Reviewerly/exHarmony huggingface dataset:
Probabilistic Analysis of Solar Power Supply Using D-Vine Copulas Based on Meteorological Variables

Solar power generation at solar plants is a strongly fluctuating non-deterministic variable depending on many influencing factors. In general, it is not clear which and how certain variables influence solar power supply at feed-in points in a distribution network. Therefore, analyzing the dependence structure of measured solar power supply and other variables is very informative and can be helpful in designing probabilistic prediction models. In this paper multivariate D-vine copulas are fitted to investigate the relationship between solar power supply and certain meteorological variables in the current time period of one hour length as well as solar power supply in previous time periods. The meteorological variables considered in this analysis are global horizontal irradiation, temperature, wind speed, humidity, precipitation and pressure. By applying parametric D-vine copulas useful insight is gained into the dependence structure of solar power supply and the considered meteorological variables. The main goal lies in determining suitable explanatory variables for the design of probabilistic prediction models for solar power supply at single feed-in points and analyzing their impact on the validation of conditional level-crossing probabilities.
Trajectory tracking control for autonomous parking using reduced-horizon model predictive control

In this paper, a reduced-horizon model predictive control strategy is proposed for autonomous parking. Given the planned trajectory, the linear time varying model is obtained by discretizing and linearizing along the reference trajectory. Then the linear time varying model is represented in an incremental form. A standard quadratic programming problem is formulated which can be solved online. The joint simulation in Simulink and CarSim in three parking scenarios shows the effectiveness of the proposed method.
Recycling of Adversarial Attacks on the DNN of Autonomous Cars

There are several DNN-driven autonomous cars being developed in the world. However, despite their splendid progress, DNNs frequently demonstrate incorrect behaviors which can lead to fatal damages. For example, an adversarial example generated by adding a small perturbation to an image causes a misclassification of the DNN. Numerous techniques have been studied so far in order to research those adversarial examples and the results are remarkable. However, the results are not good on the huge and complex ImageNet dataset. In this paper, we propose the recycling of adversarial attacks, which shows a high success rate of the ImageNet attack. Our method is highly successful and relatively fast by recycling adversarial examples which failed once. We also compare our method with the state-of-the-art techniques and prove that our method is more effective to generate adversarial examples of the ImageNet dataset through experiments.
A One-Page Text Entry Method Optimized for Rectangle Smartwatches

In this paper, we provide the design and implementation of UOIT, a text entry method optimized for smartwatches. UOIT uses only one page where a user can see and tap directly for entry without any additional actions, such as zoom-in/zoom-out and swipes, which are required in the existing entry methods. To fully utilize the constrained screen space and to address the “fat finger” problem, we use a technique called “drawing-like typing”, which reduces the 26 small alphabetic keys into 13 large keys with a dual input property. To evaluate the performance of UOIT, we conducted two user studies while varying the learning period. In the short-term experiments (i.e., two days), we observed a fast learning curve of users when using the UOIT keyboard. Moreover, with the long-term experiments (i.e., a month), we show that users can type as fast as QWERTY keyboard but with much less errors. Moreover, UOIT outperforms the state-of-the-art keyboard in both speed and error rate.
An Adaptive Image Contrast Enhancement Algorithm Based on Retinex

Image contrast enhancement is an essential operation for various image processing applications, especially for segmentation and feature extraction. However, different contrast enhancement methods contain various performance on the same images. To automatically choose the suitable algorithm for image contrast enhancement, in this paper, we put forward a different Retinex based method according to image feature fusion. First, five features, including gray value, contrast, definition, edge and entropy, are defined to characterize various image. Then, five fusion rules are designed to distinguish images with low contrast, foggy, monotonous color, rich in detail or color. Finally, the single scale Retinex (SSR), the multi-scale Retinex (MSR) and MSR in HSV color space (MSR_HSV) are introduced to improve image contrast. Experiments on various images drawn from different data sets prove the effectiveness of our proposed method.
Block Mobilenet: Align Large-Pose Faces with &lt;1MB Model Size

3D face alignment methods based on deep models have become very popular due to their empirical success. However, high time and space complexities make these methods difficult to be applied to mobile devices and embedded devices. To decrease the time and space complexity, we propose a novel Depthwise Separable Block (DSB) which consists of a depthwise block and a pointwise block. The depthwise block is constructed by stacking depthwise convolution layers and concatenating the low layer, and the pointwise block has only pointwise convolution layers stacking together. Moreover, we develop a light-weight Block-Mobilenet by using our DSBs to reconstruct Mobilenet. It is worth noting that our Block-Mobilenet successfully reduces network parameters from MB to KB. Experiments on four popular datasets verify that Block Mobilenet has better overall performance (mean NME on 68 points: 3.81%; speed on CPU: 91 FPS; storage size: 876 KB) than the state-of-the-art methods.
Beyond MAGIC: Matching Collaborative Gestures using an optimization-based Approach

