Reviewerly/exHarmony huggingface dataset:
A Deep Ordinal Distortion Estimation Approach for Distortion Rectification

Radial distortion has widely existed in the images captured by popular wide-angle cameras and fisheye cameras. Despite the long history of distortion rectification, accurately estimating the distortion parameters from a single distorted image is still challenging. The main reason is that these parameters are implicit to image features, influencing the networks to learn the distortion information fully. In this work, we propose a novel distortion rectification approach that can obtain more accurate parameters with higher efficiency. Our key insight is that distortion rectification can be cast as a problem of learning an ordinal distortion from a single distorted image. To solve this problem, we design a local-global associated estimation network that learns the ordinal distortion to approximate the realistic distortion distribution. In contrast to the implicit distortion parameters, the proposed ordinal distortion has a more explicit relationship with image features, and significantly boosts the distortion perception of neural networks. Considering the redundancy of distortion information, our approach only uses a patch of the distorted image for the ordinal distortion estimation, showing promising applications in efficient distortion rectification. In the distortion rectification field, we are the first to unify the heterogeneous distortion parameters into a learning-friendly intermediate representation through ordinal distortion, bridging the gap between image feature and distortion rectification. The experimental results demonstrate that our approach outperforms the state-of-the-art methods by a significant margin, with approximately 23% improvement on the quantitative evaluation while displaying the best performance on visual appearance.
Evolutionary-based image encryption using biomolecules and non-coupled map lattice

Image encryption is an effective way of protecting multimedia data; it is done by transferring multimedia data into an undetectable format within the network context. In this paper, a new Chaotic Evolutionary Biomolecules Model (CEBM) based on the concepts of biological molecules (DNA and RNA) is presented for image encryption. The proposed model consists of four phases. In the first phase, a secret 256-bit key using the SHA-256 algorithm is assumed for the initial value of the non-coupled map lattice function. In the second phase, the Chaotic Rate operator is used for permutation. In the third phase, DNA XOR operators and RNA codons complement operator based on chaos are used for Diffusion. In the final phase, the steps taken 1, 2, and 3 are repeated by the number of the population of the early countries of Imperialist Competition Algorithm. The population is then optimized using imperialist competition algorithm operators. The results show the superiority of the proposed model over other models. In addition, the proposed model is highly resistant to common attacks and has an entropy value average of 7.99944.
Detection of Phishing Websites from URLs by using Classification Techniques on WEKA

The Internet is getting stronger day by day and it makes our lives easier with many applications that are executed on cyberworld. However, with the development of the internet, cyber-attacks have increased gradually and identity thefts have emerged. It is a type of fraud committed by intruders by using fake web pages to access people's private information such as userid, password, credit card number and bank account numbers, etc. These scammers can also send e-mail from many important institutions and organizations by using phishing attacks which imitate these web pages and acts as if they are original. Traditional security mechanisms can not prevent these attacks because they directly target the weakest part of connection: end-users. Machine learning technology has been used to detect and prevent this type of intrusions. The anti-phishing method has been developed by detecting the attacks made with the technologies used. In this paper, we combined the websites used by phishing attacks into a dataset, then we obtained some results using 4 classification algorithms with this dataset. The experimental results showed that the proposed systems give very good accuracy levels for the detection of these attacks.
Anomaly detection in multivariate streaming PMU data using density estimation technique in wide area monitoring system

