Reviewerly/exHarmony huggingface dataset:
Novel Multi Security and Privacy Benchmarking Framework for Blockchain-Based IoT Healthcare Industry 4.0 Systems

The evaluation, importance and variation nature of multiple security and privacy properties are the main issues that make the benchmarking of blockchain-based IoT healthcare Industry 4.0 systems fall under the multi-criteria decision-making (MCDM) problem. In this article, one of the recent MCDM weighting methods called fuzzy weighted with zero inconsistency (FWZIC) is effective for weighting the evaluation criteria subjectively without any inconsistency issues. However, considering the advantages of spherical fuzzy sets in providing a wide range of options to decision-makers and efficiently dealing with vagueness, hesitancy and uncertainty, this article formulated a new version of FWZIC for weighting the security and privacy properties, that is, spherical FWZIC (S-FWZIC). Moreover, an integrated MCDM framework was developed for benchmarking blockchain-based IoT healthcare Industry 4.0 systems on the basis of multi security and privacy properties. In the first phase of the methodology, a decision matrix is formulated based on the intersection of “blockchain-based Internet of Things healthcare Industry 4.0 systems” and “security and privacy properties” (i.e., user authentication, access control, privacy protection, integrity availability and anonymity). In the second phase, the weights of each security and privacy property are calculated through the S-FWZIC method. Then, these weights are employed to benchmark blockchain-based IoT healthcare Industry 4.0 systems through the combined grey relational analysis–technique for order of preference by similarity to ideal solution (GRA-TOPSIS) and the bald eagle search (BES) optimization method. Results indicate the following: First, the S-FWZIC method efficiently weighs the security and privacy properties, indicating that access control has the highest significance weight of 0.2070, while integrity has the lowest weight (0.0646); and second, the combination of the GRA-TOPSIS and the BES optimization method effectively ranks the systems. The evaluation was conducted using sensitivity analysis, revealing high correlation results over all the discussed scenarios of changing the weights of the criteria. The implications of this article can assist medical organisation administrators in selecting the most secure and appropriate system and the developers of such systems in future directions.
Spatio-Temporal Feature Encoding for Traffic Accident Detection in VANET Environment

In the Vehicular Ad hoc Networks (VANET) environment, recognizing traffic accident events in the driving videos captured by vehicle-mounted cameras is an essential task. Generally, traffic accidents have a short duration in driving videos, and the backgrounds of driving videos are dynamic and complex. These make traffic accident detection quite challenging. To effectively and efficiently detect accidents from the driving videos, we propose an accident detection approach based on spatio–temporal feature encoding with a multilayer neural network. Specifically, the multilayer neural network is used to encode the temporal features of video for clustering the video frames. From the obtained frame clusters, we detect the border frames as the potential accident frames. Then, we capture and encode the spatial relationships of the objects detected from these potential accident frames to confirm whether these frames are accident frames. The extensive experiments demonstrate that the proposed approach achieves promising detection accuracy and efficiency for traffic accident detection, and meets the real-time detection requirement in the VANET environment.
OpenGAN: Open-Set Recognition via Open Data Generation

Real-world machine learning systems need to analyze novel testing data that differs from the training data. In K-way classification, this is crisply formulated as open-set recognition, core to which is the ability to discriminate open-set data outside the K closed-set classes. Two conceptually elegant ideas for open-set discrimination are: 1) discriminatively learning an open-vs-closed binary discriminator by exploiting some outlier data as the open-set, and 2) unsupervised learning the closed-set data distribution with a GAN and using its discriminator as the open-set likelihood function. However, the former generalizes poorly to diverse open test data due to overfitting to the training outliers, which unlikely exhaustively span the open-world. The latter does not work well, presumably due to the instable training of GANs. Motivated by the above, we propose OpenGAN, which addresses the limitation of each approach by combining them with several technical insights. First, we show that a carefully selected GAN-discriminator on some real outlier data already achieves the state-of-the-art. Second, we augment the available set of real open training examples with adversarially synthesized "fake" data. Third and most importantly, we build the discriminator over the features computed by the closed-world K-way networks. Extensive experiments show that Open-GAN significantly outperforms prior open-set methods.
Deep image retrieval using artificial neural network interpolation and indexing based on similarity measurement

