Extraction of Dynamic Trajectory on Multi-Stroke Static Handwriting Images Using Loop Analysis and Skeletal Graph Model

The recovery of handwriting’s dynamic stroke is an effective method to help improve the accuracy of any handwriting’s authentication or verification system. The recovered trajectory can be considered as a dynamic feature of any static handwritten images. Capitalising on this temporal information can significantly increase the accuracy of the verification phase. Extraction of dynamic features from static handwritings remains a challenge due to the lack of temporal information as compared to the online methods. Previously, there are two typical approaches to recover the handwriting’s stroke. The first approach is based on the script’s skeleton. The skeletonisation method has highly computational efficiency whereas it often produces noisy artifacts and mismatches on the resulted skeleton. The second approach deals with the handwriting’s contour, crossing areas and overlaps using parametric representations of lines and thickness of strokes. This method can avoid the artifacts, but it requires complicated mathematical models and may lead to computational explosion. Our paper is based on the script’s extracted skeleton and provides an approach to processing static handwriting’s objects, including edges, vertices and loops, as the important aspects of any handwritten image. Our paper is also to provide analysing and classifying loops types and human’s natural writing behavior to improve the global construction of stroke order. Then, a detailed tracing algorithm on global stroke reconstruction is presented. The experimental results reveal the superiority of our method as compared with the existing ones

A Deep Learning Model for Splicing Image Detection

With the advancement of digital technology, manipulating images has become relatively easy through many photo editing techniques. One of the techniques is the splicing image method, which crops parts of images and puts them into another image creating a new composite image. The image splicing detection system is soon regarded as an exciting topic for many researchers to solve the problems of forgery images on the Internet, especially in social networks. ResNet-50 and VGG-16 are powerful architectures of convolutional neural networks, but they reveal many weaknesses when operating on low-end computers. The ultimate goal of this research is to create a model for image splicing detection working well in limited memory machines. The study proposes the model, which is the improvement of VGG-16 applying residual network (ResNet). As a result, the proposed model achieves a test accuracy of 92.5% while the ResNet-50 gives an accuracy of 85.6% after 20 epochs of training 9,319 images from the CASIA v2.0 dataset, which are used for forgery classification. The result proves the efficiency of the proposed model for image splicing detection, especially when working on low-end computers

Historic Building Renovation in San Francisco; A Business Plan

This report explores the history and thought process behind an idea for a historical restoration company based out of San Francisco, California. This is a vital service for both building owners and the public, as historical buildings with deferred maintenance pose a threat to the lives and safety of civilians in the streets below. The city of San Francisco recognized that historical buildings may pose a threat to public safety and recently put an ordinance in place to mandate inspections and repairs of these buildings. There seems to be a gap between firms that profess the ability to assign qualified individuals to perform façade inspections and the actual result that comes from these inspections. A highly experienced team has been put together to run this business, and this report covers the logistics of how this business will operate. Based upon industry leaders’ opinions in the niche, the author’s own experience in the niche, as well as financial data reviewed by a finance major, this plan seems to be viable for a historical restoration business offering consulting and construction management services

Multi-Objective Optimization for IRS-Aidded Multi-user MIMO SWIPT Systems

In this paper, we investigate an intelligent reflecting surface (IRS) assisted simultaneous wireless information and power transfer (SWIPT) system in which users equipped with multiple antennas exploit power-splitting (PS) strategies for simultaneously information decoding (ID) and energy harvesting (EH). Different from the majority of previous studies which focused on single objective optimization problems (SOOPs) and assumed the linearity of EH models, in this paper, we aim at studying the multi-objective optimization problem (MOOP) of the sum rate (SR) and the totalharvested energy (HE) subject to the maximum transmit power (TP) constraint, the user quality of service (QoS), and HE requirements at each user with taking a practical non-linear EH (NLEH) model into consideration. To investigate insightful tradeoffs between the achievable SR and total HE, we adopt the modified weighted Tchebycheff method to transform the MOOP into a SOOP. However, solving the SOOPs and modified SOOP is mathematically difficult due to the non-convexity of the object functions and the constraints of the coupled variables of the  base station (BS) transmit precoding matrices (TPMs), the user PS ratios (PSRs), and the IRS phase shift matrix (PSM). To address these challenges, an alternating optimization (AO) framework is used to decompose the formulated design problem into sub-problems. In addition, we apply the majorization-minimization (MM) approach to transform the sub-problems into convex optimization ones. Finally,  numerical simulation results are conducted to verify the tradeoffs between the SR and the total amount of HE. The numerical results also indicate that the considered system using the IRS with optimal phase shifts provides considerable performance improvement in terms of the achievable SR and HE as compared to the counterparts without using the IRS or with the IRS of fixed phase shifts

