Reviewerly/exHarmony huggingface dataset:
Robust Nonlocal Low-Rank SAR Time Series Despeckling Considering Speckle Correlation by Total Variation Regularization

Outliers and speckle both corrupt time series of synthetic aperture radar (SAR) acquisitions. Owing to the coherence between SAR acquisitions, their speckle can no longer be regarded as independent. In this study, we propose an algorithm for nonlocal low-rank time series despeckling, which is robust against outliers and also specifically addresses speckle correlation between acquisitions. By imposing total variation regularization on the signal's speckle component, the correlation between acquisitions can be identified, facilitating the extraction of outliers from unfiltered signals and the correlated speckle. This robustness against outliers also addresses matching errors and inaccuracies in the nonlocal similarity search. Such errors include mismatched data in the nonlocal estimation process, which degrade the denoising performance of conventional similarity-based filtering approaches. Multiple experiments on real and synthetic data assess the performance of the approach by comparing it with state-of-the-art methods. It provides filtering results of comparable quality but is not adversely affected by outliers. The source code is available at https://github.com/gbaier/nllrtv.
When past is not a prologue: Adapting informatics practice during a pandemic

Abstract Data and information technology are key to every aspect of our response to the current coronavirus disease 2019 (COVID-19) pandemic—including the diagnosis of patients and delivery of care, the development of predictive models of disease spread, and the management of personnel and equipment. The increasing engagement of informaticians at the forefront of these efforts has been a fundamental shift, from an academic to an operational role. However, the past history of informatics as a scientific domain and an area of applied practice provides little guidance or prologue for the incredible challenges that we are now tasked with performing. Building on our recent experiences, we present 4 critical lessons learned that have helped shape our scalable, data-driven response to COVID-19. We describe each of these lessons within the context of specific solutions and strategies we applied in addressing the challenges that we faced.
WooKong: A Ubiquitous Accelerator for Recommendation Algorithms with Custom Instruction Sets on FPGA

Recommendation algorithms, such as Neighborhood-based Collaborative- Filtering (CF), have been widely applied in various emerging machine learning applications. However, under the circumstance of the explosive big data, it poses significant challenges to CF recommendation algorithms as it is becoming quite time and energy-consuming. It has to be optimized and accelerated by powerful engines to process on large data scale. To solve these problems, in this article, we propose WooKong, a ubiquitous accelerator architecture for the collaborative-filtering recommendation on FPGA. It is able to accommodate three types of CF recommendation algorithms, including User-based CF, Item-based CF, and SlopeOne recommendations algorithms, with five different similarity analysis metrics including Jaccard, Cosine, CosineIR, euclidean, and Pearson. To maintain flexibility for these different CF algorithms and metrics, we adopt custom instruction sets to manipulate the learning and prediction accelerators. We implement a hardware prototype on a real Xilinx Zynq FPGA development board. Experimental results show that the proposed learning and prediction accelerators can achieve 8.0X speedup and 1.7X speedup compared with an Intel i7 processor respectively. The accelerator has the energy benefits of up to 137.4X compared with an NVIDIA Tesla K40C GPU, with the affordable hardware cost.
Intelligent and Application-Aware Network Traffic Prediction in Smart Access Gateways

Network measurement and management are critical to the future smart network QoS and enhancing user QoE. Accurate prediction of network status can support network measurement and provide extra time for network management. However, it is challenging to achieve high accuracy in real-time due to the complexity of user behavior as well as the diversity of network applications. The future SDN will enable data-driven techniques to provide accurate network traffic prediction from access gateways. To demonstrate the concept, a DL-based encrypted packet classifier is first developed in this work to identify network applications. With the achievement of application awareness, a DL-based network traffic prediction scheme is further proposed and developed to provide accurate network traffic prediction. Datasets of network packets from an open-source as well as traffic flow collected in real life are applied to conduct evaluations and case studies. The evaluation results demonstrate that the proposed network traffic classification and prediction framework can successfully predict network traffic in access networks. The proposed framework will contribute significantly to the transition toward intelligent communication and networking to enhance user QoE.
The application of machine learning methods to aggregate geochemistry predicts quarry source location: An example from Ireland

