Reviewerly/exHarmony huggingface dataset:
EFFECT: An End-to-End Framework for Evaluating Strategies for Parallel AI Anomaly Detection

Neural networks achieve high accuracy in tasks like image recognition or segmentation. However, their application in safety-critical domains is limited due to their black-box nature and vulnerability to specific types of attacks. To mitigate this, methods detecting out-of-distribution or adversarial attacks in parallel to the network inference were introduced. These methods are hard to compare because they were developed for different use cases, datasets, and networks. To fill this gap, we introduce EFFECT, an end-to-end framework to evaluate and compare new methods for anomaly detection, without the need for retraining and by using traces of intermediate inference results. The presented workflow works with every preexisting neural network architecture and evaluates the considered anomaly detection methods in terms of accuracy and computational complexity. We demonstrate EFFECT's capabilities, by creating new detectors for ShuffleNet and MobileNetV2 for anomaly detection as well as fault origin detection. EFFECT allows us to design an anomaly detector, based on the Mahalanobis distance as well as CNN based detectors. For both use cases, we achieve accuracies of over 85 %, classifying inferences as normal or abnormal, and thus beating existing methods.
A Dual-Path Multi-Scale Feature Fusion Decoder for SegFormer

The encoder-decoder structure is the basic structure of most semantic segmentation models and is adopted by a large number of segmentation models. How to effectively extract image features and achieve high-precision mapping through the optimal design of encoder and decoder is the key issue of current research. SegFormer designs an encoder with excellent performance, which fully extracts the feature information of different semantic granularity in the image with a large receptive field. Even if a simple fully connected layer decoder is used, excellent segmentation results can also be achieved. However, this simplified decoder does not make full use of the advantages of the SegFormer encoder. Therefore, a decoder structure with dual-path multi-scale feature fusion is designed in this paper, and the decoder is redesigned according to the characteristics of the SegFormer encoder. The decoder adopts a dual-path structure, one path passes the abstract global information layer by layer to the local detail information through the layer-by-layer upsampling fusion module (LFM), and gradually upsamples the feature maps obtained from the encoder, and then use the channel fusion module to learn the importance of different channels in the deep abstract semantic feature map and the shallow local detail feature map, and perform dynamic fusion to obtain a feature map containing both abstract semantic information and local details. The other path takes advantage of the large receptive field of the feature map output by the SegFormer encoder, and uses the weighted hybrid multi-scale feature extraction module (WMF) to extract multi-scale features containing global semantics from the deep semantic feature map finally output by the encoder. Finally, the Deep Feature Fusion Module (DFM) is used to fuse the outputs of the first two modules, fully mining the multi-scale global information in the encoder, and obtaine the feature maps with rich semantic information, which effectively improves the algorithm model performance.
Boosting BERT-Based Knowledge Graph Completion with Contrastive Learning and Hard Sample Training

Knowledge graph (KG), which is often described by a set of triplets (head, relation, tail), has shown to be very useful for many downstream applications, but suffers from the issue of incomplete connections. Knowledge graph completion (KGC) is to predict the missing connections among entities. Driven by recent advances of BERT in extracting meaningful textual representations, some recent KGC methods have been proposed to leverage the associated texts of triplets to assist the completion. Under the BERT-based framework, we argue that if more meaningful representations can be learned from the texts, better performance can be expected. Following this framework, inspired by recent successes of contrastive learning, we propose to adapt it to the KGC task by introducing a novel dual-encoder architecture to produce semantic-similar positive pairs, which have been widely shown to be pivotal in inducing semantic-rich representations for contrastive learning. To further improve the performance, a hard negative sampling strategy is developed to train the model. Extensive experimental results on three public datasets show that the proposed techniques can improve the performance of existing KGC methods effectively.
Exploring the Impact of Synthetic Data on Human Activity Recognition Tasks

This paper investigates the potential of synthetic data in enhancing the performance of Machine Learning classifiers. Our focus lies primarily on the Human Activity Recognition task, Specifically through wearable device sensors. We analyze the performance of three Generative Adversarial Networks (GANs) and a Diffuse model, considering fidelity, diversity, and generalization metrics. In addition, we assess the relationship between the addition of synthetic samples to the training data and the impact of imbalanced classes on the generative model in the production of synthetic samples. We also introduce a novel GAN designed to generate synthetic samples of time series data from wearable devices. After conducting nearly 400 experiments, our results suggest that synthetic samples can significantly improve the performance of Machine Learning models when real data are scarce. More importantly, our findings underline that data quality precedes the quantity of synthetic data added to training samples.
One-class Classification-Based Machine Learning Model for Estimating the Probability of Wildfire Risk