Gestures are a key aspect of communication during collaboration: through gestures we can express ideas, inquires and formalize instructions as we collaborate. Nevertheless, gesture analysis is not currently used to assess quality of task collaboration. One possible reason for this is that there is no consensus on how to represent and compare gestures from the semantic standpoint. To address this, this paper introduces three novel approaches to compare gestures performed by individuals as they collaborate to complete a physical task. Our approach relies on solving three variations of an integer optimization assignment problem, i.e. based on gesture similarity, based on temporal synchrony, and based on a combination of both. We collected the gestures of 40 participants (divided into 20 pairs) as they performed two collaborative tasks, and generated a human baseline that compared and matched their gestures. Afterwards, our gesture comparison approach was evaluated against other gestures comparison approaches based on how well they replicated the human baseline. Our approach outperformed the other approaches, agreeing with the human baseline over 85% of the times. Thus, the obtained results support the proposed technique for gesture comparison. This in turn can lead to the development of better methods to evaluate collaborative physical tasks.
Detecting Hierarchical Changes in Latent Variable Models

This paper addresses the issue of detecting hierarchical changes in latent variable models (HCDL) from data streams. There are three different levels of changes for latent variable models: 1) the first level is the change in data distribution for fixed latent variables, 2) the second one is that in the distribution over latent variables, and 3) the third one is that in the number of latent variables. It is important to detect these changes because we can analyze the causes of changes by identifying which level a change comes from (change interpretability). This paper proposes an information-theoretic framework for detecting changes of the three levels in a hierarchical way. The key idea to realize it is to employ the MDL (minimum description length) change statistics for measuring the degree of change, in combination with DNML (decomposed normalized maximum likelihood) code-length calculation. We give a theoretical basis for making reliable alarms for changes. Focusing on stochastic block models, we employ synthetic and benchmark datasets to empirically demonstrate the effectiveness of our framework in terms of change interpretability as well as change detection.
GAN Generated Portraits Detection Using Modified VGG-16 and EfficientNet

Generative Adversarial Networks can generate deceptive portraits of people who do not exist. The misuse of this technology leads to severe security issues such as fake identities and credentials. Since 2017, many works on Deep Learning have focused on detecting GAN synthesized images to prevent the threat against credibility in media. In this work, the authors propose a lightweight VGG-like model to detect state-of-the-art StyleGAN generated portraits. The authors also adopt EfficientNet-B0 to train a classifier on the same StyleGAN architecture. The VGG-like model and EfficientNet-based model achieve 98.9% and 100%, respectively, on the StyleGAN dataset published by Nvidia in 2019. Both models show the potential in generalizing to other GAN architectures and synthetic technologies.
On the Security of Off-the-Shelf Microcontrollers: Hardware Is Not Enough

We complete the state-of-the-art on the side-channel security of real-world devices by analysing two 32-bit microcontrollers equipped with an unprotected co-processor. Our results show that (i) the lack of understanding of their hardware architecture can be circumvented with standard detection tools – for this purpose, we combine a simple variation of the Test Vector Leakage Assessment methodology with Signal-to-Noise Ratio estimations, which enables the efficient identification of attack vectors; (ii) standard distinguishers then lead to powerful key recoveries with less than 5,000 traces; and (iii) preprocessing like the continuous wavelet transform can be useful in such a black box evaluation context.
Enhanced LSTM: a Text Matching Aggregation Model

Text matching is widely used in various natural language tasks, such as Knowledge Inference, Search Engine and Question Answering System. In the matching aggregation algorithm, previous researches focused on the design of various complex attentions to improve the performance ability of the model on the task, but there were few researches on the sequence representation of text and the fusion of interactive information. Based on this, we propose an enhanced LSTM text matching aggregation model. First, the word vector is pre-trained by Fasttext method to solve the OOV(out of vocabulary) problem. Then an enhanced LSTM is used to encode the sentences to better represent the sequence information of the sentence pairs. Next, we extract their local interaction information by soft attention. Besides, we design a new network model combining bidirectional LSTM(BiLSTM) with Multi-window CNN(MCNN) to integrate interactive information. Finally, based on the integrated vector, a decision is made through a fully connected layer. We evaluate our model on Quora Question Pairs set and our accuracy is 88.09%, which is improved by 0.6% compared with the previous ESIM(enhanced sequential inference model) model. Besides, unlike the previous top models that use very complicated network architectures, our model is more efficient.
Noise Level Estimation Based on Block SVD

