Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Representation of Sound Speed Profiles Based on Dictionary Learning

The perturbations of sound speed profiles (SSPs) has great influence on sound propagation. Empirical orthogonal functions (EOFs) are often used to simplify the description of sound speed profiles. However, when the unevenness of seawater, such as internal wave and turbulence exists, the regularization operation will result in a significant decrease in the reconstruction accuracy of sound speed. In this paper, the dictionary learning, a form of unsupervised machine learning, is used to generate non-orthogonal entries of sound speed profiles, OMP algorithm is used in sparse coding, while K-SVD algorithm is used in dictionary updating. Because dictionary learning does not require the use of orthogonal conditions, it is more flexible for training data, and thus can use fewer atomic combinations to achieve higher reconstruction accuracy. The reconstruction performance of EOFs and LDs was tested with HYCOM data. The results show that compared with EOFs, LDs can better explain the perturbations of sound speed profiles with a few entries. Dictionary learning can improve the sparsity of sound speed profiles and improve the reconstruction accuracy of sound speed profiles.
Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network

Unconstrained handwritten text recognition is a major step in most document analysis tasks. This is generally processed by deep recurrent neural networks and more specifically with the use of Long Short-Term Memory cells. The main drawbacks of these components are the large number of parameters involved and their sequential execution during training and prediction. One alternative solution to using LSTM cells is to compensate the long time memory loss with an heavy use of convolutional layers whose operations can be executed in parallel and which imply fewer parameters. In this paper we present a Gated Fully Convolutional Network architecture that is a recurrence-free alternative to the well-known CNN+LSTM architectures. Our model is trained with the CTC loss and shows competitive results on both the RIMES and IAM datasets. We release all code to enable reproduction of our experiments: https://github.com/FactoDeepLearning/LinePytorchOCR.
DC-DC Converter and Rectifier with Resonator for Underwater Wireless Power Transfer Module

This paper designs a DC-DC converter and rectifier with resonator for wireless power transfer module (WPTM). The proposed boost DC-DC power converter serving as DC-to-AC converter with an embedded DC rectifier to provide the first-stage DC-DC converter, and charge pump support AC-to-DC converter with an input from the DC rectifier. Two differential, cross-coupled rectifiers as the charge pump circuit mitigate the reverse-leakage issue and they are cascaded to double the output voltage. The resonator uses the coil in multi-mode to drive Bluetooth low energy (BLE) and wireless charging functions for power receiving unit (PRU).
The HHD Dataset

Benchmark datasets are important in document image processing field, as they allow to analyze different approaches and compare their performances in a fair manner. There exist benchmark datasets for several alphabets such as Latin, Arabic and Chinese, but not the Hebrew alphabet. In this paper, a handwritten Hebrew dataset, HHD, is introduced. The HHD dataset is collected from hand-filled forms, and accompanied by their ground truth at character, word and text line levels. Presently, the dataset contains around 1000 document images, and we continue to further enlarge it. To the best of our knowledge, this is the first comprehensive corpus of Hebrew handwritten documents, and we believe it will help leveraging Hebrew documents processing and document processing in general. The dataset can be useful for various research applications, such as word spotting, word recognition, text line alignment, and writer identification. The initial small subset of the HDD for character classification can be downloaded from https://www.cs. bgu.ac. illr-vberatldatalhhd_dataset. zip together with the training and test sets subdivisions. We also provide baseline results for character classification on this initial subset. In the near future, the full HHD dataset will be made freely available to the research community.
Faster R-CNN and DenseNet Regression for Glaucoma Detection in Retinal Fundus Images

Glaucoma is one of the main retinal diseases. Glaucoma affects older people more often, and it can lead to vision loss. Until now there is no medicament for Glaucoma, but early detection is important, wherein it can limit the increase of vision loss or blindness. In this paper, we propose a deep learning approach based on two steps for Glaucoma detection in retinal fundus images. In the first step, we use a faster region proposal neural network (RCNN) to detect the optical disc (OD). Then in a second step, we train a regression network to estimate the cup-to-disc ratio (CDR) by analyzing reign around the detected OD. Experimental results of this method are demonstrated on the MESSIDOR and Magrabi datasets.
Radiography Classification: A Comparison between Eleven Convolutional Neural Networks

