Reviewerly/exHarmony huggingface dataset:
Estimation of the area under the ROC curve for non-normal data

The Receiver Operating Characteristic curve is one of the widely used classification tools that helps in assessing the performance of the diagnostic test as well as accommodates for comparing two diagnostic tests/statistical procedures using its intrinsic and accuracy measures, such as, sensitivity; specificity, and the Area under the Curve. The conventional and standard ROC model is the Bi-normal ROC model which is based on the assumption that the test scores/marker values underlie Normal distributions. Over the years, several researchers have developed various bi-distributional ROC models where the data possess the pattern of Exponential, Gamma, the combination of Half Normal and Rayleigh, etc. However, there are many practical situations, particularly in the field of medicine, where these available distributions may not be of fit for the data at hand. In this article, we attempted to propose two new ROC models and showed that these models have a better fit and explain better accuracy than that of the existing ROC models. The work is supported by a real dataset and simulated datasets.
A Difference-Equation-Based Robust Image Encryption Scheme with Chaotic Permutations and Logic Gates

Abstract Image encryption has become an indispensable tool for achieving highly secure image-based communications. Numerous encryption approaches have appeared and demonstrated varying degrees of robustness to adversarial attacks. In this paper, an efficient and robust image encryption algorithm is established based on randomized difference equations, random permutations and randomized logic circuits. Specifically, hyperchaotic and chaotic systems are used to generate pseudo-random sequences. These sequences are thus used to define random first-order difference equations, chaotic permutations and logic circuits. Image encryption based on these three randomized modules shows high computational efficiency as well as strong robustness against statistical, differential, and chosen-plaintext attacks. The proposed scheme leads to almost zero correlation in the encrypted images, entropy values of more than 7.99 for the test images, and a key space size of $$2^{572}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>572</mml:mn> </mml:msup> </mml:math> . Furthermore, differential analysis shows that the number of pixel change rate (NPCR) and the unified average change intensity (UACI) for the proposed technique are on average 99.61 and 33.35%, respectively.
A lightweight ensemble discriminator for Generative Adversarial Networks

While Generative Adversarial Networks (GANs) have brought immense success in various content-generation tasks, they still face enormous challenges in generating high-quality visually realistic images because of the model collapse or instability during GAN training. One common accepted explanation for the model collapse and instability is that the learning signal provided by the discriminator to the generator become inadequate when the discriminator overconcentrates on the most discriminative difference between real and synthetic images and ignores the less discriminative parts. To this end, we propose a lightweight ensemble discriminator to evaluate the generator from multi-perspective. Borrowing the insights from ensemble learning, several auxiliary discriminators are embedded into one deep model. A novel ensemble loss function is designed to promote the complementariness within the ensemble and train the whole framework in an end-to-end manner. Extensive experiments on datasets of varying resolutions and data sizes prove significant performance improvements over the state-of-the-art GANs. The proposed method can be easily embedded into various GAN frameworks and combined with different loss functions.
PyHAPT: A Python-based Human Activity Pose Tracking data processing framework

We propose a novel Python-based Human Activity Pose Tracking data processing framework (PyHAPT). It provides the functionality to efficiently process annotated human pose tracking raw video data collected in unconstrained environments. Besides, PyHAPT provides the functionalities of interpolation to recover the missing joints data and data visualization that gives insights into the spatial–temporal skeletal information. The processed data could be readily used for developing new human activity recognition deep learning models, which could be deployed on mobile service robots.
Application of Addition and Multiplication Noise Model Parameter Estimation in INSAR Image Processing

INSAR images are inevitably contaminated by noise during the process of generation, transmission, compression, and reception. Noise not only affects the quality of the INSAR image, but also affects subsequent operations such as the design of corresponding filters, INSAR image segmentation, compression, restoration, and feature recognition. The INSAR image noise model is mainly divided into additive noise and multiplicative noise. Compared with additive noise, multiplicative noise is more complicated due to INSAR image correlation and non-Gaussian. Based on least squares algorithm system of additive and multiplicative mixed noise model, this paper proposes a method of using PCA to remove multiplicative gamma distribution noise. The pure noise coefficient is obtained by subtracting the original coefficient from the diagonal wavelet coefficient of the noisy image, and the mode of the local variance is calculated as the estimation value of the noise standard deviation. Experiments show that the proposed method can obtain more accurate estimation of noise; in particular in the case of less noise and more detailed image information, its effect is more obvious.
Pre-Service Teachers’ Competence and Pedagogical Use of ICT: Are They Ready to Develop Collaborative Activities with Students?