In content-based image retrieval (CBIR), primitive image signatures are critical because they represent the visual characteristics. Image signatures, which are algorithmically descriptive and accurately recognized visual components, are used to appropriately index and retrieve comparable results. To differentiate an image in the category of qualifying contender, feature vectors must have image information's like colour, objects, shape, spatial viewpoints. Previous methods such as sketch-based image retrieval by salient contour (SBIR) and greedy learning of deep Boltzmann machine (GDBM) used spatial information to distinguish between image categories. This requires interest points and also feature analysis emerged image detection problems. Thus, a proposed model to overcome this issue and predict the repeating pattern as well as series of pixels that conclude similarity has been necessary. In this study, a technique called CBIR-similarity measure via artificial neural network interpolation (CBIR-SMANN) has been presented. By collecting datasets, the images are resized then subject to Gaussian filtering in the pre-processing stage, then by permitting them to the Hessian detector, the interesting points are gathered. Based on Skewness, mean, kurtosis and standard deviation features were extracted then given to ANN for interpolation. Interpolated results are stored in a database for retrieval. In the testing stage, the query image was inputted that is subjected to pre-processing, and feature extraction was then fed to the similarity measurement function. Thus, ANN helps to get similar images from the database. CBIR-SMANN have been implemented in the python tool and then evaluated for its performance. Results show that CBIR-SMANN exhibited a high recall value of 78% with a minimum retrieval time of 980 ms. This showed the supremacy of the proposed model was comparatively greater than the previous ones.
From concept drift to model degradation: An overview on performance-aware drift detectors

The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradation during the system’s life cycle. Recent advances that study non-stationary environments have mainly focused on identifying and addressing such changes caused by a phenomenon called concept drift. Different terms have been used in the literature to refer to the same type of concept drift and the same term for various types. This lack of unified terminology is set out to create confusion on distinguishing between different concept drift variants. In this paper, we start by grouping concept drift types by their mathematical definitions and survey the different terms used in the literature to build a consolidated taxonomy of the field. We also review and classify performance-based concept drift detection methods proposed in the last decade. These methods utilize the predictive model’s performance degradation to signal substantial changes in the systems. The classification is outlined in a hierarchical diagram to provide an orderly navigation between the methods. We present a comprehensive analysis of the main attributes and strategies for tracking and evaluating the model’s performance in the predictive system. The paper concludes by discussing open research challenges and possible research directions.
Experimental photonic quantum memristor

Abstract Memristive devices are a class of physical systems with history-dependent dynamics characterized by signature hysteresis loops in their input–output relations. In the past few decades, memristive devices have attracted enormous interest in electronics. This is because memristive dynamics is very pervasive in nanoscale devices, and has potentially groundbreaking applications ranging from energy-efficient memories to physical neural networks and neuromorphic computing platforms. Recently, the concept of a quantum memristor was introduced by a few proposals, all of which face limited technological practicality. Here we propose and experimentally demonstrate a novel quantum-optical memristor (based on integrated photonics) that acts on single-photon states. We fully characterize the memristive dynamics of our device and tomographically reconstruct its quantum output state. Finally, we propose a possible application of our device in the framework of quantum machine learning through a scheme of quantum reservoir computing, which we apply to classical and quantum learning tasks. Our simulations show promising results, and may break new ground towards the use of quantum memristors in quantum neuromorphic architectures.
Exploiting Temporal Contexts With Strided Transformer for 3D Human Pose Estimation

Despite the great progress in 3D human pose estimation from videos, it is still an open problem to take full advantage of a redundant 2D pose sequence to learn representative representations for generating one 3D pose. To this end, we propose an improved Transformer-based architecture, called Strided Transformer, which simply and effectively lifts a long sequence of 2D joint locations to a single 3D pose. Specifically, a Vanilla Transformer Encoder (VTE) is adopted to model long-range dependencies of 2D pose sequences. To reduce the redundancy of the sequence, fully-connected layers in the feed-forward network of VTE are replaced with strided convolutions to progressively shrink the sequence length and aggregate information from local contexts. The modified VTE is termed as Strided Transformer Encoder (STE), which is built upon the outputs of VTE. STE not only effectively aggregates long-range information to a single-vector representation in a hierarchical global and local fashion, but also significantly reduces the computation cost. Furthermore, a full-to-single supervision scheme is designed at both full sequence and single target frame scales applied to the outputs of VTE and STE, respectively. This scheme imposes extra temporal smoothness constraints in conjunction with the single target frame supervision and hence helps produce smoother and more accurate 3D poses. The proposed Strided Transformer is evaluated on two challenging benchmark datasets, Human3.6 M and HumanEva-I, and achieves state-of-the-art results with fewer parameters. Code and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Vegetebird/StridedTransformer-Pose3D</uri> .
End-to-End Optimized Versatile Image Compression With Wavelet-Like Transform

