Reviewerly/exHarmony huggingface dataset:
An intelligent hybrid classification algorithm integrating fuzzy rule-based extraction and harmony search optimization: Medical diagnosis applications

Uncertainty is a critical factor in medical datasets needed to be overcome for increasing diagnosis efficiency. This paper proposes an intelligent classification algorithm comprising a fuzzy rule-based approach, a harmony search (HS) algorithm, and a heuristic algorithm to classify medical datasets intelligently. Two fuzzy approaches, as well as orthogonal and triangular fuzzy sets, are first utilized to define the attributes of data. Then, an HS algorithm is integrated with a heuristic to generate fuzzy rules to select the best rules in the fuzzy rule-based systems. Moreover, to improve the performance of the proposed classification approach, a three-phase parameter tuning approach is applied. First, the Taguchi method (phase I) is employed to tune the parameters of the HS algorithm using a fixed number of training data and find the central points of the parameters’ values. Then, a nested cross-validation (CV) approach consisting of an outer CV (phase II) and an inner CV (phase III) is utilized. Using the Taguchi approach gives the advantage of not considering a wide range of parameters by the nested CV which produces better results on the medical dataset. Nine well-known medical datasets are used to evaluate the efficiency of the proposed hybrid algorithm. To this aim, the results obtained by the algorithm are compared with the ones of several related works in the literature where several statistical tests and graphical approaches are used for comparisons. The results show that the proposed methods are robust in data analysis and classification of clinical datasets.
Text to Image Generation with Semantic-Spatial Aware GAN

Text-to-image synthesis (T2I) aims to generate photorealistic images which are semantically consistent with the text descriptions.Existing methods are usually built upon conditional generative adversarial networks (GANs) and initialize an image from noise with sentence embedding, and then refine the features with fine-grained word embedding iteratively.A close inspection of their generated images reveals a major limitation: even though the generated image holistically matches the description, individual image regions or parts of somethings are often not recognizable or consistent with words in the sentence, e.g."a white crown".To address this problem, we propose a novel framework Semantic-Spatial Aware GAN for synthesizing images from input text.Concretely, we introduce a simple and effective Semantic-Spatial Aware block, which (1) learns semantic-adaptive transformation conditioned on text to effectively fuse text features and image features, and (2) learns a semantic mask in a weakly-supervised way that depends on the current text-image fusion process in order to guide the transformation spatially.Experiments on the challenging COCO and CUB bird datasets demonstrate the advantage of our method over the recent stateof-the-art approaches, regarding both visual fidelity and alignment with input text description.
Robust SVD-based schemes for medical image watermarking

Nowadays, several information security issues can compromise the medical images administration. The source and origin of the medical image must be authenticated to verify that it corresponds to the right patient. A second issue is to avoid detachment between an electronic medical record and the corresponding images. One possible solution is the use of digital watermarking techniques applied to medical images, considering the imperceptibility and robustness requirements of the medical imaging watermarking. To increase the security of medical image sharing and transfer, we present in this work a watermarking approach to protect medical images. This innovative approach consists precisely of carefully inserting hospital signature information and patient data into the medical image. The ambitious goal of our work is to properly integrate the watermark with as little distortion as possible to typically retain the medical information in the image. In this flexible approach DWT decomposition is applied to the image which allows a remarkably satisfactory adjustment during the insertion. An SVD is then applied to the three subbands LL, LH and HL, which allows retaining the maximum energy of the image in a necessary minimum of singular values. A possible combination of the three resulting singular value matrices is then performed for watermark integration. A possible combination of the three resulting singular value matrices is then performed for watermark integration. The proposed approach ensures information integrity, patient confidentiality when sharing data, and robustness to several conventional attacks.
A real-time video surveillance system for traffic pre-events detection

In this study, a conceptual framework is proposed for the development of a video surveillance-based system for improving road safety. Based on the framework, a set of algorithms are developed which are capable of detecting various traffic pre-events from traffic videos, such as speed violation, one-way traffic, overtaking, illegal parking, and wrong drop-off location of passengers. After detecting the pre-events, an alarm will be automatically generated in the control room which helps to take precautionary measures to avoid any potential mishap on road, thereby, improving the road safety. In previous studies, a single system can handle either one or two pre-events. Whereas, in our present study, five anomalies can be detected in a single system using five different algorithms. Our study further contributes to the detection of “wrong drop-off location of passengers”. The effectiveness of the developed algorithms is demonstrated over 132 traffic videos acquired from an integrated plant in India. Some additional comparative studies for overtaking and illegal parking are done using two benchmark datasets, namely ‘CamSeq01’ and ‘ISLab-PVD’. Through an extensive study, it can be concluded that our developed algorithms are superior to some state-of-the-art algorithms in the detection of pre-events on road.
Stance Detection Benchmark: How Robust is Your Stance Detection?

