This paper describes an innovative e-learning project which is the development of a digital workbook that helps teaching handwriting at school. In this work, we propose a new qualitative and quantitative analysis process of cursive handwriting. This process detects automatically mistakes, gives a real-time feedback and helps teachers evaluate children’s writing skills. The main aim of this digital workbook is to help children learn how to write correctly. The proposed process is composed of five main criteria: shape,direction, stroke order, position respect to the reference lines and kinematics of the trace. It analyzes the handwriting quality and gives automatically feedback based on the Beta-Elliptic Model using similarity detection (SD) and dissimilarity distance (DD) measure. Our work apprehends dynamic and visual representation of the acquired traces and selects efficient features adapted to various handwriting styles and multiple script languages such as Arabic, Latin, digits, and symbols drawing. It demonstrates that beta-elliptic is not only a model for segmentation and recognition but also a tool to evaluate handwriting. Our application offers two interactive interfaces respectively dedicated to learners, and experts or teachers who can adapt it easily to the specificity of each child. The validation of the proposed system is done on a database collected in Tunisia primary schools with 400 children. Experimental results show that the efficiency and robustness of our suggested framework that do help teachers and children by offering positive feedback throughout the handwriting learning process using tactile digital devices.

The role of the source geometry in the the radiation of focusing beams by conformal antennas is examined by the comparison of their directivity functions at different maximum directions. An inverse source problem approach is adopted, where solutions stable with respect to data uncertainties are to be found by relying on the analysis of the pertinent operator by the Singular Values Decomposition. This general framework allows to connect the mean value of the mazimum directivity function to the Number of Degrees of Freedom of the conformal source, which depends only on its electrical length. For each source geometry the focusing far field ensuring the maximum directivity for every pointing direction and the corresponding source current are obtained. The comparison with the ones achieved by the usual phase compensation technique of the source current reveals their optimal behavior. The usefulness of the approach as a tool in antenna synthesis is shown by comparing different geometries for those applications where identical beams are required to be rdiated for the coverage of a larga angular domain.

Deep-BEJT: A New Human Activity Recognition System basedon Beta Elliptical Joint Trajectory (BEJT) and Long Short TermMemory (LSTM) New journal paper

We proposed RTP, a composite neural network model that captures knowledge from remote transportation pollution events (RTPEs) to improve the local PM2.5 prediction. To the best of our knowledge, this is the first deep learning work to include knowledge from remote pollutants for PM2.5 prediction. RTP consists of two neural network components: a pre-trained base model and STRI model. The base model captures knowledge from local factors that influence PM2.5 concentrations and STRI captures knowledge from RTPEs by learning spatial-temporal characteristics of Satellite base AOD data and weather features from remote areas. In addition, given the size of the STRI model, to facilitate training and improve results we divide the full STRI model into two components: STRI\_fe, which is used to extract spatial-temporal features from remote areas, and STRI\_p, which predicts local PM2.5 concentrations using both remote and local features. The prediction results from STRI\_p show that the prediction error is reduced when remote features are added to the model, demonstrating that the STRI model indeed captures knowledge from RTPEs. To characterize the occurrence of RTPEs in northern Taiwan, we also developed an algorithm to classify PM2.5 concentrations attributable to RTPEs. We use the STRI model for the prediction of two EPA stations located at the northern tip of Taiwan and apply the classification algorithm to the results. This yields improvements in accuracy when remote features are added to the model, which demonstrates the impact of RTPEs at the stations.

Our work aims to conduct a comprehensive literature review of deep learning methods applied in the fashion industry and, especially, the image-based virtual fitting task by citing research works published in the last years. We have summarized their challenges, their main frameworks, the popular benchmark datasets, and the different evaluation metrics. Also, some promising future research directions are discussed to propose improvements in this research field.

