Reviewerly/exHarmony huggingface dataset:
MC-OCR Challenge 2021: A Multi-modal Approach for Mobile-Captured Vietnamese Receipts Recognition

Mobile captured receipts OCR (MC-OCR) recognizes text from structured and semi-structured receipts and invoices captured by mobile devices. This process plays a critical role in streamlining document-intensive processes and office automation in many financial, accounting, and taxation areas. Although many efforts have been devoted, MC-OCR still faces significant challenges due to mobile captured images’ complexity. First, receipts might be crumpled, or the content might be blurred. Second, different from scanned images, the quality of photos taken by mobile devices shows high diversity due to the light condition and the dynamic environment (e.g., indoor, out-door, complex background, etc.) where the receipts were captured. These difficulties lead to a low accuracy of the recognition results. In this challenge, we target two tasks to address these issues, including (1) evaluating the quality of the captured receipts, and (2) recognizing required fields of the receipts. Our idea is to leverage a multi-modal approach which can take advantage of both areas: computer vision and natural language processing, two of the main interests of the RIVF community. The paper presents the BK-OCR team’s methodology and results in the Mobile-Captured Image Document Recognition for Vietnamese Receipts 2021.
Analogue to NTRU public key cryptosystem by multi-dimensional algebra with high security

In this paper, we present TOTRU a new multi-dimensional public-key cryptosystem. TOTRU is based on the octonion algebra as an alternative to the NTRU-mathematical structure which non-commutative, non-associative. It has been differentiated from NTRU by creating two public keys. This structure increases the complexity and security of TOTRU. We also show that TOTRU is an improvement over NTRU in terms of security and efficiency.
Deriving the Wug-Shaped Curve: A Criterion for Assessing Formal Theories of Linguistic Variation

In this review, I assess a variety of constraint-based formal frameworks that can treat variable phenomena, such as well-formedness intuitions, outputs in free variation, and lexical frequency-matching. The idea behind this assessment is that data in gradient linguistics fall into natural mathematical patterns, which I call quantitative signatures. The key signatures treated here are the sigmoid curve, going from zero to one probability, and the wug-shaped curve, which combines two or more sigmoids. I argue that these signatures appear repeatedly in linguistics, and I adduce examples from phonology, syntax, semantics, sociolinguistics, phonetics, and language change. I suggest that the ability to generate these signatures is a trait that can help us choose between rival frameworks.
Image Synthesis of Warli Tribal Stick figures using Generative Adversarial Networks

With the advancement in deep learning methods, Generative Adversarial Neural Networks(GANs) have been successful in the application of Computer Vision, Natural Language Processing, and medical. It is observed to have great research potential of using GAN for Image Synthesis. GANs can generate meaningful samples from complex patters or distributions. We present a method to generate Asian Tribal human synthetic stick figures using GAN. With variety of applications the output are limited to low resolution and are unrealistic. The work includes image to image translation where the inputs are low resolution images and as the training progresses, addition of new layers lead to better or high resolution output. For this purpose, it was mandatory to train the generator and discriminator following the principles governing the concept of transfer learning and to fetch evidence of how the generated output resembles the original data set. We also extend additional visualizations of our approach and support our GAN model with images of our generated output. This piece of work includes customized data set as input, where the input images are constructed based on laws of coordinate geometry and spherical polar coordinates and final generated images to illustrate its resemblance with the actual input images. The generated images were quite efficient in faking the discriminator, contributing towards robustness, precision and viability of the stated architecture.
A Comprehensive Review on Security and Privacy Preservation in Cloud Environment

With the widespread growth of the Internet of Things (IoT) devices and emergent data generated at the edge computing network, the conventional cloud computing (CC) model encounters various bottleneck issues like resource constraints and bandwidth constraints. Thus, edge computing devices help in processing and storing data at the edge level and intended as a promising solution for the past few years. Moreover, the unique nature of computing devices like parallel processing, content perception, and real-time computing have introduced various challenges in privacy preservation and data security. These two factors are considered as the key factors in this review. In this survey, an empirical analysis of the privacy and security threats, protection methodologies, and related countermeasures are inherited in cloud computing. Specifically, an overview of basic CC definitions, applications, and architectural models are discussed. Subsequently, an extensive analysis is performed specifically with data security, privacy, challenges, requirements, and mechanisms in CC. Various cryptographic-based methods based on privacy preservation and security are discussed in this review. The existing issues related to security and privacy in CC are reviewed and finally, various open research directions related to privacy and security in the CC field are also discussed.
WPU: A FPGA-based Scalable, Efficient and Software/Hardware Co-design Deep Neural Network Inference Acceleration Processor

