Reviewerly/exHarmony huggingface dataset:
Likhai Database Urdu Handwritten Text Using Sampling Strategies

This research presents an Urdu handwriting database named as LIKHAI database, which is designed to facilitate Urdu handwritten text recognition. A standard Urdu database ‘LIKHAI’ was developed consisting of 456 forms. The database is then divided in seven different categories to get the variety of words. These forms are written by different writers of different ages, education level etc. Sampling strategy was used for realistic representation of the Urdu handwriting, two different samples, that is writer sample and text sample, were selected. The writer sample helps to find balanced writer samples and the text sample helps to find a compact collection of text that covers all the characters of Urdu language. Then the handwritten forms are scanned on 300 dpi and saved as in .XML format. This database covers all the shapes of the Urdu Language characters and it contains all natural and real Urdu handwritten words. Then the filters are applied on the first page of each form in order to extract the information of writers. This database can be used for multiple tasks like text recognition, Ground Truth, and Line segmentation. Statistics of underlying research showed that database provides an excellent representation of Urdu handwriting.
Rethinking the Bottleneck in Diversifying the Cybersecurity Talent Pool

Although the number of cybersecurity programs is increasing, the field remains challenged in sourcing talent, particularly among underrepresented groups (URGs). The need for inclusion and diversity is critical to a better workforce and to better problem solving which is critical in cybersecurity. Many people want to contribute to diversifying cybersecurity but don't know where/how to start. In this BoF session, participants will explore the following questions: (1) what are the most significant barriers preventing wider diversity in cybersecurity, (2) what are the most significant enablers to increase URGs participation in cybersecurity, (3) what are some actionable items to improve the diversity landscape in cybersecurity, and (4) how to measure the success of these actions. Participants should exit this session with a better understanding on what change they can make to have a positive impact on diversifying the cybersecurity workforce at their institution. This BoF will plant a seed for a community of educators who want to contribute to the cybersecurity diversification effort through evidence-based interventions. A summary of the BoF discussion will be disseminated publicly and shared with the BoF participants for possible future collaboration. The BoF facilitators will create a working group for the participants to follow up on diversification efforts and will hold periodic virtual meetings to discuss contributions, observations, outcomes and recommendations.
Integrating Blockchain and Edge Computing in Internet of Things: Brief Review and Open Issues

Blockchain has catched significant attention and has been used in several contexts, like Internet of Things (IoT). Anyway, blockchain has a scalability problem that limits its capability to sustain services with several operations. Edge computing is an emerging computing paradigm introduced to distribute services at the network edge, leveraged by the need of meeting low response requirements, mainly for IoT applications. However, edge systems currently face challenges in their decentralized administration and security. The combination of edge computing and blockchain in IoT gives the possibility to solve these challenges. In this paper, we inquire the synergistic combination of blockchain with edge computing in the context of IoT systems. In detail, we first discuss about Edge Computing and its challenges. Then, we introduce the blockchain in Edge-based IoT and analyze several proposal present in the literature. In the end, we draw the open research direction.
Geometric Distribution

After finishing the chapter, readers will be able to … Understand geometric distributions. Explore properties of geometric distribution. Discuss arithmetico-geometric distribution. Apply geometric distribution to practical problems.
Data Sets and Challenges

In this chapter, we describe some of the most prominent shared tasks and data sets used in neural approaches to text production.
Securing Digital Image u sing Chaotic Based Cryptosystem

As the unfold boom of Internet get entry to the need for multimedia specifically digital images are widely applicable in various areas such as military, medical, science, education, advertising, entertainment and many others. As human beings around the world exchange these digital images assuring the privacy and reliability of images has turn out to be a principal concern. Thus ciphering the image comes into account. The relationship between the pixel values of neighboring pixels of a plain digital image is strong. The proposed cryptosystem breaks the relationship of neighboring pixel values by altering its position with the use of Arnold cat map equation. Then the shuffled image is diffused using a symmetric keys to yield an encrypted image. The experimental result shows that our cryptosystem effectively cipher/decipher the digital image with external secret key. The experimental result of the proposed cryptosystem was done using MATLAB R2018a and shows it is capable of producing an encrypted image with extremely low relationship coefficients of neighboring pixels. We also found that it is very sensitive to any input key and parameters change. Consequently the proposed cryptosystem has strong encryption quality and invulnerable way for a colored image. Considering the security necessities, this work can have a focus in securing medical end results, country wide secrets and plenty of others which are exchanged over the Internet.
Dual Patient – Healthcare Provider Experience Mapping and Implications for Information Technology Deployment and Clinic Layout