Attempts using geochemical data to classify quarry sources which provided reactive rock aggregate, composed of Carboniferous aged pyritic mudrocks and limestones, which has caused structural damage to over 12, 500 homes across Ireland have not yet succeeded. In this paper, a possible solution to this problem is found by performing machine learning models, such as Logistic regression and Random Forest, upon a geochemical dataset obtained through the scanning electron microscope energy-dispersive X-ray spectroscopy (SEM-EDS) and Laser ablation-quadrupole-inductively couple plasma mass spectrometry (LA-Q-ICPMS) of pyrite and Isotope ratio mass spectrometry (IRMS) of bulk rock aggregate, to predict quarry source location. When comparing the classification scores, the LA-Q-ICPMS dataset achieved the highest average classification score of 55.38% for Random Forest and 67.73% for Logistic regression based on 10-fold cross validation testing. As a result, this dataset was then used to classify a set of known unknown samples and achieved average classification accuracies of 40.30% for random forest and 66.80% for logistic regression, based on a systematic train-test procedure. There is scope to enhance these classification scores to an accuracy of 100% by combining the geochemical datasets together. However, due to the difficulty in linking pyrites analysed by SEM-EDS to those analysed by LA-Q-ICPMS, and relating a bulk rock analytical technique (IRMS) to mineral geochemistry (SEM-EDS, LA-Q-ICPMS), median values have to be used when combining IRMS (Fe, S) and SEM-EDS (TS and δ34S) datasets with LA-Q-ICPMS data. Therefore, if these combined datasets were used as part of an applied quarry classification system, statistically meaningful mean values taken from a near normally distributed dataset would have to be used in order to accurately represent the quarry composition.
Block Chain Technology based Electoral Franchise

In recent years, technology plays a key role in many applications like smart city, online banking and others. In democratic countries, voting system is emerging as an indispensable model to select the right leader. Despite the hype, the current voting system requires more integrity and privacy to protect confidential information. This can be achieved by utilizing various state-of-the-art technical approaches present in this digital world. Hence, to improve the security features of the voting system, the proposed work has implemented blockchain technology into the existing voting system. This technology has unique features like immutable and distributive nature to reduce the attacks and threats on the voting system. By considering the sensitivity of the voters' data, here Dagger Hash algorithm and ETH/TRX crypto coins are used to develop a BCT based voting system. These methods can help in achieving the specified features.
Cybercrimes on the Streets of the Netherlands? An Exploration of the Intersection of Cybercrimes and Street Crimes

This article explores the intersections of street crimes and cybercrimes.Fourteen Dutch criminal investigations into networks that committed cybercrimes were analyzed to gain insight into offline and local dimensions of these activities.Firstly, we examined whether the networks in these cases are also involved in other criminal activities than cybercrimes.Secondly, we analyzed the origin and growth of these networks, with specific attention for the role or offline interactions on the physical streets.Thirdly, we explored whether the cases contained information that would indicate the presence of a street culture informing the activities of the offenders.Our analysis of both the criminal activities and the origin and development of the cybercriminal networks highlights the ongoing importance of the offline world.However, our results shed light on more than just the "hidden face of cybercrime."Based on their linguistic practices and motives and neutralizations, we see examples of how core members, recruiters, and money mules in different cases are embedded in Dutch street culture.Our findings, therefore, indicate that cybercrime cases could also be interpreted as digital diversifications of traditional street (economic) street crimes and, thereby, as empirical examples of street offenders adapting to the rise of technology.
Design of reversible logic based full adder in current-mode logic circuits

Demand of Very Large Scale Integration (VLSI) circuits with very high speed and low power are increased due to communication system's transmission speed increase. During computation, heat is dissipated by a traditional binary logic or logic gates. There will be one or more input and only one output in irreversible gates. Input cannot be reconstructed using those outputs. In low power VLSI, reversible logic is commonly preferred in recent days. Information is not lost in reversible gates and back computation is possible in reversible circuits with reduced power dissipation. Reversible full adder circuits are implemented in the previous work to optimize the design and speed of the circuits. Reversible logic gates like TSG, Peres, Feynman, Toffoli, Fredkin are mostly used for designing reversible circuits. However it does not produced a satisfactory result in terms of static power dissipation. In this proposed research work, reversible logic is implemented in the full adder of MOS Current-Mode Logic (MCML) to achieve high speed circuit design with reduced power consumption. In VLSI circuits, reliable performance and high speed operation is exhibited by a MCML when compared with CMOS logic family. Area and better power consumption can be produced implementing reversible logic in full adder of MCML. Minimum garbage output and constant inputs are used in reversible full adder. The experimental results shows that the proposed designed circuit achieves better performance compared with the existing reversible logic circuits such as Feynman gate based FA, Peres gate based FA, TSG based FA in terms of average power, static power dissipation, static current and area.
A message passing approach for multiple maneuvering target tracking