Digital training plays a major role in the initial training for preschool, primary, and high school teachers in Québec, Canada. Since the mastery of information and communications technology (ICT) is prescriptive, measures have been implemented in university teacher training programs to ensure its development. Nevertheless, after this training, pre-service teachers’ (PSTs’) ability to transfer competency to a real context remains uncertain. We identified PSTs’ levels of mastery of digital tools and competency, as well as their pedagogical usage of them during their school practicums. We sought to ascertain whether they were ready to organize collaborative learning activities with their students. Their mastery of digital tools did not go beyond the level of “good,” except for tools related to office suites. They perceived themselves as having average mastery of the ability to integrate ICT. In addition, the transfer to a professional context is progressing via the pedagogical usage of ICT reported by PSTs during their school practicums. We observed moderate correlations between the mastery of collaborative and communication tools and of collaborative learning activities.
Blockchain Technology: Applications and Open Issues

Due to its distributed, decentralized and secure nature, data stored within blockchain can not be modified easily by an unauthorized party and because of this it has become the preferred technology for record keeping. Today, every application domain where data or records need to be stored in a secure manner, are migrating to blockchain. Though blockchain provides a secure and convenient way of record keeping, like any other technology, it too suffers from some challenges such as high transaction processing time, resource wastage, and certain types of attacks. This paper provides a systematic review of the various application areas of blockchain and the challenges there in.
Motion Recognition Based on Deep Learning and Human Joint Points

In order to solve the problem that the traditional feature extraction methods rely on manual design, the research method is changed from the traditional method to the deep learning method based on convolutional neural networks. The experimental results show that the larger average DTW occurs near the 55th calculation, that is, about the 275th frame of the video. In the 55th calculation, the joint angle with the largest DTW distance is the right knee joint. A multiscene action similarity analysis algorithm based on human joint points has been realized. In the fitness scene, by analyzing the joint angle through cosine similarity, the time of fitness key posture in the action sequence can be recognized. In the sports scene, through the similarity analysis of joint angle sequences by the DTW algorithm, we can get the similarity between people's actions in the sports video and the joint positions with large differences in some time intervals, and the real validity of the experiment is verified. The accuracy of motion recognition before and after the improvement is 95.2% and 97.1%, which is 0.19% higher than that before the improvement. The methods and results are widely used in the fields of sports recognition, movement specification, sports training, health management, and so on.
Characterizing Java Streams in the Wild

Since Java 8, streams ease the development of data transformations using a declarative style based on functional programming. Some recent studies aim at shedding light on how streams are used. However, they consider only small sets of applications and mainly apply static analysis techniques, leaving the large-scale analysis of dynamic metrics focusing on stream processing an open research question. In this paper, we present the first large-scale empirical study on the use of streams in Java. We present a novel dynamic analysis for collecting runtime information and key metrics that enable the fine-grained characterization of sequential and parallel stream processing. We massively apply our dynamic analysis using a fully automated approach, supported by a distributed infrastructure to mine public software projects hosted on GitHub. Our findings advance the understanding of the use of streams, both confirming some of the results of previous studies at a much larger scale, as well as revealing previously unobserved findings in the use of streams.
Efficient Uncertain Sequence Pattern Mining Based on Hadoop Platform

In the Internet of Things (IoT) era, information is collected by sensor devices, resulting in data loss or uncertain data and other consequences. We need to represent the uncertain data collected using probabilities to extract the useful information for production and application from a huge indeterminate data warehouse. The data in the database has a particular order in time or space, so the High-Utility Probability Sequential Pattern Mining (HUPSPM) has become a new investigation and analysis topic in data processing. After the progress of timestamp, many efficient algorithms for sequential mining have been developed. However, these algorithms have a limitation: they can only be executed in a stand-alone environment and are only suitable for small datasets. Therefore, introducing an advanced graph framework for processing large datasets addresses the shortcomings of the existing methods. The proposed algorithm can avoid repeated database searching, splitting the database, and improve the parallel computing capability. The initial database is pruned according to the existing pruning strategy to effectively reduce the number of candidate sets effectively. Experiments show that the algorithm presented in this paper has excellent advantages in mining high-utility probability sequences in large datasets.
A Novel Reversible Data Hiding Scheme Based on Pixel-Residual Histogram