Abstract Stance detection (StD) aims to detect an author’s stance towards a certain topic and has become a key component in applications like fake news detection, claim validation, or argument search. However, while stance is easily detected by humans, machine learning (ML) models are clearly falling short of this task. Given the major differences in dataset sizes and framing of StD (e.g. number of classes and inputs), ML models trained on a single dataset usually generalize poorly to other domains. Hence, we introduce a StD benchmark that allows to compare ML models against a wide variety of heterogeneous StD datasets to evaluate them for generalizability and robustness. Moreover, the framework is designed for easy integration of new datasets and probing methods for robustness. Amongst several baseline models, we define a model that learns from all ten StD datasets of various domains in a multi-dataset learning (MDL) setting and present new state-of-the-art results on five of the datasets. Yet, the models still perform well below human capabilities and even simple perturbations of the original test samples (adversarial attacks) severely hurt the performance of MDL models. Deeper investigation suggests overfitting on dataset biases as the main reason for the decreased robustness. Our analysis emphasizes the need of focus on robustness and de-biasing strategies in multi-task learning approaches. To foster research on this important topic, we release the dataset splits, code, and fine-tuned weights.
Single-shot 3D shape measurement using an end-to-end stereo matching network for speckle projection profilometry

Speckle projection profilometry (SPP), which establishes the global correspondences between stereo images by projecting only a single speckle pattern, has the advantage of single-shot 3D reconstruction. Nevertheless, SPP suffers from the low matching accuracy of traditional stereo matching algorithms, which fundamentally limits its 3D measurement accuracy. In this work, we propose a single-shot 3D shape measurement method using an end-to-end stereo matching network for SPP. To build a high-quality SPP dataset for training the network, by combining phase-shifting profilometry (PSP) and temporal phase unwrapping techniques, high-precision absolute phase maps can be obtained to generate accurate and dense disparity maps with high completeness as the ground truth by phase matching. For the architecture of the network, a multi-scale residual subnetwork is first leveraged to synchronously extract compact feature tensors with 1/4 resolution from speckle images for constructing the 4D cost volume. Considering that the cost filtering based on 3D convolution is computationally costly, a lightweight 3D U-net network is proposed to implement efficient 4D cost aggregation. In addition, because the disparity maps in the SPP dataset should have valid values only in the foreground, a simple and fast saliency detection network is integrated to avoid predicting the invalid pixels in the occlusions and background regions, thereby implicitly enhancing the matching accuracy for valid pixels. Experiment results demonstrated that the proposed method improves the matching accuracy by about 50% significantly compared with traditional stereo matching methods. Consequently, our method achieves fast and absolute 3D shape measurement with an accuracy of about 100 µm through a single speckle pattern.
Generalized Nesterov's Acceleration-Incorporated, Non-Negative and Adaptive Latent Factor Analysis

A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful knowledge from non-negative data represented by high-dimensional and sparse (HiDS) matrices arising from various service-oriented applications. However, its convergence rate is slow. To address this issue, this study proposes a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> eneralized Nesterov's acceleration-incorporated, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> on-negative and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> daptive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> atent <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> actor (GNALF) model. It results from a) incorporating a generalized Nesterov's accelerated gradient (NAG) method into an SLF-NMU algorithm, thereby achieving an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> AG-incorporated and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> lement-oriented <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</u> on-negative (NEN) algorithm to perform efficient parameter update; and b) making its regularization and acceleration parameters self-adaptive via incorporating the principle of a particle swarm optimization algorithm into the training process, thereby implementing a highly adaptive and practical model. Empirical studies on six large sparse matrices from different recommendation service applications show that a GNALF model achieves very high convergence rate without the need of hyper-parameter tuning, making its computational efficiency significantly higher than state-of-the-art models. Meanwhile, such efficiency gain does not result in accuracy loss, since its prediction accuracy is comparable with its peers. Hence, it can better serve practical service applications with real-time demands.
Spatially Conditioned Graphs for Detecting Human–Object Interactions

