Reviewerly/exHarmony huggingface dataset:
Experimenting with Unsupervised Multilingual Event Detection in Historical Newspapers

To prevent historical knowledge’s fading, research in event detection could facilitate access to digitized collections. In this paper, we propose a method for annotating multilingual historical documents for event detection in an unsupervised manner by leveraging entities and semantic notions of event types. We automatically annotate the documents by relying on dependency parse trees and automatic semantic mapping to event-based frames, with a focus on the multilingual transfer between frames and candidate events. The documents are afterward verified by native speakers, Digital Humanities researchers. We also report on experimental results of event detection in historical newspapers with a state-of-the-art model. We demonstrate that our approach allows for easy language adaptation by presenting two study cases with knowledge extracted from German newspapers from 1911 to 1933 regarding events surrounding International Women’s Day and from French newspapers between 1900 and 1944 related to the abolition of guillotine executions in France. Our preliminary findings show that this type of approach could alleviate the need for manual annotation by also providing a practical course of action toward unsupervised event detection from multilingual digitized and historical documents.
A High-Accuracy Slotted Hole Detector

Slotted holes are often integrated into industrial parts to flexibly adjust the position of the parts. The measurement of the geometric parameters of the slotted hole is the key to ensure the mechanical properties of the parts. Identifying and detecting the edges of slotted holes on the image is the basis for the slotted hole measurement with high-accuracy. This is a challenging problem because the projection of the slotted hole on the image is not a primitive geometry. In this paper, a high-accuracy slotted hole detector is designed. First, a curvature-based algorithm is introduced to detect the dominant points on the curves of interest after edge detection with Canny detector. Then, a dominant point verification algorithm based on a new projective invariant is proposed to significantly remove incorrect dominant points on the edge of the slotted holes. Finally, a dominant point tuning algorithm based on line and ellipse fitting is developed for accurately finding the segmentations between lines and ellipse arcs to calculate slotted hole parameters. Extensive experiments on real images and synthetic images demonstrate that the proposed slotted hole detector has high detection accuracy and strong robustness.
Combinations of L-Complex Fuzzy t-Norms and t-Conorms

This paper investigates the study of L-complex fuzzy sets. The L-complex fuzzy set, where L is a completely distributive lattice, is a generalization of the complex fuzzy set. The fundamental set theoretic operations on L-complex fuzzy sets are discussed properly, including L-complex fuzzy complement, union and intersection. New procedures are presented to combine the novel concepts of L-complex fuzzy t-norms and t-conorms and look into the conditions that lead to a comparable representation theorem. We have used the axiomatic method, in the sense that our underlying assumptions, especially about L, are abstract; it can thus be ascertained to what extent our results apply to some new problem. On the other hand, our method shows that if mathematics, as we use it, is consistent, so is fuzziness, as we formulate it.
Comparing New Relic with other Performance Monitoring Tools

This paper focuses on how new relic works, its components with their working and how it is different from other tools. New Relic is basically a tool or platform where the developers and any member from the team will monitor its application performance and any infrastructure. The other main usage is that it monitors the errors and sends you an alert when your site or application status is down. From new relic we can easily detect that at what level our online service work, whether it is work as per expectations or not. This paper mainly focuses on the new relic working and to study parameters which makes new relic different from other tools which are available in market for the same purpose.
Breast cancer treatment at home during COVID pandemic using IoT, cloud computing and machine learning

It is crucial that Breast Cancer should be detected early. Breast cancer time series forecasting is a novel data - driven approach to breast cancer diagnosis. Instead of looking at static images of the medical records, it analyses the dynamics in the tumour’s growth rate, especially in its early stages. It uses machine learning models to find patterns that are not readily observable in static images, but are predictive of later outcomes. During COVID-19 it is necessary to monitor patient from home and IOT devices can be used that give moment forecast to the client and doctor during their typical day by day routine. Various Machine learning models are reviewed for classification of Breast Cancer symptoms. It is observed that data visualization and feature engineering play a crucial role in the classification before applying any model on data set. For human protection during COVID-19 it is better to depend on IoT enabled wearable device for automatic detection and appointment. The IoT enabled devices can use power of cloud computing and machine learning models to complete the framework of getting treated at home. Security of the data is another aspect to be taken into consideration. Solutions are available for the whole process and their aggregation will result in generating the desired model. In this paper, model is proposed to diagnose breast cancer at home using IoT, Blockchain, Machine learning and Cloud Computing.
Person Centroid Estimation and Tracking Using an RGB-D Camera