Prediction-error expansion (PEE) is the most popular reversible data hiding (RDH) technique due to its efficient capacity-distortion tradeoff. With the generated prediction-error histogram (PEH) and adaptively selected expansion bins, the image redundancy is well exploited by PEE. However, for the most widely used rhombus predictor, the rounding operation which groups different prediction-errors into one value is completely unnecessary. The embedding can be extended to a general case by removing the rounding operation, and more histogram bins can be derived for expansion with a new mapping mechanism. Therefore, in this article, instead of pixel prediction-error, we propose to compute the pixel residuals without the rounding operation, and a new embedding mechanism based on pixel-residual histogram (PRH) modification is devised. In PRH, four bins correspond to one bin in PEH. Then, different from the one-to-one mapping between the prediction-error and pixel modification, a four-to-one mapping between the pixel-residual and pixel modification is established, and the performance is optimized by adaptively selecting four expansion bin pairs for embedding. Since more modification selections are considered, better performance can be obtained. Moreover, the proposed scheme is extended to the two-dimensional (2D) histogram and multiple histograms based embedding, and the performance is further enhanced. The superiority of the proposed method is experimentally verified by comparing it with some state-of-the-art works.
Monocular Depth and Velocity Estimation Based on Multi-Cue Fusion

Many consumers and scholars currently focus on driving assistance systems (DAS) and intelligent transportation technologies. The distance and speed measurement technology of the vehicle ahead is an important part of the DAS. Existing vehicle distance and speed estimation algorithms based on monocular cameras still have limitations, such as ignoring the relationship between the underlying features of vehicle speed and distance. A multi-cue fusion monocular velocity and ranging framework is proposed to improve the accuracy of monocular ranging and velocity measurement. We use the attention mechanism to fuse different feature information. The training method is used to jointly train the network through the distance velocity regression loss function and the depth loss as an auxiliary loss function. Finally, experimental validation is performed on the Tusimple dataset and the KITTI dataset. On the Tusimple dataset, the average speed mean square error of the proposed method is less than 0.496 m2/s2, and the average mean square error of the distance is 5.695 m2. On the KITTI dataset, the average velocity mean square error of our method is less than 0.40 m2/s2. In addition, we test in different scenarios and confirm the effectiveness of the network.
Exploiting Parallelism with Vertex-Clustering in Processing-In-Memory-based GCN Accelerators

Recently, Graph Convolutional Networks (GCNs) have shown powerful learning capabilities in graph processing tasks. Computing GCNs with conventional von Neumann architectures usually suffers from limited memory bandwidth due to the irregular memory access. Recent work has proposed Processing-In-Memory (PIM) architectures to overcome the bandwidth bottleneck in Convolutional Neural Networks (CNNs) by performing in-situ matrix-vector multiplication. However, the performance improvement and computation parallelism of existing CNN-oriented PIM architectures is hindered when performing GCNs because of the large scale and sparsity of graphs. To tackle these problems, this paper presents a parallelism enhancement framework for PIM-based GCN architectures. At the software level, we propose a fixed-point quantization method for GCNs, which reduces the PIM computation overhead with little accuracy loss. We also introduce the vertex clustering algorithm to the graph, minimizing the inter-cluster links and realizing cluster-level parallel computing on multi-core systems. At the hardware level, we design a Resistive Random Access Memory (RRAM) based multi-core PIM architecture for GCN, which supports the cluster-level parallelism. Besides, we propose a coarse-grained pipeline dataflow to cover the RRAM write costs and improve the GCN computation throughput. At the software/hardware interface level, we propose a PIM-aware GCN mapping strategy to achieve the optimal tradeoff between resource utilization and computation performance. We also propose edge dropping methods to reduce the inter-core communications with little accuracy loss. We evaluate our framework on typical datasets with multiple widely-used GCN models. Experimental results show that the proposed framework achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$698\times, 89\times$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$41\times$</tex> speedup with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7108\times,255\times$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$31\times$</tex> energy efficiency enhancement compared with CPUs, GPUs, and ASICs, respectively.
The VISTA datasets, a combination of inertial sensors and depth cameras data for activity recognition

This paper makes the VISTA database, composed of inertial and visual data, publicly available for gesture and activity recognition. The inertial data were acquired with the SensHand, which can capture the movement of wrist, thumb, index and middle fingers, while the RGB-D visual data were acquired simultaneously from two different points of view, front and side. The VISTA database was acquired in two experimental phases: in the former, the participants have been asked to perform 10 different actions; in the latter, they had to execute five scenes of daily living, which corresponded to a combination of the actions of the selected actions. In both phase, Pepper interacted with participants. The two camera point of views mimic the different point of view of pepper. Overall, the dataset includes 7682 action instances for the training phase and 3361 action instances for the testing phase. It can be seen as a framework for future studies on artificial intelligence techniques for activity recognition, including inertial-only data, visual-only data, or a sensor fusion approach.
An Approach Using Transformer-based Model for Short-term PV generation forecasting