We address the problem of detecting human–object interactions in images using graphical neural networks. Unlike conventional methods, where nodes send scaled but otherwise identical messages to each of their neighbours, we propose to condition messages between pairs of nodes on their spatial relationships, resulting in different messages going to neighbours of the same node. To this end, we explore various ways of applying spatial conditioning under a multi-branch structure. Through extensive experimentation we demonstrate the advantages of spatial conditioning for the computation of the adjacency structure, messages and the refined graph features. In particular, we empirically show that as the quality of the bounding boxes increases, their coarse appearance features contribute relatively less to the disambiguation of interactions compared to the spatial information. Our method achieves an mAP of 31.33% on HICO-DET and 54.2% on V-COCO, significantly outperforming state-of-the-art on fine-tuned detections.
Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution

Brain tumor segmentation is a critical task for patient’s disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly with deep supervision and stochastic weight averaging, on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. Two independent ensembles of models from two different training pipelines were trained, and each produced a brain tumor segmentation map. These two labelmaps per patient were then merged, taking into account the performance of each ensemble for specific tumor subregions. Our performance on the online validation dataset with test time augmentation were as follows: Dice of 0.81, 0.91 and 0.85; Hausdorff (95%) of 20.6, 4, 3, 5.7 mm for the enhancing tumor, whole tumor and tumor core, respectively. Similarly, our solution achieved a Dice of 0.79, 0.89 and 0.84, as well as Hausdorff (95%) of 20.4, 6.7 and 19.5 mm on the final test dataset, ranking us among the top ten teams. More complicated training schemes and neural network architectures were investigated without significant performance gain at the cost of greatly increased training time. Overall, our approach yielded good and balanced performance for each tumor subregion. Our solution is open sourced at https://github.com/lescientifik/open_brats2020 .
Solving Multitask Optimization Problems With Adaptive Knowledge Transfer via Anomaly Detection

Evolutionary multitask optimization (EMTO) has recently attracted widespread attention in the evolutionary computation community, which solves two or more tasks simultaneously to improve the convergence characteristics of tasks when individually optimized. Effective knowledge between tasks is transferred by taking advantage of the parallelism of population-based search. Without any prior knowledge about tasks, it is a challenging problem of how to adaptively transfer effective knowledge between tasks and reduce the impact of negative transfer in EMTO. However, these two issues are rarely studied simultaneously in the existing literature. Besides, in complex many-task environments, the potential relationships among individuals from highly diverse populations associated with tasks directly determine the effectiveness of cross-task knowledge transfer. Keeping those in mind, we propose a multitask evolutionary algorithm based on anomaly detection (MTEA-AD). Specifically, each task is assigned a population and an anomaly detection model. Each anomaly detection model is used to learn the relationship among individuals between the current task and the other tasks online. Individuals that may carry negative knowledge are identified as outliers, and candidate transferred individuals identified by the anomaly detection model are selected to assist the current task, which may carry common knowledge across the current task and other tasks. Furthermore, to realize the adaptive control of the degree of knowledge transfer, the successfully transferred individuals that survive to the next generation through the elitism are used to update the anomaly detection parameter. The fair competition between offspring and candidate transferred individuals can effectively reduce the risk of negative transfer. Finally, the empirical studies on a series of synthetic benchmarks and a practical study are conducted to verify the effectiveness of MTEA-AD. The experimental results demonstrate that our proposal can adaptively adjust the degree of knowledge transfer through the anomaly detection model to achieve highly competitive performance compared to several state-of-the-art EMTO methods.
Multi-level graph neural network for text sentiment analysis

Text sentiment analysis is a fundamental task in the field of natural language processing (NLP). Recently, graph neural networks (GNNs) have achieved excellent performance in various NLP tasks. However, a GNN only considers the adjacent words when updating the node representations of the graph, and thus the model can only focus on the local features while ignoring global features. In this paper, we propose a novel multi-level graph neural network (MLGNN) for text sentiment analysis. To consider both local features and global features, we apply node connection windows with different sizes at different levels. Particularly, we integrate a scaled dot-product attention mechanism as a message passing mechanism into our method for fusing the features of each word node in the graph. The experimental results demonstrated that the proposed model outperformed other models in text sentiment analysis tasks.
Radio Frequency Fingerprint Identification for LoRa Using Spectrogram and CNN