In this paper, we address the problem of computing the dimension of data space in phase retrieval problem. Starting from the quadratic formulation of the phase retrieval, the analysis is performed in two steps. First, we exploit the lifting technique to obtain a linear representation of the data. Later, we evaluate the dimension of data space by computing analytically the number of relevant singular values of the linear operator that represents the data. The study is done with reference to a 2D scalar geometry consisting of an electric current strip whose square amplitude of the electric radiated field is observed on a two-dimensional extended domain in Fresnel zone.

Web applications are exposed to many threats and, despite the best defensive efforts, are often successfully attacked. Reverting the effects of an attack on the state of such an application requires a profound knowledge about the application, to understand what data did the attack corrupt. Furthermore, it requires knowing what steps are needed to revert the effects without modifying legitimate data created by legitimate users. Existing intrusion recovery systems are capable of reverting the effects of the attack but they require modifications to the source code of the application, which may be unpractical. We present Sanare, a pluggable intrusion recovery system designed for web applications that use different data storage systems to keep their state. Sanare does not require any modification to the source code of the application or the web server. Instead, it uses Matchare, a new deep learning scheme we introduce to learn the matches between the HTTP requests and the database statements, file system operations and web services requests that the HTTP requests caused. We evaluated Sanare with three open source web applications: WordPress, GitLab and ownCloud. In our experiments Matchare achieved precision and recall higher than 97.5%.

Blockchain technology has the potential to revolutionize industries by offering decentralized, transparent, data provenance, auditable, reliable, and trustworthy features. However, cross-chain interoperability is one of the crucial challenges preventing widespread adoption of blockchain applications. Cross-chain interoperability represents the ability for one blockchain network to interact and share data with another blockchain network. Contemporary cross-chain interoperability solutions are centralized and require re-engineering of the core blockchain stack to enable inter-communication and data sharing among heterogeneous blockchain networks. In this paper, we propose an application-based cross-chain interoperability solution named appXchain which allows blockchain networks of any architecture type and industrial focus to inter-communicate, share data, and make requests. Our solution utilizes the decentralized applications as a distributed translation layer that is capable of communicating and understanding multiple blockchain networks, thereby delegating requests and parameters among them. The architecture uses incentivized verifier nodes that maintain the integrity of shared data facilitating them to be readable by the entities of their network. We define and describe the roles and requirements of major entities of inter-operating blockchain networks in the context of healthcare. We present a detailed explanation of the sequence of interactions needed to share an Electronic Medical Record (EMR) document from one blockchain network to another along with the required algorithms. We implement the appXchain solution with Ethereum-based smart contracts for two hospitals and also present its cost and security analysis. We have made our smart contracts code and testing scripts publicly available.

Currently, deep learning approaches have proven successful in the areas of handwriting recognition. Despite this, research in this field is still needed, especially in the context of multilingual online handwriting recognition scripts by adopting new network architectures and combining relevant parametric models. In this paper, we propose a multi-stage deep learning-based algorithm for multilingual online handwriting recognition based on hybrid deep Bidirectional Long Short Term Memory (DBLSTM) and SVM networks. The main contributions of our work lie in partly in the composition of a new multi-stage architecture of deep learning networks associated with effective feature vectors that integrate dynamic and visual characteristics. First, the proposed system proceeds by pretreating the acquired script and delimiting its Segments of Online Handwriting Trajectories (SOHTs). Second, two types of feature vectors combining BetaElliptic Model (BEM) and Convolutional Neural Network (CNN) are extracted for each SOHT in order to fuzzy classify them into k sub-groups using DBLSTM neural networks for both online and offline branches trained using an unsupervised fuzzy k-means algorithm. Finally, we combine the trained models to strengthen the discrimination power of the global system using SVM engine. Extensive experiments on three data sets were conducted to validate the performance of the proposed method. The experimental results show the effectiveness and complementarities of the individual modules and the advantage of their fusion.

