sinh-Normal noise addition mechanism to achieve truncated concentrated differential privacy (tCDP) – as demonstrated by our rigorous analysis. Our experimental evaluations also validate the effectiveness of the proposed algorithm.
Nonlinear feedforward enabling quantum computation

Measurement-based quantum computation with optical time-domain multiplexing is a promising method to realize a quantum computer from the viewpoint of scalability. Fault tolerance and universality are also realizable by preparing appropriate resource quantum states and electro-optical feedforward that is altered based on measurement results. While linear feedforward has been realized and become a common experimental technique, nonlinear feedforward was unrealized until now. In this paper, we demonstrate that a fast and flexible nonlinear feedforward realizes the essential measurement required for fault-tolerant and universal quantum computation. Using non-Gaussian ancillary states, we observed 10% reduction of the measurement excess noise relative to classical vacuum ancilla.
Two Sides of the Same Coin: Exploiting the Impact of Identifiers in Neural Code Comprehension

Previous studies have demonstrated that neural code comprehension models are vulnerable to identifier naming. By renaming as few as one identifier in the source code, the models would output completely irrelevant results, indicating that identifiers can be misleading for model prediction. However, identifiers are not completely detrimental to code comprehension, since the semantics of identifier names can be related to the program semantics. Well exploiting the two opposite impacts of identifiers is essential for enhancing the robustness and accuracy of neural code comprehension, and still remains under-explored. In this work, we propose to model the impact of identifiers from a novel causal perspective, and propose a counterfactual reasoning-based framework named CREAM. CREAM explicitly captures the misleading information of identifiers through multi-task learning in the training stage, and reduces the misleading impact by counterfactual inference in the inference stage. We evaluate CREAM on three popular neural code comprehension tasks, including function naming, defect detection and code classification. Experiment results show that CREAM not only significantly outperforms baselines in terms of robustness (e.g., +37.9% on the function naming task at F1 score), but also achieve improved results on the original datasets (e.g., +0.5% on the function naming task at F1 score).
Prescient Collision-Free Navigation of Mobile Robots With Iterative Multimodal Motion Prediction of Dynamic Obstacles

To explore safe interactions between a mobile robot and dynamic obstacles, this letter presents a comprehensive approach to collision-free navigation in dynamic indoor environments. The approach integrates Multimodal Motion Predictions (MMPs) of dynamic obstacles with predictive control for obstacle avoidance. MMP is achieved by a deep-learning method that predicts multiple plausible future positions. By repeating the MMP for each time offset in the future, multi-time-step MMPs are obtained. A nonlinear Model Predictive Control (MPC) solver uses the prediction outcomes to achieve collision-free trajectory tracking for the mobile robot. The proposed integration of multimodal motion prediction and trajectory tracking outperforms other non-deep-learning methods in complex scenarios. The approach enables safe interaction between the mobile robot and stochastic dynamic obstacles.
A Classification of Software-Architectural Uncertainty Regarding Confidentiality

In our connected world, ensuring and demonstrating the confidentiality of exchanged data becomes increasingly critical for software systems. However, especially in early system design, uncertainty exists about the software architecture itself and the software’s execution environment. This does not only impede early confidentiality analysis but can also cause data breaches due to the lack of awareness of the impact of uncertainty. Classifying uncertainty helps in understanding its impact and in choosing proper analysis and mitigation strategies. There already exist multiple taxonomies, e.g., from the domain of self-adaptive systems. However, they do not fit the abstraction of software architecture and do not focus on security-related quality properties like confidentiality. To address this, we present a classification of architectural uncertainty regarding confidentiality. It enables precise statements about uncertain influences and their impact on confidentiality. It raises awareness of uncertainty properties, enables knowledge transfer to non-experts, and serves as a baseline for discussion. Also, it can be directly integrated into existing notions of data flow diagrams for uncertainty-aware confidentiality analysis. We evaluate the structural suitability, applicability, and purpose of the classification based on a real-world case study and a user study. The results show increased significance compared to existing taxonomies and raised awareness of the impact of uncertainty on confidentiality.
Sarcasm detection in online comments using machine learning

Purpose Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning. Design/methodology/approach For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared. Findings The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models. Originality/value This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.