In this paper, we use an RGB-D camera to estimate and track the centroid of a person. In order for a robot to run safely in the crowd, it is important to recognize and track people with high accuracy, and the RGB-D camera is suitable for human tracking because it can not only recognize people with high accuracy based on images but also obtain distance information of the recognized people. The proposed method improves the performance of centroid estimation and tracking by removing noise from the distance information and considering the motion model of the person. The effectiveness of the proposed method is demonstrated by comparing the estimated position and labeling of a person with the true values in an experiment of simultaneous tracking of multiple persons.
UAV-Coin: Blockchain assisted UAV as a Service

The vast majority of 5G users are in eMBB content which requires high bandwidth and moderate latency under a few milliseconds. Today's applications are irregular and not predictable and current fixed wireless access cannot meet these requirements of eMBB traffic. This leads to the use of unmanned aerial vehicles (UAVs) as mobile base stations to increase the capacity of physical infrastructure. However; due to serving eMBB users, the replacement of UAVs for recharging becomes frequent. There are many studies proposing to increase the serving time without any replacement in the literature. Due to the economic limitations of service providers, they prefer partial recovery on the topology. All of these leads use UAV as a service between multi-providers by hiring it from infrastructure on-demand. On the other hand, security is an urgent requirement of 5G while integrating multi-providers. Therefore, this paper proposes blockchain-assisted UAV as a service to handle the smart contracts between providers and UAVs in exchange for UAV-Coin. Thanks to that, multi-providers can hire a UAV on-demand to enhance network capacity without any full recovery on the physical topology. This is implemented by using edge servers that execute UAV, Content, and Database manager modules, respectively. The proof algorithm for blockchain-assisted UAVaaS is proposed. Here, the Proof of Stake (PoS) is preferred for the validation of transactions and adding them into a blockchain in order to meet the latency requirement of eMBB content. According to performance evaluation, the blockchain can be used by up to 10 service providers while considering the processing delay. Moreover, the service providers can earn much more UAV-Coin by being selected as a winner more than once for validation in proof of algorithm.
The Human Side of Cyber Takedowns: Theory and Evidence from GitHub

This paper presents a revealed preference approach to evaluate the welfare of the widely adopted content takedown policy that secures copyright in cyberspace. In our analytical framework, unauthorized reproduction occurs in an open-access platform, either as an unproductive investment to steal the content or as a cost-saving method to expand the content. The first kind of reproduction reduces original contributions, whereas the second kind of reproduction invites them. The takedown policy is only socially beneficial in removing the former, but it is welfare reducing in removing the latter. An event study design using data from GitHub provides evidence that reproduction on this platform is most likely driven by the motive for content expansion, and enforcing the takedown policy is thus inefficient. This welfare loss can be addressed by an appropriate fair-use condition.
Types

In a programming language context, types are used to distinguish values that have different properties. The main reason for using types is to avoid errors: If a value is used in a context where it is expected to have a certain property, and it does not have that property, this is an error.
Finger-Touch Direction Feature Using a Frequency Distribution in the Writer Verification Base on Finger-Writing of a Simple Symbol

In this study, individuals were asked to draw a symbol by using their fingertips on a digital device screen. This study focused on finger-touch direction information that can be extracted from a smartphone screen. To suppress rapid changes in the detected direction data, preprocessing was introduced, and its effectiveness was confirmed by evaluating the verification performance. Finally, representing the direction data as a frequency distribution was introduced as a new feature, which was demonstrated to improve the verification performance.
Research on Security Access Technology of Power Internet of Things Gateway Equipment Based on Artificial Intelligence

Aiming at the problem that the security access of traditional power Internet of things gateway equipment is vulnerable to network attack, resulting in the decline of security, a security access technology of power Internet of things gateway equipment based on artificial intelligence is proposed. Analyze the functional requirements of power Internet of things equipment, and carry out data protection processing of gateway equipment. Based on artificial intelligence technology, network attack characteristics are extracted to realize the secure access of power Internet of things gateway equipment. By means of comparative experiment, it is verified that the safety performance of the new technology is better and has great popularization value.
Point Symmetry