Solar power forecasting has already become a key role in energy market. However, forecasting PV generation is a challenging task because solar energy strongly depends on weather conditions, day/night cycles and meteorological variables such as solar irradiance, temperature, humidity, etc. Therefore, integrating solar power into grids requires accurate prediction methods, which is also improve the quality of the power systems and reduce operational cost. Beside traditional approaches for forecasting solar PV power generation, new techniques arise every year to enhance the performance of models with the main objective to reduce uncertainties. In this paper, a novel deep learning model based on transformer has been established for one-hour-ahead PV generation forecasting. In practice, this work uses PV power output data with one-hour resolution from the North PV sites in Taiwan, and 2 years of Numerical Weather Prediction (NWP) data from Central Weather Bureau (CWB). Furthermore, forecasting results show that the proposed model outperforms other models such as Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) in terms of NRMSE and NMAPE.
A Solution for Monitoring the Eye's Viewing Distance from Electronical Displays

Recent working environment due to pandemic covid 19 leads to numerous medical ailments for those who sits in front of the Computer Display for a longer duration. This type of activity leads to two types of eye strains such as Radiation Strain and Proximate Strain. Out of the two, proximate strain is the more potent cause of eye defects. There are proximity sensors in many devices, but their function is to turn off the display when kept in proximity to any surface. This has been done to prevent any mis-clicks on the display. There also exist many measures of safety to the eye by the use of blue light filters, dark mode, focus modes, etc. The proposed work focuses on monitoring the proximity of the eyes of the user to display with the use of the inbuilt device camera. The proposed algorithm maps the user's face and prevents them from using their devices too close to their eyes. Built on Python, OpenCV and MediaPipe, this project aims to do this using the camera on a device, it is very affordable and does not require any proprietary sensor or powerful and expensive hardware. Being very light, this algorithm can even run smoothly on a Raspberry Pi4.
Design and Implementation of Smart Tourism Service Platform from the Perspective of Artificial Intelligence

With the advancement of science and technology and the further improvement of people’s living standards, tourism has become a hot topic in today’s society. This paper is aimed at studying the development of a smart tourism service platform using artificial intelligence recognition technology and automatic program design. This paper proposes functions such as online ticket booking through the design of intelligent access control in scenic spots (combined with intelligent voice, face recognition, and other technologies), scenic miniprograms, and WeChat public accounts. Tourists can collect tickets automatically through the intelligent assistant designed by the intelligent service system. This allows tourists to consult and interact with intelligent machine assistants, reducing human-to-human contact during travel, thereby reducing the probability of epidemic infection (mainly spread by coughing or sneezing, close contact with sick people, touching contaminated surfaces, etc.). Finally, the smart tourism service platform is promoted, and an online questionnaire survey is conducted on 2,000 tourists who use the service platform to understand the actual use of the system. The results of the questionnaire survey showed that among the 2,000 tourists’ evaluations, 55.15% were satisfied with the system, 41.5% were very satisfied, 3.1% felt average, 0.25% felt dissatisfied, and 0% were very dissatisfied. At the same time, a large number of positive evaluation words were received in the feedback results of tourists, which shows that the smart service system designed in this paper is very popular with tourists. Meanwhile, a great deal of positive evaluation words were received in the feedback results of tourists (such as full of technology, special convenience, and special safety). This shows that the intelligent service system designed in this paper is very popular with tourists.
Variational Rician Noise Removal via Splitting on Spheres

We propose a novel variational method for Rician noise removal in magnitude-based magnetic resonance (MR) imaging. We first explore the link between the Gaussian noise removal for complex images and the Rician noise removal for magnitude images. Then we establish the constraint optimization model via signal-noise splitting, consisting of a total variation regularizer, two quadratic terms, and a constraint on the field of spheres. Specifically, this constraint represents the forward model of calculating the magnitude of complex images corrupted by Gaussian noises. Namely, the proposed model is completely different from the existing maximum a posteriori based methods, which inevitably involved the sophisticated Bessel function causing high computation costs. It is further efficiently solved by the alternating direction method of multipliers with convergence guarantee. Numerical comparisons with existing variational methods show that the proposed method produces comparable results in terms of image quality, but saves about 50% of overall computational cost on average.