Two-coil wireless power transfer (WPT) systems are composed of two circuits tuned at the same resonance frequency, one containing the source, and other containing the load, both connected to each other by the mutual inductance. The power delivered to the load circuit (Po) divided by the total power supplied by the source (PT) and by the maximum ideal amount of power which can be delivered to the load circuit are usual figures of merit known as efficiency (n) and power transfer capability (P*), respectively. Additionally, it can be defined a third figure of merit (I*) as the power dissipated at the source circuit divided by PT. It has been recently demonstrated that n and P* are related to I* . In this paper, it is presented a simple method to monitor I*, allowing consequently the determination of n and/or P* without any direct measurement at the load circuit. The qualities and limitations of the proposed method are discussed in details. Practical results are included to verify the proposal.

Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline multi-lingual handwriting. Our framework is based on an integrated sequence-to-sequence attention model. The proposed system involves extracting a hidden representation from an image using a Convolutional Neural Network (CNN) and a Bidirectional Gated Recurrent Unit (BGRU), and decoding the encoded vectors to generate dynamic information using a BGRU with temporal attention. We validate our framework using an online recognition system applied to a benchmark Latin, Arabic and Indian On/Off dual-handwriting character database. The performance of the proposed multi-lingual system is demonstrated through a low error rate of point coordinates and high accuracy system rate.

Inspired by compressive sensing principles, we propose novel error control coding techniques for communication systems. The information bits are encoded in the support and the non-zero entries of a sparse signal. By selecting a dictionary matrix with suitable dimensions, the codeword for transmission is obtained by multiplying the dictionary matrix with the sparse signal. Specifically, the codewords are obtained from the sparse linear combinations of the columns of the dictionary matrix. At the decoder, we employ variations of greedy sparse signal recovery algorithms. Using Gold code sequences and mutually unbiased bases from quantum information theory as dictionary matrices, we study the block error rate (BLER) performance of the proposed scheme in the AWGN channel. Our results show that the proposed scheme has a comparable and competitive performance with respect to the several widely used linear codes, for very small to moderate block lengths. In addition, our coding scheme extends straightforwardly to multi-user scenarios such as multiple access channel, broadcast channel, and interference channel. In these multi-user channels, if the users are grouped such that they have similar channel gains and noise levels, the overall BLER performance of our proposed scheme will coincide with an equivalent single-user scenario.

This article demonstrates how experiential learning could be used to develop argumentative essay writing skills in STEM students. Written feedback, when delivered in a timely manner, is an effective way of advancing students' understanding of the writing process. Unfortunately, large class sizes and the limited backgrounds of instructors do not always make formative feedback possible. STEM students are especially disadvantaged since approaches to teaching written communication tend to differ from the trial-and-error strategies compatible with many STEM areas. An experiential learning approach to writing instruction can have a positive impact on developing writing skills in STEM learners. Implementing algorithms for providing STEM students with immediate, dependable, formative feedback is expected to improve their performance in writing. This paper discusses an experiential learning project for teaching argumentative writing was delivered to computer science and engineering freshmen. Also discussed are automated analysis of content and argumentation in the essays, using NLP methods.

A document by Wei Jiang . Click on the document to view its contents.

The manufacturing process of STT-MRAM requires unique steps to fabricate and integrate magnetic tunnel junction (MTJ) devices which are data-storing elements. Thus, understanding the defects in MTJs and their faulty behaviors are paramount for developing high-quality test solutions. This article applies the advanced device-aware test to intermediate (IM) state defects in MTJ devices based on silicon measurements and circuit simulations. An IM state manifests itself as an abnormal third resistive state, which differs from the two bi-stable states of MTJ. We performed silicon measurements on MTJ devices with diameter ranging from 60nm to 120nm; the results show that the occurrence probability of IM state strongly depends on the switching direction, device size, and bias voltage. We demonstrate that the conventional resistor- based fault modeling and test approach fails to appropriately model and test such a defect. Therefore, device-aware test is applied. We first physically model the defect and incorporate it into a Verilog-A MTJ compact model and calibrate it with silicon data. Thereafter, this model is used for a systematic fault analysis based on circuit simulations to obtain accurate and realistic faults in a pre-defined fault space. Our simulation results show that an IM state defect leads to intermittent write transition faults. Finally, we propose and implement a device-aware test solution to detect the IM state defect.