The concept of point group is defined, and the wo-dimensional point groups and three-dimensional groups are presented as the groups of non-translational symmetry, both of the lattices and of the crystals. The symbol is described using the international notation of Hermann–Mauguin, showing also that of Schoenflies. The directions of symmetry and their relation to the crystallographic directions for each type of lattice, both two and three-dimensional, are presented. The equivalence between the point groups and the crystalline classes is shown, since in many texts of mineralogy they appear extensively described. The concept of crystalline form is defined, the types that exist, and how they can be named using a symbol or a name that alludes to a polyhedral form. The concept of the crystalline system is defined as a set of point groups compatible with certain types of Bravais lattices. From the study of the faces of a crystal, its geometrical and symmetrical relations were deduced by the laws of classical crystallography, and they are presented. The representation of the symmetry and faces of a crystal through a projection is described. It starts with the three-dimensional projection on the pole sphere and then the stereographic projection in two dimensions. To do this, the concept of a normal bundle to the faces is used.
Implementation of Reed–Solomon Coder and Decoder Using Raspberry PI for Image Applications

The Reed–Solomon codes are multilevel forward error-correcting block codes in which the encoder operates on byte stream data. Reed–Solomon codes are represented as RS (n = 23, k = 17) block codes. The encoder takes input symbol data (k = 17) and adds ‘n − k’ (t = 6) redundant information. The parity bits/symbols contribute an ‘n’ (23) symbol code word. A Reed–Solomon decoder capacity of correction is up to ‘t’ (3) symbols of the code word, where 2t = n− k (6). The output of the encoder is n symbols comprising of ‘n − k’ parity symbols and k information symbols. The burst error is the condition due to noise interference in the digital data due to reasons such as thunder, white noise, and scratch on a DVD which can be handled optimistically by the proposed block codes. The proposed RS coder and decoder will be able to detect the error and recover the original data from redundant information during encoding. The algorithm is being developed and implemented using Raspberry Pi using Python programming; various test sequences are given as input to the system with and without error to evaluate the error-correcting capability of the system.
Promoting Informatics Workforce Development Through Global Initiatives

This chapter focuses on initiatives that aim to promote the development of the healthcare informatics workforce and discuss the importance of fostering global and interprofessional communities to drive innovation and technology worldwide. This chapter will showcase resources and tools for students, educators, and healthcare professionals co-developed by the Healthcare Information and Management Systems Society (HIMSS) global communities in partnership with other expert organizations. These resources will cover a wide range of health informatics topics—from core competencies recommendation frameworks to adoption models for imaging professionals. Finally, this chapter will describe the current state of the digital healthcare workforce and address the impact of COVID-19 on its development.
Data Driven Modelling and Prediction Of Rainfall

The prediction of weather and is difficult because these phenomena are highly non-linear and complicated phenomena. Technology based on artificial intelligence enables knowledge processing and is utilised in predicting. Synthetic neural network (ANN) has emerged as an alluring substitute for conventional statistical techniques for anticipating the behaviour of nonlinear systems The purpose of this paper is to prevent tools to model and predict rainfall behavior form past observations based on past observation. There are two fundamentally different approaches that are used in the paper to develop a model, both based on statistical methods based on ANNs. The prediction efficiency was evaluated based on 115years of mean annual rainfall between 1901and 2015.
Impact of software product improvement for quality and social requirements

A fast procedure that utilizes hopeless improvement and prototyping is by and large used in various business drives as a lightweight improvement methodology that can meet the necessities. Short practices use what you need for green thing development. The strategy for standard programming program improvement isn’t for every circumstance incomprehensible in managing the rapid substitute of necessities. Enhancements to the product improvement measure incorporate more steady necessities, prior flaw location, decreased testing times, expanded interoperability, and expanded versatility. Various approaches require various changes in programming advancement and the board culture. Various elements can, straightforwardly and in a roundabout way, influence improvement projects in the deft system. The reception of nimble improvement strategies decidedly affects both usefulness and quality. Thus, both the advancement group and the client are happy with its execution in programming improvement measures. Dismissing the advantages of perfection, the grievance of the speedy strategy isn’t consistently enchanted by both turn of events and plan, and no little coordinators can pass on little makers. Here, In this paper, we concentrate on a deft system to check the impact of the program improvement technique for quality, focal and social necessities.
Development of an automatic musical instrument using analog sound source media

In this paper, I describe an automatic musical instrument that uses cams to enable analog-style performance. The purpose of this paper is to propose a new musical expression and performance experience by creating a playback device that allows the user to be involved in the performance without any special skills. The contents of the research and experiments include the investigation of the instrument to be used, the basic experiment of the recording method using the cam mechanism, and other important mechanisms. Through this instrument, we will propose a new musical experience by enabling automatic performance that cannot be expressed by existing music boxes.
Multi-Target Multi-Camera Tracking Based On Mutual Information-Temporal Weight Aggregation Person Re-Identification