In recent years, the advancement in technology and the availability of a large number of sensors has led to a huge amount of real-time streaming data. The real-time data measured by Phasor Measurement Units (PMU), are of streaming data type and their analysis can identify the status of the power system failure which leads to system instability. Hence online monitoring of PMU data is extremely important which includes preprocessing, filtering, and analysis before taking any corrective actions in the power system. One of the crucial analyses that can be performed over PMU data is the identification of anomalies that arise due to several factors. Early detection of anomalies in the PMU streaming data is highly useful though in real-time it is difficult to detect anomalies consistently. This paper proposes a novel framework for detecting anomalies in multivariate streaming PMU data based on the Density Estimation technique. The main novelty of this paper is that the proposed model is suitable for any kind of anomaly detection in multivariate streaming PMU data which is not addressed in literatures. The proposed system implements Principal Component Analysis in streaming real-time data to select the relevant features. The K-Means algorithm is performed over the selected feature vectors to find the number of clusters in the streaming PMU data. The Gaussian Mixture Model (GMM) is developed to represent the normal PMU data of the system. The minimum and maximum value of the probability of the training samples being generated by the model is selected as a minimum and maximum threshold. The samples with probability less than the minimum threshold or greater than the maximum threshold are considered as anomalies. The main advantage of this proposed model is that it can perform anomaly detection in multivariate streaming PMU data even online within the specified window. The proposed system can identify real time anomalies of multivariate dataset even by offline training model of GMM. The proposed model is developed in MATLAB 2018b version and is tested in streaming PMU data both for offline and online datasets. The performance of the proposed model is validated using performance metrics and the results show that F1 score is 0.95 for 2 features, 0.91 for 3 features, and 0.99 for 8, 12 and 16 features and 1 for 10 features in offline mode. The performance evaluation of testing data in online mode for 2, 3, 8, 10, 12 and 16 features are obtained which proves that the system is adapting to the streaming PMU data with reduced false positive rate and F1 score of 1 for 8, 10, 12 and 16 features. The experimental results prove that the proposed framework for anomaly detection in streaming data is more effective and accurate in identifying the anomalies in real time PMU streamed dataset.
IoT based Smart Shoe for the Blind

IoT based Smart shoe system for the blind is a system made with the help of ultrasonic sensors paired to an Arduino UNO board. Internet of things is all about making physical objects communicate with other objects or even with humans. It is an enabling technology which has a rapid development and growth in the market. In our India there are almost 40 million blind people among which 1.6 million are children. Blind people face great difficulty to travel independently. They have to depend on others in many aspects of their life. The Major problem is when they walk on the road. With a stick in hand they cannot detect every obstacle that comes in their way. The Smart shoe design provides a longterm solution for the blind to walk on roads independently. The smart shoe will help the Blind person to reach his destination independently. It is built using IoT Technology in which the shoe will be embedded with various sensors, Microcontroller and buzzers. The shoe warns the user by making noise with the buzzer when he/she walks in front of an obstacle. To improve the efficiency, Smart glasses are designed using IoT which is also embedded with sensors and helps in detecting the objects by covering a larger area. The smart shoe and the smart glasses communicate and coordinate with each other to ensure that the user does not collide with any obstacle in his way.
Stomach 3D Reconstruction Using Virtual Chromoendoscopic Images

Gastric endoscopy is a golden standard in the clinical process that enables medical practitioners to diagnose various lesions inside a patient's stomach. If a lesion is found, a success in identifying the location of the found lesion relative to the global view of the stomach will lead to better decision making for the next clinical treatment. Our previous research showed that the lesion localization could be achieved by reconstructing the whole stomach shape from chromoendoscopic indigo carmine (IC) dye-sprayed images using a structure-from-motion (SfM) pipeline. However, spraying the IC dye to the whole stomach requires additional time, which is not desirable for both patients and practitioners. Our objective is to propose an alternative way to achieve whole stomach 3D reconstruction without the need of the IC dye. We generate virtual IC-sprayed (VIC) images based on image-to-image style translation trained on unpaired real no-IC and IC-sprayed images, where we have investigated the effect of input and output color channel selection for generating the VIC images. We validate our reconstruction results by comparing them with the results using real IC-sprayed images and confirm that the obtained stomach 3D structures are comparable to each other. We also propose a local reconstruction technique to obtain a more detailed surface and texture around an interesting region. The proposed method achieves the whole stomach reconstruction without the need of real IC dye using SfM. We have found that translating no-IC green-channel images to IC-sprayed red-channel images gives the best SfM reconstruction result. Clinical impact We offer a method of the frame localization and local 3D reconstruction of a found gastric lesion using standard endoscopy images, leading to better clinical decision.
A New Recurrent Neural Network Fuzzy Mean Square Clustering Method

