Reviewerly/exHarmony huggingface dataset:
Secured E-voting System Using Two-factor Biometric Authentication

Building a secure voting system that offers privacy of conventional voting system with proper voter authentication & transparency has been a challenge for a due course of time. The research work proposes a secured and robust electronic voting system based on popular machine learning based facial recognition algorithms and biometric authentication methodologies for the purpose of building a secure voting system. In particular, it focuses on the potential working of face detection and recognition and bio-metric authentication namely bio-metric scan, and the implementation procedure, which improves the security and decreases the duplicate vote and fraudulent to make the system as more efficient and user friendly in nature.
Addressing Accuracy Paradox Using Enhanched Weighted Performance Metric in Machine Learning

Accuracy metric has become a gold standard for measuring various models and systems. However, the latest development and research have shown its limitations, especially during training/testing of the machine learning models. It fails to keep the integrity of the reliability of the outcome for unbalanced data modeling. This problem is known as Accuracy Paradox. This paper presents a unique approach utilizing the underlying standard metrics and creating internals 3D evaluation of fitness factors, a part of enhanced machine learning engine engineering. The model is trained under the internal tuning of parameters and trade-off is handled as a collateral benefit using built-in parallelism. Moreover, the way this metric is engineered, it opens up generalized options for data engineers and model creators to incorporate (mathematically) the metrics (standard or the new one) of their choice based on how the model is trained for any type of data domain. This approach shows the improved integrity of the internal metrics measurements using mathematical constructs and governing algorithm for enhanced Weighted Performance Metric. Included sample experiments on real-world datasets, and results support the contribution of this research.
A Time Convolutional Network Based Outlier Detection for Multidimensional Time Series in Cyber-Physical-Social Systems

With the development of the Cyber-Physical-Social Systems(CPSS), a large number of multidimensional time series have been generated in today's world, such as: sensor data for industrial equipment operation, vehicle driving data, and cloud server operation and maintenance data and so on.The key task of Cloud-Fog-Edge Computing in managing these systems is how to detect anomalous data in a specific time series to facilitate operator action to solve potential system problems.So multidimensional time series outlier detection become an important direction of CPSS data mining and Cloud-Fog-Edge Computing research, and it has a wide range of applications in industry, finance, medicine and other fields.This paper proposes a framework called Multidimensional time series Outlier detection based on a Time Convolutional Network AutoEncoder (MOTCN-AE), which can detect outliers in time series data, such as identifying equipment failures, dangerous driving behaviors of cars, etc.Specifically, this paper first uses a feature extraction method to transform the original time series into a feature-rich time series.Second, the proposed TCN-AE is used to reconstruct the feature-rich time series data, and the reconstruction error is used to calculate outlier scores.Finally, the MOTCN-AE framework is validated by multiple time series datasets to demonstrate its effectiveness in detecting time series outliers.
On Twitter Purge: A Retrospective Analysis of Suspended Users

Abuse and spam in Twitter have long been a pressing issue, and in response, Twitter regularly purges (i.e., suspends in mass) accounts that violate Twitter Rules. However, there is no available information about the characteristics and activities of these regularly purged users. We have developed a novel and comprehensive measurement mechanism to identify millions of purged Twitter users and collect their tweets. We have identified 2.4M purged users and collected 1M tweets made by them over eight months. Using our dataset, we perform a retrospective analysis to characterize their account properties and behavioral activities. We analyze their tweet content to identify their role and abuse strategy over-time.
Fault diagnosis of rotating machinery components using a deep kernel extreme learning machine under different working conditions

Deep-learning-based algorithms have shown great achievements in fault diagnosis of rotating machinery components in recent years. However, training deep networks is cumbersome and time-consuming. Furthermore, when working conditions change, their diagnostic performance degrades significantly. To address these problems, a novel deep kernel extreme learning machine (DK-ELM) is presented in this paper. First, we propose a discriminative manifold ELM auto-encoder (DM-ELM-AE), which exploits both the geometry of the training sample's marginal distribution and the label information. Then, multiple DM-ELM-AEs are stacked to form a deep feature extractor to extract discriminative and robust high-level features from vibration measurements automatically. Based on the extracted features, final fault pattern classification is carried out using a kernel ELM instead of a conventional ELM classifier. In this way, good diagnostic performance can be attained, regardless of working conditions. The experimental results show that the DK-ELM yields more promising results than other state-of-the-art algorithms in terms of diagnosis accuracy and adaptation ability to working conditions. It achieved 2% improvement in testing accuracy compared with the kernel ELM, and nearly 5% improvement compared with classical deep learning algorithms under different working conditions. In addition, it inherits the superb training efficiency from the ELM and is much easier to implement than deep learning algorithms.
Utilising Low Complexity CNNs to Lift Non-Local Redundancies in Video Coding