Convolutional neural network (CNN) based algorithms are becoming dominant in a broad range of applications. Due to the high computation requirement, accelerator design for CNN has drawn many research interests in recent years. Besides GPU and ASIC, FPGA is also a major resource for CNN computation due to its re-programmability and flexibility for architecture designs [9].In this paper, we propose a software-hardware combined CNN accelerator design for FPGAs. Our focus is on architecture design for computation efficiency per DSP and the adaptability for different sizes of FPGAs. We designed a scheme of network pruning algorithm which enables simpler sparse data selection logic in hardware. Our software-hardware combined solution achieves 4x computation speedup with the same number of DSPs. Full pipeline of data transferring and computing reduces the requirement of BRAM usage significantly. Our test on Yolo-v2 [8] network shows a DSP efficiency enhancement of 3x over RTX 2070 GPU and 58% over an existing FPGA design in [10], respectively.
Let'sTrace — Blockchain, Federated Learning and TUF/In-ToTo Enabled Cyber Supply Chain Provenance Platform

&#x201C;Let&#x0027;sTrace&#x201D; is a blockchain-enabled cyber supply chain provenance platform. It enables cyber supply chain verification with TUF (The update framework) and In-ToTo frameworks. In this paper, we discuss a prototype where the TUF and In-ToTo frameworks have been integrated into a blockchain smart contract platform to facilitate robust supply chain verification functions. In this paper, we describe the TUF and In-ToTo frameworks and how they are implemented as the secure software update system which can verify the software update and use In-ToTo metadata files as the software supply chain end-to-end verification. Further, we have proposed a mechanism to analyze cyber supply chain information and software updates on different parties using a blockchain-enabled federated learning system. With this approach, we make the cyber supply chain more secure, reliable, and meaningful to conduct further analytics efficiently. This paper aims to demonstrate how through the use of our integrated approach, cyber defenders can harden relevant SSCs thus reducing a multitude of attack vectors leading to a stronger overall security posture.
Packet Format Detection and Modulation Classification of Wireless LAN Using Distributed Convolutional Neural Network

This paper proposes a method to detect the packet format and classify signal modulation type of wireless LAN signals using the distributed model of Convolutional Neural Network (CNN). The main advantages of this method compared to the conventional are the high accuracy of the model and the flexibility for lowering the complexity. These are achieved because the CNN models are trained to perform multiple small classification tasks instead of a single big classification task. This method makes retraining much easier. The five-fold cross-validation is applied to assess the performance for training and testing the model. To validate the model, actual Wi-Fi signals are used to test the model using Software Define Radio (SDR). The result shows the proposed method can accurately classify different packet formats and signal modulation types above 90%, even when different timing offsets occur.
An Image Compression Algorithm Based on Quantization and DWT-BP Neural Network

The traditional BP (Back Propagation) neural network can realize image compression, but because it extracts the overall features of images, there is still a problem of low compression rate. In this paper, an image compression algorithm based on DWT-BP (Discrete Wavelet Transformation - Back Propagation) network is proposed, which separates the low-frequency information and the high-frequency information of images, and extracts the eigenvalues of the frequency coefficients. These eigenvalues are normalized and then put into the network for repeated training, and quantifies the network output-stream. Experimental results show that the compression time of the two algorithms is similar with the same parameters. When the quality of decompressed image meets the requirement of human eyes for image quality, the compression rate of the proposed algorithm is 10.04% higher than that of BP neural network algorithm on average. This algorithm can store more digital information in the same space, save bandwidth, and achieve fast transmission.
Impact Assessment of IT Security Breaches in Cyber-Physical Systems: Short paper

The increased cyber-attack surface in cyber-physical systems, the close coupling to vulnerable physical processes, and the potential for human casualties necessitate a careful extension of traditional safety methodologies, e.g., error propagation analysis (EPA), with cybersecurity capabilities. We propose a model-driven Information Technology/Operational Technology impact analysis method that supports identifying vulnerabilities, most critical attack strategies, and most dangerous threat actors by analyzing attack scenarios on an abstract functional model of the system. Our solution extends EPA, initially developed for dependability and safety analysis, with cybersecurity aspects to explore the safety impact of a cyber attack on a cyber-physical system. The paper presents the impact analysis workflow, the threat model, the pilot analysis tool, and a case study.
*.chain: automatic coding of smart contracts and user interfaces for supply chains