Built on deep networks, end-to-end optimized image compression has made impressive progress in the past few years. Previous studies usually adopt a compressive auto-encoder, where the encoder part first converts image into latent features, and then quantizes the features before encoding them into bits. Both the conversion and the quantization incur information loss, resulting in a difficulty to optimally achieve arbitrary compression ratio. We propose iWave++ as a new end-to-end optimized image compression scheme, in which iWave, a trained wavelet-like transform, converts images into coefficients without any information loss. Then the coefficients are optionally quantized and encoded into bits. Different from the previous schemes, iWave++ is versatile: a single model supports both lossless and lossy compression, and also achieves arbitrary compression ratio by simply adjusting the quantization scale. iWave++ also features a carefully designed entropy coding engine to encode the coefficients progressively, and a de-quantization module for lossy compression. Experimental results show that lossy iWave++ achieves state-of-the-art compression efficiency compared with deep network-based methods; on the Kodak dataset, lossy iWave++ leads to 17.34 percent bits saving over BPG; lossless iWave++ achieves comparable or better performance than FLIF. Our code and models are available at https://github.com/mahaichuan/Versatile-Image-Compression.
Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold values

Image thresholding is one of the most important approaches for image segmentation. Among multilevel thresholding techniques, cross entropy has been widely used by researchers to find multilevel threshold values. In multilevel cross entropy thresholding techniques, main target is to find an optimal combination of threshold values at different levels for minimizing the cross entropy. In this paper, Teaching-Learning-based Optimization (TLBO) algorithm is used to find an optimal combination of threshold values at different levels for minimizing the cross entropy. TLBO algorithm is inspired by passing on knowledge within a classroom environment where students first gain knowledge from a teacher and then through mutual interaction. This new proposed approach is called the TLBO-based minimum cross entropy thresholding (TLBO-based MCET) algorithm. The performance of the proposed algorithm is tested on a number of standard test images. For comparative analysis, the results of TLBO-based MCET algorithm are compared with the results of Firefly-based minimum cross entropy thresholding (FF-based MCET), Honey Bee Mating Optimization-based minimum cross entropy thresholding (HBMO-based MCET) and Quantum Particle Swarm Optimization-based minimum cross entropy thresholding (Quantam PSO-based MCET). Comparative analysis is done based on two most popular objective performance measures, Peak Signal to Noise Ratio (PSNR) and Uniformity. From experimental results, it is observed that the proposed method is an efficient and feasible method to search an optimal combination of threshold values at 2nd, 3rd, 4th and 5th levels.
How do we talk about doctors and drugs? Sentiment analysis in forums expressing opinions for medical domain

The main goal of this study is to examine how people express their opinion in medical forums. We analyze the language used in order to determine the best way to tackle sentiment analysis in this domain.We have applied supervised learning and lexicon-based sentiment analysis approaches over two different corpora extracted from social web. Specifically, we have focused on two aspects: drugs and doctors. We have selected two forums and we have collected corpora for each one: (i) DOS, a Spanish corpus of drug reviews and (ii) COPOS, a Spanish corpus of patients' opinions about physicians.The classification results show that drug reviews are more difficult to classify than those about physicians. In order to understand the difference in the results, we have studied the linguistic features of both corpora.Although opinions about physicians and drugs are written in most cases by non-professional users, reviews about physicians are characterized by the use of an informal language while reviews about drugs are characterized by a combination of informal language with specific terminology (e.g. adverse effects, drug names) with greater lexical diversity, making the task of sentiment analysis difficult.
SG-FCN: A Motion and Memory-Based Deep Learning Model for Video Saliency Detection

Data-driven saliency detection has attracted strong interest as a result of applying convolutional neural networks to the detection of eye fixations. Although a number of image-based salient object and fixation detection models have been proposed, video fixation detection still requires more exploration. Different from image analysis, motion and temporal information is a crucial factor affecting human attention when viewing video sequences. Although existing models based on local contrast and low-level features have been extensively researched, they failed to simultaneously consider interframe motion and temporal information across neighboring video frames, leading to unsatisfactory performance when handling complex scenes. To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance. By simulating the memory mechanism and visual attention mechanism of human beings when watching a video, we propose a step-gained fully convolutional network by combining the memory information on the time axis with the motion information on the space axis while storing the saliency information of the current frame. The model is obtained through hierarchical training, which ensures the accuracy of the detection. Extensive experiments in comparison with 11 state-of-the-art methods are carried out, and the results show that our proposed model outperforms all 11 methods across a number of publicly available datasets.
Ensemble of convolutional neural networks for bioimage classification