The implementation of information technology, in general, and electronic medical records (EMR), in particular, can have implications on various aspects of the healthcare process. This paper describes the development and implementation of a dual experience mapping technique to identify the mutual experience of patients and their families, on the one hand, and healthcare providers, on the other hand, as part of the preparation for deploying a new EMR system in a hospital environment. The dual experience mapping focused on identifying mutual pain points associated with the use of the new system as a function of various physical locations within hospital clinics. The findings facilitated the identification of mutual needs and definition of solution directions. Specifically, the recommendations addressed the layout of clinics with the deployed system in a way that addresses identified pain points and needs. The effectiveness of using the dual experience mapping is demonstrated in this project as a technique that can help in patient-oriented design and patient empowerment in healthcare.
Information Fusion for Driver Distraction Studies Using Eye Tracking Glasses

Eye tracking research about driver distraction, applied to real world driving tasks, has so far demanded a massive amount of manual intervention, for the annotation of hundreds of hours of head camera videos. We present a novel methodology that enables the automated integration of arbitrary gaze localizations onto a visual object and its local surrounding in order to draw heat maps directly onto the environment. Gaze locations are tracked in video frames of the eye tracking glasses’ head camera, within the regions about the driver’s environment, using optical flow methodology. The high robustness and accuracy of the optical flow based tracking - measured with a residual mean error of ca. 0.3 pixels on sequences, captured and verified in 576 individual trials - enables a fully automated estimation of the driver’s attention processes, for example in the context of roadside objects. We present results from a typical driver distraction study and visualize the performance of fully aggregated human attention behavior.
Correlation properties in worldwide and synthetic earthquake networks

In this work, we have studied correlation properties of earthquake networks, by the assortativity analysis of global and synthetic earthquakes networks.We have used data from the worldwide earthquake catalog for the period between 2002 and 2016 of earthquakes with magnitude greater than or equal to 4.5, with shallow events (depth up to 70 km) and deep events (depth greater than 70 km) being analyzed separately, and an earthquakes catalog simulated with a modified version of the OFC model.The study was conducted for two methodologies of connections: the successive model and the time window model.We calculated the nearest-neighbors average connectivity of nodes and the degree correlation coefficient for all networks.It was observed that only the networks built from shallow earthquakes and synthetic data have assortative mixing.These results are in concordance with previous works; however, the present work is more comprehensive.We observed that the networks of deep earthquakes, are neutral.Our results contribute to a better understanding of the seismological dynamics and the correlations existing between different seismological regions.
Development of machine-learning based anomaly detection system for manufacturing

IoT is widely applied in the modern manufacturing system to acquire the time-series data from each individual equipment. Based on such time-series data, a variety of anomaly detection methods have been proposed for improvement in safety and productivity. However, conventional methods have some problems such as insufficient supervised anomaly data and need of extra procedure of feature extraction. As a solution, we developed a machine-learning based anomaly detection model which combines AE (Autoencoder) with LOF (Local Outlier Factor) in a simple way. This model requires the normal data only for AE at the training phase, to extract the features from the raw time-series data. At the detection phase, LOF is applied to calculate the deviation of input data in feature space. The model has been verified by use of the phase-shifted sine waves generated artificially. As a result, we conclude our model can discern the normal data from anomalies.
Preparation and photocatalytic performance of fly ash based nanocomposites

In order to solve the problems of low accuracy and high time consuming in the traditional photocatalytic performance analysis methods of nanocomposites, a new method for preparation and photocatalytic performance analysis of fly ash nanocomposites was proposed. Mesoporous molecular sieve was constructed, and the obtained grey massive nano TiO2/fly ash composite was inserted into the pore channels of mesoporous molecular sieve to complete the preparation of fly ash based nanocomposites. Methyl orange was used as a degradation compound to test the photocatalytic performance of the material, and the first-order kinetic model and degradation rate calculation results of methyl orange were obtained, so as to realise the photocatalytic performance analysis of the material. The experimental results show that the photocatalytic performance of the materials prepared by this method is high, and the average accuracy of photocatalytic performance analysis is 95.5%, the average time is 0.63 s.
Institutional support for data management plans: case studies for a systematic approach