Digital media is ubiquitous and produced in ever-growing quantities. This necessitates a constant evolution of compression techniques, especially for video, in order to maintain efficient storage and transmission. In this work, we aim at exploiting non-local redundancies in video data that remain difficult to erase for conventional video codecs. We design convolutional neural networks with a particular emphasis on low memory and computational footprint. The parameters of those networks are trained on the fly, at encoding time, to predict the residual signal from the decoded video signal. After the training process has converged, the parameters are compressed and signalled as part of the code of the underlying video codec. The method can be applied to any existing video codec to increase coding gains while its low computational footprint allows for an application under resource-constrained conditions. Building on top of High Efficiency Video Coding, we achieve coding gains similar to those of pretrained denoising CNNs while only requiring about 1% of their computational complexity. Through extensive experiments, we provide insights into the effectiveness of our network design decisions. In addition, we demonstrate that our algorithm delivers stable performance under conditions met in practical video compression: our algorithm performs without significant performance loss on very long random access segments (up to 256 frames) and with moderate performance drops can even be applied to single frames in high-resolution low delay settings.
In situ investigation and detection of opto-mechanical properties of polymeric fibres from their digital distorted microinterferograms using machine learning algorithms

During the dynamic investigation of opto-mechanical properties of the fibres, hundreds of microinterferograms that carry the fibres information may be produced. These microinterferograms may be classified into four main classes due to focus plane of CCD camera and the inclination of the fibre relative to the fringes. Each class of them has a specific analysis method according to its class. The manual classification for these microinterferograms is a complex process and needs a professionally expert in the field. So, the automatic classification and investigation for these classes are main goal of the present study. Hence, an experimental dataset, including 400 microinterferograms for the four classes is established. To perform this task, Mach–Zehnder interferometer with a stretched device is used. To automate the classification for the classes in our dataset, we fine-tuned a pre-trained AlexNet neural network. After training our dataset using this network, we obtained an 89.46% validation accuracy (VA). To increase the accuracy for the constructed network, two methods are proposed. The first method contains a combination between the AlexNet and the support vector machine and this network achieved an 94.11% VA. The second method includes a combination between the AlexNet and the K- Nearest Neighbor and an VA 96.64% is yielded. After the classification process, each microinterferogram is analyzed according to its class and the 2D refractive index of their fibres are investigated. Dynamic cold drawing and creep experiments are carried out for iPP fibres by considering AlexNet and the K- Nearest Neighbor network to give the relation between its refractive indices and strains.
Large-scale global optimization based on hybrid swarm intelligence algorithm

There are numerous large-scale global optimization problems encountered in real-world applications including engineering, manufacturing, economics, networking fields. Over the last two decades different varieties of swarm intelligence and nature inspired based evolutionary algorithms (EAs) were dev eloped and still. Among them, particles swarm optimization, Firefly algorithm, Ant colony optimization, Bat algorithm are the most popular and recently developed leading swarm intelligence based approaches. They are mainly inspired by the social and cooperative behaviors of swarm likewise herds of animals, flocking of birds, schooling of fish, ant colonies, herds of bisons and packs of wolves working together for their common benefit. Due to easy implementation and high capability in achieving of absolute optimum, swarm intelligence based algorithms have attained a great deal attention in both academic and industrial applications. This paper proposes a hybrid swarm intelligence (HSI) algorithm that employs the Bat Algorithm (BA) and the Practical Swarm Optimization (PSO) as constituents to perform their search process for dealing with recently designed benchmark functions in the special session of the 2017 IEEE congress of evolutionary computation (CEC'17) [3]. The approximate solutions for most of the CEC'17 benchmark functions obtained by the suggested algorithm in its twenty five independent runs of trails are much promising as compared to its competitors.
Equivalence of Classical and Quantum Electromagnetic Scattering in the Far-Field Regime

Quantum remote sensing, also known as quantum detection and ranging (QUDAR), is the use of entangled photon states to detect targets at a stand-off distance. It inherently relies on sending many single photons through free space, bouncing off of a target and returning to the sensor. It is therefore necessary to understand how single photons interact and scatter from targets of macroscopic size. This article relates quantum and classical scattering in the far-field regime. Specifically, we show that due to the photon's position uncertainty, the path over which the photon traverses is not well defined, and this causes quantum interference. The result of this interference exactly replicates classical scattering behavior of electromagnetic waves. We will show that one can exactly derive the classical electric field scattering integral using a purely quantum construction. Although this article focuses on the context of QUDAR, it is very general to any application involving far-field electromagnetic scattering. Finally, we delve into the QUDAR multiphoton quantum scattering advantage shown in previous literature and further develop the theory. Specifically, we provide explanations as to why this advantage has not been observed in the classical regime, as well as provide insight as to the experimental requirements necessary to achieve this cross-section enhancement.
FIID: Feature-Based Implicit Irregularity Detection Using Unsupervised Learning From IoT Data for Homecare of Elderly