Noise level estimation is crucial in many image processing applications, such as blind image denoising. In this paper, a block SVD based noise level estimation method is proposed, which aims to reduce the computation complexity of the existing SVD domain algorithm and ensure the accuracy and stability of the estimate results at the same time. Extensive experimental results show that the proposed method can reliably estimate the noise level for a variety of image types, and according to simulation results, the new algorithm can reduce the computation complexity greatly in comparison with the existing method in SVD domain.
Simulation Research on Hydraulic Loading System of Wind Turbine Based on Particle Swarm PID Control

As the key part of the wind turbine transmission chain to bear the load of the wind turbine, the quality of its working characteristics directly affects the basis of important performance such as the safety, stability and reliability of the system. This paper studies the hydraulic loading system of wind turbines. The mathematical model of the valve-controlled hydraulic loading system was established and simulated in MATLAB. The PID control method and the fuzzy PID control method based on particle swarm algorithm are selected to model and simulate the closed-loop control system of the hydraulic loading system. Through comparison, it is concluded that the hydraulic loading system based on PSO PID control has the advantages of fast response speed and stable system.
Eye Tracking Methodology for Diagnosing Neurological Diseases: A Survey

As the global aging and climbing rate of aging diseases causing more and more attention to elderly illness, a systematic review explores the part eye-tracking plays in reflecting cognitive status and being an indicator and assistive tool for diagnosis of neurological diseases. Neuropsychological tests are commonly seen in current clinical diagnostic process, while massive labor and equipment support are a must. To look for alternatives, various eye movement parameters have been investigated covering smooth saccade pursuit, inhibitory gaze, fixation of duration, pupil diameter, saccade length, scan path and ROI using diverse visual stimuli. Nonetheless, existing limitations in studies are presented. Overall, current research validated that eye movements reflect cognitive impairment based on different distinct manifestation and provide a contact-free and efficient methodology for pre-diagnostic domain.
Synthesizing safe policies under probabilistic constraints with reinforcement learning and Bayesian model checking

We propose to leverage epistemic uncertainty about constraint satisfaction of a reinforcement learner in safety critical domains. We introduce a framework for specification of requirements for reinforcement learners in constrained settings, including confidence about results. We show that an agent's confidence in constraint satisfaction provides a useful signal for balancing optimization and safety in the learning process.
A Temperature Identification Method Based on Chromaticity Statistical Features of Raw Format Visible Image and K-nearest Neighbor Algorithm

Temperature monitoring is important to ensure the safe operation of power grid. The fault temperature is generally in the normal range; therefore, infrared detection is generally used. In this paper, the chromaticity information of raw format visible images of aluminum plate is studied. First, establish image library of aluminum plate at different temperatures, extract gray values of R, G, and B components of images according to the pixel arrangement of filter, and calculate gray frequency to obtain the gray frequency distribution. Then the statistical features of the gray frequency distribution are calculated and selected by Fisher discrimination. Finally, the selected features are combined into input feature vector, and the KNN algorithm is used for temperature identification. The results show that the accuracy of temperature prediction model is about 1.1 °C. The above results provide a new technical route for detecting normal temperature using visible image information.
Short-term Photovoltaic Power Prediction based on Sparse Representation Method

Abstract In the research of solar power prediction, providing accurate prediction data in real time is one of the most effective means to enhance the capacity of wind power acceptance and improve the power reliability and economy. The existing prediction models based on statistical methods are often unavoidable in data preprocessing and model training stage, and their adaptive ability needs to be improved. Considering that the sparse coding method does not require model training, and has the characteristics of high solving efficiency and strong self-adaptability, an online solar energy prediction model using sparse coding is proposed.Firstly, the historical time series data is composed of input-output pairs with delay, and the dictionary is respectively constructed in atomic form. Then, the sparse weight is calculated for the delay input data vector to be predicted, and the corresponding predicted output is obtained by borrowing the dictionary. Taking the actual solar power data of Alberta, Canada as sample, the simulation was carried out in MATLAB. The simulation results show that the model can accurately predict the solar power and improve the effectiveness and practicability of the prediction
The Revival of Thoreauvian Resistance

Recent years have witnessed a revival of interest in Thoreau’s views of political resistance. Eager to transcend liberal (and especially Rawlsian) ideas of civil disobedience, scholars of political obligation are tapping Thoreau to justify a broader understanding of legitimate lawbreaking than Rawls and many others countenanced. Despite some strengths, the Thoreauvian revival among theorists of political obligation relies on a selective interpretation of its progenitor, sometimes anachronistically transforming him into a defender of a strict duty to political resistance as a path to radical change. Others now following Thoreau reproduce his ambivalent views of democratic politics and far-reaching antilegalism, both of which impede a proper understanding of politically motivated lawbreaking as a potentially legitimate mode of democratic political action. This article briefly considers the possibility that Thoreau can nonetheless help us understand modern individual whistleblowing: some of its key features seem to mesh with his ideas.
The Study of Chinese Calligraphy Font Style Based on Edge-Guided Filter and Convolutional Neural Network