Strictures caused by esophageal cancer can narrow down the esophageal lumen, leading to dysphagia. Palliation of dysphagia has driven the development of a Robotic Soft Esophagus (RoSE) to provide a novel in vitro platform for esophageal stent testing and food viscosity studies. In RoSE, peristaltic wave generation and control were done in an open-loop manner since the conduit lacked visibility and embedded sensing capability. Hence, in this work, RoSE version 2.0 (RoSEv2.0) was designed with embedded Time Of Flight (TOF) and pressure sensors to measure conduit displacement and air pressure, respectively, for modeling and control. Model Predictive Control (MPC) of RoSEv2.0 was implemented to govern the peristalsis and air pressure profile autonomously. The implemented MPC used Sparse Identification Nonlinear Dynamics with Control (SINDYC) models to estimate the future states of ROSEv2.0. The dynamic models were discovered from the TOF and pressure sensors captured data. Peristalsis waves of speed 20 mm/s, wavelength 75 mm, and amplitudes 5, 7.5, and 10 mm were successfully generated by the MPC. Additionally, RoSEv2.0 with the MPC was employed to perform stent migration testing with various food boluses consistencies.

Channel estimation is one of the main tasks in realizing practical intelligent reflecting surfaceassisted multi-user communication (IRS-MUC) systems. However, different from traditional communication systems, an IRS-MUC system generally involves a cascaded channel with a sophisticated statistical distribution. In this case, the optimal minimum mean square error (MMSE) estimator requires the calculation of a multidimensional integration which is intractable to be implemented in practice. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a deep residual learning (DReL) approach to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy. As a realization of the developed DReL framework, a convolutional neural network (CNN)-based DRN (CDRN) is then proposed for channel estimation in IRS-MUC systems, in which a CNN denoising block equipped with an element-wise subtraction structure is specifically designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously. In particular, an explicit expression of the proposed CDRN is derived and analyzed in terms of Bayesian estimation to characterize its properties theoretically. Finally, simulation results demonstrate that the performance of the proposed method approaches that of the optimal MMSE estimator requiring the availability of the prior probability density function of channel.

Peer-to-Peer (P2P) Networking have a lot of practical applicability’s over the years. File storage and resource sharing are few key areas among the others where such peered network architecture is widely successful. The common building block for P2P networking is to store or locate an identifiable resource, for which there are basically 3 approaches namely (1) local storage/broadcast search (2) global storage/local search (3) distributed storage/distributed search. With the development of mobile hardware and wireless technology, it further became feasible to use mobile devices in these P2P networks. This computing architecture is widely used in Mobile Ad-hoc Network (MANET) for building content sharing applications. Search algorithm and file transfer schemes are the basic components of such content sharing systems. In this article, we provide a brief overview of the resource discovery approaches for peer-to-peer file sharing applications over MANET. We analyze and classify the search techniques into 4 broad schemes, mainly the flooding-based schemes, Distributed Hash Table (DHT) based schemes, advertisement-based schemes, and social network-based schemes. The pros and cons of each technique is summarized. Further, a one-to-one comparison is done across the classes for quick interpretation. We also outline the various issues, and complications that should be taken into consideration while designing any resource discovery algorithm. Further, we briefly discuss the security threats, and present state-of-the-art countermeasures for it. Also, we highlight some important guidelines that need to be focused while designing efficient file sharing applications and services in Mobile Edge Computing (MEC) enabled networks. Comprehensive and in-depth assessments of the related approaches are presented providing clear insights for the future research directions.