Reviewerly/exHarmony huggingface dataset:
Lightweight convolutional neural network with knowledge distillation for cervical cells classification

Cervical cancer is among the most lethal human malignancies. Women in developing countries are overwhelmingly vulnerable to the cervical cancer because of the lack of medical resources. Although artificial intelligence technologies have witnessed some great progress in healthcare and medical practices, most studies in cervical cells classification focus on improving the accuracy without considering the resource limitations. To build a compact and effective model that meets design requirements for embedded devices, a lightweight convolutional neural network (CNN) architecture is chosen for establishing a highly efficient model with minimal parameters and calculations. Furthermore, knowledge distillation is utilized to improve the representation power of the lightweight CNN. This paper also investigates the importance of model selection in the proposed method. Experiments are conducted on the Herlev Pap smear dataset for the fine-grained 7-class classification tasks. The lightweight Xception, MobileNet and MobileNetV2 all achieved enhanced results (Xception from 71.39% to 72.25%, MobileNet from 62.12% to 64.14%, MobileNetV2 from 60.92% to 61.20%), and the best performing Xception model can achieve a comparable accuracy (72.25% compared to 73.58%) with only 40% of the model size of the powerful Inception-ResnetV2 model. Results shown that the proposed method can be used to develop a lightweight CNN model with improved accuracy, which is believed to be the first in this area of studying cervical cells classification tasks under limited resources. Furthermore, the compact model can potentially lead to a more economical and effective computer-aided system towards the diagnosis and prevention of the cervical cancer.
Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis

Abstract The importance and pervasiveness of emotions in our lives makes affective computing a tremendously important and vibrant line of work. Systems for automatic emotion recognition (AER) and sentiment analysis can be facilitators of enormous progress (e.g., in improving public health and commerce) but also enablers of great harm (e.g., for suppressing dissidents and manipulating voters). Thus, it is imperative that the affective computing community actively engage with the ethical ramifications of their creations. In this article, I have synthesized and organized information from AI Ethics and Emotion Recognition literature to present fifty ethical considerations relevant to AER. Notably, this ethics sheet fleshes out assumptions hidden in how AER is commonly framed, and in the choices often made regarding the data, method, and evaluation. Special attention is paid to the implications of AER on privacy and social groups. Along the way, key recommendations are made for responsible AER. The objective of the ethics sheet is to facilitate and encourage more thoughtfulness on why to automate, how to automate, and how to judge success well before the building of AER systems. Additionally, the ethics sheet acts as a useful introductory document on emotion recognition (complementing survey articles).
Human-AI interaction

research-article Share on Human-AI interaction: intermittent, continuous, and proactive Authors: Niels van Berkel Aalborg University Aalborg UniversityView Profile , Mikael B. Skov Aalborg University Aalborg UniversityView Profile , Jesper Kjeldskov Aalborg University Aalborg UniversityView Profile Authors Info & Claims InteractionsVolume 28Issue 6November - December 2021 pp 67–71https://doi.org/10.1145/3486941Online:10 November 2021Publication History 1citation3,116DownloadsMetricsTotal Citations1Total Downloads3,116Last 12 Months3,116Last 6 weeks450 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Forecasting Hourly Solar Radiation Using Artificial Intelligence Techniques

Recently, photovoltaic (PV) cell technology has witnessed significant developments and improvements. However, solar power is still unattractive to some consumers due to its unpredictability. Therefore, accurate prediction of solar irradiance continues to be critical for stable solar power. In this article, a proposed methodology to improve PV systems' reliability and stability, using state-of-the-art machine learning techniques, is introduced. Time-series long short-term memory (LSTM) network, convolutional LSTM (ConvLSTM), convolutional neural network (CNN), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) regression models were used to predict the city of Johannesburg's solar irradiance. These models were separately trained using the historical meteorological data of Johannesburg. Using the normalized root mean square error (nRMSE) as a performance indicator, the ConvLSTM model outperformed all the other models. When the models were individually trained with 80% of one-year input data, the results obtained showed that ConvLSTM recorded an nRMSE value of 4.05%. Results obtained using LSTM, XGBoost, and SVM models came next with nRMSE values of 4.72%, 6.63%, and 7.8%, respectively. The RF model gave an nRMSE value of 19.8%, whereas the CNN time-series model recorded an nRMSE value of 12.61%. The best experimental result obtained was an nRMSE value of 1.51% when the ConvLSTM model was trained at 500 epochs using 80% of ten years of actual historical solar irradiance as the dataset. These results suggest that the adoption of the ConvLSTM model for solar irradiance forecasts by solar farm operators in South Africa could improve electricity generation stability for PV connection to the power grid.
SimROD: A Simple Adaptation Method for Robust Object Detection