Advances in wireless sensor networks and increasing Internet-of-Things devices give great opportunities for smart homecare of the elderly. Smart homecare has been a promising issue and received much attention recently. Irregularity detection is one of the most important issues in smart homecare for assessing the health condition of the elderly. However, most of the researches focused on the explicit irregularity detections which are usually based on the drastic changes of sensor data, such as falling. Existing mechanisms for detecting implicit irregularity rely on the subjective assessment of behaviors' importance by the elder and simply outputs the binary detection results. This article proposes a feature-based implicit irregularity detection mechanism (FIID), which extracts the regularity features using unsupervised learning and outputs the probability of implicit irregularity. The proposed FIID identifies the regular behaviors which satisfy the time-regular and happen-frequently properties as the regularity features of daily behaviors. These features then construct a multidimensional feature space to calculate the implicit irregularity probability of the daily health condition. Performance results show that the proposed FIID outperforms the existing implicit irregularity mechanism in terms of precision, recall as well as F-measure.
Data-Driven Maintenance: Combining Predictive Maintenance and Mixed Reality-supported Remote Assistance

Predictive Maintenance and Mixed Reality are two enabling technologies, which enable effective ways to support future maintenance work in factories. Predictive maintenance techniques are designed to help determine the condition of machines and machinery parts in order to estimate when maintenance should be performed. Mixed reality describes ways to merge digital (or virtual) information with real environments, where both worlds can interact in real-time. Both technologies show potential to support workers in industrial settings to accomplish their tasks more effectively. Especially the field of remote assistance (or remote support) where workers are visually guided through maintenance tasks via smartphones or smartglasses and mixed reality-supported information, are described to be disruptive. In this paper, we describe our concepts and ideas to integrate both technologies into future learning factories. Following this idea, predictive maintenance algorithms trigger a specific maintenance task and inform a technician. If the technician needs further support at the component in need of a maintenance check, she can trigger a remote support call with an expert. Both can make use of anchored annotations to discuss and solve the problem. We present our current research, as well as a design proposal to combine both technologies in future learning factories.
A regional soil and river sediment geochemical study in Baoshan area, Yunnan province, southwest China

This study aims to better understand, at a regional scale, the concentration and distribution of selected chemical elements in surface environments and the influence of natural factors (geological background, geomorphology) and anthropogenic factors (mining activities, land-use, agricultural fertilization) on their distribution in the Baoshan area, Yunnan province, southwest China. Mineral resources (such as copper, lead, zinc, mercury, gold, silver) and carbonate and basalt parent materials in this area often lead to elevated potentially toxic metals (PTMs), which are widely distributed where mining activities have rapidly developed since last 20–30 years. This study makes use of 1574 surface soil data and 425 deep soil data from the National Geochemistry Survey of Land Quality project (NGSLQ), and 1585 archived river sediment data which were collected and analyzed for the Regional Geochemistry-National Reconnaissance program (RGNR). These data, once log-ratio (clr) transformed have been studied by compositional data analysis (CoDA), principle components analysis (PCA) and spatial analysis. In the Baoshan area, the concentration of elements As, B, (Br), Co, Cr, Cu, Hg, (I), Mn, (N), Ni, Sb, (Sc), (Se), V, Fe2O3Total and (COrgonic) are enriched compared to the China Soil Geochemical Baselines program (CGB) and RGNR data sets; while elements Sr and Na2O are depleted compared to CGB and RGNR data sets. Influencing factors on surface soil, deep soil and river sediment composition of the study area are revealed. The distribution of most elements in surface soil, deep soil and river sediment are influenced by natural factors such that: Co, Cr, Cu, Ni, Sc, Ti, V, Fe2O3Total and MgO are controlled by the distribution of basic igneous rocks; Ca, Mg, Sr and pH are controlled by the distribution of carbonate rocks and basic igneous rocks; As, Hg, Sb, (Ag), Cd, Pb and Zn are controlled by mineralization; and other elements normally controlled by the distribution of clastic rocks and acid-intermediate rocks; while the organic matter such as Br, N, (P), S, COrgonic and CTotal in soil data set are controlled by geomorphic (altitude and temperature). Anthropogenic factors are also recognized in the soil data set. For example: high pH values in paddy land and grassland, and low pH values in forested land, garden plot and dry cultivated land; in agricultural area fertilization has caused enrichment of the nutrient elements (COrgonic, CTotal, N, P, etc.,) in surface soil; while in areas of mining, surface soils are contaminated by PTMs (incl. Hg, As, Sb, Cu, Pb, Zn and Cd).
Performance Comparisson Activity Recognition using Logistic Regression and Support Vector Machine