This paper investigates the classification of normal and abnormal radiographic images. Eleven convolutional neural network architectures (GoogleNet, Vgg-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, Vgg-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2) were used to classify a series of x-ray images from Stanford Musculoskeletal Radiographs (MURA) dataset corresponding to the wrist images of the data base. For each architecture, the results were compared against the known labels (normal / abnormal) and then the following metrics were calculated: accuracy (labels correctly classified) and Cohen’s kappa (a measure of agreement) following MURA guidelines. Numerous experiments were conducted by changing classifiers (Adam, Sgdm, RmsProp), the number of epochs, with/without data augmentation. The best results were provided by InceptionResnet-v2 (Mean accuracy = 0.723, Mean Kappa = 0.506). Interestingly, these results lower than those reported in the Leaderboard of MURA. We speculate that to improve the results from basic CNN architectures several options could be tested, for instance: pre-processing, post-processing or domain knowledge, and ensembles.
Response Format, Not Semantic Activation, Influences the Failed Retrieval Effect

In educational settings, tests are mainly used to measure the extent to which learners’ knowledge and skill have been acquired. However, the act of taking a test also promotes learning itself. In particular, making errors on tests (i.e., searching for erroneous information) promotes learning. This is called the “failed retrieval effect” (FRE) and has been the subject of considerable study. Previous research shows that enhanced learning does not occur if feedback correcting an error is delayed. This is attributed to the relative absence of activated information. In this study, we manipulated both the amount of information to be retrieved prior to learning and the delay time until feedback is given to investigate their effects on learning. As a result, even when multiple incorrect answers were given to increase the degree of semantic activation, learning was not promoted beyond that found with traditional procedures that rely on only one incorrect response. The timing of feedback (immediate, short-delay, long-delay) also did not impact FRE. However, the manipulation of response format for erroneous information resulted in degraded performance when responses were typed and feedback was delayed. Based on this result, we suggested that the failed retrieval effect was not affected by semantic activation at the time of retrieval but was affected by response format. Moreover, the processing necessary for typing may affect FRE under the delayed feedback condition.
One-pass Synthesis for Digital Microfluidic Biochips: A Survey

With the advances of the microfluidic technology, the design of Digital Microfluidic Biochips (DMFBs) received significant attention in the recent past. The corresponding design process usually consists of multiple, consecutive design tasks, namely binding, scheduling, placement, and routing. These tasks, however, are often considered and solved separately. This can lead to design gaps between the individual steps, yielding less-than-optimal overall solutions or prevent to solve the next step altogether. In order to address these shortcomings, the concept of one-pass synthesis for DMFBs has been introduced. In this survey paper, we review the main ideas of this concept and provide an overview on two different implementations of it: (1) an exact, SAT-based approach which guarantees optimal solutions with respect to area and/or timing and (2) a heuristic graph-based approach which focuses on run-time efficiency. We discuss the (dis-)advantages of those approaches with respect to their quality as well as scalability and illustrate them on various benchmark assays.
Study on the classification of beatbox sounds based on timbre features

Beatbox is a performance form of vocal percussion, which is gradually favored by people with continuous development in recent years. With the abundance of Beatbox content on the Internet, the research on its automatic classification becomes very significant. In this article, we mainly using machine learning methods, to identify the beatbox timbre. First of all, we collected 900 Beatbox audio materials and constructed a database. Secondly, 14 acoustic parameters of audio material are extracted by the signal processing method. Finally, we used MultilayerPerceptron, SMO, BayesNet, RandomForest in current machine learning to build a training model for BB timbre classification. The Experimental results showed that the model could classify BB timbre well.
Skin Lesion Classification: An Optimized Framework of Optimal Color Features Selection

Melanoma is the most common and deadly kind of malignancy among all the existing types of cancers, worldwide. Globally, the incidence rate of melanoma rising in recent decades. Responses on a survey, in USA about 192,310 new cases are diagnosed while 7,230 deaths have been occurred due to melanoma in 2019. This ratio can be decreased if it is detected at an early stage. A novel systematic approach for skin cancer detection based on optimal feature selection is proposed in this work. In the normalization step, it differentiates the lesion region from the surrounding skin region by using a linear contrast stretching technique. Later, various type features are computed and put to optimal feature selection approach name higher entropy value features (HEVF). Optimized and best features are selected and classified using SVM classifier and evaluated on ISBI 2017 dataset. As a result, the proposed systems get a performance of 96.2% which is improved as compared to existing techniques.
Usability and Accessibility Evaluation of Banking Websites

Websites are used by almost all businesses as a competitive tool in this electronic media age. These websites have become a necessity and play a vital role to gather information and to provide different services and ease to their users. Therefore, the design and interface of websites should provide ease of use and satisfaction to their users in every aspect. The objective of this study is to evaluate the usability, accessibility and vulnerability issues in Pakistani and International Banking Websites. The evaluations were performed manually and by using automated tools. Also, a USE questionnaire was used to get users’ perspectives. Results of the study show that majority of the websites do not adhere to usability principles leading to lack of user satisfaction. Similarly, the websites under the study do not follow WCAG 2.0 principles and guidelines. Considerable issues were also reported in vulnerability tests. Further, it was observed that International Banking Websites have achieved better results than Pakistani Banking Websites in terms of usability, accessibility and vulnerability. However, none of the websites passed all the tests with 100% result.
A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition

Human Activity Recognition (HAR) has recently received remarkable attentions in numerous applications such as assisted living and remote monitoring. Existing solutions based on sensors and vision technologies have obtained achievements but still suffering from considerable limitations in the environ-mental requirement. Wireless signals like WiFi-based sensing has emerged as a new paradigm since it is convenient and not restricted in the environment. In this paper, a new WiFi-based and video-based neural network (WiNN) is proposed to improve the robustness of activity recognition where the synchronized video serves as the supplement for the wireless data. Moreover, a wireless-vision benchmark (WiVi) is collected for 9 class actions recognition in three different visual conditions, including the scenes without occlusion, with partial occlusion, and with full occlusion. Both machine learning method - support vector machine (SVM) as well as deep learning methods are used for the accuracy verification of the data set. Our results show that WiVi data set satisfies the primary demand and all three branches in proposed pipeline keep more than 80% of activity recognition accuracy over multiple action segmentation from Is to 3s. In particular, WiNN is the most robust method in terms of all the actions on three action segmentation compared to the others.
Computer Vision Based Intelligent 3D CNC Machines

Innovative, digital, embedded processing units and systems and easy-to-use design tools help deliver the digitalization of systems to enable smarter, safer, more efficient machines and factories. All these new technologies and innovations help us to design what’s new and what’s next in industrial processing, monitoring, sensing, command and control, communication, functional safety and power management. This study presents a collection of practical applications of computer vision and a plan to prepare utility software, which is based on latest technology of computer vision, which makes easy to prepare a CAD drawing from an image for CNC machines. This method could be useful in the field of reverse engineering for reproducing existing products which has not a vector drawing or the restoration of a historical item.
Mirante: A visualization tool for analyzing urban crimes

Visualization assisted crime analysis tools used by public security agencies are usually designed to explore large urban areas, relying on grid-based heatmaps to reveal spatial crime distribution in whole districts, regions, and neighborhoods. Therefore, those tools can hardly identify micro-scale patterns closely related to crime opportunity, whose understanding is fundamental to the planning of preventive actions. Enabling a combined analysis of spatial patterns and their evolution over time is another challenge faced by most crime analysis tools. In this paper, we present Mirante, a crime mapping visualization system that allows spatiotemporal analysis of crime patterns in a street-level scale. In contrast to conventional tools, Mirante builds upon street-level heatmaps and other visualization resources that enable spatial and temporal pattern analysis, uncovering fine-scale crime hotspots, seasonality, and dynamics over time. Mirante has been developed in close collaboration with domain experts, following rigid requirements as scalability and versatile to be implemented in large and medium-sized cities. We demonstrate the usefulness of Mirante throughout case studies run by domain experts using real data sets from cities with different characteristics. With the help of Mirante, the experts were capable of diagnosing how crime evolves in specific regions of the cities while still being able to raise hypotheses about why certain types of crime show up.
Machine Learning and Hardware security: Challenges and Opportunities -Invited Talk-

Machine learning techniques have significantly changed our lives. They helped improving our everyday routines, but they also demonstrated to be an extremely helpful tool for more advanced and complex applications. However, the implications of hardware security problems under a massive diffusion of machine learning techniques are still to be completely understood. This paper first highlights novel applications of machine learning for hardware security, such as evaluation of post quantum cryptography hardware and extraction of physically unclonable functions from neural networks. Later, practical model extraction attack based on electromagnetic side-channel measurements are demonstrated followed by a discussion of strategies to protect proprietary models by watermarking them.
Theoretical Analysis of ON-State Performance Limit of 4H-SiC IGBT in Field-Stop Technology

In this article, the ON-state performance limits of 4H-silicon carbide (SiC) IGBTs in field-stop technology are theoretically estimated for the first time and compared against silicon counterparts. The theoretical analysis is based on static modeling of a high-current p-i-n diode and the calculation results are examined with TCAD simulations. Owing to conductivity modulation effect, the ON-state losses of 4H-SiC IGBTs do not show any significant increase with the increase in breakdown voltage (BV). However, the large built-in potential of SiC poses an inherent limit on the reduction of ON-state voltage drop. Compared with 4H-SiC IGBTs, the silicon-based IGBTs exhibit superior ON-state performance limits within the BV range considered, although their drift layer thicknesses are ten times higher than that of 4H-SiC IGBTs. Therefore, silicon IGBTs remain as efficient technologies for enhancing high power conversion efficiency.
Point Cloud Denoising Algorithm Based on Noise Classification