This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD). To overcome the challenging issues of domain shift and pseudo-label noise, our method integrates a novel domain-centric data augmentation, a gradual self-labeling adaptation procedure, and a teacher-guided fine-tuning mechanism. Using our method, target domain samples can be leveraged to adapt object detection models without changing the model architecture or generating synthetic data. When applied to image corruptions and high-level cross-domain adaptation benchmarks, our method outperforms prior baselines on multiple domain adaptation benchmarks. SimROD achieves new state-of-the-art on standard real-to-synthetic and cross-camera setup benchmarks. On the image corruption benchmark, models adapted with our method achieved a relative robustness improvement of 15-25% AP50 on Pascal-C and 5-6% AP on COCO-C and Cityscapes-C. On the cross-domain benchmark, our method outperformed the best baseline performance by up to 8% and 4% AP50 on Comic and Watercolor respectively. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Meta Pairwise Relationship Distillation for Unsupervised Person Re-identification

Unsupervised person re-identification (Re-ID) remains challenging due to the lack of ground-truth labels. Existing methods often rely on estimated pseudo labels via iterative clustering and classification, and they are unfortunately highly susceptible to performance penalties incurred by the inaccurate estimated number of clusters. Alternatively, we propose the Meta Pairwise Relationship Distillation (MPRD) method to estimate the pseudo labels of sample pairs for unsupervised person Re-ID. Specifically, it consists of a Convolutional Neural Network (CNN) and Graph Convolutional Network (GCN), in which the GCN estimates the pseudo labels of sample pairs based on the current features extracted by CNN, and the CNN learns better features by involving high-fidelity positive and negative sample pairs imposed by GCN. To achieve this goal, a small amount of labeled samples are used to guide GCN training, which can distill meta knowledge to judge the difference in the neighborhood structure between positive and negative sample pairs. Extensive experiments on Market-1501, DukeMTMC-reID and MSMT17 datasets show that our method outperforms the state-of-the-art approaches.
A novel machine learning approach for breast cancer diagnosis

Breast cancer disease is a major public health problem among women worldwide. This article proposes an expert system for the diagnosis of breast cancer disease based on an evolutionary algorithm known as Differential Evolution (DE) of a Radial-Based Function Kernel Extreme Learning Machines (RBF-KELM). In the structure of the RBF-KELM, there are two adjustable parameters of the RBF-kernel which are the penalty parameter C and the RBF-kernel's parameter (σ). These parameters play a major role in the efficiency of RBF-KELM. In this study, the optimal values of these parameters have been obtained using a differential evolution (DE) algorithm. To validate the effectiveness of the suggested approach, DE-RBF-KELM was examined on the two datasets: The Mammographic Image Analysis Society (MIAS) and the wisconsin breast cancer database (WBCD) and the results were satisfactory compared to conventional approaches.
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution With Weight Transfer

Neural architecture search has proven to be highly effective in the design of efficient convolutional neural networks that are better suited for mobile deployment than hand-designed networks.Hypothesizing that neural architecture search holds great potential for human pose estimation, we explore the application of neuroevolution, a form of neural architecture search inspired by biological evolution, in the design of 2D human pose networks for the first time.Additionally, we propose a new weight transfer scheme that enables us to accelerate neuroevolution in a flexible manner.Our method produces network designs that are more efficient and more accurate than state-of-the-art hand-designed networks.In fact, the generated networks process images at higher resolutions using less computation than previous hand-designed networks at lower resolutions, allowing us to push the boundaries of 2D human pose estimation.Our base network designed via neuroevolution, which we refer to as EvoPose2D-S, achieves comparable accuracy to SimpleBaseline while being 50% faster and 12.7x smaller in terms of file size.Our largest network, EvoPose2D-L, achieves new state-of-the-art accuracy on the Microsoft COCO Keypoints benchmark, is 4.3x smaller than its nearest competitor, and has similar inference speed.The code is publicly available at https://github.com/wmcnally/evopose2d.
SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting

Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches combine monocular depth networks with inpainting networks to achieve compelling results. A drawback of these techniques is the use of hard depth layering, making them unable to model intricate appearance details such as thin hair-like structures. We present SLIDE, a modular and unified system for single image 3D photography that uses a simple yet effective soft layering strategy to better preserve appearance details in novel views. In addition, we propose a novel depth-aware training strategy for our inpainting module, better suited for the 3D photography task. The resulting SLIDE approach is modular, enabling the use of other components such as segmentation and matting for improved layering. At the same time, SLIDE uses an efficient layered depth formulation that only requires a single forward pass through the component networks to produce high quality 3D photos. Extensive experimental analysis on three view-synthesis datasets, in combination with user studies on in-the-wild image collections, demonstrate superior performance of our technique in comparison to existing strong baselines while being conceptually much simpler. Project page: https://varunjampani.github.io/slide
Zero-Shot Visual Question Answering Using Knowledge Graph