Daily activities has become leading problems in human physical analysis. Autonomous system as application in several area of human physical analysis was increase along with several machine learning methods. Fall detection, medical rehabilitation or other smart home application in physical analysis application has increase degree of life. Accelerometer and gyroscope was popular sensor for physical analysis. Several research was used these sensor with various position in human body part. Activities was separated in three class, static activity, transition activity, and dynamic activity. Basic activities has same pattern in each activity. From public HAR dataset, wich have three static activity (standing, sitting, and laying) each pattern has same shape and patterns. Dataset were used in this paper have acquire from 30 volunters. Seven basic machine learning alghoritm Logistic Regression, Support Vector Machine, Decission Tree, Random Forest, Gradien Boosted and K-Nearest Neighbor. The purposed method Logistic Regression achieves 98% accuracy same as SVM with linear kernel, with same result hyperparameter tuning for both method has same accuracy. Likewise for result where this main problems static activity was successful detected with logistic regression and SVM.
Guaranteed Recovery of One-Hidden-Layer Neural Networks via Cross Entropy

We study model recovery for data classification, where the training labels are generated from a one-hidden-layer neural network with sigmoid activations, also known as a single-layer feedforward network, and the goal is to recover the weights of the neural network. We consider two network models, the fully-connected network (FCN) and the non-overlapping convolutional neural network (CNN). We prove that with Gaussian inputs, the empirical risk based on cross entropy exhibits strong convexity and smoothness uniformly in a local neighborhood of the ground truth, as soon as the sample complexity is sufficiently large. This implies that if initialized in this neighborhood, gradient descent converges linearly to a critical point that is provably close to the ground truth. Furthermore, we show such an initialization can be obtained via the tensor method. This establishes the global convergence guarantee for empirical risk minimization using cross entropy via gradient descent for learning one-hidden-layer neural networks, at the near-optimal sample and computational complexity with respect to the network input dimension without unrealistic assumptions such as requiring a fresh set of samples at each iteration.
Bat-inspired algorithm for feature selection and white blood cell classification

We have already seen applications of nature-inspired algorithms to a wide variety of optimization problems. This chapter discusses another field of application which has not quite been explored to great depths yet. The problem of feature selection is approached by employing the bat algorithm on a medical dataset of white blood cells (WBCs). Feature selection, from a bird's eye view, is choosing an optimal subset of features from all features such that it does not hinder the classification accuracy. Since the differential count of WBCs is a key aspect for medical diagnostics and analysis, feature selection gives the advantage of faster processing and computing. The proposed methodology selects 11 features out of 35 features and has an accuracy of 97.69%. The chapter concludes with a detailed comparison with other recent nature-inspired algorithms, including the optimized crow search algorithm and the optimized cuttlefish algorithm.
Lattice-Based Privacy-Preserving and Forward-Secure Cloud Storage Public Auditing Scheme

Aiming at reducing the local storage burden and computational costs, numerous individuals and enterprises are willing to outsource their data to the cloud server. Meanwhile, due to the loss of the actual physical control over their data files once outsourced to the cloud server, how to guarantee the cloud server keep user's data integrity is an important security issue to be addressed urgently. Accordingly, multiple data integrity checking schemes based on the traditional cryptosystem have been proposed. However, with the advent and development of quantum computer, these existing data integrity checking schemes are no longer secure. Thus, it is necessary to study the new scheme which can resist quantum attack to adapt to the quantum era. In this work, we put forward a novel scheme named lattice-based privacy-preserving and forward-secure cloud storage public auditing scheme (LB-PPFS). Our proposed scheme is not only quantum-attack-against, but also enjoy the privacy-preserving and forward-secure property. In the proposed scheme, a curious auditor cannot learn any knowledge of user's data because the original data is encapsulated with a random number. In addition, the lattice basis delegation technique is adopted to achieve forward security for resisting key exposure attack. Based on the hardness assumptions of SIS problem from lattice, we prove that the proposed scheme can achieve formally provable security. Besides, the theoretical analysis and performance evaluation demonstrate that the proposed scheme is effective and feasible to guarantee the quantum security for the data integrity in cloud storage.