In this paper we present a framework for automatically coding blockchain based supply chain management systems starting from a Domain Specific Graphical Language (DSGL) interface modeling the typical interactions of the actors of a value chain. For each asset defined in the DSGL, a solidity smart contract is created and for each interaction a specific method is defined. The DSGL allows the specification of the roles of the actors involved in the value chain, and of a set of constraints in order to permit the execution of operations on the assets only to users with specific roles. Besides the smart contracts implementing the supply chain management system, two web based user interfaces are produced by our framework for the management of the supply chain designed through the DSGL: one for the supply chain administrator and the other for the supply chain participant.
A Mobile DNN Training Processor With Automatic Bit Precision Search and Fine-Grained Sparsity Exploitation

In this article, an energy-efficient deep learning processor is proposed for deep neural network (DNN) training in mobile platforms. Conventional mobile DNN training processors suffer from high-bit precision requirement and high ReLU-dependencies. The proposed processor breaks through these fundamental issues by adopting three new features. It first combines the runtime automatic bit precision searching method addition to both conventional dynamic fixed-point representation and stochastic rounding to realize low-precision training. It adopts bit-slice scalable core architecture with the input skipping functionality to exploit bit-slice-level fine-grained sparsity. The iterative channel reordering unit helps the processor to maintain high core utilization by solving the workload unbalancing problem during zero-slice skipping. It finally achieves at least 4.4× higher energy efficiency compared with the conventional DNN training processors.
Interactive Poster Design System for Movies with StyleGAN

A movie poster design system is proposed by introducing interactive evolutionary computation (IEC) into StyleGAN. This system suggests a poster layout and colors, which are suitable for the movie, in order to help designers to create nice posters efficiently. In this system, first, StyleGAN generates multiple movie posters with various layouts and colors, and then IEC is applied to refine the design based on the user’s subjective evaluation. IEC updates the latent space of StyleGAN by evolutionary process. Since each part in the latent space corresponds to the rough structure, detailed structure, and color of the poster respectively, the design can be controlled by manipulating each part of the latent space. Computer simulations shows effective performance of this method.
Registration and Login Scheme of Charity Blood Donation System Based on Blockchain Zero-Knowledge Proof

With the development of Internet technology, Internet technology provides a platform for charity to allow people to participate together. Currently, each charitable blood donation registration agency operates independently, donors need to fill in personal information multiple times to register and log in when they want to register with different institutions. The information is collected by various platforms, which increases the risk of personal information leakage. In order to protect the security of the personal information used by the user during registration, and relieve the user from the many troubles caused by the leakage of personal information. This article proposes a user registration and login method based on blockchain technology, through the construction of a alliance chain, the blockchain technology is integrated with the registration and login system. To ensure that the user’s information is kept confidential to the charity blood donation platform and use the mobile phone number to register in the alliance chain under the premise of hiding the user’s login application record, successful registration can log in to all platforms on the alliance chain. The alliance chain system issues unique identification numbers to the charitable blood donation platform nodes in the chain, and users use their accounts to post transactions to apply for login nodes, and generate zero-knowledge range proofs to hide login records during the application login process.
A Study on the Privacy Threat Analysis of PHI-Code

During the period of epidemic prevention and control, personal health information code (PHI-code) is widely used in China and has brought significant benefits, including helping to manage the flow of people effectively and to slow down the spread of corona virus. However, the personal information security issues involved in the use of PHI-code has also attracted people’s attention. To solve this problem, we can use the security threat modeling to strategize and protect personal information. There are various security threat modeling techniques exist today, such as LINDDUN, PASTA and NIST. In this paper, we analyze all possible privacy threats contained in PHI-code by using LINDDUN, which is a threat modeling technique for personal information protection and is not used in the analysis of domestic PHI-code. Finally, we derive threat mitigation strategies based on the security evaluation results. This study will improve the future public health information collection system towards more privacy protection and promote the stable development of society.
Towards Transformer-Based Real-Time Object Detection at the Edge: A Benchmarking Study

Recent work has demonstrated the success of end-to-end transformer-based object detection models. These models achieve predictive performance that is competitive with current state-of-the-art detection model frameworks without many of the hand-crafted components needed by previous models (such as non-maximal suppression and anchor boxes). In this paper, we provide the first benchmarking study of transformer-based detection models focused on real-time and edge deployment. We show that transformer-based detection model architectures can achieve 30FPS detection rates on NVIDIA Jetson edge hardware and exceed 40FPS on desktop hardware. However, we observe that achieving these latency levels within the design space that we specify results in a drop in predictive performance, particularly on smaller objects. We conclude by discussing potential next steps for improving the edge and IoT deployment performance of this interesting new class of models.
Arpanet and Its Evolution - A Report Card