Aiming at the problem of different kinds of noise in 3D point cloud data, we propose a point cloud denoising method based on noise classification. This algorithm first divides noise in point cloud data into inner points and outer points, and uses radius filtering and statistical filtering to remove the outer points. Then, normal and curvature information of point cloud are estimated by the principal component analysis. At the same time, curvature information is introduced into the bilateral filtering factor to improve the existing algorithm. Ultimately, we smooth inner points mixed in point cloud by utilizing the modified algorithm. Comparing the improved algorithm with the bilateral filtering algorithm on the bunny, horse and dragon model, experimental results indicate that the maximum error and the average error are reduced. The algorithm in this paper makes models’ features maintained better while models are smoothed.
Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks

Scene change detection is an image processing problem related to partitioning pixels of a digital image into foreground and background regions. Mostly, visual knowledge-based computer intelligent systems, like traffic monitoring, video surveillance, and anomaly detection, need to use change detection techniques. Amongst the most prominent detection methods, there are the learning-based ones, which besides sharing similar training and testing protocols, differ from each other in terms of their architecture design strategies. Such architecture design directly impacts on the quality of the detection results, and also in the device resources capacity, like memory. In this work, we propose a novel Multiscale Cascade Residual Convolutional Neural Network that integrates multiscale processing strategy through a Residual Processing Module, with a Segmentation Convolutional Neural Network. Experiments conducted on two different datasets support the effectiveness of the proposed approach, achieving average overall F-measure results of 0.9622 and 0.9664 over Change Detection 2014 and PetrobrasROUTES datasets respectively, besides comprising approximately eight times fewer parameters. Such obtained results place the proposed technique amongst the top four state-of-the-art scene change detection methods.
Searching for Malware Dataset: a Systematic Literature Review

Malware is one of the exciting topics widely discussed by both academicians and researchers, but the source list of malware rarely provided. Therefore, this paper aims to write a Systematic Literature Review (SLR) to find which datasets are commonly used by previous researchers. The three journal databases were used in this study, including IEEE, science direct, and ACM. The PRISMA statement was applied to maintain transparency during the literature review. To facilitate the search, the authors also provide limitations during the SLR process (inclusion and exclusion). The inclusion includes: (1) full article fully written in English; (2) peer-reviewed papers; (3) explicitly mentioning the name of dataset or database; and (4) explicitly mentioning the method to find malware characteristics and behavior. While the exclusion consists of: (1) articles written before 2015; (2) book and white paper; (3) article already indexed in another database journal; and (4) paper which is less than four pages. After both filter processes, there are 42 out of 245 articles eligible to answer the stated research question (RQ), which were: (1) where does the researcher usually find the malware database or dataset? ; (2) what kind of methods applied by previous researchers to find the malware’s characteristics or behavior? ; and (3) which platforms that malware usually attacks are? Based on the three RQs, we could conclude that RQ1 recorded for 37 datasets, RQ2 recorded for 47 methods, and RQ3 recorded for six platforms.
Distinguishing True and False Buy/Sell Triggers from Financial Technical Indicators

The objective of this study is to develop a method to distinguish True and False Buy/Sell recommendations. Various recommendation schemes, like 30/70 RSI (Relative Strength Index) scheme, are effectively used by many traders. However, the triggers produced by such recommendation schemes are found suspicious most of the time, and hence are non-actionable. In this study we develop a dynamic programming formulation to extract an optimal trade pattern from the price datasets. Such patterns are further augmented with several financial indicators to obtain binary classification model which is going to be consulted online. So, our system assists investors with removing uncertainties left from the primary recommenders. We show that our dynamic programming formulation runs efficiently in linear time. The approach is experimentally evaluated on BIST-100 stocks. The technical indicators used as predictor features are RSI, Trend Normalized RSI, Percentage Price Oscillator, Bollinger Band Percentage, Stochastic Oscillator, Rate of change (ROC), and Commodity Channel Index (CCI). We use Support Vector Machines as the binary classification algorithm. Up to 70% accuracies are obtained in this very hard binary classification task.
Classification of Small Region of Interest from Remote Images Using Neural Networks

The classification and segmentation of small region of interest from ground images is important in many domains: flood detection and evaluation, crop monitoring, environment monitoring, defense, etc. The paper uses a fusion scheme to classify small regions from remote images in four classes (different for satellite and unmanned aerial vehicle -UAVs). As novelty, two neural networks, considered as primary classifiers, AlexNet and Perceptron (the last based on ten textural image features) are combined in a convolutional scheme to make up the global classifier. The weights from the convolutional layer are calculated according to the performances of the primary classifiers in a validation phase. Two datasets with different images are used for classifier learning, validation, and testing, one consisting in images from satellite and another consisting in images from UAVs. The global system performances were better than those of the individual neural network (system components) accuracies.