Researchers have to ensure that their projects comply with Research Data Management (RDM) requirements. Consequently, the main funding agencies require Data Management Plans (DMPs) for grant applications. So, institutions are investing in RDM tools and implementing RDM workflows in order to support their researchers. In this context, we propose a collaborative DMP-building method that involves researchers, data stewards and other parties if required. This method was applied as part of an RDM workflow in research groups across several scientific domains. We describe it as a systematic approach and illustrate it through a set of case studies. We also address the DMP monitoring process during the life cycle of projects. The feedback from the researchers highlighted the advantages of creating DMPs and their growing need. So, there is motivation to improve the DMP support process according to the machine-actionable DMPs concept and to the best practices in each scientific community.
Interlinking and enrichment of disparate organisational data with LOD at application run-time

The present work focuses on the semantic integration, enrichment and interlinking of data that is semi-automatically generated by documenting artworks and their creators. In this work, we have been experimenting with RDFization and links discovery tools, W3C standards and widely accepted vocabularies. This work has been already evaluated with museum data, emphasising the discovery of links between disparate data sets and external data sources at the back-end of the proposed approach. In this paper, while contributing a number of other new and extended features at the back-end of this approach, we emphasise links discovery at the front-end in order to interlink and enrich cultural data with LOD at application run-time and facilitate the real-time and up-to-date exploitation of semantically integrated and LOD-enriched data. This is achieved by implementing a custom links discovery method and evaluating it within a web application using LOD cloud data sources such as DBpedia and Europeana.
The Inherent Text Difficulty Consistency of Parallel Translation Tasks in CET-4

College English Test 4 (CET-4) is a high-stakes test in China. Since December 2013, its translation task has been reformed and there are three parallel translation tasks for one test from which only one is randomly assigned to a test taker. Therefore, the text difficulty consistency issue becomes particularly important for CET-4 translation tasks. This paper collected 51 translation task texts from December 2013 to December 2021 and examined their inherent text difficulty with the corpus tool, the Chi-Editor.&#x0D; The texts were divided into groups randomly, by their text difficulty values, and by the test months. The text difficulty values were then analyzed with a paired and independent t-test through SPSS 23.0. The results showed that there were significant differences in the inherent text difficulty level among the three parallel translation tasks if they were divided based on their text difficulty level indexed by Chi-Editor, but the difficulty differences in parallel translation tasks were not salient if they were grouped randomly or by test months. It was suggested that more carefulness should be exercised with the development of CET-4 parallel translation tasks to secure the inherent text difficulty consistency and thus guarantee fairness for each individual test taker.
Accurate prediction of nano impermeability and antifreeze concrete ratio in high-rise buildings

In order to solve the problem of large error between the ratio prediction result and the actual value in the traditional method, an accurate ratio prediction method of nano frost resistant concrete for high-rise buildings is proposed. The NPCA method is used to eliminate the abnormal data, the principal component analysis method is used to obtain the data combination and its score vector and load vector at each time point, and the weight in the structure is updated in the form of top-down to obtain the effective characteristics of the data, so as to realise the accurate prediction of concrete ratio. The simulation results show that the difference between the predicted value and the measured value of this method is small, and the RMSE value is low, indicating that its prediction accuracy is high, which verifies the effectiveness of this method in concrete proportioning prediction.
Speech and video processing for semi-automatic generation of surgery reports in paranasal sinus surgery

The aim of the project is to develop a documentation tool that generates a surgical report based on voice input and endoscopic video recordings during paranasal sinus surgery. Thus, the documentation time shall be shortened and the report quality shall be improved.
CIRS: A Confidence Interval Radius Slope Method for Time Series Points Based on Unsupervised Learning

The rise of big data has brought various challenges and revolutions to many fields. Even though its development in many industries has gradually become perfect or even mature, its application and development in complex industrial scenarios is still in its infancy. We run research on single-dimensional time series point anomaly detection based on unsupervised learning: Unlike periodic time series, aperiodic or weakly periodic time series in industrial scenarios are more common. Considering the need for online real-time monitoring, we need to solve the problem of point anomaly detection of oil chromatographic characteristic gases. Thus, we propose a sliding window-based method for the unsupervised single-dimensional time series point anomaly detection problem called the confidence interval radius slope method (CIRS). CIRS is a fusion of knowledge-driven and data-driven methods to realize online real-time monitoring of possible data quality problems. From the experimental results, CIRS has obtained higher PR values than other unsupervised methods by the subject data.
Variability Handling in Multi-Mode Service Composition