Chinese calligraphy is a unique visual art and one of the material bases of Chinese traditional cultural heritage. However, due to the passage of time, ancient calligraphy works have been weathering and damages, so it is necessary to utilize advanced technologies to protect those works. The image features are not only used to represent the image content or structure but also used to express the style of images. In our work, we propose an extraction method to obtain traditional Chinese calligraphy (TCC) features of five major styles by fuzzy threshold segmentation and edge-guided filter, and then discriminate TCC styles using a convolutional neural networks (CNNs) of deep learning framework. At the same time, we use cross-validation to make the training results more convincing, and after considering the number of convolutional layers, training set, and training time, we chose VGGNet and ResNet as classifiers. Ultimately, we can achieve high-accuracy classify within less running time. And the method that we propose can also be applied to other image style classification problems.
A Comparative Analysis of Potential Factors and Impacts that Affect Blockchain Technology in Software: Based Applications

A new technology that considerably provides help in case of application of distributed ledger system by use of decentralized transactional databases is introduced as Blockchain. The functions of Blockchain can be categorized into three phases: Firstly, it emphasizes on decentralization i.e. no centralized body can dictate on other blocks. Secondly, it emphasizes on transparency i.e. easy validated transactions take place through it permits verifiable transparency in case of each mechanism. Finally, it has an immutable feature which never permits tampering of database/data as any change in data may affect or agitate the entire data-chain. For the purpose of security, the immutable pillar is basically used in this technology. In Blockchain technology, generally the peer to peer network is applied that allows new block to join in the chain without affecting all other blocks in the system. It is a well accepted view or proposition that for obtaining block chain of a high quality some basic attributes like security, privacy, input, size and bandwidth, usability, performance, scalability and data integrity are highly essential. Block chain based applications across multiple domains or in other words across diverse sectors such as Medical care, Agriculture, Social medias, Banking sector, Education, Digital voting, Travelling, Crypto-currency, Information Technology and Data management along with the limitations of this technology-a-viz a systematic literature review of Blockchain enabled applications is altogether an interesting field of analysis as well as a predominant area of research for scholars and software industries.
Secure Data Transmission in BPEL (Business Process Execution Language)

In the world of computation, the encryption is a technique by which the plaintext or any type of data which is converted from the readable form is transformed into an encoded form. That encoded form can only be read by another entity if they have corrected key for decryption. The proposed technique providing the security to the data in inefficient way that can be further use in implementation in new upcoming task and enhancement in current running projects of SOA (Service Oriented Architecture) BPEL (Business Process Execution Language).
Research on Users' Trust of Chatbots Driven by AI: An Empirical Analysis Based on System Factors and User Characteristics

AI chatbots have been widely used in e-commerce, which can improve service efficiency and reduce labor cost markedly. However, consumers' evaluation of intelligent customer service is mixed. This paper explores the impact of system factors and user characteristics on service perception and users' trust. Based on the social presence theory, this study constructs the trust research model of AI chatbots. The main results show that perceived personalization, perceived media richness and past usage experience positively influence social presence, cognitive reactance has a negative impact on social presence, social presence has a positive impact on users' trust. By investigating the features influencing trust, this study provides relevant technical improvement and marketing suggestions for managers.
3D Bone Shape Reconstruction from 2D X-ray Images Using MED Generative Adversarial Network

Three-dimensional (3D) objects are always better than two-dimensional (2D) images for visualization. Very delicate information can be detected in 3D objects compared to 2D objects. The human brain can easily infer 3D information by observing 2D objects. But retrieving 3D information from a 2D object is very challenging for machines. For this reason, 3D shape reconstruction is one of the most researched topics in computer vision, deep learning, and medical science. 3D bone shapes are needed for pre-operative surgery planning and visualization. But the conventional methods of generating high-quality 3D bone-shape from 2D images are very time-consuming. This paper proposes a framework of Generative Adversarial Network Medical Generative Adversarial Network (MED-GAN) to generate high-quality 3D bone-shape from 2D images. It generates 3D bone-shape using recent advances in convolution networks and generative adversarial networks. X-ray images are fed to the convolution network which is then converted to a D-dimensional vector by the convolution network. D-dimensional vector is fed to the GAN to reconstruct 3D bone shape.