Fuzzy mean square clustering is one of the simplest and most performant versions of the k-means non-hierarchical clustering methods. In this work, we extend and improve this method by a recurrent neural network, leading to a new clustering method called Recurrent Neural Network Fuzzy Mean Square. In this approach the fuzzy mean square error is modeled by a constrained non-linear optimization program. The latter is solved by a recurrent neural network in which an original energy function is defined. The energy function makes a compromise between the objective function and the constraints by using appropriate Lagrange relaxation scales. The Euler-Cauchy method is then used to calculate the centers and the membership functions. Simulation results on academic datasets show the effectiveness of the proposed method.
Photovoltaic Nowcasting With Bi-Level Spatio-Temporal Analysis Incorporating Sky Images

Today many large-scale photovoltaic (PV) plants have been equipped with sky imaging systems. The sky images contain abundant spatio-temporal information of the local climate condition at the PV plant. Meanwhile, power outputs of the PV systems located at different sites in a certain geographical area also exhibit spatio-temporal correlation. It is of interest to improve the accuracy of PV nowcasting by fully utilizing spatio-temporal information embedded in both sky images and PV output measurements of distributed PV systems. In this article, we incorporate the above two aspects into a unified framework and propose the Bi-level spatio-temporal (BILST) PV nowcasting model. The proposed model learns features from local spatio-temporal information embedded in sky images, global spatio-temporal correlations embedded in PV output datasets of a number of distributed PV systems and weather characteristics embedded in exogenous dataset simultaneously. Then the obtained three types of hidden features are aggregated and applied to predict the PV output at the PV site of interest. Experiments using real-world datasets show that the proposed BILST model can enable sky images to contribute to the PV nowcasting task and achieve the desirable accuracy of PV nowcasting consistently.
PA-Net: Learning local features using by pose attention for short-term person re-identification

Person re-identification (Re-ID) is an important but challenging task in video surveillance applications. In Re-ID tasks, pose is an extremely useful cue to identify a person, even from the back view. Therefore, pose-detection models may learn the features that are beneficial to the Re-ID task and improve the Re-ID performance by fusing the feature maps into the Re-ID model. Two key problems in integrating the pose cues are addressed in this study. One is how to reduce the noise caused by cross-domain datasets. The other is how to fuse the feature maps to better utilize high-level semantic pose cues. To address these two key problems, we first propose PA-Net by combining the pose attention stream and the global attention stream, where the global attention stream distinguishes persons with different global appearances, and the pose attention stream distinguishes persons with similar global appearance but different poses. Then, we present a pose attention stream that learns local features to reduce the noise in the pose cues caused by the cross-domain datasets and provide more semantic information for the Re-ID task. The effects of the proposed pose attention are demonstrated in an ablation study, and comparative experiments show that PA-Net achieves state-of-the-art performance.
Empowerment as a Pivotal Deterrent to Employee Silence: Evidence from the UAE Hotel Sector

Based on time-lagged data focusing on frontline employees (N=285) operating in five-star hotels in the United Arab Emirates (UAE), we examined the individual and combined effects of empowerment, job satisfaction, and perceived organizational support on employee silence. Using structural equation modeling for data analysis, we explored a hypothetical model. The results indicated that empowerment had a significant negative impact on employee silence while job satisfaction and perceived organizational support had no significant direct impact. However, these latter two variables had an impact when feelings of empowerment were also present. The result suggests indicates that empowerment plays a central (pivotal) role in silence behavior. The implications of these findings for both management practice and future research are discussed.
Improved End-to-End Dysarthric Speech Recognition via Meta-learning Based Model Re-initialization

Dysarthric speech recognition is a challenging task as dysarthric data is limited and its acoustics deviate significantly from normal speech. Model-based speaker adaptation is a promising method by using the limited dysarthric speech to fine-tune a base model that has been pre-trained from large amounts of normal speech to obtain speaker-dependent models. However, statistic distribution mismatches between the normal and dysarthric speech data limit the adaptation performance of the base model. To address this problem, we propose to re-initialize the base model via meta-learning to obtain a better model initialization. Specifically, we focus on end-to-end models and extend the model-agnostic meta learning (MAML) and Reptile algorithms to meta update the base model by repeatedly simulating adaptation to different dysarthric speakers. As a result, the re-initialized model acquires dysarthric speech knowledge and learns how to perform fast adaptation to unseen dysarthric speakers with improved performance. Experimental results on UASpeech dataset show that the best model with proposed methods achieves 54.2% and 7.6% relative word error rate reduction compared with the base model without finetuning and the model directly fine-tuned from the base model, respectively, and it is comparable with the state-of-the-art hybrid DNN-HMM model.
Properties and interrelationships of skeptical, weakly skeptical, and credulous inference induced by classes of minimal models