Wildfires have caused devastating consequences to property and human and animal lives, which has become a global problem. Consequently, advanced wildfire prediction models are required to treat complex features and climate conditions. As a result, Machine Learning and Deep Learning models are becoming popular. However, creating a balanced true and false labeled dataset in the wild-fire domain is often challenging. Hence, One-class classification models are a promising approach to overcome this concern. In this paper, several One-class classification models are investigated; linear models (Principal Component Analysis and One-Class Support Vector Machines), outlier ensemble models (Lightweight On-line Detector of Anomalies and Locally Selective Combination of Parallel Outlier Ensembles), proximity-based models (Histogram-based Outlier Score and Rotation-based Outlier Detection), probabilistic models (Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions and Copula-Based Outlier Detection), neural network-based models (Deep One-class Classification and Adversarially Learned Anomaly Detection) is used in two case studies for California and Western Australian states. In conclusion, it was found that Deep learning-based One-class classification models outperform other models in terms of performance and feature importance of showcasing the effectiveness of deep neural network models in the wildfire prediction domain.
Towards the Prognosis of Patients in Coma using Echo State Networks for EEG Analysis

The electroencephalogram (EEG) exam registers the brain's electrical signals using positioned electrodes over the scalp. The signals can be used in many medical applications, such as the prognosis and monitoring of diseases. However, EEG data also require efficient learning solutions in order to deal with their inherent noises and artifacts. In this paper, we propose simple, fast and accurate architectures of Echo State Networks (ESNs) capable of considering the sequential patterns of the exam. To be specific, we develop three ESNs based on a baseline, a single-reservoir and a multi-reservoir architectures, which are responsible for mapping temporal (or sequential) inputs into a high-dimensional space. To evaluate the ESN architectures, we addressed the problem of prognostic of patients in coma (PPC) from EEG records considering two formulations: binary and multi-class. Experiments on both scenarios considering the three ESN architectures as well as traditional techniques (without reservoir) were performed. The results revealed the potential of our single and multi-reservoir architectures over the baseline architecture and other techniques without the reservoir structure. Interestingly, the ESN single-reservoir architecture achieved the best predictive performance for both binary and multi-class formulations.
A Markovian Error Model for False Negatives in DNN-based Perception-Driven Control Systems

This paper presents an improved Markovian error model for Deep Neural Network (DNN) based perception in autonomous vehicles and other perception-driven control systems. Many modern autonomous systems rely on DNN-driven perception-based control/planning methodologies such as autonomous navigation, where the perception errors significantly affect the control/planning performance and the systems’ safety. The traditional independent, identically-distributed (IID) perception error model is inadequate for perception-based control/planning applications because image sequences supplied to a DNN-based perception module are not independent in the real world. Based on this observation, we develop a novel Markov model to describe the error behavior of a DNN perception model—an error in one frame is likely to signal errors in successive frames, effectively reducing sample rate for the control command. We evaluate the effect of Markovian perception errors on a drone-control simulator and show that the Markovian error model provides a better estimate of control performance than does a traditional independent, identically-distributed (IID) model.
Method for inferring the number of clusters based on a range of attribute values with subsequent automatic data labeling

Machine learning is a suitable pattern recognition technique for detecting correlations between data. In the case of unsupervised learning, the groups formed from these correlations must receive a label, which consists of describing them in terms of their most relevant attributes and their respective ranges of values so that they are understood automatically. In this research work, this process is called labeling. However, a challenge for researchers is to establish the optimal number of groups submitted to grouping, which influences the performance of this process. Therefore, this research aims to provide an inference approach to the number of clusters used in the clustering based on the range of attribute values, followed by automatic data labeling to maximize the understanding of the groups obtained. This methodology was applied to four databases, and the results show that it contributes to the interpretation of groups since it generates more accurate labels with a hit rate above 93%.
EGPose: Explicit and Geometric Self-Supervision for 3D Human Pose Estimation