Incorporating external knowledge to Visual Question Answering (VQA) has become a vital practical need. Existing methods mostly adopt pipeline approaches with different components for knowledge matching and extraction, feature learning, etc. However, such pipeline approaches suffer when some component does not perform well, which leads to error cascading and poor overall performance. Furthermore, the majority of existing approaches ignore the answer bias issue—many answers may have never appeared during training (i.e., unseen answers) in real-word application. To bridge these gaps, in this paper, we propose a Zero-shot VQA algorithm using knowledge graph and a mask-based learning mechanism for better incorporating external knowledge, and present new answer-based Zero-shot VQA splits for the F-VQA dataset. Experiments show that our method can achieve state-of-the-art performance in Zero-shot VQA with unseen answers, meanwhile dramatically augment existing end-to-end models on the normal F-VQA task.
ECANet: Explicit cyclic attention-based network for video saliency prediction

Video saliency prediction has received increasing attention in the field of computer vision research. How to model the spatio-temporal information in video frames is a key issue for saliency prediction. Most of the existing methods either show a limited temporal memory ability or fail in jointly processing spatial and temporal information. Besides, these methods take each pixel of video frames as input indiscriminately, which may result in incorrect results because of redundant pixels. To solve the above problems, we propose a novel Explicit Cyclic Attention-based Network (ECANet), which is a two-stream encoder-decoder model. Specifically, an explicit cyclic attention mechanism is proposed for temporal modeling and pixel emphasizing, in which an attentional stream is built, whose input is cropped patches based on the model’s previous prediction. For spatio-temporal information aggregation, 3D convolutional neural network is adopted as the backbone of our encoders and decoder. Stacks of video frames and attentional patches from past steps build up the input of our model. Extensive experimental results over three benchmark datasets show that our model achieves competitive performance against state-of-the-art methods.
Transformer-based Dual Relation Graph for Multi-label Image Recognition

The simultaneous recognition of multiple objects in one image remains a challenging task, spanning multiple events in the recognition field such as various object scales, inconsistent appearances, and confused inter-class relationships. Recent research efforts mainly resort to the statistic label co-occurrences and linguistic word embedding to enhance the unclear semantics. Different from these researches, in this paper, we propose a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, i.e., structural relation graph and semantic relation graph. The structural relation graph aims to capture long-range correlations from object context, by developing a cross-scale transformer-based architecture. The semantic graph dynamically models the semantic meanings of image objects with explicit semantic-aware constraints. In addition, we also incorporate the learnt structural relationship into the semantic graph, constructing a joint relation graph for robust representations. With the collaborative learning of these two effective relation graphs, our approach achieves new state-of-the-art on two popular multi-label recognition benchmarks, i.e. MS-COCO and VOC 2007 dataset.
Underwater Image Enhancement via Physical-Feedback Adversarial Transfer Learning

This article proposes a domain adaptive learning framework based on physical model feedback for underwater image enhancement. Underwater image enhancement involves mapping from low-quality underwater images to their dewatered counterparts. Due to the lack of dewatered images as ground truth, most learning-based methods are trained using synthetic datasets. However, they usually ignored the domain gap between synthetic training data and real-world testing data, which seriously reduces the generalization ability of those models when testing on real underwater images. We solve the problem by embedding a domain adaptive mechanism in a learning framework to eliminate the domain gap. However, the basic formulation of a domain adaptive-based learning framework does not generate realistic images in color and details. Motivated by an observation that the estimated results should be consistent with the physical model of underwater imaging, we propose a physics constraint as a feedback controller so that it can guide the estimation of underwater image enhancement. Extensive experiments validate the superiority of the proposed framework.
Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond

To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requires prohibitive human labors. In this paper, we seek to achieve the similar goal but do not involve more human efforts. To this end, we introduce a novel framework, which successfully encodes both geometric local features and global representations to distinguish vehicle instances, optimized only by the supervision from official ID labels. Specifically, given our insight that objects in ReID share similar geometric characteristics, we propose to borrow self-supervised representation learning to facilitate geometric features discovery. To condense these features, we introduce an interpretable attention module, with the core of local maxima aggregation instead of fully automatic learning, whose mechanism is completely understandable and whose response map is physically reasonable. To the best of our knowledge, we are the first that perform self-supervised learning to discover geometric features. We conduct comprehensive experiments on three most popular datasets for vehicle ReID, i.e., VeRi-776, CityFlow-ReID, and VehicleID. We report our state-of-the-art (SOTA) performances and promising visualization results. We also show the excel-lent scalability of our approach on other ReID related tasks, i.e., person ReID and multi-target multi-camera (MTMC) vehicle tracking.
Self-supervised 3D Skeleton Action Representation Learning with Motion Consistency and Continuity

Recently, self-supervised learning (SSL) has been proved very effective and it can help boost the performance in learning representations from unlabeled data in the image domain. Yet, very little is explored about its usefulness in 3D skeleton-based action recognition understanding. Directly applying existing SSL techniques for 3D skeleton learning, however, suffers from trivial solutions and imprecise representations. To tackle these drawbacks, we consider perceiving the consistency and continuity of motion at different playback speeds are two critical issues. To this end, we propose a novel SSL method to learn the 3D skeleton representation in an efficacious way. Specifically, by constructing a positive clip (speed-changed) and a negative clip (motion-broken) of the sampled action sequence, we encourage the positive pairs closer while pushing the negative pairs to force the network to learn the intrinsic dynamic motion consistency information. Moreover, to enhance the learning features, skeleton interpolation is further exploited to model the continuity of human skeleton data. To validate the effectiveness of the proposed method, extensive experiments are conducted on Kinetics, NTU60, NTU120, and PKUMMD datasets with several alternative network architectures. Experimental evaluations demonstrate the superiority of our approach and through which, we can gain significant performance improvement without using extra labeled data.
An ensemble feature selection algorithm based on PageRank centrality and fuzzy logic

One of the crucial processes in machine learning algorithms to improve the performance as well as, in some cases, to reduce the computational cost is feature selection. In other words, when there are many features, some are irrelevant; thus, irrelevant features will badly affect the learning process, so selecting the most fitting features can lead to increasing the accuracy of models in high-dimensional datasets. Recent researches benefit from an integration of various feature selection methods to find relevant features more accurately. Despite the fact that applying several feature selection methods can be efficient, the accuracy of current methods and combining the ranks of different feature selection methods are still challenging problems. In this paper, a novel ensemble of feature selection based on weighted PageRank and fuzzy logic, named EFSPF, is proposed to overcome these drawbacks. First, an M × M matrix, called Euclidean Distance Matrix (EDM), is set up, where M is the number of features. Secondly, a fuzzy Type-I is applied individually to tackle uncertainties in ranks. In other words, to specify a reliability rate for each feature, fuzzy Type-I is utilized. Then, EFSPF forms a reliable weighted graph where each feature is considered as a node. Furthermore, the weight between two nodes expresses a combination of their Euclidean Distance in EDM and the calculated reliability rates through fuzzy Type-I. Finally, the rank of each graph node is calculated via the PageRank algorithm. To demonstrate the performance of EFSPF, we have compared it with five ensemble algorithms and eight primary feature selection algorithms. The proposed method is evaluated based on Accuracy, Precision, Recall, and F1 score metrics. The analysis results show that the proposed method outperforms its rivals. The source code of the proposed method is accessible from https://github.com/mehdijoodaki/EFSPF .
WarpedGANSpace: Finding non-linear RBF paths in GAN latent space

This work addresses the problem of discovering, in an unsupervised manner, interpretable paths in the latent space of pretrained GANs, so as to provide an intuitive and easy way of controlling the underlying generative factors. In doing so, it addresses some of the limitations of the state-of-the-art works, namely, a) that they discover directions that are independent of the latent code, i.e., paths that are linear, and b) that their evaluation relies either on visual inspection or on laborious human labeling. More specifically, we propose to learn non-linear warpings on the latent space, each one parametrized by a set of RBF-based latent space warping functions, and where each warping gives rise to a family of non-linear paths via the gradient of the function. Building on the work of [34], that discovers linear paths, we optimize the trainable parameters of the set of RBFs, so as that images that are generated by codes along different paths, are easily distinguishable by a discriminator network. This leads to easily distinguishable image transformations, such as pose and facial expressions in facial images. We show that linear paths can be derived as a special case of our method, and show experimentally that non-linear paths in the latent space lead to steeper, more disentangled and interpretable changes in the image space than in state-of-the art methods, both qualitatively and quantitatively. We make the code and the pretrained models publicly available at: https://github.com/chi0tzp/WarpedGANSpace.