This paper considers the problem of detecting and tracking multiple maneuvering targets, which suffers from the intractable inference of high-dimensional latent variables that include target kinematic state, target visibility state, motion mode-model association, and data association. A unified message passing algorithm that combines belief propagation (BP) and mean-field (MF) approximation is proposed for simplifying the intractable inference. By assuming conjugate-exponential priors for target kinematic state, target visibility state, and motion mode-model association, the MF approximation decouples the joint inference of target kinematic state, target visibility state, motion mode-model association into individual low-dimensional inference, yielding simple message passing update equations. The BP is exploited to approximate the probabilities of data association events since it is compatible with hard constraints. Finally, the approximate posterior probability distributions are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue between the estimations of target kinematic state and target visibility state and decisions on motion mode-model association and data association. The performance of the proposed algorithm is demonstrated by comparing with the well-known multiple maneuvering target tracking algorithms, including interacting multiple model joint probabilistic data association, interacting multiple model hypothesis-oriented multiple hypothesis tracker and multiple model generalized labeled multi-Bernoulli.
Fraud Detection and Prevention by using Big Data Analytics

A retail sector is a group of organization or people who sell goods or services for gaining income. Fraud is wrongful or criminal activities for the economic and personal benefits. Fraud detection is finding actual or expected fraud which takes place in an organization and in the retail market is one of the challenging aspects. Fraud is mischievous activities occur in retail sector includes shoplifting, skimming, replicating cards from skimmed data, counterfeiting, bar code or POS(Point-of-Sale) manipulation, contamination, mislabeling, substitute cheaper ingredients instead of high-quality ingredients. Fraudulent activities occur in the retail sector by both consumer and supplier. Analyzing financial crimes related to fraudulent activities is difficult where traditional data mining techniques fail to address all of them. Big data analytics is used to identify an unusual pattern to detect and prevent fraud in the retail sector. Various predictive analytics tools are used to handle massive data and their pattern.
Autonomous Face Detection System from Real-time Video Streaming for Ensuring the Intelligence Security System

Face detection is a popular research topic to the researchers at present in the Biometric system. The aim of this research is to develop a system which can detect human face or faces from a live video streaming and warn by giving the siren for ensuring the significant role in the surveillance security aspects. The core of this control system includes ATMega328p Microcontroller based Arduino Uno with Pan-Tilt mechanism and Open Source Computer Vision System (OpenCV) is the platform for the image processing system. For detecting human face or faces existing methods are Haar-Cascade, Camshift, Hausdorff distance, AdaBoost, viola jones, etc. Amongst all of this we used Haar classifier Cascade algorithm for face detection in this research.
Multi scrolls chaotic encryption for physical layer security in OFDM-PON

We propose a novel 3-dimension (3-D) multi scrolls chaotic encryption for physical layer security in OFDM-PON. Compared with the hyper Chen scheme, the proposed encryption method has lower encryption complexity and larger key space due to the 3-D multi scrolls characteristic of self-contained limiting amplitude and extreme initial value sensitivity. We verify the proposed multi scrolls encryption algorithm in 25-Gb/s DD-OFDM-PON system. The experimental results show that normal decryption is possible only if the key is fully acquired without any tiny error. The key space of the proposed scheme is up to 10332. We also transmit classical images 'Lena' in the experimental system to verify the practicability of the proposed encryption algorithm. The experimental result show that our method is promising for physical layer security of OFDM-PON.
Clustering-Aware Graph Construction: A Joint Learning Perspective

Graph-based clustering methods have demonstrated the effectiveness in various applications. Generally, existing graph-based clustering methods first construct a graph to represent the input data and then partition it to generate the clustering result. However, such a stepwise manner may make the constructed graph not fit the requirements for the subsequent decomposition, leading to compromised clustering accuracy. To this end, we propose a joint learning framework, which is able to learn the graph and the clustering result simultaneously, such that the resulting graph is tailored to the clustering task. The proposed method is formulated as a well-defined nonnegative and off-diagonal constrained optimization problem,which is optimized by an alternative iteration method with the convergence of the value of the objective function guaranteed. The advantage of the proposed model is demonstrated by comparing with 19 state-of-the-art clustering methods on 10 datasets with 4 clustering metrics.
TensorOpt: Exploring the Tradeoffs in Distributed DNN Training With Auto-Parallelism

A good parallelization strategy can significantly improve the efficiency or reduce the cost for the distributed training of deep neural networks (DNNs). Recently, several methods have been proposed to find efficient parallelization strategies but they all optimize a single objective (e.g., execution time, memory consumption) and produce only one strategy. We propose FT, an efficient algorithm that searches for an optimal set of parallelization strategies to allow the trade-off among different objectives. FT can adapt to different scenarios by minimizing the memory consumption when the number of devices is limited and fully utilize additional resources to reduce the execution time. For popular DNN models (e.g., vision, language), an in-depth analysis is conducted to understand the trade-offs among different objectives and their influence on the parallelization strategies. We also develop a user-friendly system, called TensorOpt, which allows users to run their distributed DNN training jobs without caring the details of parallelization strategies. Experimental results show that FT runs efficiently and provides accurate estimation of runtime costs, and TensorOpt is more flexible in adapting to resource availability compared with existing frameworks.
Experimental Verification of Three-Degree-of-Freedom Electromagnetic Actuator for Image Stabilization