Despite the recent progress of 3D human pose estimation, reconstructing an accurate 3D human posture from a single image without 3D annotation is yet challenging due to the following reasons. First, the reconstructing process is inherently ambiguous, as multiple 3D poses can be projected onto the same 2D pose. Second, camera rotation is difficult to measure precisely without laborious camera calibration. Some approaches resort to traditional computer vision algorithms to address these issues, but they are not differentiable and cannot be optimized through training. In this paper, we propose two geometrically explicit modules to solve the problems without any 3D ground-truth or camera parameters. Relative depth estimation module effectively mitigates depth ambiguity, reducing a number of possible depths for each joint to only two candidates. Differentiable pose alignment module calculates camera rotation via aligning poses from different views. The two modules are geometrically interpretable, reducing the training difficulty and leading to superior performance. Our method achieves state-of-the-art on the standard benchmark datasets among self-supervised methods and even outperforms several fully-supervised approaches that rely on 3D annotations.
A Meta-learning Based Generative Model with Graph Attention Network for Multi-Modal Recommender Systems

With increased data in various e-commerce domains or in different online streaming platforms like Amazon Prime, Netflix, etc, it has become more challenging to give a personalized recommendation to a user. In terms of the movie recommendation system, different multimodal information such as audio, video, and text are effectively fused together to tackle this problem. However, in real-world scenarios this information may not always be present, leading to the problem of missing modality. Existing works in Recommender Systems (RSs) may fail to recommend effectively if their model is not susceptible to missing modality. In this work, we propose a meta-learning model to deal with the missing modality problem. Our proposed model tries to reconstruct the missing features with the help of the existing modalities and once reconstructed, we pass them to novel trident architecture for solving the recommendation task. We have performed experiments on the newly created multimodal version of the MovieLens dataset. Additionally, we have incorporated a new modality where we extracted subtitles of the movies to increase the effectiveness of our missing modality model.
A Mechanism for Supporting the Peak/Trough Detection in the Concept Drifting Environment

The primary objective of this research endeavor is to tackle the difficult challenge of identifying the elusive aspect peak/trough of the business cycle, which is known to exhibit the tendency of concept drift. To effectively confront this issue, we have put forth a supervised learning approach referred to as PLM E. This approach encompasses the integration of the envelope concept and the sequence-based sliding window mechanism along with the pupil learning mechanism (PLM), aimed at effectively detecting the elusive aspect peak/trough of the business cycle within changing data patterns. To confirm the effectiveness of our proposed PLM E approach, this study has conducted some experiments utilizing a real-world copper price forecasting dataset.
Evaluating Robustness of Reconstruction Models with Adversarial Networks

With the advent of adversarial robustness as a research area, much novel work attempts to design creative defense mechanisms against adversarial vulnerabilities that arise. While classification models are the most common target of adversarial robustness research, reconstruction models are often underestimated though they play essential roles in many applications. This work evaluates reconstruction models regarding their adversarial robustness. We constructed two frameworks: a standard and a universal-attack framework. The standard framework requires an input to find its perturbation, and the universal-attack framework generates adversarial perturbation from the distribution of a dataset. Extensive experimental evidence discussed in this paper suggests that both frameworks can effectively alter how images are reconstructed and classified using classic reconstruction models trained on MNIST and Cropped Yale Face datasets. Further, these frameworks outperform state-of-the-art adversarial attacks. Moreover, we showcase using the proposed framework to retrain a reconstruction model to improve its resilience against adversarial perturbations. Furthermore, for the sake of reconstruction models, an attack may desire not to alter the latent space. Thus, we also include the analysis of the latent space.
Anticipate, Ensemble and Prune: Improving Convolutional Neural Networks via Aggregated Early Exits

Today, artificial neural networks are the state of the art for solving a variety of complex tasks, especially in image classification.Such architectures consist of a sequence of stacked layers with the aim of extracting useful information and having it processed by a classifier to make accurate predictions.However, intermediate information within such models is often left unused.In other cases, such as in edge computing contexts, these architectures are divided into multiple partitions that are made functional by including early exits, i.e., intermediate classifiers, with the goal of reducing the computational and temporal load without extremely compromising the accuracy of the classifications.In this paper, we present Anticipate, Ensemble and Prune (AEP), a new training technique based on a weighted ensemble of early exits, which aims at exploiting the information in the structure of networks to maximise their performance.Through a comprehensive set of experiments, we show how the use of this approach can yield average accuracy improvements of up to 15% over traditional training.AEP's internal pruning operation also allows reducing the number of parameters by up to 41%, lowering the number of multiplications and additions by 18% and the latency time to make inference by 16%.By using AEP, it is also possible to learn weights that allow early exits to achieve better accuracy values than those obtained from singleoutput reference models.The code will be available on GitHub after acceptance of the paper.
Attention Link: An Efficient Attention-Based Low Resource Machine Translation Architecture

