Low-complexity Multiclass Encryption by Compressed Sensing

The idea that compressed sensing may be used to encrypt information from unauthorised receivers has already been envisioned, but never explored in depth since its security may seem compromised by the linearity of its encoding process. In this paper we apply this simple encoding to define a general private-key encryption scheme in which a transmitter distributes the same encoded measurements to receivers of different classes, which are provided partially corrupted encoding matrices and are thus allowed to decode the acquired signal at provably different levels of recovery quality. The security properties of this scheme are thoroughly analysed: firstly, the properties of our multiclass encryption are theoretically investigated by deriving performance bounds on the recovery quality attained by lower-class receivers with respect to high-class ones. Then we perform a statistical analysis of the measurements to show that, although not perfectly secure, compressed sensing grants some level of security that comes at almost-zero cost and thus may benefit resource-limited applications. In addition to this we report some exemplary applications of multiclass encryption by compressed sensing of speech signals, electrocardiographic tracks and images, in which quality degradation is quantified as the impossibility of some feature extraction algorithms to obtain sensitive information from suitably degraded signal recoveries.Comment: IEEE Transactions on Signal Processing, accepted for publication. Article in pres

Artificial Intelligence in Invoice Recognition: a Systematic Literature Review

In the era marked by a flourishing economy and rapid advancements in information technology, the proliferation of invoice data has accentuated the urgent need for automated invoice recognition. Traditional manual methods, long relied upon for this task, have proven to be inefficient, error-prone, and incapable of coping with the rising volume of invoices. This research endeavours to addresses the imperative of automating invoice recognition by exploring, assessing, and advancing cutting-edge algorithms, techniques, and methods within the domain of Artificial Intelligence (AI). This research conducts a comprehensive Systematic Literature Review (SLR) to investigate Computer Vision (CV) approaches, encompassing image preprocessing, Layout Analysis (LA), Optical Character Recognition (OCR), and Information Extraction (IE). The objective is to provide valuable insights into these fundamental components of invoice recognition, emphasizing their significance in achieving accuracy and efficiency. This exploration aims to contribute to the development of more effective automated systems for extracting information from invoices, addressing the challenges posed by diverse formats and content. The results indicate that in LA, the combination of Mask Region-based Convolutional Neural Networks (M-RCNN) and Feature Pyramid Network (FPN) achieves goods results. In OCR, algorithms like Convolutional Recurrent Neural Network (CRNN), You Only Look Once version 4 (YOLOv4) and models inspired by M-RCNN and Faster Region-based Convolutional Neural Network (F-RCNN) with ResNetXt-101 as the backbone demonstrate remarkable performance. When it comes to IE, algorithms inspired by F-RCNN and Region Proposal Network (RPN), Grid Convolutional Neural Network (G-CNN) and Layer Graph Convolutional Networks (LGCN), and Gated Graph Convolutional Network (GatedGCN) consistently deliver the best results

Impact Analysis of OCR Quality on Research Tasks in Digital Archives

Humanities scholars increasingly rely on digital archives for their research in place of time-consuming visits to physical archives. This shift in research methodology has the hidden cost of working with digi- tally processed historical documents: how much trust can a scholar place in noisy representations of source texts? In a series of interviews with historians about their use of digital archives, we found that scholars are aware that optical character recognition (OCR) errors may bias their results. They were, however, unable to quantify this bias or to indicate what information they would need to estimate it. Based on the interviews and a literature study, we provide a classification scheme relating schol- arly research tasks to their specific OCR-induced uncertainty and the data required for more reliable uncertainty estimations. We conducted a use case study on a national newspaper archive with example research tasks. From this we learned what data is typically available in digital archives and how it could be used to reduce and/or assess the uncer- tainty in result sets. We conclude that the current knowledge situation on the users’ side as well as on the tool makers and data providers’ side is insufficient and needs further research to be improved

Class II Division 1 malocclusion treatment with extraction of maxillary first molars:Evaluation of treatment and post-treatment changes by the PAR Index

OBJECTIVE To investigate occlusal result and post-treatment changes after orthodontic extraction of maxillary first permanent molars in patients with a Class II division 1 malocclusion. SETTING AND SAMPLE Retrospective longitudinal study in a private practice, with outcome evaluation by an independent academic hospital. Ninety-six patients (53 males, 43 females) consecutively treated by one orthodontist with maxillary first permanent molar extraction were studied, divided into three facial types, based on pre-treatment cephalometric values: hypodivergent (n = 18), normodivergent (n = 21) and hyperdivergent (n = 57). METHODS Occlusal outcome was scored on dental casts at T1 (pre-treatment), T2 (post-treatment) and T3 (mean follow-up 2.5 ± 0.9 years) using the weighted Peer Assessment Rating (PAR) Index. The paired sample t test and one-way ANOVA followed by Tukey's post hoc test were used for statistical analysis. RESULTS PAR was reduced by 95.7% and 89.9% at T2 and T3, respectively, compared with the start of treatment. The largest post-treatment changes were found for overjet and buccal occlusion. Linear regression analysis did not reveal a clear effect (R-Square 0.074) of age, sex, PAR score at T1, incremental PAR score T2-T1, overjet and overbite at T1, and facial type on the changes after treatment (incremental PAR score T3-T2). CONCLUSIONS The occlusal outcome achieved after Class II division 1 treatment with maxillary first permanent molar extractions was maintained to a large extent over a mean post-treatment follow-up of 2.5 years. Limited changes after treatment were found, for which no risk factors could be discerned