Image deteriorations due to vibrations have become a problem in autonomous systems such as unmanned aerial vehicles, robots, and autonomous cars. To suppress the vibration, a camera stabilizer using a gimbal mechanism is widely used. However, the size and weight of the system increase because the conventional image stabilization systems require some actuators and links to drive in multi-axes. In order to solve these problems, we proposed a novel three-degree-of-freedom (3DOF) electromagnetic actuator for image stabilization. The actuator can be driven by only three-phase and has a simple structure and control system. This paper describes the experimental verification of the proposed actuator. The torque characteristics are clarified, and the analysis and measured torque characteristics are compared to verify the analysis validity. For verifying the dynamic performance, the frequency characteristics are measured. The effectiveness of the proposed magnetic structure and operating principle are investigated.
The Influence of Image Augmentation on Breast Lesion Classification Using Transfer Learning

Transfer learning and data augmentation techniques play an essential role in implementing an effective deep learning model when there is a lack of sufficient training samples. Popular pre-trained networks such as AlexNet, VGG, GoogLeNet, and ResNet showed high level of accuracy on many image classification tasks, however these networks were trained on general purpose datasets such as ImageNet which does not contain labeled images related to breast lesions. In this paper, we have fine-tuned the pre-trained VGG-19 network on breast lesions images before and after applying the augmentation methods to differentiate between benign and malignant type of cancers. Image augmentation methods are carefully chosen to further overcome the lack of breast x-ray images, improve the classification accuracy, and reduce over-fitting by generating new training samples based on the existing breast x-ray images modifications. These augmentation methods include rotation, flipping, zooming, brightness, and contrast. The performance of VGG-19 classification is compared with similar pre-trained models used in the literature such as ResNet50 and VGG-16. Our approach surpassed the previous approaches with classification accuracy, sensitivity, specificity, and AUC of 90.4%, 94.01%, 85.9%, and 95.9% respectively.
Intelligent Controller for 7-DOF Manipulator Based upon Virtual Reality Model

A robot is an option to improve productivity in industrial automation. Automated manipulators have been applied to hazardous environments and routine manufacturing functions. Because automated manipulators are nonlinear dynamic systems with a high degree of uncertainty, it is difficult to obtain precise dynamic equations to design control laws. VR is an important part of applications in industrial, medicine, statistics, and other areas where 3D object can help understand complex systems. In this application, interaction with the virtual system can be enhanced by a sense of touch, and rapid feedback can be used to apply representative forces from the virtual environment to a human user. The ANFIS approach has become one of the main areas of interest because it gains the benefits of neural networks (NN) as well as mysterious logic systems and eliminates individual defects by combining them with common features. The artificial neural network (ANN) has injected new momentum into the mysterious literature. ANN can be used as a universal learning model for any smooth parameter models, including the mysterious inference system. The mixed learning base used to combine the gradient ratios technique and the Least Square Estimator (LSE) to train the ANFIS network for a particular problem. This chapter introduces the design of the ANFIS for the 7-DOF manipulator model built by the VR environment and simulates this model by connecting Matlab / Simulink with VR to execute commands produced by the system-based ANFIS console. Satisfactory results are obtained in simulations which improve the design as a basic application of this control design.
A Boolean Load-Matrix Based Frequent Pattern Mining Algorithm

Frequent Pattern Mining (FPM) plays an essential role in many data analytic tasks. In literature, many techniques have been proposed to discover interesting patterns. However, the complexity in terms of computational time is a major concern. In this paper, we have proposed a novel FPM algorithm using boolean matrix. Here the transactional dataset is transformed into a boolean matrix, that further decomposed vertically into multiple loads. Our proposed algorithm improves efficiency in terms of computational time and further it may be used in a distributed environment to deal with huge dataset.
Out-of-Distribution Detection for Reliable Face Recognition

In real applications, face recognition systems are always faced with non-face inputs and low-quality faces due to the complicated conditions like mis-detections by face detectors. However, in deep learning based methods, these outliers are always ignored during training phase and the models tend to make unreasonable decisions on these images. For example, matching a texture-rich patch to an old-man face overconfidently. We formulate this challenge on the task of out-of-distribution detection (OOD), where a network must determine whether or not an input is outside of the set on which the network can safely perform. In this paper, we propose to detect out-of-distribution samples based on uncertainty prediction and the L2-norm of features, so as to effectively filter out non-face and low-quality faces. We demonstrate that the proposed method can reliably detect out-of-distribution samples and improve the performance of face recognition, without the need of labelled OOD data.