There are multiple ways of defining nonmonotonic inference relations based on a conditional knowledge base. While the axiomatic system P is an important standard for such plausible nonmonotonic reasoning, inference relations obtained from system Z or from c-representations have been designed which go beyond system P by selecting preferred models for inference. For any class of models M, we propose the notion of weakly skeptical inference, first introduced in an ECAI conference paper this article revises and extends, that lies between skeptical and credulous inference with respect to M. Weakly skeptical c-inference properly extends skeptical c-inference, but avoids disadvantages of a too liberal credulous c-inference. We extend the concepts of skeptical, weakly skeptical, and credulous c-inference modes by taking models obtained from different minimality criteria into account. We illustrate the usefulness of the obtained inference relations and show that they fulfill various desirable properties put forward for nonmonotonic reasoning. Furthermore, we elaborate in detail the interrelationships among the inference relations when taking the different inference modes and various classes of minimal models into account.
AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting

We address the problem of image-based crowd counting. In particular, we propose a new problem called <i>unlabeled scene-adaptive crowd counting</i>. Given a new target scene, we would like to have a crowd counting model specifically adapted to this particular scene based on the target data that capture some information about the new scene. In this paper, we propose to use one or more unlabeled images from the target scene to perform the adaptation. In comparison with the existing problem setups (e.g. fully supervised), our proposed problem setup is closer to the real-world applications of crowd counting systems. We introduce a novel <monospace>AdaCrowd</monospace> framework to solve this problem. Our framework consists of a crowd counting network and a guiding network. The guiding network predicts some parameters in the crowd counting network based on the unlabeled images from a particular scene. This allows our model to adapt to different target scenes. The experimental results on several challenging benchmark datasets demonstrate the effectiveness of our proposed approach compared with other alternative methods. Code is available at <uri>https://github.com/maheshkkumar/adacrowd</uri>
Phase-tilt iteration: Accurate and robust phase extraction from random tilt-shift interferograms

An advanced phase-tilt iteration method is proposed for accurate and robust phase extraction from the interferograms with random tilt shift. The method constructs a system of linear equations about the tilt parameters via the spatial vector transform-based decoupling, and then establishes a least-squares alternate optimization. Using the two steps of initial estimation and fine iteration in the orthogonal one-dimensional interference intensity, the phase distribution is extracted with high accuracy and robustness. The simulations and experiments verify the high performance of the proposed method. It is promised to provide a high-accuracy and low-cost solution for temporal phase-shifting interferometers in the vibrating environment without modifying the optical hardware.
An inductive construction of minimal codes

We provide new families of minimal codes in any characteristic, useful for the construction of secret sharing schemes. Also, an inductive construction of minimal codes is presented.
A kind of improved quantum key distribution scheme

In this paper, the problem on quantum key distribution (QKD) is discussed. Firstly, based on the framework of BB84 protocol and Heisenberg's uncertainty principle, a kind of QKD scheme is proposed; then, theoretical analysis is conducted to justify the QKD scheme; finally, some examples have been included, and the results show that the given QKD scheme not only inherits the simple and practical property from BB84, but also effectively reduces the resource loss, and possesses the better performance of monitoring eavesdropping.
A decentralized dynamic PKI based on blockchain

The central role of the certificate authority (CA) in traditional public key infrastructure (PKI) makes it fragile and prone to compromises and operational failures. Maintaining CAs and revocation lists is demanding especially in loosely-connected and large systems. Log-based PKIs have been proposed as a remedy but they do not solve the problem effectively. We provide a general model and a solution for decentralized and dynamic PKI based on a blockchain and web of trust model where the traditional CA and digital certificates are removed and instead, everything is registered on the blockchain. Registration, revocation, and update of public keys are based on a consensus mechanism between a certain number of entities that are already part of the system. Any node which is part of the system can be an auditor and initiate the revocation procedure once it finds out malicious activities. Revocation lists are no longer required as any node can efficiently verify the public keys through witnesses included in the blockchain.