Transformers have emerged as a pivotal tool in machine translation. Nonetheless, their effectiveness typically hinges on extensive training with millions of bilingual parallel corpora. This paper presents a novel architecture, termed as Attention Link (AL), which is specifically designed to bolster the performance of transformer models in situations where training resources are scarce. We furnish theoretical substantiation that underscores the superiority of the AL architecture in such resource-limited settings. Experimental outcomes lend further credence to the enhancements brought about by our methodology, marking it as a stride towards enhancing the performance of transformer models.
Curriculum Compositional Continual Learning for Neural Machine Translation

Current trends in language modelling leverage large language models pre-trained on a huge corpus of data to achieve state of the art results on several NLP tasks. On the other hand, humans acquire language from small amount of data using cognitive principles. Recently, a continual learning approach using compositionality to disentangle the syntax and semantics of an input sentence for downstream sequence to sequence tasks was proposed. In this work, we show how curriculum learning can be incorporated with this framework to improve performance. More specifically, first, we show that using the model of interest with reduced hidden size as the auxiliary model to generate curriculum is not necessarily optimal and second, we propose a novel variant of the one best score approach for curriculum learning where, a sequence to sequence model is used as the auxiliary model to generate the conditional probabilities of word predictions (proxy for difficulty) and consequently used this to generate a curriculum. Results on a variety of translation tasks, demonstrate the superiority of the proposed approach compared to several baselines, enabling the improvement of sentence accuracy with respect to knowledge transfer and catastrophic-forgetting both by at least a significant margin of 35% with respect to the best performing baseline on the English-French translation task.
Langevin Algorithms for Very Deep Neural Networks with Application to Image Classification

Training a very deep neural network is a challenging task, as the deeper a neural network is, the more non-linear it is. We compare the performances of various preconditioned Langevin algorithms with their non-Langevin counterparts for the training of neural networks of increasing depth. For shallow neural networks, Langevin algorithms do not lead to any improvement, however the deeper the network is and the greater are the gains provided by Langevin algorithms. Adding noise to the gradient descent allows to escape from local traps, which are more frequent for very deep neural networks. Following this heuristic we introduce a new Langevin algorithm called Layer Langevin, which consists in adding Langevin noise only to the weights associated to the deepest layers. We then prove the benefits of Langevin and Layer Langevin algorithms for the training of popular deep residual architectures for image classification.
Mining Implicit Behavioral Patterns via Attention Networks for Sequential Recommendation

Sequential recommender systems (SRSs) seek to model users’ dynamic preferences based on their interaction sequences to suggest items they may be interested in. Existing methods suffer from two major flaws in modeling user interest representations. First, most methods only encode the absolute positions of items in an interaction sequence, but ignore the relative position information between items, impeding the modeling of temporal patterns of user behaviors. At the same time, the multi-layer stacked network structure will cause the dilution of position information. Second, historical interactions usually contain many items that are irrelevant to the user's subsequent choices, while existing work lacks approaches to explicitly distinguish them from interest-related items. In this work, we alleviate the foregoing issues by proposing a user behavioral patterns enhanced attention network (BPERec), where a novel position encoding module enables the enhancement of temporal patterns implicit in interaction sequences. Additionally, an explicit denoising attention mechanism is proposed to enhance user interest representations. Experimental results on three benchmark datasets reveal that our method outperforms state-of-the-art baseline methods.
AC-MMOE: A Multi-gate Mixture-of-experts Model Based on Attention and Convolution

Multi-task learning (MTL), an important branch of machine learning, has been successfully applied to many fields, and its effectiveness in practice has been proved. However, at present, the soft parameter sharing model represented by multi-gate mixtureof-experts (MMOE) still has some disadvantages, including negative transfer, seesaw phenomenon, and inadequate utilization of shared information. Although the existing research has improved these issues, they also bring some new problems, such as high model complexity and difficulty in hyperparameter tuning. To address these issues, we propose a multi-gate mixture-of-experts model base on attention and convolution (AC-MMOE), which incorporates a multi-layer perception based attention module and a column convolution module. AC-MMOE applies attention to achieve feature extraction and convolution to integrate the output of shared substructures, which improves the feature extraction ability and information fusion ability of the model without significantly increasing the model training cost. We validate the performance of AC-MMOE on several MTL datasets, the experimental results show that our model achieves better results than other baselines on various datasets with different task correlations.