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on intrin-sic hardware characteristics of wireless devices. We designed an RFFI scheme for Long Range (LoRa) systems based on spectrogram and convolutional neural network (CNN). Specifically, we used spectrogram to represent the fine-grained time-frequency characteristics of LoRa signals. In addition, we revealed that the instantaneous carrier frequency offset (CFO) is drifting, which will result in misclassification and significantly compromise the system stability; we demonstrated CFO compensation is an effective mitigation. Finally, we designed a hybrid classifier that can adjust CNN outputs with the estimated CFO. The mean value of CFO remains relatively stable, hence it can be used to rule out CNN predictions whose estimated CFO falls out of the range. We performed experiments in real wireless environments using 20 LoRa devices under test (DUTs) and a Universal Software Radio Peripheral (USRP) N210 receiver. By comparing with the IQ-based and FFT-based RFFI schemes, our spectrogram-based scheme can reach the best classification accuracy, i.e., 97.61% for 20 LoRa DUTs.
Feature and Subfeature Selection for Classification Using Correlation Coefficient and Fuzzy Model

This article presents an analysis of data extraction for classification using correlation coefficient and fuzzy model. Several traditional methods of data extraction are used for classification that could not provide sufficient information for further step of data analysis on class. It needs refinement of features data to distinguish a class that differs from a traditional class. Thus, it proposes the feature tiny data (subfeature data) to find distinguish class from a traditional class using two methods such as correlation coefficient and fuzzy model to select features as well as subfeature for distinguishing class. In the first approach, the correlation coefficient methods with gradient descent technique are used to select features from the dataset and in the second approach, the fuzzy model with supreme of minimum value is considered to get subfeature data. As per the proposed model, some features (i.e., three features from the acoustic dataset, two features from the QCM dataset, and eight features from the audit dataset, etc.) and subfeatures (as per threshold value like 20 for acoustic; 10 for QCM, and 20 for audit, etc.) are selected based on correlation coefficient as well as fuzzy methods, respectively. Further, the probability approach is used to find the association and availability of subfeature data from the dimensional reduced database. The experimental results show the proposed framework identifies and selects both feature and subfeature data with the effectiveness of the new class. The comparison results of several classifiers on several datasets are explained in the experimental section.
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation

Sequential recommendation (SR) is to accurately recommend a list of items for a user based on her current accessed ones. While new-coming users continuously arrive in the real world, one crucial task is to have inductive SR that can produce embeddings of users and items without re-training. Given user-item interactions can be extremely sparse, another critical task is to have transferable SR that can transfer the knowledge derived from one domain with rich data to another domain. In this work, we aim to present the holistic SR that simultaneously accommodates conventional, inductive, and transferable settings. We propose a novel deep learning-based model, Relational Temporal Attentive Graph Neural Networks (RetaGNN), for holistic SR. The main idea of RetaGNN is three-fold. First, to have inductive and transferable capabilities, we train a relational attentive GNN on the local subgraph extracted from a user-item pair, in which the learnable weight matrices are on various relations among users, items, and attributes, rather than nodes or edges. Second, long-term and short-term temporal patterns of user preferences are encoded by a proposed sequential self-attention mechanism. Third, a relation-aware regularization term is devised for better training of RetaGNN. Experiments conducted on MovieLens, Instagram, and Book-Crossing datasets exhibit that RetaGNN can outperform state-of-the-art methods under conventional, inductive, and transferable settings. The derived attention weights also bring model explainability.
Application of virtual reality teaching method and artificial intelligence technology in digital media art creation

With the progress and innovation of science and technology, virtual reality technology emerges as the times require, and its application in digital media art creation is more and more extensive. The application of virtual reality technology in digital media art creation helps to enrich artistic creation means and improve the quality of artistic creation process. This paper first gives the application of virtual reality teaching method in digital media art creation. Virtual reality education has obvious characteristics of vividness and autonomy. In the teaching mode and teaching design, it emphasizes the comprehensive improvement of teachers and students' teaching experience, so as to drive teachers and students to separate from the traditional teaching concept and mode, and achieve twice the teaching effect with half the effort. Then, it gives the technical advantages of artificial intelligence technology for digital media art creation when using virtual reality teaching method. Finally, we give the application of virtual reality and artificial intelligence technology in the teaching of digital media art creation. The experimental results show that the virtual reality teaching method, which combines the advantages of artificial intelligence algorithm, has potential application value and good practical significance in the teaching of digital media art creation.