In order to solve the problem of multi-target multi-camera tracking, the method of detection and tracking is used to complete single-camera multi-target tracking, and a person re-identification model using mutual information time weight aggregation is proposed to extract appearance features, and determine whether the camera is in the camera. A new method for cross-camera multi-target tracking is finally performed. We use the deep learning framework Pytorch to build network models and tune model-related parameters and compare experiments with existing models on the dataset. The results show that the model has better tracking performance.
Measurer calibration method based on phase triangulation for measurements in limited volume with refraction of optical signals

The paper presents a method for calibration of a three-dimensional geometry measurement system based on the phase triangulation for measurements in the conditions of limited volume with optical signals refraction. The calibration algorithm implements calibration procedures directly in the measurement volume in the presence of all the limiting objects that cause optical signals refraction. The method is universal for various conditions of limiting volume with optical signals refraction. The developed calibration method was tested in the case the limiting volume in the form of a plexiglass tube with the transverse size of 200 mm ´ 200 mm and the wall thickness of 20 mm and the measurement volume of 100 mm ´ 100 mm ´ 100 mm. The accuracy of 12 microns was achieved in the measurement of flat object three-dimensional geometry.
FedALP: An Adaptive Layer-Based Approach for Improved Personalized Federated Learning

Personalized federated learning (PFL) is an improved framework that can facilitate the handling of data heterogeneity by learning personalized models. As personalization performance directly depends on the global model, it is desired to acquire a global model with a decent generalization capability under data heterogeneity. This paper proposes a novel PFL scheme, FedALP, integrating the clustering method with an adaptive layer-based fusion algorithm. Experiments are performed using various neural network models on three standard datasets. Experimental results demonstrate that, compared with the FedAvg method, our scheme can significantly improve the local model’s performance with a negligible decrease in the generalization capability of the global model. Furthermore, our scheme is customizable for specific PFL applications; hence it may provide a flexible strategy to effectuate a balanced performance for both the global and the local models.
Chapter 13 A Logical Conceptualization of Knowledge on the Notion of Language Communication

The main objective of the paper is to provide a conceptual apparatus of a general logical theory of language communication. The aim of the paper is to outline a formal-logical theory of language in which the concepts of the phenomenon of language communication and language communication in general are defined and some conditions for their adequacy are formulated. The theory explicates the key notions of contemporary syntax, semantics, and pragmatics. The theory is formalized on two levels: token-level and type-level. As such, it takes into account the dual—token and type—ontological character of linguistic entities. The basic notions of the theory: language communication, meaning and interpretation are introduced on the second, type-level of formalization, and their required prior formalization of some of the notions introduced on the first, token-level; among others, the notion of an act of communication. Owing to the theory, it is possible to address the problems of adequacy of both empirical acts of communication and of language communication in general. All the conditions of adequacy of communication discussed in the presented paper, are valid for one-way communication (sender-recipient); nevertheless, they can also apply to the reverse direction of language communication (recipient-sender). Therefore, they concern the problem of two-way understanding in language communication.
Quick Response Code Beautification Based on Mask Pattern Optimization

Quick response (QR) code is widely used for mobile payment to convey information. However, the traditional QR code has no semantic content and cannot be understood by human. To remedy this, the QR code beautification is designed to transform the black-white pattern into a semantic content, and make the code more appealing for commercial applications. In this paper, we propose an improved QR code beautification method based on mask pattern optimization. By selecting the optimal mask pattern, the proposed method can reduce the interference of the masking process on the QR code beautification and improve the visual quality of the QR code. The key issue lies in encoding the QR pattern into a given visual content as similar as possible while keeping the acceptable decoding accuracy. The experimental results show that the proposed method can generate a more appealing aesthetic QR code and preserve the high decoding rate for various devices.
Imperfect Competition and Sanitation: Evidence from Randomized Auctions in Senegal

We study the extent to which collusion can explain the under-provision of clean sanitation technologies in developing countries. Using desludging services in Dakar as a case-study, we document that prices are 66% higher in areas where prices are likely coordinated by a large trade association, compared to nearby neighborhoods supplied by unaffiliated companies. We then develop an experimental just-in-time auction platform with random variation in several design features aimed at learning about the extent of competition. Consistent with the collusion hypothesis, we find that most bidders systematically avoid competition by placing round bids and refusing to undercut rivals.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.