This article is a “back to the future” report card on the Internet's seedling, the Arpanet. The vision and goals for the Arpanet are evaluated along several aspects of functionality, performance, and evolution. The resulting grades suggest what direction future evolution might take to allow the Internet to evolve into what it was intended to be.
Review of OFC 2021: The future of optical networks and communications. Virtual conference 6–10 June 2021

This is a detailed review of the latest developments presented at the last OFC 2921 virtual conference. The review covers: Game-Changing developments, specifically Hollow Core Fibers (HCF), Neural Networks (NN) Applications, and Quantum Key Distribution (QKD); and Incremental developments, in particular, Sensors and Data Centers, High-Speed Coherent Optics Transmission, with the latter's emphasis on Optical Switching.
Hyperledger Fabric Node Types and Performance Study

Permissioned blockchain option is emerging as an alternative solution to business administration for its trustworthiness in place of or in tandem with conventional solutions. Hyperledger Fabric is a well-defined permissioned blockchain. It consists of various types of nodes such as nodes for endorsement, ordering and commitment, and they play a role in the performance equation, and a sound understanding of their performance is essential to address and resolve the issue of performance optimization in the perspective from the backend architectures and systems. This paper presents quantitative models for the performance of those above-mentioned primary types of nodes in the permissioned blockchains. Each type of nodes is characterized in terms of transaction/block queue size and waiting time, and the transaction/block arrival rates and the transaction/block service rates are considered for simulation purpose. The proposed models for the various types of nodes in Hyperledger Fabric provide a quantitative basis to assure and optimize the design of each type of nodes and ultimately the overall performance of the permissioned blockchain. Extensive numerical simulations are conducted and presented.
A Multi-Objective Optimization Approach for Multi-Vehicle Path Planning Problems Considering Human–Robot Interactions

Abstract There has been unprecedented development in the field of unmanned ground vehicles (UGVs) over the past few years. UGVs have been used in many fields including civilian and military with applications such as military reconnaissance, transportation, and search and research missions. This is due to their increasing capabilities in terms of performance, power, and tackling risky missions. The level of autonomy given to these UGVs is a critical factor to consider. In many applications of multirobotic systems like “search-and-rescue” missions, teamwork between human and robots is essential. In this article, given a team of manned ground vehicles (MGVs) and UGVs, the objective is to develop a model that can minimize the number of teams and total distance traveled while considering human–robot interaction (HRI) studies. The human costs of managing a team of UGVs by a MGV are based on human–robot interaction (HRI) studies. In this research, we introduce a combinatorial, multi-objective ground vehicle path planning problem that takes human–robot interactions into consideration. The objective of the problem is to find: ideal number of teams of MGVs-UGVs that follow a leader–follower framework where a set of UGVs follow an MGV and path for each team such that the missions are completed efficiently.
Computational Solutions for Human Falls Classification

In the last two decades, studies about using technology for automatic detection of human fall increased considerably. The automatic detection of falls allows for quicker aid that is key to increasing the chances of treatment and mitigating the consequences of falls. However, each type of fall has its specificities, and determining the correct type of fall can help treat the person who has fallen. Although it is essential to use computational methods to classify falls, there are few studies about that in the literature, especially compared to the studies that propose solutions for fall detection. In this sense, we execute a systematic literature review (SLR) using the Kitchenham (2009) [1] method to investigate the computational solutions used to classify the different types of falls. We performed a search on Scopus, Web of Science and PubMed scientific databases looking for computational methods to falls classification in their papers. We use the grounded theory methodology for a more detailed qualitative analysis of the papers. As a result of our search, we selected a total of 36 studies for our review and found two different computational methods for classifying falls. Related to the steps used in each method, we found fourteen different types of sensors, four different techniques for background and foreground extraction of videos, twenty-one techniques for feature extraction, and seven different fall classification strategies. Finally, we also identified fifty-one different types of falls. In conclusion, we believe that the methods and techniques analyzed in our study can help developers to create new and better systems for classification, detection, and prevention of falls and falls database. Besides, we identified gaps that can be explored in future research related to the automatic classification of falls.
Challengeable Legislation Against Fatal Occupational Accidents in Republic of Korea

The new legislation, the Major Accident Punishment Act, is effective on January 2022. The author discuss the background of legislation and estimated effects caused by the Act.