Regional, multilateral, and unilateral trade policies on MERCOSUR for growth and poverty reduction in Brazil

The authors estimate that the Free Trade Agreement of the Americas (FTAA), the EU-MERCOSUR agreement, and multilateral trade policy changes will all be beneficial for Brazil. The Brazilian government strategy of simultaneously negotiating the FTAA and the EU-MERCOSUR agreement, while supporting multilateral liberalization through the Doha Agenda, will increase the benefits of each of these policies. The authors estimate that the poorest households typically gain roughly three to four times the average for Brazil from any of the policies considerethe United States protects its most highly protected markets. Both the FTAA and the EU-MERCOSUR agreements are net trade-creating for the countries involved, but excluded countries almost always lose from the agreements. The authors estimate that multilateral trade liberalization of 50 percent in tariffs and export subsidies results in gains to the world more than four times greater than either the FTAA or the EU-MERCOSUR agreement. This shows the continued importance to the world trading community of the multilateral negotiations.Trade Policy,Economic Theory&Research,Rules of Origin,Environmental Economics&Policies,Payment Systems&Infrastructure,TF054105-DONOR FUNDED OPERATION ADMINISTRATION FEE INCOME AND EXPENSE ACCOUNT,Environmental Economics&Policies,Economic Theory&Research,Trade and Regional Integration,Trade Policy

Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system

Automatic number plate recognition (ANPR) systems are becoming vital for safety and security purposes. Typical ANPR systems are based on three stages: number plate localization (NPL), character segmentation (CS), and optical character recognition (OCR). Recently, high definition (HD) cameras have been used to improve their recognition rates. In this paper, four algorithms are proposed for the OCR stage of a real-time HD ANPR system. The proposed algorithms are based on feature extraction (vector crossing, zoning, combined zoning, and vector crossing) and template matching techniques. All proposed algorithms have been implemented using MATLAB as a proof of concept and the best one has been selected for hardware implementation using a heterogeneous system on chip (SoC) platform. The selected platform is the Xilinx Zynq-7000 All Programmable SoC, which consists of an ARM processor and programmable logic. Obtained hardware implementation results have shown that the proposed system can recognize one character in 0.63 ms, with an accuracy of 99.5% while utilizing around 6% of the programmable logic resources. In addition, the use of the heterogenous SoC consumes 36 W which is equivalent to saving around 80% of the energy consumed by the PC used in this work, whereas it is smaller in size by 95%

TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents

Recently, automatically extracting information from visually rich documents (e.g., tickets and resumes) has become a hot and vital research topic due to its widespread commercial value. Most existing methods divide this task into two subparts: the text reading part for obtaining the plain text from the original document images and the information extraction part for extracting key contents. These methods mainly focus on improving the second, while neglecting that the two parts are highly correlated. This paper proposes a unified end-to-end information extraction framework from visually rich documents, where text reading and information extraction can reinforce each other via a well-designed multi-modal context block. Specifically, the text reading part provides multi-modal features like visual, textual and layout features. The multi-modal context block is developed to fuse the generated multi-modal features and even the prior knowledge from the pre-trained language model for better semantic representation. The information extraction part is responsible for generating key contents with the fused context features. The framework can be trained in an end-to-end trainable manner, achieving global optimization. What is more, we define and group visually rich documents into four categories across two dimensions, the layout and text type. For each document category, we provide or recommend the corresponding benchmarks, experimental settings and strong baselines for remedying the problem that this research area lacks the uniform evaluation standard. Extensive experiments on four kinds of benchmarks (from fixed layout to variable layout, from full-structured text to semi-unstructured text) are reported, demonstrating the proposed method's effectiveness. Data, source code and models are available

Information for Impact: Liberating Nonprofit Sector Data

This paper explores the costs and benefits of four avenues for achieving open Form 990 data: a mandate for e-filing, an IRS initiative to turn Form 990 data into open data, a third-party platform that would create an open database for Form 990 data, and a priori electronic filing. Sections also discuss the life and usage of 990 data. With bibliographical references