Information services are designated for information processing intensive tasks and may require different levels of human involvement in their execution, e.g., in information processing and analysis. The handling of variability in information service systems incorporates the concern of human involvement in service execution or, as we denote it, functioning mode of service. The existence of different functioning modes of services raises a problem of multi-mode service composition. In the paper we propose the use and the extension of variability representation model to represent variability in the information service system and present the multi-mode service composition approach to derive consistent flows of both abstract and concrete services.
Conclusion and Future Directions

According to the cognitive viewpoint of IR research, search systems are essentially interactive and hence users should not be abstracted out of system design and evaluation processes (Belkin, 2015; Ingwersen, 1996). To understand the role of users in search interaction and to evaluate IR systems with users, IIR researchers often design user studies of different types to examine various elements, dimensions, and stages of the information search and evaluation process. Therefore, to sharpen the instruments of IIR user studies and to improve the reliability and validity of the associated conclusions, it is critical to develop a comprehensive, generalizable framework for characterizing, reporting, evaluating IIR user studies.
Introduction

We are living in an information age where various information systems, devices, and recommendations are becoming ubiquitous. The ubiquity of systems and available information supports the diverse interactions of people with information within a variety of environments and largely expands the role of Information Retrieval (IR) in modern society. Specifically, the adoption and usage of search systems have gone beyond traditional libraries and workplaces and have become increasingly more common in everyday–life situations (e.g., checking weather before travel, finding a nearby gas station, checking the distance between a conference venue and the airport). New modes and interfaces of information retrieval (e.g., mobile search, spoken/conversational search, tangible infor– mation retrieval) continue to emerge from the development of a series of cutting–edge technologies (e.g., Jansen et al., 2010; Kamvar and Baluja, 2006; Trippas et al., 2018; White, 2018).
ASPEK-ASPEK KESULITAN PESERTA DIDIK DALAM MEMAHAMI MATA PELAJARAN IPA DI KELAS V SDN PETAPA KECAMATAN PARIGI TENGAH KABUPATEN PARIGI MAUTONG

This study aims to determine the aspects of students' learning difficulties in science subjects in class V at SDN Petapa and to find out the teacher's efforts to overcome students' difficulties in understanding science subjects in class V at SDN Petapa. This type of research is descriptive research with a qualitative approach. The subjects in this study were 16 students with learning difficulties in science and class teachers. Data collection techniques using interview techniques, observation and documentation. The data analysis of this research used the Interactive Analysis Model. In obtaining the validity of the data, the researchers conducted a credibility and confirmability test. The results of this study indicate that aspects of students' difficulties in understanding science subjects consist of two aspects, namely internal aspects which include aspects of interest, motivation and self-readiness aspects of students. Next is the external aspect, namely school facilities, teachers and infrastructure. Meanwhile, the teacher's efforts in overcoming learning difficulties in science subjects are diagnosing learning difficulties with the aim of identifying the causes of learning difficulties.
End-to-End Call Authorization in Public Telephony

More than a decade of papers talking about preventing SPIT (SPam over Internet Telephony), yet we still receive spam calls these days. We recover here an old proposal about issuing secret codes to potential callers, which they are to provide on each call-in order to prove legitimacy. As an up-to-date contribution, we suggest an out […]
Data Pipelines

Most of our data ingestion into Snowflake is via one of two routes: flat file submission loaded via S3 or directly landed using ELT/ETL tooling. We do not address ELT/ETL tooling specifically in this chapter but do call out entry points where relevant, instead of focusing on the Snowflake components and interactions.
Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

We present SymLearn, a method to automatically infer fault tree (FT) models from data. SymLearn takes as input failure data of the system components and exploits evolutionary algorithms to learn a compact FT matching the input data. SymLearn achieves scalability by leveraging two common phenomena in FTs: (i) We automatically identify symmetries in the failure data set, learning symmetric FT parts only once. (ii) We partition the input data into independent modules, subdividing the inference problem into smaller parts. We validate our approach via case studies, including several truss systems, which are symmetric structures commonly found in infrastructures, such as bridges. Our experiments show that, in most cases, the exploitation of modules and symmetries accelerates the FT inference from hours to under three minutes.
Pinpointing Axioms in Ontologies via ASP

Axiom pinpointing is the task of identifying the axiomatic causes for a consequence to follow from an ontology. Different approaches have been proposed in the literature for finding one or all the subset-minimal subontologies that preserve a description logic consequence. We propose an approach that leverages the capabilities of answer set programming for transparent axiom pinpointing. We show how other associated tasks can be modelled without much additional effort.