This work presents a system based on an ensemble of Convolutional Neural Networks (CNNs) and descriptors for bioimage classification that has been validated on different datasets of color images. The proposed system represents a very simple yet effective way of boosting the performance of trained CNNs by composing multiple CNNs into an ensemble and combining scores by sum rule. Several types of ensembles are considered, with different CNN topologies along with different learning parameter sets. The proposed system not only exhibits strong discriminative power but also generalizes well over multiple datasets thanks to the combination of multiple descriptors based on different feature types, both learned and handcrafted. Separate classifiers are trained for each descriptor, and the entire set of classifiers is combined by sum rule. Results show that the proposed system obtains state-of-the-art performance across four different bioimage and medical datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni .
Rejecting Motion Outliers for Efficient Crowd Anomaly Detection

Crowd anomaly detection is a key research area in vision-based surveillance. Most of the crowd anomaly detection algorithms are either too slow, bulky, or power-hungry to be applicable for battery-powered surveillance cameras. In this paper, we present a new crowd anomaly detection algorithm. The proposed algorithm creates a feature for every superpixel that includes the contribution from the neighboring superpixels only if their direction of motion conforms with the dominant direction of motion in the region. We also propose using univariate Gaussian discriminant analysis with the K-means algorithm for classification. Our method provides superior accuracy over numerous deep learning-based and handcrafted feature-based approaches. We also present a low-power FPGA implementation of the proposed method. The algorithm is developed such that features are extracted over non-overlapping pixels. This allows gating inputs to numerous modules resulting in higher power efficiency. The maximum energy required per pixel is 2.43 nJ in our implementation. 126.65 Mpixels can be processed per second by the proposed implementation. The speed, power, and accuracy performance of our method make it competitive for surveillance applications, especially battery-powered surveillance cameras.
Predicting cadmium concentration in soils using laboratory and field reflectance spectroscopy

Visible and near-infrared spectroscopy (VNIRS, 350-2500 nm) is a promising alternative to rapidly investigate soil contamination by heavy metals. To explore the possibility of predicting heavy metal concentration in soils using laboratory and field reflectance spectroscopy and examine transferability of the prediction method, 46 soil samples from a mining area, 42 soil samples from an agricultural land, and the corresponding two sets of field soil spectra were collected. Cadmium (Cd) was taken as an example in this study. The collected soil samples were air-dried, ground, sieved, and then used for laboratory spectral measurement and chemical analysis. Soil reflectance spectroscopy associated with organic matter was extracted from the VNIRS and used to predict Cd concentration based on strong sorption and retention of Cd on soil organic matter. Genetic algorithm (GA) was adopted for band selection, and the selected bands were used to calibrate the prediction model with partial least squares regression (PLSR). Compared with the prediction using entire VNIR region, the ratio of prediction to deviation (RPD) and the coefficient of determination (R2) were improved from 1.473 and 0.508 to 2.997 and 0.881 for laboratory spectra and 1.437 and 0.484 to 1.992 and 0.731 for field spectra by using spectral bands associated with organic matter in the mining area. The RPD and R2 values were improved from 1.919 and 0.707 to 3.727 and 0.923 for laboratory spectra and 1.057 and 0.036 to 1.747 and 0.646 for field spectra by the prediction method in the agricultural land. The improvement was further revealed by prediction of Cd concentration with a selected subset of soil samples from the mining area. The results suggest that predicting Cd concentration in soils with GA-PLSR using reflectance spectroscopy associated with organic matter is feasible and the prediction method could have the potential to be applied to field conditions.
Wearable Computing for Internet of Things: A Discriminant Approach for Human Activity Recognition

With the rapid development of the wireless sensor network and the continuous improvement of its key technologies, the concept of Internet of Things has been encouraged and extended due to its wide applications in scenarios, such as smart homes and healthcare. Under the background, human activity recognition has drawn great attention in recent years. In this paper, we present a discriminant approach to recognize daily human activities recorded through accelerometer sensor. In the proposed approach, we first use S transform (ST) to extract features, and then introduce a supervised regularization-based robust subspace (SRRS) learning method to learn low-dimensional intrinsic feature representation from the original feature subspace. Particularly, ST has been described as a joint time-frequency representation, which is insensitive to noise. SRRS can learn more robust and discriminative features to reinforce the descriptions of samples while removing noise and redundancy. Experiments are conducted on three publicly available datasets, i.e., wireless sensor data mining, SCUT-NAA, and mHealth demonstrating the superior performance of our proposed scheme compared with state-of-the-art methods.