Identification and characteristics of MYB4 transcription factor related to regulation of abiotic stress tolerance in peanut

Peanut (Arachis hypogaea L.), an economically valuable crop, provides protein and oil for human and animal consumption. The transcription factor MYB4 has been identified as a potential drought tolerance gene in peanut. This study aimed to isolate and characterize the MYB4 gene in the L14 peanut cultivar. The isolated AhL14_MYB4 gene was found to be 1.1 kb long, with a 663 bp coding sequence containing 3 exons and 2 introns. In silico analysis showed that AhL14_MYB4 possesses a nuclear localization signal and two DNA-binding domains characteristic of transcription factors. The findings revealed key molecular features of AhL14_MYB4 and provided insights into improving drought resistance in peanut varieties. Further research on AhL14_MYB4 may aid efforts to enhance drought tolerance in local peanut cultivars through molecular breeding or genetic engineering. Overall, this finding about preliminary characterization of the peanut MYB4 gene lays the groundwork for potential genetic improvements to this economically important crop

On the Interference Alignment Designs for Secure Multiuser MIMO Systems

In this paper, we propose two secure multiuser multiple-input multiple-output transmission approaches based on interference alignment (IA) in the presence of an eavesdropper. To deal with the information leakage to the eavesdropper as well as the interference signals from undesired transmitters (Txs) at desired receivers (Rxs), our approaches aim to design the transmit precoding and receive subspace matrices to minimize both the total inter-main-link interference and the wiretapped signals (WSs). The first proposed IA scheme focuses on aligning the WSs into proper subspaces while the second one imposes a new structure on the precoding matrices to force the WSs to zero. When the channel state information is perfectly known at all Txs, in each proposed IA scheme, the precoding matrices at Txs and the receive subspaces at Rxs or the eavesdropper are alternatively selected to minimize the cost function of an convex optimization problem for every iteration. We provide the feasible conditions and the proofs of convergence for both IA approaches. The simulation results indicate that our two IA approaches outperform the conventional IA algorithm in terms of average secrecy sum rate.Comment: Updated version, updated author list, accepted to be appear in IEICE Transaction

THE INTERACTION BETWEEN SELF-REGULATED LEARNING STRATEGIES AND EFL TEENAGER LEARNERS’ POSTCARD WRITING AT ENGLISH CENTER IN MEKONG DELTA, VIETNAM

The current study aims at investigating the possible interaction of EFL teenage learners’ postcard writing performance (according to A2 level) and their self-regulated learning strategies at an English center. The research also helps to determine the level of interaction between EFL teenagers’ self-regulated learning (SRL) strategies and their postcard writing performance. Thus, it also examined the frequency of use of SRL strategies in writing among those learners. A total of 74 learners completed 32 items in the self-regulated learning strategies questionnaire including six dimensions of three categories namely environmental processes, behavioral processes, and personal processes. Then, three successful writers and three less successful writers were invited into the semi-structured interview. The findings indicated that SRL strategies had a positive impact on EFL teenage learners' postcard writing. The more SRL strategies used in writing, the higher the learners' score. Among these strategies, environmental factors may have a stronger influence than behavioral or personal factors. Specifically, environmental structuring and help-seeking strategies are most frequently used. The findings also showed that EFL teenage learners use SRL strategies to a moderate degree when given writing tasks. Besides, the results of the interview reveal that successful learners self-regulated better than less successful ones. They also self-evaluated their writing more frequently than those who are less successful. Based on the findings of this study, pedagogical implications and recommendations for further study are presented.  Article visualizations